Search

Artificial intelligence (AI) and natural language processing technologies have fuelled the growth of Pedagogical Conversational Agents (PCAs) with empathic conversational capabilities. However, no systematic literature review has explored the intersection between conversational agents, education and emotion. Therefore, this study aimed to outline the key aspects of designing, implementing and evaluating these agents. The data sources were empirical studies, including peer‐reviewed conference papers and journal articles, and the most recent publications, from the ACM Digital Library, IEEE Xplore, ProQuest, ScienceDirect, Scopus, SpringerLink, Taylor & Francis Online, Web of Science and Wiley Online Library. The remaining papers underwent a rigorous quality assessment. A filter study meeting the objective was based on keywords. Comparative analysis and synthesis of results were used to handle data and combine study outcomes. Out of 1162 search results, 13 studies were selected. The results indicate that agents promote dialogic learning, proficiency in knowledge domains, personalized feedback and empathic abilities as essential design principles. Most implementations employ a quantitative approach, and two variables are used for evaluation. Feedback types play a vital role in achieving positive results in learning performance and student perceptions. The main limitations and gaps are the time range for literature selection, the level of integration of the empathic field and the lack of a detailed development stage report. Moreover, future directions are the ethical implications of agents operating beyond scheduled learning times and the adoption of Responsible AI principles. In conclusion, this review provides a comprehensive framework of empathic PCAs, mostly in their evaluation. The systematic review registration number is osf.io/3xk6a.Practitioner notesWhat is already known about this topicEmotions play a pivotal role in shaping the interaction process, making it essential to consider them when designing methodological strategies or learning tools.Empathic Pedagogical Conversational Agents (PCAs) have emerged as a crucial approach for enhancing and personalizing the learning experience (24/7) for pupils and supporting human teachers in their teaching process.Despite the creation of numerous empathic PCAs, there is a scarcity of Systematic Literature Reviews (SLRs) on their application in the educational field, particularly concerning the integration of emotional abilities in combination with the competencies of each subject.What this paper addsIt offers new insights into the design principles underlying the integration of the empathic field.It reviews different approaches for incorporating students' prior knowledge in real time.It provides a comprehensive and up‐to‐date overview of the research designs used for implementation, including quantitative, qualitative and mixed methods.It examines the factors that influence the effectiveness of empathic PCA in teaching and learning.It evaluates the types of feedback that enhance the impact of the empathic field on learning outcomes.Implications for practice and/or policyIt is crucial to grasp the topics that this paper introduces in order to effectively integrate new learning tools into any context.Techno‐pedagogical designers seeking to gain insights into empathic PCAs will find immense value in this SLR, as it comprehensively covers each stage of the process.For future research endeavours, this study offers a wealth of ideas to draw upon, enabling researchers to address the challenges outlined and explore new avenues of investigation. [ABSTRACT FROM AUTHOR]

Preparing preservice teachers (PSTs) to be able to notice, interpret, respond to and orchestrate student ideas—the core practices of responsive teaching—is a key goal for contemporary science and mathematics teacher education. This mixed‐methods study, employing a virtual reality (VR)‐supported simulation integrated with artificial intelligence (AI)‐powered virtual students, explored the frequent patterns of PSTs' talk moves as they attempted to orchestrate a responsive discussion, as well as the affordances and challenges of leveraging AI‐supported virtual simulation to enhance PSTs' responsive teaching skills. Sequential analysis of the talk moves of both PSTs (n = 24) and virtual students indicated that although PSTs did employ responsive talk moves, they encountered difficulties in transitioning from the authoritative, teacher‐centred teaching approach to a responsive way of teaching. The qualitative analysis with triangulated dialogue transcripts, observational field notes and semi‐structured interviews revealed participants' engagement in (1) orchestrating discussion by leveraging the design features of AI‐supported simulation, (2) iterative rehearsals through naturalistic and contextualized interactions and (3) exploring realism and boundaries in AI‐powered virtual students. The study findings provide insights into the potential of leveraging AI‐supported virtual simulation to improve PSTs' responsive teaching skills. The study also underscores the need for PSTs to engage in well‐designed pedagogical practices with adaptive and in situ support. Practitioner notes What is already known about this topic Developing the teaching capacity of responsive teaching is an important goal for preservice teacher (PST) education. PSTs need systematic opportunities to build fluency in this approach. Virtual simulations can provide PSTs with the opportunities to practice interactive teaching and have been shown to improve their teaching skills. Artificial intelligence (AI)‐powered virtual students can be integrated into virtual simulations to enable interactive and authentic practice of teaching. What this paper adds AI‐supported simulation has the potential to support PSTs' responsive teaching skills. While PSTs enact responsive teaching talk moves, they struggle to enact those talk moves in challenging teaching scenarios due to limited epistemic and pedagogical resources. AI‐supported simulation affords iterative and contextualized opportunities for PSTs to practice responsive teaching talk moves; it challenges teachers to analyse student discourse and respond in real time. Implications for practice and/or policy PSTs should build a teaching repertoire with both basic and advanced responsive talk moves. The learning module should adapt to PSTs' prior experience and provide PSTs with in situ learning support to navigate challenging teaching scenarios. Integrating interaction features and AI‐based virtual students into the simulation can facilitate PSTs' active participation. What is already known about this topic Developing the teaching capacity of responsive teaching is an important goal for preservice teacher (PST) education. PSTs need systematic opportunities to build fluency in this approach. Virtual simulations can provide PSTs with the opportunities to practice interactive teaching and have been shown to improve their teaching skills. Artificial intelligence (AI)‐powered virtual students can be integrated into virtual simulations to enable interactive and authentic practice of teaching. What this paper adds AI‐supported simulation has the potential to support PSTs' responsive teaching skills. While PSTs enact responsive teaching talk moves, they struggle to enact those talk moves in challenging teaching scenarios due to limited epistemic and pedagogical resources. AI‐supported simulation affords iterative and contextualized opportunities for PSTs to practice responsive teaching talk moves; it challenges teachers to analyse student discourse and respond in real time. Implications for practice and/or policy PSTs should build a teaching repertoire with both basic and advanced responsive talk moves. The learning module should adapt to PSTs' prior experience and provide PSTs with in situ learning support to navigate challenging teaching scenarios. Integrating interaction features and AI‐based virtual students into the simulation can facilitate PSTs' active participation. [ABSTRACT FROM AUTHOR]

A key goal of educational institutions around the world is to provide inclusive, equitable quality education and lifelong learning opportunities for all learners. Achieving this requires contextualized approaches to accommodate diverse global values and promote learning opportunities that best meet the needs and goals of all learners as individuals and members of different communities. Advances in learning analytics (LA), natural language processes (NLP), and artificial intelligence (AI), especially generative AI technologies, offer potential to aid educational decision making by supporting analytic insights and personalized recommendations. However, these technologies also raise serious risks for reinforcing or exacerbating existing inequalities; these dangers arise from multiple factors including biases represented in training datasets, the technologies' abilities to take autonomous decisions, and processes for tool development that do not centre the needs and concerns of historically marginalized groups. To ensure that Educational Decision Support Systems (EDSS), particularly AI‐powered ones, are equipped to promote equity, they must be created and evaluated holistically, considering their potential for both targeted and systemic impacts on all learners, especially members of historically marginalized groups. Adopting a socio‐technical and cultural perspective is crucial for designing, deploying, and evaluating AI‐EDSS that truly advance educational equity and inclusion. This editorial introduces the contributions of five papers for the special section on advancing equity and inclusion in educational practices with AI‐EDSS. These papers focus on (i) a review of biases in large language models (LLMs) applications offers practical guidelines for their evaluation to promote educational equity, (ii) techniques to mitigate disparities across countries and languages in LLMs representation of educationally relevant knowledge, (iii) implementing equitable and intersectionality‐aware machine learning applications in education, (iv) introducing a LA dashboard that aims to promote institutional equality, diversity, and inclusion, and (v) vulnerable student digital well‐being in AI‐EDSS. Together, these contributions underscore the importance of an interdisciplinary approach in developing and utilizing AI‐EDSS to not only foster a more inclusive and equitable educational landscape worldwide but also reveal a critical need for a broader contextualization of equity that incorporates the socio‐technical questions of what kinds of decisions AI is being used to support, for what purposes, and whose goals are prioritized in this process. [ABSTRACT FROM AUTHOR]

This study investigated the added value of real‐time adaptive feedback on seventh graders' performances in tablet‐based geometry learning. To isolate the effects of the medium (ie, tablet) from those of the feedback, three groups were compared: paper‐and‐pencil, pen‐based tablet without feedback and pen‐based tablet with feedback. The feedback was provided by a tutoring system based on an artificial intelligence that automatically interpreted students' pen strokes on the screen. A total of 85 French students drew three geometric shapes, either on paper or on a tablet, and then performed a transfer task on paper. Results showed that using a tablet without feedback did not improve learning but seemed to enhance interest in the task compared to the paper‐and‐pencil group. Students in the tablet with feedback group performed significantly better than the other two groups on learning, as well as on transfer. This study was the first to combine media comparison and added‐value approaches to test the effects on students' geometry performances of using a new educational app on a pen‐based tablet in a naturalistic classroom environment. Results showed that it was not the medium used but the intelligent tutoring system‐based feedback that improved students' performance. Our data therefore indicate that artificial intelligence is a promising way of providing learners with real‐time adaptive feedback in order to improve their performances. Practitioner notesWhat is already known about this topic Previous meta‐analyses have investigated the effects of tablet‐based learning.Tablet computers have been proven to increase students' motivation.Yet, the influence of tablet computers on learning outcomes remains inconclusive.Other studies show that certain features of environments, such as feedback, have positive effects on learning.What this paper adds Most of the previous studies adopted a media comparison approach (paper‐ vs. tablet‐based instruction).We combine this approach with an added‐value approach by adding or not real‐time AI‐based feedback.Results showed that tablet use increased children's interest but not their learning outcomes.Feedback improved children's performance in a training task and a later transfer paper task.Implications for practice and/or policy Tablet computers can promote students' interest in the task during geometry instruction.App features play a critical role in improving students' learning.Specifically, IA‐based adaptive feedback helps children to perform better on a geometry task. [ABSTRACT FROM AUTHOR]

This paper presents a multidimensional view of AI's role in education, emphasising the intricate interplay among AI, analytics and human learning processes. Here, I challenge the prevalent narrow conceptualisation of AI as tools in Education, exemplified in generative AI tools, and argue for the importance of alternative conceptualisations of AI for achieving human–AI hybrid intelligence. I highlight the differences between human intelligence and artificial information processing, the importance of hybrid human–AI systems to extend human cognition and posit that AI can also serve as an instrument for understanding human learning. Early learning sciences and AI in Education Research (AIED), which saw AI as an analogy for human intelligence, have diverged from this perspective, prompting a need to rekindle this connection. The paper presents three unique conceptualisations of AI: the externalisation of human cognition, the internalisation of AI models to influence human mental models and the extension of human cognition via tightly coupled human–AI hybrid intelligence systems. Examples from current research and practice are examined as instances of the three conceptualisations in education, highlighting the potential value and limitations of each conceptualisation for human competence development, as well as the perils of overemphasis on approaches that replace human learning opportunities with AI tools. The paper concludes with advocacy for a broader approach to AIED that goes beyond considerations on the design and development of AI and includes educating people about AI and innovating educational systems to remain relevant in an AI ubiquitous world. [ABSTRACT FROM AUTHOR]

A core focus of self‐regulated learning (SRL) research lies in uncovering methods to empower learners within digital learning environments. As digital technologies continue to evolve during the current hype of artificial intelligence (AI) in education, the theoretical, empirical and methodological nuances to support SRL are emerging and offering new ways for adaptive support and guidance for learners. Such affordances offer a unique opportunity for personalised learning experiences, including adaptive interventions. Exploring the application of adaptivity to enhance SRL is an important and emerging area of research that requires further attention. This editorial introduces the contributions of seven papers for the special section on adaptive support for SRL within digital learning environments. These papers explore various themes related to enhancing SRL strategies through technological interventions, offering valuable insights and paving the way for future advancements in this dynamic area. [ABSTRACT FROM AUTHOR]

Training to run is not straightforward since without proper personalised supervision and planning, people will not improve their performance and will increase the risk of injuries. This study aims to identify the different factors that influence running training programmes, examining the benefits, challenges or limitations of personalised plans. Moreover, this study explores how multimodal, immersive and artificial intelligence technologies can support personalised training. We conducted an exploratory sequential mixed research consisting of interviews with 11 running coaches from different countries and a survey of 12 running coaches. Based on the interviews and survey analysis, we identified and extracted relevant factors of the training process. We identified four relevant aspects for running training: physical, technical, mental and body awareness. Using these aspects as a reference, we derived a framework using a bottom‐up approach. This framework proposes multimodal, immersive and artificial intelligence technologies to facilitate personalised running training. It also allows coaches to personally guide their athletes on each aspect. Practitioner notesWhat is already known about this topic Running is a popular sport that provides health benefits and is practised by many people around the world.Training is a process that enables athletes to improve their development in various aspects of their sport; in the case of running, it helps them to increase their speed and endurance.Personalised training supports the needs and abilities of athletes, by helping them to achieve their potential through individualised activities or programmes.Sports science research indicates that personalised training can be improved by applying technology to tackle its challenges and limitations.What this paper adds We show that personalising the training requires not only focusing on the runners' physical condition but also on their mental, technical and body awareness aspects, where each of them has a different adaptation to training.We show that multimodal and immersive technologies offer suitable and portable ways to measure and target the mental and body awareness aspects during running training.Implications for practice and/or policy This paper presents a list of factors, measures and devices that coaches can use to plan and design their training sessions in a more personalised manner.This study can serve as a foundation for future research that aims to identify and target the various factors that influence the learning and training of sports. [ABSTRACT FROM AUTHOR]

Previous research showed that the parents acknowledged the technology's benefits for their young children's learning, however, they are still worried about the extended screen time, lack of physical activity and lack of social interactions. To address these concerns, we developed Kid Space to enable pedagogically appropriate technology use for children in early childhood education by combining various sensing technologies with a multi‐modal conversational artificial intelligence system that can interact with children, understand individual progress and provide personalised learning experiences. To understand the impact of Kid Space on the parents' initial concerns about technology use by their young children, we conducted a multi‐method user study: (1) a quasi‐experimental design and (2) formative research method using an exploratory case study with a set of children and their parents experiencing Kid Space in their homes. The results show that after experiencing Kid Space with their children, the parents felt significantly less concerned about screen time, social interactions and physical activity and reported positive perceptions towards pedagogical value of Kid Space. Detailed analysis on the multi‐modal data quantitatively and qualitatively validated why Kid Space alleviated these concerns. Future research is needed to validate long‐term educational value of Kid Space and generate insights for improvement for next iterations. Practitioner notesWhat is already known about this topic Play‐based learning is critical for young children's education, but digital games create major concerns around extended screen time, lack of physical activity and lack of social interactions.Blending digital and physical spaces could support pedagogically appropriate technology use for young children. Towards this end, there are some exemplary studies in the state–of‐the‐art reporting positive educational outcomes as an effect of utilising such spaces. However, none of these studies supported children's most natural mode of communication in their interactions with the systems—speaking.Pedagogical conversational agents (PCAs) are promising, but they are tricky when it comes to young children's speech because of unique technical challenges resulted from how children use language and communicate with digital systems.What this paper adds To our best knowledge, Kid Space is one of the earliest implementations of a PCA with a multi‐modal artificial intelligence (AI) system utilising physical and digital learning manipulatives for maths learning with a focus on early childhood education. The key contributions of this paper are (1) the design and development of an end‐to‐end multi‐modal system enabling Wizard‐of‐Oz experimentation for initial evaluations with users, (2) the creation of a multi‐modal, in‐the‐wild labelled dataset with children–agent, children–parent and children–physical/digital space interactions enabling advancements for AI components for later evaluations with users and (3) the generation of rich insights from an initial research study on user perceptions and engagement as well as actionable findings to improve Kid Space experiences for next iterations and inform key design features for similar systems.Implications for practice and/or policy The results of the study implied a set of areas for improvement—or design features—for Kid Space and other similar pedagogical conversational systems developed for children's home usages: (1) easier setup and usage with optimised setup size addressing diverse space limitations at homes, (2) minimised latency between Oscar (the conversational pedagogical agent) and child interactions (eg, adding multimodal dialogue system to reduce the need for a human wizard), (3) more advanced personalisation, social (including more verbal interactions) and pedagogical skills for Oscar with increased contextual awareness (eg, sending children's engagement), (4) scalability and higher visual quality of content with diverse games and learning outcomes, (5) parental control features over Kid Space platform and Oscar (eg, time limit, content, etc.) and (6) accessibility features (eg, captions turned on for multilingual children) and support for neurodiversity. [ABSTRACT FROM AUTHOR]

Conversational user interfaces (CUI), including voice interfaces, which allow users to converse with computers via voice, are gaining wide popularity. VoiceBots allow users to receive a response in real‐time, regardless of the communication device. VoiceBots have been explored in fields such as customer service to automate repetitive queries and help reduce redundant tasks; however, they have not been widely applied in the classroom. This study aimed to explore the effects of VoiceBot implementation on student learning. A pre‐test–post‐test design was implemented with 65 participating undergraduate students in multimedia programming who were randomly allocated to scenarios representing a 2 × 2 design (experimental and control cohorts). Data were collected using an academic achievement test and semi‐structured interviews, which allowed for a more in‐depth analysis of the students' experiences with the VoiceBot. The results showed that how the VoiceBot was applied positively influenced student learning in the experimental cohort. Moreover, the focus group data demonstrated that the VoiceBot can be a valuable assistant for students and could be easily replicated in other courses. To the best of our knowledge, this study was the first to use VoiceBot to engage undergraduate students in Ghana, thus contributing to the growing literature stream on the development of VoiceBots to improve student learning experiences. This study elucidates the design process using a zero‐coding technique, which is considered a suitable approach for educational institutions with limited resources. Practitioner notes What is already known about this topic Conversational user interfaces (CUIs), including voice interfaces, have gained popularity and are used to interact with computers through natural language. VoiceBots have been utilised in various fields such as customer service to automate tasks and reduce redundancy. Instant messaging systems such as WhatsApp and Telegram have been used for communication in educational contexts. Advances in artificial intelligence (AI) and natural language processing (NLP) have led to significant improvements in voice‐enabled CUIs (VoiceBots). Existing studies indicate that chatbots affect students' motivation, learning experiences, and achievements; however, research on using VoiceBots for learning improvement is limited. What this paper adds A VoiceBot was introduced as an assistant to facilitate learning in a multimedia programming course. The study used an experimental design with an experimental cohort using a WhatsApp group platform equipped with a zero‐coding VoiceBot and a control cohort without the bot. The study found that students interacting with VoiceBot demonstrated better learning achievement than the control group. The study also provides clear suggestions on integrating VoiceBots into educational institutions. Implications for practice and/or policy The study's findings suggest that VoiceBots can play a significant role in improving student learning achievements, especially in subjects such as multimedia programming. Educational institutions could establish learning design and technology centres with subject matter experts to integrate VoiceBots effectively into the learning process. Instructors must possess adequate technological proficiency to engage students with VoiceBots and targeted in‐service training may be necessary. Future research can explore VoiceBot use across various academic domains and levels of education, analyse the impact of usage patterns on learning outcomes, and assess its long‐term effects on student engagement and motivation. What is already known about this topic Conversational user interfaces (CUIs), including voice interfaces, have gained popularity and are used to interact with computers through natural language. VoiceBots have been utilised in various fields such as customer service to automate tasks and reduce redundancy. Instant messaging systems such as WhatsApp and Telegram have been used for communication in educational contexts. Advances in artificial intelligence (AI) and natural language processing (NLP) have led to significant improvements in voice‐enabled CUIs (VoiceBots). Existing studies indicate that chatbots affect students' motivation, learning experiences, and achievements; however, research on using VoiceBots for learning improvement is limited. What this paper adds A VoiceBot was introduced as an assistant to facilitate learning in a multimedia programming course. The study used an experimental design with an experimental cohort using a WhatsApp group platform equipped with a zero‐coding VoiceBot and a control cohort without the bot. The study found that students interacting with VoiceBot demonstrated better learning achievement than the control group. The study also provides clear suggestions on integrating VoiceBots into educational institutions. Implications for practice and/or policy The study's findings suggest that VoiceBots can play a significant role in improving student learning achievements, especially in subjects such as multimedia programming. Educational institutions could establish learning design and technology centres with subject matter experts to integrate VoiceBots effectively into the learning process. Instructors must possess adequate technological proficiency to engage students with VoiceBots and targeted in‐service training may be necessary. Future research can explore VoiceBot use across various academic domains and levels of education, analyse the impact of usage patterns on learning outcomes, and assess its long‐term effects on student engagement and motivation. [ABSTRACT FROM AUTHOR]

In the ever‐evolving AI‐driven education, integrating AI technologies into teaching practices has become increasingly imperative for aspiring STEM educators. Yet, there remains a dearth of studies exploring pre‐service STEM teachers' readiness to incorporate AI into their teaching practices. This study examined the factors influencing teachers' willingness to integrate AI (WIAI), especially from the perspective of pre‐service STEM teachers' attitudes towards the application of AI in teaching. In the study, a comprehensive survey was conducted among 239 pre‐service STEM teachers, examining the influences and interconnectedness of Technological Pedagogical Content Knowledge (TPACK), Perceived Usefulness (PU), Perceived Ease of Use (PE), and Self‐Efficacy (SE) on WIAI. Structural Equation Modeling (SEM) was employed for data analysis. The findings illuminated direct influences of TPACK, PU, PE, and SE on WIAI. TPACK was found to directly affect PE, PU, and SE, while PE and PU also directly influenced SE. Further analysis revealed significant mediating roles of PE, PU, and SE in the relationship between TPACK and WIAI, highlighting the presence of a chain mediation effect. In light of these insights, the study offers several recommendations on promoting pre‐service STEM teachers' willingness to integrate AI into their teaching practices. Practitioner notes What is already known about this topic? The potential of AI technologies to enrich learning experiences and improve outcomes in STEM education has been recognized. Pre‐service teachers' willingness to integrate AI into teaching practice is crucial for shaping the future learning environment. The TAM and TPACK frameworks are used to analyse teacher factors in technology‐supported learning environments. Few studies have been conducted for examining factors of pre‐service teachers' willingness to integrate AI into teaching practices in the context of STEM education. What this paper adds? A survey was designed and developed for exploring pre‐service STEM teachers' WIAI and its relationships with factors including TPACK, PE, PU, and SE. TPACK, SE, PU, and PE have direct impact on pre‐service STEM teachers' WIAI. SE, PU, and PE have been identified as mediating variables in the relationship between TPACK and WIAI. Two sequential mediation effects, TPACK → PE → SE → WIAI and TPACK → PU → SE → WIAI, among pre‐service STEM teachers were further identified. Implications of this study for practice and/or policy Pre‐service STEM teachers are encouraged to explore and utilize AI technology to enhance their confidence and self‐efficacy in integrating AI into teaching practices. Showcasing successful cases and practical experiences is essential for fostering awareness of AI integration in STEM education. It is recommended to introduce AI education courses in teacher training programs. Offering internship and practicum opportunities related to AI technologies can enhance their practical skills in integrating AI into education. What is already known about this topic? The potential of AI technologies to enrich learning experiences and improve outcomes in STEM education has been recognized. Pre‐service teachers' willingness to integrate AI into teaching practice is crucial for shaping the future learning environment. The TAM and TPACK frameworks are used to analyse teacher factors in technology‐supported learning environments. Few studies have been conducted for examining factors of pre‐service teachers' willingness to integrate AI into teaching practices in the context of STEM education. What this paper adds? A survey was designed and developed for exploring pre‐service STEM teachers' WIAI and its relationships with factors including TPACK, PE, PU, and SE. TPACK, SE, PU, and PE have direct impact on pre‐service STEM teachers' WIAI. SE, PU, and PE have been identified as mediating variables in the relationship between TPACK and WIAI. Two sequential mediation effects, TPACK → PE → SE → WIAI and TPACK → PU → SE → WIAI, among pre‐service STEM teachers were further identified. Implications of this study for practice and/or policy Pre‐service STEM teachers are encouraged to explore and utilize AI technology to enhance their confidence and self‐efficacy in integrating AI into teaching practices. Showcasing successful cases and practical experiences is essential for fostering awareness of AI integration in STEM education. It is recommended to introduce AI education courses in teacher training programs. Offering internship and practicum opportunities related to AI technologies can enhance their practical skills in integrating AI into education. [ABSTRACT FROM AUTHOR]

Students' physical and digital lives are increasingly entangled. It is difficult to separate students' digital well‐being from their offline well‐being given that artificial intelligence increasingly shapes both. Within the context of education's fiduciary and moral duty to ensure safe, appropriate and effective digital learning spaces for students, the continuing merger between artificial intelligence and learning analytics not only opens up many opportunities for more responsive teaching and learning but also raises concerns, specifically for previously disadvantaged and vulnerable students. While digital well‐being is a well‐established research focus, it is not clear how AI‐Powered Educational Decision Support Systems (AI‐EDSS) might impact on the inherent, situational and pathogenic vulnerability of students. In this conceptual paper, we map the digital well‐being of previously disadvantaged and vulnerable students in four overlapping fields, namely (1) digital well‐being research; (2) digital well‐being research in education; (3) digital well‐being research in learning analytics; and (4) digital well‐being in AI‐informed educational contexts. With this as the basis, we engage with six domains from the IEEE standard 7010–2020—IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well‐Being and provide pointers for safeguarding and enhancing disadvantaged and vulnerable student digital well‐being in AI‐EDSS. Practitioner notesWhat is already known about this topic Digital well‐being research is a well‐established focus referring to the impact of digital engagement on human well‐being.Digital well‐being is effectively inseparable from general well‐being as it is increasingly difficult to disentangle our online and offline lives and, as such, inherently intersectional.Artificial Intelligence shows promise for enhancing human digital well‐being, but there are concerns about issues such as privacy, bias, transparency, fairness and accountability.The notion of 'vulnerable individuals' includes individuals who were previously disadvantaged, and those with inherent, situational and/or pathogenic vulnerabilities.While current advances in AI‐EDSS may support identification of digital wellness, proxies for digital wellness should be used with care.What this study contributes An overview of digital well‐being research with specific reference how it may impact on vulnerable students.Illustrates specific vulnerabilities in five domains from the IEEE standard 7010–2020—IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well‐Being selected for their significance in online learning environments.Pointers for the design and implementation of fair, ethical, accountable, and transparent AI‐EDSS with specific reference to vulnerable students.Implications for practice and/or policy Fairness, equity, transparency and accountability in AI‐EDSS affect all students but may have a greater (positive or negative) impact on vulnerable students.A critically informed understanding of the nature of students' vulnerability—whether as inherent, situational and/or pathogenic, as well as temporal/permanent aspects—is crucial.Since AI‐EDSS can exacerbate existing vulnerabilities resulting in pathogenic vulnerability, care is needed when designing AI‐EDSS. [ABSTRACT FROM AUTHOR]

The greater the proliferation of AI in educational contexts, the more important it becomes to ensure that AI adheres to the equity and inclusion values of an educational system or institution. Given that modern AI is based on historic datasets, mitigating historic biases with respect to protected classes (ie, fairness) is an important component of this value alignment. Although extensive research has been done on AI fairness in education, there has been a lack of guidance for practitioners, which could enhance the practical uptake of these methods. In this work, we present a practitioner‐oriented, step‐by‐step framework, based on findings from the field, to implement AI fairness techniques. We also present an empirical case study that applies this framework in the context of a grade prediction task using data from a large public university. Our novel findings from the case study and extended analyses underscore the importance of incorporating intersectionality (such as race and gender) as central equity and inclusion institution values. Moreover, our research demonstrates the effectiveness of bias mitigation techniques, like adversarial learning, in enhancing fairness, particularly for intersectional categories like race–gender and race–income. Practitioner notesWhat is already known about this topic AI‐powered Educational Decision Support Systems (EDSS) are increasingly used in various educational contexts, such as course selection, admissions, scholarship allocation and identifying at‐risk students.There are known challenges with AI in education, particularly around the reinforcement of existing biases, leading to unfair outcomes.The machine learning community has developed metrics and methods to measure and mitigate biases, which have been effectively applied to education as seen in the AI in education literature.What this paper adds Introduces a comprehensive technical framework for equity and inclusion, specifically for machine learning practitioners in AI education systems.Presents a novel modification to the ABROCA fairness metric to better represent disparities among multiple subgroups within a protected class.Empirical analysis of the effectiveness of bias‐mitigating techniques, like adversarial learning, in reducing biases in intersectional classes (eg, race–gender, race–income).Model reporting in the form of model cards that can foster transparent communication among developers, users and stakeholders.Implications for practice and/or policy The fairness framework can act as a systematic guide for practitioners to design equitable and inclusive AI‐EDSS.The fairness framework can act as a systematic guide for practitioners to make compliance with emerging AI regulations more manageable.Stakeholders may become more involved in tailoring the fairness and equity model tuning process to align with their values. [ABSTRACT FROM AUTHOR]

Even highly motivated undergraduates drift off their STEM career pathways. In large introductory STEM classes, instructors struggle to identify and support these students. To address these issues, we developed co‐redesign methods in partnership with disciplinary experts to create high‐structure STEM courses that better support students and produce informative digital event data. To those data, we applied theory‐ and context‐relevant labels to reflect active and self‐regulated learning processes involving LMS‐hosted course materials, formative assessments, and help‐seeking tools. We illustrate the predictive benefits of this process across two cycles of model creation and reapplication. In cycle 1, we used theory‐relevant features from 3 weeks of data to inform a prediction model that accurately identified struggling students and sustained its accuracy when reapplied in future semesters. In cycle 2, we refit a model with temporally contextualized features that achieved superior accuracy using data from just two class meetings. This modelling approach can produce durable learning analytics solutions that afford scaled and sustained prediction and intervention opportunities that involve explainable artificial intelligence products. Those same products that inform prediction can also guide intervention approaches and inform future instructional design and delivery.Practitioner notesWhat is already known about this topicLearning analytics includes an evolving collection of methods for tracing and understanding student learning through their engagements with learning technologies.Prediction models based on demographic data can perpetuate systemic biases.Prediction models based on behavioural event data can produce accurate predictions of academic success, and validation efforts can enrich those data to reflect students' self‐regulated learning processes within learning tasks.What this paper addsLearning analytics can be successfully applied to predict performance in an authentic postsecondary STEM context, and the use of context and theory as guides for feature engineering can ensure sustained predictive accuracy upon reapplication.The consistent types of learning resources and cyclical nature of their provisioning from lesson to lesson are hallmarks of high‐structure active learning designs that are known to benefit learners. These designs also provide opportunities for observing and modelling contextually grounded, theory‐aligned and temporally positioned learning events that informed prediction models that accurately classified students upon initial and later reapplications in subsequent semesters.Co‐design relationships where researchers and instructors work together toward pedagogical implementation and course instrumentation are essential to developing unique insights for feature engineering and producing explainable artificial intelligence approaches to predictive modelling.Implications for practice and/or policyHigh‐structure course designs can scaffold student engagement with course materials to make learning more effective and products of feature engineering more explainable.Learning analytics initiatives can avoid perpetuation of systemic biases when methods prioritize theory‐informed behavioural data that reflect learning processes, sensitivity to instructional context and development of explainable predictors of success rather than relying on students' demographic characteristics as predictors.Prioritizing behaviours as predictors improves explainability in ways that can inform the redesign of courses and design of learning supports, which further informs the refinement of learning theories and their applications. [ABSTRACT FROM AUTHOR]

Scaffolds that support self‐regulated learning (SRL) have been found to improve learning outcomes. The effects of scaffolds can differ depending on how learners use them and how specific scaffolds might influence learning processes differently. Personalized scaffolds have been proposed to be more beneficial for learning due to their adaptivity to learning progress and individualized content to learning needs. The present study investigated finer‐grained effects of how personalized scaffolds driven by a rule‐based artificial intelligence system influenced SRL processes, especially how students learned with them. Using a pre‐post experimental design, we investigated personalized scaffolds based on university students' real‐time learning processes in a technologically enhanced learning environment. Students in the experimental group (n = 30) received personalized scaffolds, while the control group (n = 29) learned without scaffolds. All students completed a 45‐minute learning task with trace data recorded. Findings indicated scaffold effects on students' subsequent learning behaviour. Additionally, only scaffold interaction correlated to essay performance and suggests that the increase in frequencies of SRL activities alone does not contribute directly to learning outcomes. As guidelines for real‐time SRL support are lacking, this study provides valuable insights to enhance SRL support with adaptive learning technologies. Practitioner notesWhat is already known about this topic Self‐regulated learning scaffolds, especially adaptive scaffolds, improve learning.Personalized scaffolds have effects on self‐regulated learning activities.Past research focused on aggregated effects of scaffolds.What this paper adds Investigates how students learn with personalized scaffolds in terms of frequencies of learning activities and scaffold interaction.Takes a closer look at which learning activities and when the effects of personalized scaffolds occur.Examines how finer‐grained effects of personalized scaffolds correspond to learning outcomes.Implications for practice and/or policy Personalized scaffold effects vary across learning, and future research should consider finer‐grained investigations of SRL support in order to better understand their influence on learning.The number of personalized scaffolds provided should be reconsidered in the future as students only use some of the support provided, especially when task demands increase.Personalized scaffold interaction is linked to improvement in task performance, so future research should also focus on students' appropriate use of self‐regulated learning support. [ABSTRACT FROM AUTHOR]

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs‐based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer‐reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state‐of‐the‐art models (eg, GPT‐3/4), embracing the initiative of open‐sourcing models/systems, and adopting a human‐centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models. Practitioner notesWhat is currently known about this topic Generating and analysing text‐based content are time‐consuming and laborious tasks.Large language models are capable of efficiently analysing an unprecedented amount of textual content and completing complex natural language processing and generation tasks.Large language models have been increasingly used to develop educational technologies that aim to automate the generation and analysis of textual content, such as automated question generation and essay scoring.What this paper adds A comprehensive list of different educational tasks that could potentially benefit from LLMs‐based innovations through automation.A structured assessment of the practicality and ethicality of existing LLMs‐based innovations from seven important aspects using established frameworks.Three recommendations that could potentially support future studies to develop LLMs‐based innovations that are practical and ethical to implement in authentic educational contexts.Implications for practice and/or policy Updating existing innovations with state‐of‐the‐art models may further reduce the amount of manual effort required for adapting existing models to different educational tasks.The reporting standards of empirical research that aims to develop educational technologies using large language models need to be improved.Adopting a human‐centred approach throughout the developmental process could contribute to resolving the practical and ethical challenges of large language models in education. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) literacy is at the top of the agenda for education today in developing learners' AI knowledge, skills, attitudes and values in the 21st century. However, there are few validated research instruments for educators to examine how secondary students develop and perceive their learning outcomes. After reviewing the literature on AI literacy questionnaires, we categorized the identified competencies in four dimensions: (1) affective learning (intrinsic motivation and self‐efficacy/confidence), (2) behavioural learning (behavioural commitment and collaboration), (3) cognitive learning (know and understand; apply, evaluate and create) and (4) ethical learning. Then, a 32‐item self‐reported questionnaire on AI literacy (AILQ) was developed and validated to measure students' literacy development in the four dimensions. The design and validation of AILQ were examined through theoretical review, expert judgement, interview, pilot study and first‐ and second‐order confirmatory factor analysis. This article reports the findings of a pilot study using a preliminary version of the AILQ among 363 secondary school students in Hong Kong to analyse the psychometric properties of the instrument. Results indicated a four‐factor structure of the AILQ and revealed good reliability and validity. The AILQ is recommended as a reliable measurement scale for assessing how secondary students foster their AI literacy and inform better instructional design based on the proposed affective, behavioural, cognitive and ethical (ABCE) learning framework. Practitioner notesWhat is already known about this topic AI literacy has drawn increasing attention in recent years and has been identified as an important digital literacy.Schools and universities around the world started to incorporate AI into their curriculum to foster young learners' AI literacy.Some studies have worked to design suitable measurement tools, especially questionnaires, to examine students' learning outcomes in AI learning programmes.What this paper adds Develops an AI literacy questionnaire (AILQ) to evaluate students' literacy development in terms of affective, behavioural, cognitive and ethical (ABCE) dimensions.Proposes a parsimonious model based on the ABCE framework and addresses a skill set of AI literacy.Implications for practice and/or policy Researchers are able to use the AILQ as a guide to measure students' AI literacy.Practitioners are able to use the AILQ to assess students' AI literacy development. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) can enhance teachers' capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un‐planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers' voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co‐design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co‐orchestration tool, called Pair‐Up. We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co‐orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI‐based tutoring systems. We leveraged qualitative content analysis to examine teachers' views about sharing control with Pair‐Up, and we describe high‐level insights about the human‐AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human‐centred learning analytics can be applied to the design of human‐AI technologies and share reflections for human‐AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co‐orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems. Practitioner notesWhat is already known about this topic: Artificial Intelligence (AI) can help teachers facilitate complex classroom activities, such as having students move between individual and collaborative learning in unplanned ways.Designers should use human‐centred design approaches to give teachers a voice in deciding what AI might do in the classroom and if or how they want to share control with it.What this paper adds: Presents teacher views about how they want to share control with AI to support students moving between individual and collaborative learning.Describes how we adapted six design methods to design AI features.Illustrates a complete, iterative process to create human‐AI interactions to support teachers as they facilitate students moving from individual to collaborative learning.Implications for practice: We share five implications for designers that teachers highlighted as necessary when designing AI‐features, including control, trust, responsibility, efficiency and accuracy.Our work also includes a reflection on our design process and implications for future design processes. [ABSTRACT FROM AUTHOR]

This paper discusses a three‐level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities. Practitioner notesWhat is already known about this topic Numerous learning theories exist with significant cross‐over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning.Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified.Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices.What this paper adds A three‐level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education.A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels.The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels.Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity.Implications for practice and policy Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems.Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory. [ABSTRACT FROM AUTHOR]

We argue in this paper that there is currently no adequate theoretical framework or model that spans the twelve odd year trajectory from non‐reader to proficient reader, nor addresses fine‐grain skill acquisition, mastery and integration. The target construct itself, reading proficiency, as often operationalized as an endpoint of formal secondary schooling, is defined and measured in imprecise, fragmented terms. Consequently, schools (and empirical research) fall back on heuristics like the Simple View of Reading, or a few stages (learn to read, read to learn, read to do) to describe reading development. Those models, however, are too general to guide instructional decisions for adaptive learning systems. Progress in engineering an adequate learning system has been inhibited by a mismatch with curriculum standards and school organization that impose good‐faith but not fully optimized developmental targets on the educational system. We propose the development of a learning and assessment framework to scaffold reading proficiency development while accounting for the diverse learning trajectories of groups or individuals across development. We then identify some key problems, challenges and opportunities that AI technologies are poised to help us address in conceiving individualized, adaptive learning systems for reading proficiency across the developmental spectrum. We close with a selective review of examples of AI‐enhanced research or products. Practitioner notesWhat is already known about this topic Reading proficiency is a vital component of education systems design.Theoretical and empirical studies across multiple disciplines have been published, but much of this research is framed in fragmented theories that do not seamlessly span the trajectory of life‐long reading development.Current assessments are time‐consuming, coarse‐grained and fail to provide a roadmap for educators and designers of adaptive learning environments.There is a significant lack of knowledge of potentially non‐linear growth in reading skills within or between years and how to design adaptive instruction for diverse subpopulations.What this paper adds We describe the initial steps towards a literacy learning and assessment framework that spans the trajectory from non‐reader to proficient reader.We provide a landscape of exemplars of artificial intelligence and computational linguistics that reflect the possibilities of a more comprehensive, cohesive literacy development system.We reflect upon key problems, challenges and opportunities that AI technologies can help address in conceiving individualized, adaptive learning systems for reading proficiency across the developmental spectrum.Implications for practice and/or policy AI and computational linguistics can help fill in the gaps in understanding and enacting a longitudinal vision of reading development.Educators would ideally know what to expect of their students at particular points of development, identify deviations and have additional tools to intervene effectively to maximize progress.There is a need to develop adaptive instruction that spans the development of proficiency from preschool to college/career levels and adapts to address common barriers among diverse subpopulations. [ABSTRACT FROM AUTHOR]

Advancements in artificial intelligence are rapidly increasing. The new‐generation large language models, such as ChatGPT and GPT‐4, bear the potential to transform educational approaches, such as peer‐feedback. To investigate peer‐feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross‐disciplinary framework that aims to facilitate the development of NLP‐based adaptive measures for supporting peer‐feedback processes in digital learning environments. To conceptualize this process, we introduce a peer‐feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer‐feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer‐feedback process model to exemplify a range of NLP‐based adaptive support measures. We also discuss the current challenges and suggest directions for future cross‐disciplinary research on the effectiveness and other dimensions of NLP‐based adaptive support for peer‐feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer‐feedback in digital learning environments. Practitioner notesWhat is already known about this topic There is considerable research in educational science on peer‐feedback processes.Natural language processing facilitates the analysis of students' textual data.There is a lack of systematic orientation regarding which NLP techniques can be applied to which data to effectively support the peer‐feedback process.What this paper adds A comprehensive overview model that describes the relevant activities and products in the peer‐feedback process.A terminological and procedural scheme for designing NLP‐based adaptive support measures.An application of this scheme to the peer‐feedback process results in exemplifying the use cases of how NLP may be employed to support each learner activity during peer‐feedback.Implications for practice and/or policy To boost the effectiveness of their peer‐feedback scenarios, instructors and instructional designers should identify relevant leverage points, corresponding support measures, adaptation targets and automation goals based on theory and empirical findings.Management and IT departments of higher education institutions should strive to provide digital tools based on modern NLP models and integrate them into the respective learning management systems; those tools should help in translating the automation goals requested by their instructors into prediction targets, take relevant data as input and allow for evaluating the predictions. [ABSTRACT FROM AUTHOR]

While AI has become more prevalent in our society than ever, many young learners are found holding various naive, erroneous conceptions of AI due to the influence of their technology and media environments. To address this issue, this study seeks to propose a novel pedagogical solution to improve upper‐elementary school students' scientific understanding of AI. Following a theory‐informed design convention, we propose an embodied, analogical and disruptive (EAD) approach which is centred on a human–AI comparison through analogical teaching and embodied interaction. To evaluate the impact of this approach, a matched‐group experimental study with pre‐ and posttest interviews was conducted among 77 Grade 6 elementary students in China. The statistical analysis showed that the experimental group, learning via the EAD approach, significantly outperformed their counterparts receiving direct instruction in terms of student growth in understanding. Qualitative analyses revealed the strengths of the EAD approach in supporting student engagement, abstract thinking and system thinking, and its limitations in cognitive overload and communication issues. Patterns of student learning were identified, including their mental schemas and strategies. The EAD approach, as an evidence‐based, age‐appropriate pedagogical solution, demonstrates the value of embodied cognition and human–AI analogy in AI education in elementary education. Practitioner notesWhat is already known about this topic Many young students hold naive, erroneous conceptions about AI.Pedagogical design has long been a critical challenge in teaching AI in K‐12 schools.Human–AI analogies are often used to teach AI, but with obvious pitfalls to be fixed.What this paper adds An embodied, analogical and disruptive (EAD) approach is proposed to improve students' understandings of AI.An experimental study shows statistically significant greater gains in students' understanding with the EAD approach than with the conventional AI‐only direct instruction approach.Qualitative analysis shows the strength of the EAD approach in supporting student engagement, abstract thinking and systems thinking, and its limitations in cognitive overload and communication issues.Implications for practice and/or policy The EAD approach offers a novel pedagogical solution to promote inclusive and quality AI education for young learners.The EAD approach highlights the pedagogical value of embodied cognition and human–AI analogy in AI education.The EAD approach can complement programming and other plugged activities in AI, and more broadly technology and engineering education. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) chatbots are gaining increasing popularity in education. Due to their increasing popularity, many empirical studies have been devoted to exploring the effects of AI chatbots on students' learning outcomes. The proliferation of experimental studies has highlighted the need to summarize and synthesize the inconsistent findings about the effects of AI chatbots on students' learning outcomes. However, few reviews focused on the meta‐analysis of the effects of AI chatbots on students' learning outcomes. The present study performed a meta‐analysis of 24 randomized studies utilizing Stata software (version 14). The main goal of the current study was to meta‐analytically examine the effects of AI chatbots on students' learning outcomes and the moderating effects of educational levels and intervention duration. The results indicated that AI chatbots had a large effect on students' learning outcomes. Moreover, AI chatbots had a greater effect on students in higher education, compared to those in primary education and secondary education. In addition, short interventions were found to have a stronger effect on students' learning outcomes than long interventions. It could be explained by the argument that the novelty effects of AI chatbots could improve learning outcomes in short interventions, but it has worn off in the long interventions. Future designers and educators should make attempt to increase students' learning outcomes by equipping AI chatbots with human‐like avatars, gamification elements and emotional intelligence. Practitioner notesWhat is already known about this topicIn recent years, artificial intelligence (AI) chatbots have been gaining increasing popularity in education.Studies undertaken so far have provided conflicting evidence concerning the effects of AI chatbots on students' learning outcomes.There has remained a paucity of meta‐analyses synthesizing the contradictory findings about the effects of AI chatbots on students' learning outcomes.What this paper addsThis study, through meta‐analysis, synthesized these recent findings about the effects of AI chatbots on students' learning outcomes.This study found that AI chatbots could have a large effect on students' learning outcomes.This study found that the effects of AI chatbots were moderated by educational levels and intervention duration.Implications for practice and/or policyAI chatbot designers could make AI chatbots better by equipping AI chatbots with human‐like avatars, gamification elements and emotional intelligencePractitioners and/or teachers should draw attention to the positive and negative effects of AI chatbots on students.Considering the importance of ChatGPT, more research is required to develop a better understanding of the effects of ChatGPT in education.More research is needed to examine the mechanisms underlying the effects of AI chatbots on students' learning outcomes. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is increasingly integrating into our society. University education needs to maintain its relevance in an AI‐mediated world, but the higher education sector is only beginning to engage deeply with the implications of AI within society. We define AI according to a relational epistemology, where, in the context of a particular interaction, a computational artefact provides a judgement about an optimal course of action and that this judgement cannot be traced. Therefore, by definition, AI must always act as a 'black box'. Rather than seeking to explain 'black boxes', we argue that a pedagogy for an AI‐mediated world involves learning to work with opaque, partial and ambiguous situations, which reflect the entangled relationships between people and technologies. Such a pedagogy asks learners locate AI as socially bounded, where AI is always understood within the contexts of its use. We outline two particular approaches to achieve this: (a) orienting students to quality standards that surround AIs, what might be called the tacit and explicit 'rules of the game'; and (b) providing meaningful interactions with AI systems. Practitioner notesWhat is already known about this topic Artificial intelligence (AI) is conceptualised in many different ways but is rarely defined in the higher education literature.Experts have outlined a range of graduate capabilities for working in a world of AI such as teamwork or ethical thinking.The higher education literature outlines an imperative need to respond to AI, as underlined by recent commentary on ChatGPT.What this paper adds A definition of an AI that is relational: A particular interaction where a computational artefact provides a judgement about an optimal course of action, which cannot be easily traced.Focusing on working with AI black boxes rather than trying to see inside the technology.Describing a pedagogy for an AI‐mediated world that promotes working in complex situations with partial and indeterminate information.Implications for practice and/or policy Focusing on quality standards helps learners understand the social regulating boundaries around AI.Promoting learner interactions with AI as part of a sociotechnical ensemble helps build evaluative judgement in weighting AI's contribution to work.Asking learners to work with AI systems prompts understanding of the evaluative, ethical and practical necessities of working with a black box. [ABSTRACT FROM AUTHOR]

The anthropomorphic characteristics of artificial intelligence (AI) can provide a positive environment for self‐regulated learning (SRL). The factors affecting adolescents' SRL through AI technologies remain unclear. Limited AI and disciplinary knowledge may affect the students' motivations, as explained by self‐determination theory (SDT). In this study, we examine the mediating effects of needs satisfaction in SDT on the relationship between students' previous technical (AI) and disciplinary (English) knowledge and SRL, using an AI conversational chatbot. Data were collected from 323 9th Grade students through a questionnaire and a test. The students completed an AI basic unit and then learned English with a conversational chatbot for 5 days. Confidence intervals were calculated to investigate the mediating effects. We found that students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot, and that satisfying the need for autonomy and competence mediated the relationships between both knowledge (AI and English) and SRL, but relatedness did not. The self‐directed nature of SRL requires heavy cognitive learning and satisfying the need for autonomy and competence may more effectively engage young children in this type of learning. The findings also revealed that current chatbot technologies may not benefit students with relatively lower levels of English proficiency. We suggest that teachers can use conversational chatbots for knowledge consolidation purposes, but not in SRL explorations. Practitioner notesWhat is already known about this topicArtificial intelligence (AI) technologies can potentially support students' self‐regulated learning (SRL) of disciplinary knowledge through chatbots.Needs satisfaction in Self‐determination theory (SDT) can explain the directive process required for SRL.Technical and disciplinary knowledge would affect SRL with technologies.What this paper addsThis study examines the mediating effects of needs satisfaction in SDT on the relationship between students' previous AI (technical) and English (disciplinary) knowledge and SRL, using an AI conversational chatbot.Students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot.Autonomy and competence were mediators, but relatedness was not.Implications for practice and/or policyTeachers should use chatbots for knowledge consolidation rather than exploration.Teachers should support students' competence and autonomy, as these were found to be the factors that directly predicted SRL.School leaders and teacher educators should include the mediating effects of needs satisfaction in professional development programmes for digital education. [ABSTRACT FROM AUTHOR]

Peer assessment has been recognised as a sustainable and scalable assessment method that promotes higher‐order learning and provides students with fast and detailed feedback on their work. Despite these benefits, some common concerns and criticisms are associated with the use of peer assessments (eg, scarcity of high‐quality feedback from peer student‐assessors and lack of accuracy in assigning a grade to the assessee) that raise questions about their trustworthiness. Consequently, many instructors and educational institutions have been anxious about incorporating peer assessment into their teaching. This paper aims to contribute to the growing literature on how AI and learning analytics may be incorporated to address some of the common concerns associated with peer assessment systems, which in turn can increase their trustworthiness and adoption. In particular, we present and evaluate our AI‐assisted and analytical approaches that aim to (1) offer guidelines and assistance to student‐assessors during individual reviews to provide better feedback, (2) integrate probabilistic and text analysis inference models to improve the accuracy of the assigned grades, (3) develop feedback on reviews strategies that enable peer assessors to review the work of each other, and (4) employ a spot‐checking mechanism to assist instructors in optimally overseeing the peer assessment process. Practitioner notesWhat is already known about this topic Engaging students in peer assessment has been demonstrated to have various benefits. However, there are some common concerns associated with employing peer assessment that raise questions about their trustworthiness as an assessment item.What this paper adds Methods and processes on how AI and learning analytics may be incorporated to address some of the common concerns associated with peer assessment systems, which in turn, can increase their trustworthiness and adoption.Implications for practice Presentation of a systematic approach for development, deployment and evaluation of AI and analytics approaches in peer assessment systems. [ABSTRACT FROM AUTHOR]

Evidence from various domains underlines the critical role that human factors, and especially trust, play in adopting technology by practitioners. In the case of Artificial Intelligence (AI) powered tools, the issue is even more complex due to practitioners' AI‐specific misconceptions, myths and fears (e.g., mass unemployment and privacy violations). In recent years, AI has been incorporated increasingly into K‐12 education. However, little research has been conducted on the trust and attitudes of K‐12 teachers towards the use and adoption of AI‐powered Educational Technology (AI‐EdTech). This paper sheds light on teachers' trust in AI‐EdTech and presents effective professional development strategies to increase teachers' trust and willingness to apply AI‐EdTech in their classrooms. Our experiments with K‐12 science teachers were conducted around their interactions with a specific AI‐powered assessment tool (termed AI‐Grader) using both synthetic and real data. The results indicate that presenting teachers with some explanations of (i) how AI makes decisions, particularly compared to the human experts, and (ii) how AI can complement and give additional strengths to teachers, rather than replacing them, can reduce teachers' concerns and improve their trust in AI‐EdTech. The contribution of this research is threefold. First, it emphasizes the importance of increasing teachers' theoretical and practical knowledge about AI in educational settings to gain their trust in AI‐EdTech in K‐12 education. Second, it presents a teacher professional development program (PDP), as well as the discourse analysis of teachers who completed it. Third, based on the results observed, it presents clear suggestions for future PDPs aiming to improve teachers' trust in AI‐EdTech. Practitioner notesWhat is already known about this topic Human factors, and especially trust, play a critical role in practitioners' adoption of technology.In recent years, AI has been incorporated increasingly into K‐12 education.Little research has been conducted on the trust and attitudes of K‐12 teachers towards the use and adoption of AI‐powered Educational Technology.What this paper adds This research emphasizes the importance of increasing teachers' theoretical and practical knowledge about AI in educational settings to gain their trust in AI‐EdTech in K‐12 education.It presents a teacher professional development program (PDP) to increase teachers' trust in AI‐EdTech, as well as the discourse analysis of teachers who completed it.It presents clear suggestions for future PDPs aiming at improving teachers' trust in AI‐EdTech.Implications for practice and/or policy Pre‐ and in‐service teacher education programs that aim to increase teachers' trust in AI‐EdTech should include a section providing teachers with a basic understanding of AI.PDPs aimed to increase teachers' trust in AI‐EdTech should focus on concrete pedagogical tasks and specific AI‐powered tools that are considered by teachers as helpful and worth the effort to learn.AI‐EdTech should not restrict teachers to follow specific pedagogical scenarios, but rather provide teachers with the freedom to design and implement various types of pedagogies that meet their preferences, students' needs, and classroom reality.Teacher agency is key to gaining their trust. AI‐EdTech should allow teachers to review, modify, and if necessary, override AI‐based recommendations before they are sent to students. [ABSTRACT FROM AUTHOR]

For the developers of next‐generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology‐mediated learning to gain insights into how students learn, especially for large cohorts, which was unthinkable only a few decades ago. This LA growth‐spurt coincides with a growing global "Ethical AI" movement focussed on resolving questions of personal agency, freedoms, and privacy in relation to AI and Analytics. At this time, there is a significant lack of actionable information and supporting technologies, which would enable the goals of these two communities to be aligned. This paper describes a collaborative research project that seeks to overcome the technical and procedural challenges of running a data‐driven collaborative research project within an agreed set of privacy and ethics boundaries. The result is a reference architecture for ethical research collaboration and a framework, or roadmap, for privacy‐preserving analytics which will contribute to the goals of an ethical application of learning analytics methods. Practitioner notesWhat is already known about this topic Privacy Enhancing Technologies, including a range of provable privacy risk reduction techniques (differential privacy) are effective tools for managing data privacy, though currently only pragmatically available to well‐funded early adopters.Learning Analytics is a relatively young but evolving field of research, which is beginning to deliver tangible insights and value to the Education and EdTech industries.A small number of procedural frameworks have been developed in the past two decades to consider data privacy and other ethical aspects of Learning Analytics.What this paper adds This paper describes the mechanisms for integrating Learning Analytics, Data Privacy Technologies and Ethical practices into a unified operational framework for Ethical and Privacy‐Preserving Learning Analytics.It introduces a new standardised measurement of privacy risk as a key mechanism for operationalising and automating data privacy controls within the traditional data pipeline;It describes a repeatable framework for conducting ethical Learning Analytics.Implications for practice and/or policy For the Learning Analytics (LA) and Education Technology communities the approach described here exemplifies a standard of ethical LA practice and data privacy protection which can and should become the norm.The privacy risk measurement and risk reduction tools are a blueprint for how data privacy and ethics can be operationalised and automated.The incorporation of a standardised privacy risk evaluation metric can help to define clear and measurable terms for inter‐ and intra‐organisational data sharing and usage policies and agreements (Author, Ruth Marshall, is an Expert Contributor on ISO/IEC JTC 1/SC 32/WG 6 "Data usage", due for publication in early 2022). [ABSTRACT FROM AUTHOR]

Stress has become one of the major reasons for many mental health related issues among students of all age groups, which has resulted in devastating personal losses including suicide. Societal and familial pressure to succeed is high, particularly in developing countries where education is highly valued as a key enabler. As part of stress management during the COVID‐19 pandemic, demand for online intelligent virtual advisors has risen and, consequently, the need for personalised explanation that is culturally sensitive to the user's context is essential to improve the user's understanding of and trust in the recommendations provided by the virtual advisor. This paper presents the mAnaging stRess at University embodied conversational agent (ECA) that has been adapted for Indian university students from an explainable agent that was found to help Western students reduce their stress by providing study tips with explanations based on the student's beliefs and/or goals. We conducted a research study with sixty students which measured the impact of providing three different patterns of tailored explanations (belief‐based, goal‐based, and belief and goal‐based explanation) on the students' intentions to change the recommended behaviours and the relationship built with the ECA. The experimental results indicate that there was stress reduction across all student groups provided with different types of explanations. Further, the students showed trust and a good working alliance with the conversational agent, along with an intention to change behaviour across all types of explanations. However, it was observed that the user context played an important role in behaviour change intention and hence explanations could be tailored further, making them culturally more relevant to Indian students. Practitioner notesWhat is already known about this topicEmbodied conversational agents (ECAs) have been mostly developed, applied and shown to be effective in developed countries. Hence, their design and development are mostly guided by the intended user's needs and preferences.In a Western context, ECAs have been found to be beneficial for reducing study stress in university students.There is a pertinent need for use of low cost, effective technology that can aid academic stress reduction in higher educational institutions in developing countries owing to their high youth populations, lack of adequate mental healthcare facilities and associated social stigma.What this paper addsThe adaptation and use of ECAs to reduce study stress in higher education students in a developing country is evaluated.The ECA technology is adapted for an Indian context in terms of its physical appearance, colour, speech dialect and dialog content so that it is culturally more aligned to the target population.The ECA engages in an empathic conversation tailored for the Indian students and their COVID‐19 context providing them with explanation‐backed behaviour recommendations that take their beliefs and goals into account.The ECA provides three types of explanation: belief‐only; goal‐only; and both belief and goal.Results of a study carried out in an Indian university with 61 students, randomly assigned to one of the explanation types, to capture their demographics, study stress statistics, behaviour change intentions and trust/working alliance with the conversational agent.The major findings include stress reduction across all explanation groups, development of a positive relationship between the ECA and the students regardless of its explanation pattern, and changes in behaviour intentions across all types of explanations for all recommended behaviours. However, differences in change intentions for certain behaviours indicate further tailoring of explanations is required based on the user context.Implications for practice and/or policyThe ECA technology has shown promise in terms of stress reduction amongst Indian students.Higher Education Institutions in developing countries could utilise low‐cost and widely accessible ECAs to overcome lack of access to human‐based support and reluctance to use available services due to stigmatized attitudes to mental health issues.This technology can be further improved and deployed into a larger number of Indian educational institutions leading to a widespread impact on overall student health and wellbeing.Digital technologies to support mental health have become more prominent during the COVID‐19 pandemic, at least in Western countries. The ECA technology evaluated in our study demonstrates its viability and potential value for use in developing countries, with appropriate tailoring. [ABSTRACT FROM AUTHOR]

Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace‐based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace‐based approach to study SSRL, there remains a paucity of evidence on how trace‐based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace‐based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace‐based data could be utilised to predict learning outcomes in collaborative learning. Practitioner notesWhat is already known about this topic Socially shared regulation has been recognised as an essential aspect of collaborative learning success.It is challenging to make the processes of learning regulation 'visible' to better understand and support student learning, especially in dynamic collaborative settings.Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning.What this paper adds Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning.Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation.Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions.Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns.Implications for practice and/or policy Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation.This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation. [ABSTRACT FROM AUTHOR]

Due to the ongoing digitalisation of workplaces and educational settings, human activity underpinning learning and work is increasingly mediated by technology. The advancement of artificial intelligence (AI) and its integration into everyday technologies influences how people are exposed to information, interact, learn and make decisions. We argue that technology, data and evolving AI applications affect how humans enact and experience life and work, changing the context for learning. Hence, as this paper argues, the current notion of lifelong learning needs a revisit to embrace technology at its foundation. To bring freely chosen goals and ownership in one's learning to the fore, in the context of the coming AI age, we argue for the telos of learning to shift from human capital to human development, with the spotlight on capabilities. The paper draws on the capability approach to inform individuals and organisations of how they can support human development throughout lifelong learning. We then move to provide examples of how technologies underpinning workplace practices can be seen with the focus on capabilities as individuals learn to create value. Practitioner notesWhat is known about the topic? The primary notion of lifelong learning refers to adult learning processes.The policy perspective that dominates organisation of lifelong learning opportunities focuses on human capital development.Technologies mediate learning and work.What this paper adds Technology is not explicitly addressed in meanings associated with lifelong learning.AI‐based technologies dynamically interact with human cognitive and social practices.The paper argues for a stronger focus on human development instead of human capital in the telos of lifelong learning opportunities.Capability approach is a viable alternative to human capital perspective on LLL.Data used to support learning can focus on learner agency and systemic factors that enable and constrain lifelong learning.Implications for practice and/or policy LLL interventions should promote systemic support for learner agency and ownership.LLL interventions should focus on negotiated value creation.Workplaces should embrace human‐machine integration but in ways that support capability and human development, not human capital. [ABSTRACT FROM AUTHOR]

Constructionism, long before it had a name, was intimately tied to the field of Artificial Intelligence. Soon after the birth of Logo at BBN, Seymour Papert set up the Logo Group as part of the MIT AI Lab. Logo was based upon Lisp, the first prominent AI programming language. Many early Logo activities involved natural language processing, robotics, artificial game players, and generating poetry, art, and music. In the 1970s researchers explored enhancements to Logo to support AI programming by children. In the 1980s the Prolog community, inspired by Logo's successes, began exploring how to adapt logic programming for use by school children. While there have been over 40 years of active AI research in creating intelligent tutoring systems, there was little AI‐flavoured constructionism after the 1980s until about 2017 when suddenly a great deal of activity started. Amongst those activities were attempts to enhance Scratch, Snap!, and MIT App Inventor with new blocks for speech synthesis, speech recognition, image recognition, and the use of pre‐trained deep learning models. The Snap! enhancements also include support for word embeddings, as well as blocks to enable learners to create, train, and use deep neural networks. Student and teacher project‐oriented resources highlighting these new AI programming components appeared at the same time. In this paper, we review this history, providing a unique perspective on AI developments—both social and technical—from a constructionist perspective. Reflecting on these, we close with speculations about possible futures for AI and constructionism. Practitioner notesWhat is already known about this topicThere exist excellent broad surveys of the current status of teaching machine learning in schools, for example Marques et al. (2020). There are historical collections of AI and education research papers that include descriptions of constructionist activities, for example Yazdani (1984).What this paper addsThis paper adds an in‐depth focus on historical and current efforts on AI and education that support constructionist teaching. This focus enables us to delve deeper than a broad survey. Uniquely, we provide a 50‐year historical perspective on constructionist AI tools, trials, and research. Grounded in this history and our survey of current tools and projects, we provide speculations about future directions.Implications for practice and/or policyWe hope our descriptions of current AI programming tools for non‐experts placed in a broad historical context will be of use to teachers wishing to introduce AI to their students in a constructionist manner, as well as to developers and researchers aiming to support such teaching. [ABSTRACT FROM AUTHOR]

To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making. Practitioner notes: What is already known about this topic Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data. What this paper adds Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in. Implications for practice and/or policy It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes). [ABSTRACT FROM AUTHOR]

A discussion forum is a valuable tool to support student learning in online contexts. However, interactions in online discussion forums are sparse, leading to other issues such as low engagement and dropping out. Recent educational studies have examined the affordances of conversational agents (CA) powered by artificial intelligence (AI) to automatically support student participation in discussion forums. However, few studies have paid attention to the safety of CAs. This study aimed to address the safety challenges of CAs constructed with educational big data to support learning. Specifically, we proposed a safety‐aware CA model, benchmarked with two state‐of‐the‐art (SOTA) models, to support high school student learning in an online algebra learning platform. We applied automatic text analysis to evaluate the safety and socio‐emotional support levels of CA‐generated and human‐generated texts. A large dataset was used to train and evaluate the CA models, which consisted of all discussion post‐reply pairs (n = 2,097,139) by 71,918 online math learners from 2015 to 2021. Results show that while SOTA models can generate supportive texts, their safety is compromised. Meanwhile, our proposed model can effectively enhance the safety of generated texts while providing comparable support. Practitioner notesWhat is already known about this topic Online discussion forums have been plagued by a lack of interaction among students due to factors such as expectations to receive no response and perceptions of topic irrelevance which lead to low motivation to participate.AI‐based conversational agents can automatically support students' interactions in online discussion forums at a large scale, and their generated responses can be human‐like, contextually coherent and socio‐emotionally supportive.Unsafe discourse exchanges between students and conversational agents can be dangerous as identity attacks, aggravation and bullying behaviours embedded in discourses can disrupt students' knowledge inquiry and negatively influence student motivation and engagement. However, few educational studies have paid attention to the safety of conversational agents.What this paper adds This study proposes and synthesized strategies to build AI‐based conversational agents that automatically support online discussions with safe and supportive discourses.This study reveals the relationship between discourse safety and social support, suggesting supportive discourses can also be unsafe.This study enriches the literature on educational conversational agents by synthesizing a conceptual framework on discourse safety and social support, and by proposing concrete algorithmic strategies to improve the safety of conversational agents.Implications for practice and/or policy Researchers and practitioners can adopt strategies in this study such as generation control, open‐sourced models and public API services to evaluate students' discourse safety for early intervention or modify existing conversational agents to be safety‐aware.Practitioners can utilize the proposed conversational agent to automatically support students both safely and socio‐emotionally at a large scale.Practitioners should be cautious when examining social support with automatic analysis, as not all supportive texts are safe. While unsafe texts can provide emotional support, it does not justify their appropriateness in a learning environment. [ABSTRACT FROM AUTHOR]

Scholars and researchers generally believe that scientific inquiry is an important activity for cultivating students' applied knowledge and high‐level thinking ability. The process of scientific inquiry can promote students' learning motivation and trigger their higher‐order thinking ability. However, students may not have enough prior knowledge or they may lack inquiry experience, which may influence the effectiveness of their inquiry learning. Besides, in a scientific inquiry environment assisted by technology, students must face abundant and diverse learning resources, and may not effectively organize and carry out advanced cogitation to solve problems. Therefore, this research proposed an integrated concept mapping and image recognition (IR) approach to help students effectively acquire and organize knowledge in the process of scientific inquiry. This study applied a quasi‐experimental design to verify the effect of this proposed approach. The experimental group conducted the concept mapping‐based IR (CM‐IR) learning approach, whilst the control group conducted the conventional IR‐based learning approach to evaluate the students' performance in terms of their learning motivation and learning achievements. The participants were two classes of 10th‐grade students in northern Taiwan. One was the experimental group with 22 students, whilst the other was the control group with 22 students. The experimental results show that the students learning with the CM‐IR learning approach had better learning achievement, attitudes and intrinsic motivation, as well as higher mental load than those learning with the conventional IR‐based learning approach. Accordingly, several suggestions are provided for future research in this field. Practitioner notesWhat is already known about this topicWith the development of information technology, AI has attracted the attention of researchers.Image recognition (IR) is one of the AI applications, and has been used to support learning in various educational fields.Researchers have indicated that concept mapping is an effective instructional strategy and promotes students' knowledge construction.What this paper addsA concept mapping‐based IR (CM‐IR) learning approach is proposed to improve students' scientific inquiry performance.A quasi‐experimental design was conducted with 10th‐grade students on the 'Plant identification' unit of the scientific inquiry course.The experimental results show that the proposed approach can significantly improve students' learning achievement, learning motivation and learning attitude.Implications for practice and/or policyThe CM‐IR learning approach could be an effective method to facilitate students' learning performances and motivation in scientific inquiry.It is worth promoting the CM‐IR learning approach in school settings since it is low‐cost and enables learning anywhere using mobile devices.Scholars and educators are encouraged to work on how AI technologies applications can be applied to support learning. [ABSTRACT FROM AUTHOR]

Computational objects (eg, algorithms, bots, surveillance technology and data) have become increasingly present in our daily lives and are consequential for our changing relations to texts, multimodality and identity. Yet, our current theories of literacy, and especially the prevalence of mediational and representational perspectives, are inadequate to account for these changing relations. What are the implications for critical literacy education when it takes seriously computational agents that interact, produce and process texts? While such work is only beginning in education, scholars in other fields are increasingly writing about how AI and algorithmic mediation are changing the landscape of online intra‐action, and business strategies and tactics for working with AI are advancing far ahead of critical literacy education. Drawing on our own and others' research into non‐human actors online, and building on posthuman theories of networks, heterogeneous actants and the assemblage, in this conceptual paper, we sketch some of the forms of critical consciousness that media education might provide in this new mixed landscape. Practitioner NotesWhat is already known about this topic AI is a hot topic in education and in public discourse, but critical literacy theories have not sufficiently accounted for how AI and computational agents change what it means to be "critically literate."Technology is an important force in shaping (and is also shaped by) literacy practices and identity.Corporate actors have an enormous influence on the texts we read and write, but this influence is often hidden.What this paper adds We bridge between critical literacy studies and posthumanist theory to conceptualize critical posthuman literacy.We argue for re‐imagining what texts, multimodality and identity are and do in the age of AI.We pose new questions of our texts and ourselves, informed by posthuman critical literacy.Implications for practice and/or policy Today's readers and composers must be able to identify and interrogate networks of computational and human agents that permeate literacy practices.Beyond identifying and understanding computational agents, posthuman critical literacy necessitates that people can actively build more ethical assemblages with computational agents. [ABSTRACT FROM AUTHOR]

Digital data are transforming higher education (HE) to be more student‐focused and metrics‐centred. In the UK, capturing detailed data about students has become a government priority, with an emphasis on using student data to measure, compare and assess university performance. The purpose of this paper is to examine the governmental and commercial drivers of current large‐scale technological efforts to collect and analyse student data in UK HE. The result is an expanding data infrastructure which includes large‐scale and longitudinal datasets, learning analytics services, student apps, data dashboards and digital learning platforms powered by artificial intelligence (AI). Education data scientists have built positive pedagogic cases for student data analysis, learning analytics and AI. The politicization and commercialization of the wider HE data infrastructure is translating them into performance metrics in an increasingly market‐driven sector, raising the need for policy frameworks for ethical, pedagogically valuable uses of student data in HE. Practitioner NotesWhat is already known about this topic Learning analytics, education data science and artificial intelligence are opening up new ways of collecting and analysing student data in higher education.UK government policies emphasize the use of student data for improvements to teaching and learning.What this paper adds A conceptual framework from "infrastructure studies" demonstrates how political objectives and commercial aims are fused to HE data systems, with data infrastructure becoming a key tool of government reform.A critical infrastructure analysis shows that student data processing technologies are being developed and deployed to measure university performance through student data.Implications for practice and/or policy Educators and managers in universities need to prepare robust institutional frameworks to govern their use of student data.Learning analytics practitioners, data scientists, learning scientists and social science researchers need to collaborate with the policy community and education technology developers on new policy frameworks to challenge narrow uses of student data as performance metrics. [ABSTRACT FROM AUTHOR]

Students' on‐task engagement during adaptive learning activities has a significant effect on their performance, and at the same time, how these activities influence students' behavior is reflected in their effort exertion. Capturing and explaining effortful (or effortless) behavior and aligning it with learning performance within contemporary adaptive learning environments, holds the promise to timely provide proactive and actionable feedback to students. Using sophisticated machine learning (ML) algorithms and rich learner data, facilitates inference‐making about several behavioral aspects (including effortful behavior) and about predicting learning performance, in any learning context. Researchers have been using ML methods in a "black‐box" approach, ie, as a tool where the input data is the learner data and the output is a given class from the chosen construct. This work proposes a methodological shift from the "black‐box" approach to a "grey‐box" approach that bridges the hypothesis/literature‐driven (feature extraction) "white‐box" approach with the computation/data‐driven (feature fusion) "black‐box" approach. This will allow us to utilize data features that are educationally and contextually meaningful. This paper aims to extend current methodological paradigms, and puts into practice the proposed approach in an adaptive self‐assessment case study taking advantage of new, cutting‐edge, interdisciplinary work on building pipelines for educational data, using innovative tools and techniques. Practitioner NotesWhat is already known about this topic Capturing and measuring learners' engagement and behavior using physiological data has been explored during the last years and exhibits great potential.Effortless behavioral patterns commonly exhibited by learners, such as "cheating," "guessing" or "gaming the system" counterfeit the learning outcome.Multimodal data can accurately predict learning engagement, performance and processes.What this paper adds Generalizes a methodology for building machine learning pipelines for multimodal educational data, using a modularized approach, namely the "grey‐box" approach.Showcases that fusion of eye‐tracking, facial expressions and arousal data provide the best prediction of effort and performance in adaptive learning settings.Highlights the importance of fusing data from different channels to obtain the most suited combinations from the different multimodal data streams, to predict and explain effort and performance in terms of pervasiveness, mobility and ubiquity.Implications for practice and/or policy Learning analytics researchers shall be able to use an innovative methodological approach, namely the "grey‐box," to build machine learning pipelines from multimodal data, taking advantage of artificial intelligence capabilities in any educational context.Learning design professionals shall have the opportunity to fuse specific features of the multimodal data to drive the interpretation of learning outcomes in terms of physiological learner states.The constraints from the educational contexts (eg, ubiquity, low‐cost) shall be catered using the modularized gray‐box approach, which can also be used with standalone data sources. [ABSTRACT FROM AUTHOR]

This study explored the effects of virtual reality (VR) application on the process and outcome of skill learning. The participants consisted of 60 new employees in the customer network cable service division of a telecommunications company, and the study focused on the learning of optical‐fibre fusion splicing skills. The skill‐learning test results revealed the following: (a) the skill‐learning effect associated with VR application was greater than that of conventional lecturing and demonstration approaches; (b) VR application had a considerable negative effect on the perception level of skill learning but a moderately positive effect on adaptation and origination levels; and (c) in the VR operation stage of learning, the behavioural transition from the cognitive phase to the associative phase was the most prominent. Practitioner notesWhat is already known about this topicVirtual reality (VR) can transmit complex knowledgeVirtual reality (VR) can facilitate the skill learningWhat this paper addsHow to apply VR to skill learningEffects of VR teaching on steps of skill learning process and learning levelsEffects of VR teaching on stages of skill learningImplications for practice and/or policyUsing VR teaching to improve skill learning effectsUsing VR teaching to improve skill learning stages and levels. [ABSTRACT FROM AUTHOR]

Artificial intelligence and data analysis (AIDA) are increasingly entering the field of education. Within this context, the subfield of learning analytics (LA) has, since its inception, had a strong emphasis upon ethics, with numerous checklists and frameworks proposed to ensure that student privacy is respected and potential harms avoided. Here, we draw attention to some of the assumptions that underlie previous work in ethics for LA, which we frame as three tensions. These assumptions have the potential of leading to both the overcautious underuse of AIDA as administrators seek to avoid risk, or the unbridled misuse of AIDA as practitioners fail to adhere to frameworks that provide them with little guidance upon the problems that they face in building LA for institutional adoption. We use three edge cases to draw attention to these tensions, highlighting places where existing ethical frameworks fail to inform those building LA solutions. We propose a pilot open database that lists edge cases faced by LA system builders as a method for guiding ethicists working in the field towards places where support is needed to inform their practice. This would provide a middle space where technical builders of systems could more deeply interface with those concerned with policy, law and ethics and so work towards building LA that encourages human flourishing across a lifetime of learning. Practitioner NotesWhat is already known about this topic Applied ethics has a number of well‐established theoretical groundings that we can use to frame the actions of ethical agents, including, deontology, consequentialism and virtue ethics.Learning analytics has developed a number of checklists, frameworks and evaluation methodologies for supporting trusted and ethical development, but these are often not adhered to by practitioners.Laws like the General Data Protection Regulation (GDPR) apply to fields like education, but the complexity of this field can make them difficult to apply.What this paper adds Evidence of tensions and gaps in existing ethical frameworks and checklists to support the ethical development and implementation of learning analytics.A set of three edge cases that demonstrate places where existing work on the ethics of AI in education has failed to provide guidance.A "practical ethics" conceptualisation that draws on virtue ethics to support practitioners in building learning analytics systems.Implications for practice and/or policy Those using AIDA in education should collect and share example edge cases to support development of practical ethics in the field.A multiplicity of ethical approaches are likely to be useful in understanding how to develop and implement learning analytics ethically in practical contexts. [ABSTRACT FROM AUTHOR]

Intelligent Tutoring systems (ITSs) and Intelligent Learning Environments (ILEs) have been developed and evaluated over the last 40 years. Recent meta‐analyses show that they perform well enough to act as effective classroom assistants under the guidance of a human teacher. Despite this success, they have been criticised as embodying a retrograde behaviourist technology. They have also been caught up in broader controversies about the role of Artificial Intelligence in society and about the entry of big data companies into the education market and the harvesting of learner data. This paper concentrates on rebutting the criticisms of the pedagogy of ITSs and ILEs. It offers examples of how a much wider range of pedagogies are available than their critics claim. These wider pedagogies operate at both the screen level of individual systems, as well as at the classroom level within which the systems are orchestrated by the teacher. It argues that there are many ways that such systems can be integrated by the teacher into the overall experience of a class. Taken together, the screen‐level and orchestration‐level dramatically enlarge the range of pedagogies beyond what was possible with the "Skinner Box." [ABSTRACT FROM AUTHOR]

Using data to understand learning and improve education has great promise. However, the promise will not be achieved simply by AI and Machine Learning researchers developing innovative models that more accurately predict labeled data. As AI advances, modeling techniques and the models they produce are getting increasingly complex, often involving tens of thousands of parameters or more. Though strides towards interpretation of complex models are being made in core machine learning communities, it remains true in these cases of "black box" modeling that research teams may have little possibility to peer inside to try understand how, why, or even whether such models will work when applied beyond the data on which they were built. Rather than relying on AI expertise alone, we suggest that learning engineering teams bring interdisciplinary expertise to bear to develop explanatory learner models that provide interpretable and actionable insights in addition to accurate prediction. We describe examples that illustrate use of different kinds of data (eg, click stream and discourse data) in different course content (eg, math and writing) and toward different goals (eg, improving student models and generating actionable feedback). We recommend learning engineering teams, shared infrastructure and funder incentives toward better explanatory learner model development that advances learning science, produces better pedagogical practices and demonstrably improves student learning. Practitioner NotesWhat is already known about this topic Researchers in learning analytics and educational data mining have been successful in creating innovative models of data that optimize prediction.Some of these models produce scientific or practical insights and fewer have been put into use and demonstrated to enhance student learning.What this paper adds We provide examples of development of explanatory models of learners that not only accurately predict data but also provide scientific insights and yield practical outcomes.In particular, researchers with expertise in cognitive science and math education content use AI‐based data analytics to discover previously unrecognized barriers to geometry student learning. They use model‐derived insights to redesign an online tutoring system and "close‐the‐loop" by experimentally demonstrating that the new system produces better student learning than the original.Implications for practice and/or policy We define explanatory learning models and provide an articulation of a process for generating them that involves interdisciplinary teams employing human–computer interaction and learning engineering methods.Based on our experiences, we recommend learning engineering teams, shared infrastructure and funder incentives toward better explanatory learner model development that advances learning science, produces better pedagogical practices and demonstrably improves student learning. [ABSTRACT FROM AUTHOR]