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India's growing economy and population proves its significant stake in the Artificial Intelligence (AI) revolution. India's education and technology sector has grown exponentially over the last few years, particularly in times of COVID-19 pandemic but owing to various reasons, it is accessible to only one in four students in India. AI has proven to be of benefit to all stakeholders alike by (a) enhancing teachers' efficiency and effectiveness in managing Multi Levelled or Multi Graded Learning Units and enabling automated development of customised teaching content; (b) using Intelligent Tutoring Systems to tailor the learning materials to a child's level of competence, style and pace of learning for improving student interactivity and attention rates; and (c) for effective teacher posting and transfer systems and predicting likely student activities such as dropout rates in government schools. The research provides a comprehensive review of the rigorous inclusion of AI in India's education sector through various policy measures. According to the findings, AI increases the efficiency of grading and encourages critical thinking and analytical skills among students. Further studies can assess effective integration of AI with educational practices and policies to increase learning needs.

This study investigated the effects of a reading assistant software on the fluency and accuracy of speech production among EFL university students, employing a one-group pretest-posttest design. The participants included 50 Thai undergraduate English majors in their first and second years. Data were analyzed using paired samples t-tests to measure L2 oral fluency, specifically pruned speech rate (PSR) and mean length of run (MLR). Accuracy rate was assessed by quantifying the proportion of error-free clauses within the participants' speeches from the pretest and posttest and calculating the average number of error-free clauses per T-unit. Results indicated significant improvements in speech fluency, evidenced by longer uninterrupted speech segments, fewer disfluencies like filled pauses and repairs, and increased overall fluency. Additionally, the post-test showed a higher percentage of error-free clauses compared to the pretest, reflecting a substantial enhancement in grammatical accuracy. This suggests that the intervention had a positive impact on the participants' ability to produce grammatically accurate and error-free clauses. These outcomes suggest that the reading assistant software effectively enhances both the fluency and grammatical accuracy of speech production in EFL learners, underscoring its potential benefits in language education contexts.

As a major shift in education technologies, Adaptive Learning Systems (ALS) use artificial intelligence and similar technologies, adapting the lessons to the needs of individual students. Emphasizing transformative pedagogy and teaching strategies that transform the learners' cognitive and interactive patterns, this study presents a comprehensive bibliometric analysis of ASL. Contrary to conventional teaching methods, ALS alters dramatically the way students think and interact with their environment. This research has utilized an all-inclusive bibliometric analysis to analyze the evolution, trends, and themes in ALS by using an extensive set of data from the Web of Science (WoS) and Scopus. The primary objective of Bibliometric analysis is to map the development of ALS in teaching and learning while marking the important trends, models, and thematic priorities. The relevance of this research lies in its comprehensive analysis of the Adaptive Learning Systems (ALS) field through bibliometric methods, offering critical insights into the trends, key contributors, and thematic developments over time. The systematic evaluation enables the appraisal of the impact created by major contributors like authors, organizations, journals, etc. The study also examines, using the advanced data collection technique, influential articles, and publications that enormously contributed to shaping ALS. Similarly, it does the rating effectively upon evaluating the mutual relationships among important terms, concepts, and factors through co-references and co-occurrences. It highlights the increasing scholarly output and identifies key contributors and influential works, underscoring the growing recognition of ALS's importance due to technological advancements. The study's findings on global research contributions, thematic analyses, and collaboration networks offer new insights into the field's dynamics, setting a foundation for future research directions. To visually represent bibliometric data, web analytic tools are used, explaining intricate relationships and thematic clusters. Identifying the unexplored areas and discussing the practical implications of ASL development, research, and analysis of combined data taken from WoS and Scopus provides a unique perspective. Consequently, researchers, educators, policymakers, etc., get valuable insights that enable advancing and understanding the area. This bibliometric analysis will undoubtedly guide future research in the area of transformative pedagogy as it is the most sought-after method in understanding the scholarly landscape of ALS.

The use of intelligent tutoring systems (ITSs) is growing rapidly in the field of education. In mathematics, adaptive and personalized scenarios mediated by these systems have been implemented to aid concept comprehension and skill development. This study presents a systematic review on the current status of the use of ITSs in mathematics education in higher education institutions (HEI). More specifically, it inquires about elements rarely addressed in reviews, such as the purposes, advantages, effectiveness, limitations, characteristics, and development and research methodologies in ITSs used for mathematics education. We analyzed 43 documents in 15 specialized and indexed journals in the years 2012-2022. In addition to providing a global overview of the implementation of ITSs in mathematics teaching and learning, we also present trends and opportunities for future research on building educational environments guided by these technologies.

In the context of contemporary technological advancements, Artificial Intelligence (AI) has gained considerable significance in the field of education. In light of ChatGPT's growing popularity, this research aims to explore how higher education students perceive the use of ChatGPT in academics, examining factors influencing its acceptance, as well as its benefits, limitations, and ethical concerns. The study applied a survey design, collecting data through Google Forms from undergraduate, postgraduate, and doctoral students. A total of 162 participants, who were using ChatGPT selected through convenience sampling. The findings indicate a positive perception among respondents regarding ChatGPT's academic applications, its benefits, limitations, acceptance factors, and ethical concerns. The study also reveals that the perception of higher education students towards ChatGPT usage is not significantly influenced by gender, academic programs, and streams. The insights gained from this study holds significant implications for the responsible and effective integration of ChatGPT in higher education environments, taking into account its perceived benefits and ethical concerns.

This study investigates the primary school teachers' perceptions towards the Intelligent Material System (IMS), which was developed to enhance the quality of inclusive environments and support the academic skills of students with Special Educational Needs (SEN). Within this scope, IMS training was conducted with 84 classroom teachers who worked at primary schools in Istanbul. Following the training, an open-ended questionnaire and two focus group interviews were used to collect data. The findings revealed that IMS has the potential to support the students with SEN academically, as it provides motivating and rich content, has a user-friendly interface and a desired level of accessibility despite the system's limitations and the need for improvements noted by the participants.

LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation in education. It supports learning researchers in addressing a broad range of issues including cognitive, social, and motivational factors in learning, educational content analysis, and educational technology innovation. LearnSphere integrates previously separate educational data and analytic resources developed by participating institutions. The web-based workflow authoring tool, Tigris, allows technical users to contribute sophisticated analytic methods, and learning researchers can adapt and apply those methods using graphical user interfaces, importantly, without additional programming. As part of our use-driven design of LearnSphere, we built a community through workshops and summer schools on educational data mining. Researchers interested in particular student levels or content domains can find student data from elementary through higher-education and across a wide variety of course content such as math, science, computing, and language learning. LearnSphere has facilitated many discoveries about learning, including the importance of active over passive learning activities and the positive association of quality discussion board posts with learning outcomes. LearnSphere also supports research reproducibility, replicability, traceability, and transparency as researchers can share their data and analytic methods along with links to research papers. We demonstrate the capabilities of LearnSphere through a series of case studies that illustrate how analytic components can be combined into research workflow combinations that can be developed and shared. We also show how open web-accessible analytics drive the creation of common formats to streamline repeated analytics and facilitate wider and more flexible dissemination of analytic tool kits.

A massive open online course (MOOC) is a powerful tool for expanding educational opportunities, but one of the major challenges facing MOOCs is the high dropout rate. Low completion rates indicate issues with student engagement and motivation. Gamification, the incorporation of game elements in non-game contexts, has shown promise in increasing engagement and completion rates in education. This systematic review aims to explore the current state of research on gamification in MOOCs, including the most commonly used gamification aspects, their impact on motivation and engagement, and the influence on dropout rates. We searched several databases, such as Google Scholar, Scopus, and ERIC, from 2014 to 2021 and included studies that focused on the application of gamification in MOOCs and its effects on motivation, engagement, and dropout rates. A total of 16 studies were analyzed, and the results were synthesized to address the research questions posed. The results of this systematic literature review indicate that while research on gamification in MOOCs is still in its early stages, several studies have shown that gamification elements such as badges, levels, leaderboards, challenges, and rewards significantly affect student motivation and engagement, and reduce dropout rates in MOOCs. However, the effectiveness of these elements varies depending on their specific design, implementation, and alignment with students' intrinsic motivations and goals.

Bayesian Knowledge Tracing (BKT) is a popular interpretable computational model in the educational mining community that can infer a student's knowledge state and predict future performance based on practice history, enabling tutoring systems to adaptively select exercises to match the student's competency level. Existing BKT implementations do not scale to large datasets and are difficult to extend and improve in terms of prediction accuracy. On the other hand, uninterpretable neural network (NN) student models, such as Deep Knowledge Tracing, enjoy the speed and modeling flexibility of popular computational frameworks (e.g., PyTorch, Tensorflow, etc.), making them easy to develop and extend. To bridge this gap, we develop a collection of BKT recurrent neural network (RNN) cells that are much faster than brute-force implementations and are within an order of magnitude of a fast, fine-tuned but inflexible C++ implementation. We leverage our implementation's modeling flexibility to create two novel extensions of BKT that significantly boost its performance. The first merges item response theory (IRT) and BKT by modeling multidimensional problem difficulties and student abilities without fitting student-specific parameters, allowing the model to easily generalize to new students in a principled way. The second extension discovers the discrete assignment matrix of problems to knowledge components (KCs) via stochastic neural network techniques and supports further guidance via problem input features and an auxiliary loss objective. Both extensions are learned in an end-to-end fashion; that is, problem difficulties, student abilities, and assignments to knowledge components are jointly learned with BKT parameters. In synthetic experiments, the skill discovery model can partially recover the true generating problem-KC assignment matrix while achieving high accuracy, even in some cases where the true KCs are structured unfavorably (interleaving sequences). On a real dataset where problem content is available, the skill discovery model matches BKT with expert-provided skills, despite using fewer KCs. On seven out of eight real-world datasets, our novel extensions achieve prediction performance that is within 0.04 AUC-ROC points of state-of-the-art models. We conclude by showing visualizations of the parameters and inferences to demonstrate the interpretability of our BKT RNN models on a real-life dataset.

This study proposes that the gestures of an agent tutor in a multimedia learning environment can generate positive and negative emotions in learners and influence their cognitive processes. To achieve this, we developed and integrated positive and negative agent tutor gestures in a multimedia learning environment directed by cognitive gestures. The effects of emotion type on cognition were examined in terms of cognitive load, learning motivation, and achievement. The subjects were 46 university students in Gyeonggi Province, South Korea. The students were divided into three learner groups: cognition, cognition + negative emotion, and cognition + positive emotion. The learners watched a tutorial lecture on the Notion note-taking app by an agent tutor. Data analysis was conducted using one-way ANOVA to determine the cognitive load, learning motivation, and achievement. The results showed that the positive emotion design was more effective in terms of intrinsic cognitive load, learning motivation, and achievement but had a higher extrinsic cognitive load. However, even the negative + passive group showed more positive learning than the cognition group. Although this study focused on gestures by an agent tutor, it implies that such gestures in multimedia learning contexts must be informed by emotional as well as cognitive design to provide a more meaningful learning experience.

A scientometric analysis and systematic review of an immersive virtual learning environment (IVLE) designed to improve the development of hard skills comprised this study. The review focused on the period between 2016 and 2023 and was conducted using the Scopus database. This study employed a scientometrics analysis and systematic review methodology. In this study, a comprehensive analysis was conducted on a corpus of 12 scholarly articles that have been published within the last 8 years. This study looked at the aggregate magnitude and trajectory of scholarly publications and looked at a number of trends in scholarly research, including annual reports, article counts, article distribution across different sources, finding the most useful sources, keyword analysis, looking into the best collaboration groups, looking at how themes have changed over time, judging contributions, and practical implications. The primary findings regarding the data and document types indicated that a total of 12 articles were published across 10 different sources within the timeframe of 2016 to 2023. The research conducted on IVLE for the development of hard skills exhibited fluctuations in its pace, alternating between deceleration and positive acceleration. The growth rate of the articles peaked in 2022. The primary publication with the greatest of source is "Lecture Notes in Networks and Systems." Keyword Plus growth rate for terms like "virtual reality," "e-learning," and "teaching." The nations exhibit a high degree of collaboration in the field of research. The findings indicated that the authors hailing from Mexico and Colombia exhibited the greatest frequency (n = 2). The thematic evolution of the study indicated two significant advancements: firstly, the emergence of e-learning studies that have propelled the field of e-learning, and secondly, the integration of virtual reality into e-learning practices. The classification of applications encompasses various components: title, solution, learning strategy, and learning tasks.

There is a nationwide shortage of special education teachers (SETs) due, in part, to unmanageable workload expectations, which has reached crisis level. SETs are expected to modify, adapt, and accommodate general education curriculum to meet the needs of their students, communicate and collaborate with parents and general education teachers, and progress monitor on IEP goals, to name a few. SETs, especially those in more restrictive self-contained settings, report spending almost half of their time completing non-teaching tasks. One emerging and innovative solution to help SETs accomplish these tasks is using Artificial Intelligence (AI). AI powers many popular educational tools, such as predictive text, adaptive learning platforms, and digital assistants. The launch of ChatGPT, Bard, and other generative pre-trained transformers (GPTs), provides an opportunity to support SETs with some of the paperwork requirements. This is due to the GPTs ability to craft human-like responses via drafting essays, emails, lists, and the like. In this article, we provide step-by-step directions to use ChatGPT. Additionally, we illustrate how GPTs can be used to operationalize, automate, and streamline many of the SET's non-teaching tasks through specific examples of its use in (1) collaboration; (2) adapting readings; and (3) developing progress monitoring assessments.

Scalable and accessible professional development programs have the potential to address the opportunity gap many teachers experience. Yet many asynchronous online programs lack interaction with and timely feedback to teachers. We addressed this problem by developing a virtual, interactive program that uses intelligent tutoring systems to provide just-in-time feedback to teachers. We conducted a randomized controlled trial with teachers across the United States in which teachers were assigned to either this program or no additional training. We found that teachers who completed our program (N = 29) used mathematically richer tasks and created a more coherent, connected learning environment for students to build conceptual understandings than did teachers who were in the business-as-usual condition (N = 23).

Underdeveloped reading comprehension skills can limit academic success; a particular challenge for English language learners (ELLs). The current study investigated whether a web-based text structure strategy, delivered via the Intelligent Tutoring of Structure Strategy (ITSS) program to adult Chinese ELLs, improved students' use of reading strategies and/or overall reading comprehension. Using a quasi-experimental nonequivalent control group design, 207 adult Chinese ELLs from four classes were assigned to intervention or control groups. The intervention group utilized the ITSS to support their English reading instruction, whereas the control group was exposed to only traditional instruction. Our results indicate that the ITSS intervention had a statistically significant positive effect on adult Chinese ELLs' reading comprehension ([beta] = 3.07, p < 0.001) with Cohen's d = 0.43, as measured by the College English Test--4 (CET-4). Furthermore, we found that Chinese ELLs reported using more higher-order reading strategies (p < 0.01) after the intervention and there was no significant change of reported reading strategy usage for the control group from pretest and post-test. However, the current study did not provide evidence that the change in use of reading strategies mediated the relationship between the intervention/control condition and Chinese ELLs' reading comprehension.

Intelligent tutoring systems (ITSs) incorporate pedagogical agents (PAs) to scaffold learners' self-regulated learning (SRL) via prompts and feedback to promote learners' monitoring and regulation of their cognitive, affective, metacognitive and motivational processes to achieve their (sub)goals. This study examines PAs' effectiveness in scaffolding and teaching SRL during learning with MetaTutor, an ITS on the human circulatory system. Undergraduates (N = 118) were randomly assigned to a condition: "Control Condition" (i.e. learners could only self-initiate SRL strategies) and "Prompt and Feedback Condition" (i.e. PAs prompted learners to engage in SRL). Learners' log-file data captured when strategies were used, the initiator of the strategy (i.e. learner and PA), and the relevance of instructional content pages in relation to learner subgoals. While results showed that PAs were effective scaffolders of SRL in which they prompted learners to engage in SRL strategies more when content was relevant towards their subgoals and as time on page and task increased, there were mixed findings about the effectiveness of PAs as teachers of SRL. Findings show how production rules guiding PA prompts can improve their scaffolding and teaching of SRL across the learning task - through contextualizing SRL strategies to the instructional content and in relation to the relevance of the content to learners' subgoals.

Reading requires appropriate strategies to spark initial interest and sustain engagement. One promising strategy is the pedagogical approach of learning-by-teaching, transforming learners into active participants. Integrating this approach into digitalized and individualized reading contexts has the potential to foster the development of young readers. Currently, AI techniques are primarily used in chatbots as tutors, with limited focus on tutee chatbots that employ the learning-by-teaching pedagogy. Therefore, this study adopted a teachable Q&A agent and probed into the effect of chatbot training, employing AI techniques and utilizing student-generated questions and answers, with the aim of enhancing students' reading interest and engagement. Ninety-five fifth graders participated in a 9-week reading program. A quasi-experimental design was conducted. The results proved that incorporating a learning-by-teaching approach into the chatbot training activity significantly enhanced their reading interest and engagement. However, the quantity of certain question types is negatively correlated with interest and engagement. This implies that asking diverse questions poses a certain level of challenge to young readers, which requires deliberate training and incubation. Additionally, the identification of four distinct student clusters exhibited the affordances and limitations of tutee chatbots for reading.

Progress in understanding students' development of psychological literacy is critical. However, generative AI represents an emerging threat to higher education which may dramatically impact on student learning and how this learning transfers to their practice. This research investigated whether ChatGPT responded in ways that demonstrated psychological literacy and whether it matched the responses of subject matter experts (SMEs) on a measure of psychological literacy. We tasked ChatGPT with providing responses to 13 psychology research methods scenarios as well as to rate each of the five response options that were already developed for each scenario by the research team. ChatGPT responded in ways that would typically be regarded as displaying a high level of psychological literacy. The response options which were previously rated by two groups of SMEs were then compared with ratings provided by ChatGPT. The Pearson's correlations were very high (r's = 0.73 and 0.80, respectively), as were the Spearman's rhos (rho's = 0.81 and 0.82, respectively). Kendall's tau were also quite high (tau's = 0.67 and 0.68, respectively). We conclude that ChatGPT may generate responses that match SME psychological literacy in research methods, which could also generalise across multiple domains of psychological literacy.

Contribution: A general-purpose model for integrating an intelligent tutoring system within a serious game for use in higher education. Additionally, this article also offers discussions of proper serious game design informed by in-classroom observations and student responses. Background: Personalized learning in higher education has become a key issue when working to improve student performance. By combining an intelligent tutoring system within a serious game, students can be engaged in their learning through gamified lessons while simultaneously receiving personalized and timely scaffolding to support their learning. Furthermore, related systems have not explored a general-purpose model for this type of system that can apply to any game or domain. Intended Outcomes: The combined intelligent tutoring system and serious game is well-received by students as determined by student surveys. Furthermore, students show better engagement in the given material and better performance on pre-post-intervention content tests. Application Design: The proposed system is a modular, general-purpose approach for integrating an intelligent tutoring system into any serious game for education. Using the machine learning paradigm of reinforcement learning, the system can adapt to student responses to improve future scaffolding. Findings: The results of the in-classroom testing are promising. Students who interacted with the intelligent game showed improved performance on content tests and positive responses on surveys regarding system usability and utility. This article also shows that students who used the intelligent game took less time and attempts to complete game sections, owing to the utility of the personalized support.

Intelligent Tutoring Systems (ITS) have been widely used to enhance math learning, wherein teacher's involvement is prominent to achieve their full potential. Usually, ITSs depend on direct interaction between the students and a computer. Recently, researchers started exploring handwritten input (e.g., from paper sheets) aiming to provide equitable access to ITSs' benefits. However, research on math ITSs ability to handle handwritten input is limited and, to our best knowledge, no study has summarized its state of the art. This article fulfills that gap with a scoping review of handwritten recognition methods, characteristics, and applications of math ITSs compatible with handwritten input. Based on a search of 11 databases, we found eight primary studies that met our criteria. Mainly, we found that all ITSs depend on receiving handwritten input from a touchscreen interface, in contrast to recognizing solutions developed on paper. We also found that most ITSs focus on similar audiences (e.g., English speakers students), subjects (e.g., algebraic questions), and applications (e.g., in-class to understand student perceptions). Thus, towards enabling equitable access to ITSs, we propose ITS Unplugged (i.e., ITSs that i) run on low-cost, resource-restricted devices with little to no internet connection and ii) receive as well as return information in the format target users usually use) and contribute a research agenda concerning challenges of developing such ITSs.

Individualized learning support is an essential part of formal educational learning processes. However, in typical large-scale educational settings, resource constraints result in limited interaction among students, teaching assistants, and lecturers. Due to this, learning success in those settings may suffer. Inspired by current technological advances, we transfer the concept of chatbots to formal educational settings to support not only a single task but a full lecture period. Grounded on an expert workshop and prior research, we design a natural language-based digital tutor acting as an intermediary among students, teaching assistants, and lecturers. The aim of the digital tutor is to support learners automated during the lecture period in natural language-based chat conversations. We implement a digital tutor in an iterative design process and evaluate it extensively in a large-scale field setting. The results demonstrate the applicability and beneficial support of introducing digital tutors as intermediaries in formal education. Our study proposes the concept of using digital tutors as intermediaries and documents the development and underlying principles.

Generative artificial intelligence has become the focus of the intelligent education field, especially in the generation of personalized learning resources. Current learning resource generation methods recommend customized courses based on learning styles and interests, improving learning efficiency. However, these methods cannot generate personalized tutorials according to learners' preferences, nor can they adjust tutorial content as moods or levels of knowledge change. Therefore, this study develops an intelligent tutorial-generating system (Self-GT) for self-aid learning, integrating cognitive computing and generative learning to capture learners' dynamic preferences. The critical components of Self-GT are the tutorial-generating model based on cyclic deep reinforcement learning (RL) and the multimodal knowledge graph containing complex relationships. Specifically, the proposed RL model dynamically explores learners' preferences from the temporal dimension, enabling RL agents to express learning behavior characteristics accurately and generate personalized tutorials. Then, relying on the internal self-developed education base and external Internet sources, a multimodal knowledge graph with multiple self-defined relationships is designed to enhance the precision of tutorial generation. Finally, the experimental results indicate that the Self-GT performs well in generating tutorials and has been successfully applied in the generating tutorial for "Hospital Network Architecture Planning and Design."

One of the Information and Communication Technology (ICT) developments used in the learning process is the Intelligent Tutoring System (ITS), and gamification can overcome boredom, lack of interest or motivation, and monotony when using the ITS. In this study, the application of ITS equipped with Gamification is called ITS + G. Currently, several studies have built the ITS + G. However, there has not been a Systematic Literature Review (SLR) that synthesizes the characteristics of the ITS and Gamification combination. Several previous SLRs have been carried out and discussed the ITS only and several other SLRs discussed gamification only. Therefore, this SLR focused on the characteristics of ITS and gamification as a unit. This study succeeded in synthesizing that ITS + G has the potential to be applied to both STEM and non-STEM subjects. Three main game elements are mostly used in ITS + G: levels, points, and progress bars, which are supported for several reasons. Several techniques that have been used to measure the success of ITS + G are synthesized. Several positive impacts of ITS + G are revealed. Some negative impacts that need to be considered and studied in future research are also noticed. The results of this study could be the basis for ITS + G research in the future and increase the repertoire of knowledge related to ITS and Gamification.

With the escalation of the COVID-19 crisis, many educational institutions have turned to distance education, especially universities and higher education institutions, which may affect the quality of learning outcomes especially those related to deeper learning and academic mindfulness. The present study aimed at investigating the effectiveness of hybrid learning in developing academic mindfulness and enhancing deeper learning of a sample of university students, in addition to shedding the light on the relationship among the study variables. The research sample consists of 350 university students from both humanities and scientific majors at two faculties in a university in Egypt during the academic year 2021\2022. A quasi-experimental research design was used in this study to investigate the relationship among the variables of the study and to answer the proposed questions. As shown through the obtained results hybrid learning is considered one of the effective teaching and learning approaches, especially in recent years. During the COVID-19 pandemic using online learning was the most effective and available way to continue learning, however after this situation ends hybrid learning should be the basic method of learning especially for university students. The present study shows a positive significant correlation among the variables of the study including academic mindfulness, deep learning competencies levels, and hybrid learning, which stresses the importance of applying hybrid learning to have a high-quality outcome of the educational process.

Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as submission, compilation, edit, and keystroke events, with keystroke-level logs being the most fine-grained of commonly used dataset types. However, the lack of open datasets, especially at the keystroke level, is notable. There are several reasons for this failing, with the most prominent being the challenges of deidentification that are peculiar to keystroke log data. In this paper, we present the public release of two fully deidentified keystroke datasets that are the first of their kind in terms of both event and metadata richness. We describe our collection technique and properties of the data along with deidentification techniques that, while not fully relieving researchers of significant effort, at least reduce and streamline manual work in hopes that researchers will release similar datasets in the future.

This article is written by two researchers and a teacher involved with the development and implementation of a web-based intelligent tutoring system for adults reading at elementary levels. A description of the tool is provided, followed by some of the challenges faced in designing, developing, and using the tool in adult literacy classrooms.

This research is part of the Artificial Intelligence Virtual Trainer (AI-VT) project which aims to create a system that can identify the user's skills from a text by means of machine learning. AI-VT is a case-based reasoning learning support system can generate customized exercise lists that are specially adapted to user needs. To attain this outcome, the relevance of the first proposed exercise must be optimized to assist the system in creating personalized user profiles. To solve this problem, this project was designed to include a preliminary testing phase. As a generic tool, AI-VT was designed to be adapted to any field of learning. The most recent application of AI-VT was in the field of computer science specifically in the context of the fundamentals of algorithmic learning. AI-VT can and will also be useful in other disciplines. Developed in Python with the Keras API and the Tensorflow framework, this artificial intelligence-based tool encompasses a supervised learning environment, multi-label text classification techniques and deep neural networks. This paper presents and compares the performance levels of the different models tested on two different data sets in the context of computer programming and algorithms.

Randomized A/B tests within online learning platforms represent an exciting direction in learning sciences. With minimal assumptions, they allow causal effect estimation without confounding bias and exact statistical inference even in small samples. However, often experimental samples and/or treatment effects are small, A/B tests are underpowered, and effect estimates are overly imprecise. Recent methodological advances have shown that power and statistical precision can be substantially boosted by coupling design-based causal estimation to machine-learning models of rich log data from historical users who were not in the experiment. Estimates using these techniques remain unbiased and inference remains exact without any additional assumptions. This paper reviews those methods and applies them to a new dataset including over 250 randomized A/B comparisons conducted within ASSISTments, an online learning platform. We compare results across experiments using four novel deep-learning models of auxiliary data and show that incorporating auxiliary data into causal estimates is roughly equivalent to increasing the sample size by 20% on average, or as much as 50-80% in some cases, relative to t-tests, and by about 10% on average, or as much as 30-50%, compared to cutting-edge machine learning unbiased estimates that use only data from the experiments. We show that the gains can be even larger for estimating subgroup effects, hold even when the remnant is unrepresentative of the A/B test sample, and extend to post-stratification population effects estimators.

Background/purpose: ChatGPT is an artificial intelligence program released in November 2022, but even now, many studies have expressed excitement or concern about its introduction into academia and education. While there are many questions to be asked, the current study reviews the literature in order to reveal the potential effects of ChatGPT on education as a whole. The potential implications, possibilities, and concerns about the use of ChatGPT in education are disclosed as mentioned in the literature. Materials/methods: The data of the study were collected and then subjected to a systematic review. Research findings were analyzed according to the themes and categories identified. Results: The results of this research were examined under themes according to the positive and negative aspects of ChatGPT. The positive categories and sub-categories of ChatGPT's integration into education were determined, and the relationship between education and artificial intelligence determined. Similarly, the negative category highlighted the potential negative impact of artificial intelligence on educational processes. Conclusion: The reviewed research evaluated and discussed the impact of AI on education and training processes. In conclusion, this review revealed the critical applications of ChatGPT for educational settings and the potential negative impact of its application. The findings established how ChatGPT and its derivatives would create a new paradigm in education as a whole.

This study aims to examine the contribution of ArtiBos, which is designed as a Gamified Adaptive Intelligent Tutoring System for students' problem-solving skills. In the study, first of all, the system's design features to improve problem-solving skills were examined, and then the effect of the system on problem-solving skills was evaluated. The study was carried out with 12 students studying in the ninth class of a High School in Türkiye and 6 mathematics teachers with different experiences working in the same school. A case study, one of the qualitative research methods were applied in this study through which ArtiBos system logs, student interviews, and teacher interviews were evaluated. Data pertaining to the number of solved problems, the number of problems created, the number of problems solved correctly, the duration of being online in the system, the rate of correct problem-solving, and the average solving time were examined to evaluate system logs. Interview questions have been prepared so that the contribution of system features to problem-solving skills can be evaluated. The data from the interview were analyzed and some codes for problem-solving skills were created. And then, sub-themes were created by combining the codes. The results show that ArtiBos affects students' problem-solving skills positively.

This research was undertaken by synthesizing theories, documents, textbooks, research articles, and related academic articles relating to the wisdom repository management process. The objective is to present a system architecture and develop a knowledge management system which culminates in a repository of crystallized intelligence with a conversational agent that can promote learning for medical students by introducing a system architecture to develop intelligent agent technology. Through mobile technology, accessible anytime, anywhere, lifelong learning for medical students will be supported via an intelligent crystallized intelligence inventory management system. This keynote includes a comprehensive implementation and has API. The application has been tested with a trial run of all commands, with satisfactory results in the communication of the system that the user accesses through chatbots.

This studied focus on the relationship between transhumanist and artificial intelligence in the Education Context; Particularly Teaching and Learning Process at private university in Makassar, South Sulawesi, Indonesia. Anchored by a qualitative analysis and participated by five teachers, the data were analyzed in-depth interview. It was designed to find out the type of artificial intelligence used in teaching and learning process. The result of the study showed that the types of Artificial intelligences are; Intelligence of tutoring system, Smart mentor virtual, Automatic assessment, Personalized system, and other finding that even though the artificial intelligence was very great tools can support teaching and learning process but the teacher roles can be not changed them, because teachers taught morality, how to respect each other, it is a role of teacher.

In this design case, we describe our first attempt to create a virtual mathematics tutoring program for students with learning disabilities. We describe in detail how the design was motivated by the pandemic which forced schools into remote learning, how a university and school collaborated on the design, the rationale for our design decisions, and aspects of the design that did not meet the intended outcomes. Three interrelated design failures included problems with flexible scheduling, challenges seeing students' work, and inconsistent use of a collaborative, communication tool. Pervasive to all these failures were underlying communication issues associated with being remote. We share our experience learning from and altering our design of these features for the future.

Advances in augmented and virtual reality (AVR) technology have allowed for the development of AVR interactive learning environments (AVR-ILEs) with increasing fidelity. When paired with a suitably capable computer tutor agent, such environments can permit adaptive and self-directed learning of procedural skills in some cases. We undertook a comprehensive review of published smart classroom (SC) solutions to facilitate hard skills training in AVR-ILEs. We established the learning context and implemented support for hard skills training for each of the seven solutions that qualified for the final analysis. Typically, AVR-ILEs mimic realistic work conditions or equipment. The aim of this study is to solutions featuring a smart classroom for hard skills training in an AVR-ILEs have been described. In the study, it is also aimed to types of AVR-ILEs from a smart classroom (SC) have been implemented to support hard skills training. This systematic review is based on the reporting criteria of PRISMA 2020 and the scope of the study consists of 9 academic studies including the keyword "smart classroom" and the keywords "augmented reality" or "virtual reality" in academic studies scanned in Scopus, IEEE Xplore, Eric, ScienceDirect and ACM Digital Library. In this study, the distribution of the studies used in the Smart Classroom for Hard Skills Training in Augmented and Virtual Reality Environments (SCHST-AVRE) by solution, domain, country, concept description, country, reviewed papers, learning objective(s), learning tasks, and the context of the technology used is included. In order to contribute to the research on the AVR-ILEs, it has been tried to determine the opportunities and risks that teaching in the SCHST-AVRE can offer to the field of education. In addition, it is thought that this study will shed a light on future studies.

This study aims to provide a comprehensive overview of the current state and potential future research in learning style detection. With the increasing number and diversity of research in this area, a quantitative approach is necessary to map out current themes and identify potential areas for future research. To achieve this goal, a bibliometric and content analysis will be conducted to map out the existing research and identify emerging topics and directions for future research. The study analyzes 1074 bibliographic sources from Scopus and visualizes the results of the bibliometric analysis through cooccurrence and thematic map analysis using VOSviewer and BibliometriX software. Content analysis is then conducted based on the results of the co-occurrence analysis. The findings reveal a significant increase in publications and citations in the field, with popular research topics including classification, adaptive learning, and MOOCs, and the most frequently used learning style models being Felder-Silverman, VARK, and Kolb. Emerging research topics include the use of EEG signals, online learning, and feature extraction. Future research may focus on classification, intelligent tutoring systems, MOOCs, online learning, adaptive learning, and deep learning. This study provides valuable insights into the current and future research trends in learning style detection, which can support the development of adaptive e-learning systems, intelligent tutoring systems, and MOOCs. By identifying popular research topics and emerging areas of study, this research can guide the design and implementation of effective online learning environments. Additionally, the study advances the field of e-learning knowledge by providing a comprehensive overview of the most frequently used learning style models and potential research areas. It sheds light on the ongoing development of learning style detection research and the potential for future advancements in the field, ultimately contributing to the growth and improvement of e-learning practices.