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Generative AI has prompted educators to reevaluate traditional teaching and assessment methods. This study examines AI's ability to write essays analysing Old English poetry; human markers assessed and attempted to distinguish them from authentic analyses of poetry by first-year undergraduate students in English at the University of Oxford. Using the standard UK University grading system, AI-written essays averaged a score of 60.46, whilst human essays achieved 63.57, a margin of difference not statistically significant (p = 0.10). Notably, student submissions applied a nuanced understanding of cultural context and secondary criticism to their close reading, while AI essays often described rather than analysed, lacking depth in the evaluation of poetic features, and sometimes failing to properly recognise key aspects of passages. Distinguishing features of human essays included detailed and sustained analysis of poetic style, as well as spelling errors and lack of structural cohesion. AI essays, on the other hand, exhibited a more formal structure and tone but sometimes fell short in incisive critique of poetic form and effect. Human markers correctly identified the origin of essays 79.41% of the time. Additionally, we compare three purported AI detectors, finding that the best, 'Quillbot', correctly identified the origin of essays 95.59% of the time. However, given the high threshold for academic misconduct, conclusively determining origin remains challenging. The research also highlights the potential benefits of generative AI's ability to advise on structuring essays and suggesting avenues for research. We advocate for transparency regarding AI's capabilities and limitations, and this study underscores the importance of human critical engagement in teaching and learning in Higher Education. As AI's proficiency grows, educators must reevaluate what authentic assessment is, and consider implementing dynamic, holistic methods to ensure academic integrity. [ABSTRACT FROM AUTHOR]

This paper outlines innovative pedagogical approaches in chemical engineering safety education, utilising three key strategies: (1) Integration of the direct experiences of experienced professional engineers to teach expertise-aligned process safety case studies; (2) Shifting from traditional short oral presentations to more comprehensive and dynamic 'engineering meeting' formats to drive constructive student learning; and (3) The use of Generative AI to enhance (1) and (2), with cost/time scalability, improved student access and by accommodating greater learning-style diversity. Evaluation of these educational innovations is performed through a mixed-methods approach and reveals positive impacts on student learning and engagement. The paper provides a detailed outline of classroom implementation, with supporting resources, for straightforward integration by other academics. • Process safety education can be significantly enhanced by including late-career professional industry consultants and semi-retired engineers. • Generative AI roleplaying as industry consultants, can provide greater scale, student access and cater to more diverse learning styles of students. • Generative AI chatbots can be used to simulate in viva style assessment formats of process safety incident case studies. Vivas encourage more comprehensive understanding and root cause analysis of process safety incidents, when compared to commonly used short oral presentations, which can drive reductive cause-effect explanations. • A small subset of students express a preference for AI teaching tools (over in-person meetings) because of social or performance anxieties. [ABSTRACT FROM AUTHOR]

This study ventures into the expanding role of artificial intelligence (AI) in higher education and its potential to revolutionize the learning experience. Through a qualitative approach, we investigate how advanced AI-driven educational tools can enhance learning systems through tailored learning experiences, AI-powered adaptive tutoring platforms, immersive digital learning environments, and AI-assisted assessment and feedback systems. From a theoretical perspective, we utilize the constructivist theory of learning as a study framework to explain the role of technology in AI-facilitated personalized and collaborative knowledge construction. This study was executed in three stages. First, we present a brief overview of AI and its recent impact on the education system, including its ethical considerations and implementation hurdles. In the second stage, we propose the potential implications for learning contexts through the constructivist lens, the process of AI-supported cognitive development, and the use of AI tools for optimizing educational processes through AI and transformation in educational paradigms and study work. The findings suggest that institutions can unlock new opportunities for efficiency, accessibility, and personalized learning by adopting AI technology, but they also highlight the need to address ethical considerations and implementation hurdles. This study also contributes to future studies by examining the impact of AI on student outcomes, teacher roles, and institutional practices in education, as well as exploring ethical and equity concerns in AI-driven educational settings. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) is revolutionizing the field of education. The possibilities of AI in education range from providing intelligent tutoring to facilitating computational thinking, a key skill in an AI-based society. As education systems around the world adapt to technological advances, the need for teachers to develop AI skills becomes crucial. This paper explores the use of AI on mobile devices to improve the preparation of future mathematics teachers. We acknowledge the need for teachers to be skilled in the use of AI tools to foster collaborative, effective, and ethical learning environments. We also provide a case study of our implementation of the generative AI ChatGPT in the preparation of future mathematics teachers at the Faculty of Mathematics, Physics and Computer Science at Comenius University in Bratislava. After this implementation, we analyzed students' opinions on the application of AI in selected areas. We present the results of this analysis and the implications for the practical use of AI in the university education of future mathematics teachers. Finally, based on our study results, we highlight the possibilities of the successful use of AI in different aspects of teaching and its temporary limitations, which are likely to be reduced by further technological developments. [ABSTRACT FROM AUTHOR]

The recent advent of large language models (LLMs), such as ChatGPT, has drawn attention to generative artificial intelligence (AI) in a number of fields. Generative AI can produce different types of data including text, images, and voice, depending on the training methods and datasets used. Additionally, recent advancements in multimodal techniques, which can simultaneously process multiple data types like text and images, have expanded the potential of using multimodal generative AI in the medical environment where various types of clinical and imaging information are used together. This review summarizes the concepts and types of LLMs, image generative AI, and multimodal AI, and it examines the status and future possibilities of generative AI in the field of radiology. [ABSTRACT FROM AUTHOR]

The advent of ChatGPT and other artificial intelligence chatbots will require adjustments by educators in their assessment strategies to prevent plagiarism and to accurately assess student learning. Through the practice of integrating oral assessments into their undergraduate courses, instructors can minimize academic dishonesty, observe and measure students' verbal communication skills, and assess their understanding of the material. Oral exams were found to increase student depth of knowledge and improve oral communication skills, but the time and effort oral examinations require means that this form of assessment is only a partial solution. [ABSTRACT FROM AUTHOR]

Generative AI presents significant opportunities for instructional designers to create content and personalize online learning environments. Alongside its benefits, generative AI also poses ethical considerations and potential risks, such as perpetuating biases or disrupting the learning process. Navigating these complexities requires an approach to design deliberation that involves careful analysis, discussion, and decision-making throughout the design process. This paper proposes a conceptual framework to support instructional designers in leveraging generative AI to promote inclusivity within their design deliberations, emphasizing the importance of addressing ethical considerations and engaging in iterative design practices. [ABSTRACT FROM AUTHOR]

Since the rise of generative artificial intelligence (GenAI) in late 2022, many scholars and thought leaders have wondered about its impact on higher education. This study used a survey methodology (three multiple choice questions and one open-ended question) to explore the perspectives of a nationally representative sample of 1,327 U.S. administrators and faculty, asking questions to understand how much change they anticipate as a result of advancements in artificial intelligence (AI) technology, how prepared their institution is for such change, and what aspects of higher education they expect to change. The researchers used Kranzberg’s Laws of Technology as a lens to interpret the findings and guide the subsequent discussion about how AI might impact higher education. The findings showed that the vast majority of participants expect that AI will change their institution over the next five years and that the majority of participants do not feel that their institution is ready for change. The comments left in response to the open-ended questions fell into one of four themes: concerns about academic integrity and rigor, issues related to AI integration (e.g., anticipated benefits, practices in teaching and learning, issues related to preparedness, and the expected scope of change), the feeling that the current AI discourse is merely hype, and feelings of uncertainty. Ultimately, AI has the potential to be both advantageous and disadvantageous to teaching and learning, with the benefits and challenges of its use varying by context. [ABSTRACT FROM AUTHOR]

Since the rise of generative artificial intelligence (GenAI) in late 2022, many scholars and thought leaders have wondered about its impact on higher education. This study used a survey methodology (three multiple choice questions and one open-ended question) to explore the perspectives of a nationally representative sample of 1,327 U.S. administrators and faculty, asking questions to understand how much change they anticipate as a result of advancements in artificial intelligence (AI) technology, how prepared their institution is for such change, and what aspects of higher education they expect to change. The researchers used Kranzberg’s Laws of Technology as a lens to interpret the findings and guide the subsequent discussion about how AI might impact higher education. The findings showed that the vast majority of participants expect that AI will change their institution over the next five years and that the majority of participants do not feel that their institution is ready for change. The comments left in response to the openended questions fell into one of four themes: concerns about academic integrity and rigor, issues related to AI integration (e.g., anticipated benefits, practices in teaching and learning, issues related to preparedness, and the expected scope of change), the feeling that the current AI discourse is merely hype, and feelings of uncertainty. Ultimately, AI has the potential to be both advantageous and disadvantageous to teaching and learning, with the benefits and challenges of its use varying by context. [ABSTRACT FROM AUTHOR]

This special issue joins the recent but growing effort to expand understanding of integrating generative AI. While generative AI tools like ChatGPT offer great opportunities for personalized learning, it is important to think critically about what type of learning we are reinforcing through the convenience and customization offered by AI. The reliance on AI for scaffolding and personalized prompts can risk undermining students' independent thinking and problem-solving abilities. Additionally, the emphasis on personalized learning could deter the development of collaboration skills. As we continue to integrate AI into educational practices, we need to work towards balancing the benefits of personalization with the goal of fostering selfreliant, critical thinkers who can collaborate and evaluate AI generated content. [ABSTRACT FROM AUTHOR]

In this systematic review, we synthesize ten empirical peer-reviewed articles published between 2019 and 2023 that used generative artificial intelligence (GenAI) for automated feedback in higher education. There are significant opportunities and challenges to integrate these tools effectively into learning environments as the demand for timely and personalized feedback grows. We examine the articles based on instructional contexts and system characteristics, identifying critical implementation possibilities for GenAI in automated feedback. Our findings reveal that GenAI provides diverse feedback across various contexts with multiple instructional purposes. GenAI systems can reduce instructor workload by automating routine grading and feedback tasks, allowing educators to focus on more complex teaching responsibilities with augmented capabilities. Additionally, these systems enhance communication, offer cognitive and emotional support, and improve accessibility by creating supportive, stress-free learning environments. Overall, implementing GenAI automated feedback systems improves educational outcomes and creates a more efficient and supportive learning environment for students and instructors. We conclude with future research directions to better integrate GenAI with human instruction by reconsidering instructors’ roles, especially in providing feedback to create more effective educational experiences. [ABSTRACT FROM AUTHOR]

This study examined pre-service teachers' perspectives on integrating generative AI (GenAI) tools into their own learning and teaching practices. Discussion posts from asynchronous online courses on ChatGPT were analyzed using the Diffusion of Innovations framework to explore awareness, willingness to apply ChatGPT to instruction, and potential benefits, challenges, and concerns about using GenAI in teaching and learning. The course discussions significantly increased pre-service teachers' awareness and foundational knowledge while reducing anxiety towards AI technologies. However, despite exposure to ChatGPT, only a few confirmed intentions to adopt AI tools in their teaching practices, potentially reflecting lingering uncertainties evidenced by emotional responses, such as worry and concern. Professional development in AI literacy can address these uncertainties and enhance teachers’ understanding about using GenAI in class. The study offers insights into responsible GenAI adoption in education and how higher education can leverage ChatGPT to enhance pre-service teacher learning. [ABSTRACT FROM AUTHOR]

The study proposes a balanced approach and flexible guidelines for incorporating generative artificial intelligence (AI) into university-level teaching and learning processes at both the university-departmental level and within individual academic autonomy. Building on the AI Ecological Education Policy Framework, the guidelines offer a suggestive frame of reference for faculty and students to integrate generative AI into their coursework. Furthermore, feedback from 118 students and 14 academics at a teacher education institution in the Philippines underscores the guidelines' potential benefits, concerns, usefulness, and necessity in their academic undertakings. While the policy may not cover every detail exhaustively, it seeks to provide practical and context-sensitive recommendations for ethical, honest, responsible, and fair use of AI in course development, implementation, and student engagement. Consequently, other higher education institutions in general, and academics in particular, may adopt and/or modify the guidelines to suit their positions, goals, needs, and directions. [ABSTRACT FROM AUTHOR]

The purpose of this exploratory research study was to examine the roles instructional designers (IDs) play in the integration of generative Artificial Intelligence (GenAI) into their higher education institutions, and how they use GenAI technologies in their own professional practices. Data were collected from 15 participants in the United States (U.S.) in an ID role or with similar job titles (e.g., educational technologist). Using a general qualitative approach, semi-structured interviews were conducted in Zoom about IDs’ use and integration of GenAI. Our analysis resulted in three primary themes related to IDs’ integration of GenAI in online and blended education: (a) the use of GenAI for instructional design; (b) collaborative guidance for faculty integration of GenAI; and (c) training, resources, and guidelines on the integration of GenAI. A common thread through all the themes was IDs’ conscientious and cautious approach and ethical concerns about GenAI integration. We unpack these themes and discuss the implications of IDs in higher education integrating GenAI to meet organizational, faculty, and student needs. [ABSTRACT FROM AUTHOR]

As generative AI tools are increasingly popular in today’s teaching and learning process, challenges and opportunities occur at the same time. Self-directed learning has been regarded as a powerful learning ability that supports learners in informal learning contexts and its importance rises in salience when incorporating AI into learning. This study employed a mixed-method design to understand how people self-direct their online language learning through the utilization of ChatGPT. Analyzing survey data from 276 survey respondents and 11 one-to-one interviews with language learners in the United States, we found that learners are motivated to use generative AI for its high flexibility and personalization which enables learners to access learning materials that align with their knowledge levels, personal interests, and learning goals. We also found self-monitoring skills that are inherent to learners help them to use ChatGPT more effectively and achieve their learning goals efficiently. This study contributes to the emerging field of AI in education and explores possibilities to use ChatGPT to foster self-directed language learning and provide educators, instructional designers, and researchers with insights to design learning integrated with AI to best fulfill learners’ diverse needs and expand learning opportunities to more people. [ABSTRACT FROM AUTHOR]

The rapid popularity of ChatGPT has brought generative AI into broad focus. The content generation model represented by AI-generated content (AIGC) has reshaped the advertising industry. This study explores the mechanisms by which the characteristics of AI-generated advertisements affect consumers' willingness to accept these advertisements from the perspectives of perceived eeriness and perceived intelligence. It found that the verisimilitude and imagination of AI-generated advertisements negatively affect the degree of perceived eeriness by consumers, while synthesis positively affects it. Conversely, verisimilitude, vitality, and imagination positively affect the perceived intelligence, while synthesis negatively affects it. Meanwhile, consumers' perceived eeriness negatively affects their acceptance of AI-generated advertisements, while perceived intelligence positively affects their willingness to accept AI-generated advertisements. This study helps explain consumers' attitudes toward AI-generated advertisements and offers strategies for brands and advertisers for how to use AI technology more scientifically to optimize advertisements. Advertisers should cautiously assess the possible impact of AI-generated advertisements according to their characteristics, allowing generative AI to play a more valuable role in advertising. [ABSTRACT FROM AUTHOR]

The emergence of Generative Artificial Intelligence (GAI) has caused significant disruption to the traditional educational teaching ecosystem. GAI possesses remarkable capabilities in generating human-like text and boasts an extensive knowledge repository, thereby paving the way for potential collaboration with humans. However, current research on collaborating with GAI within the educational context remains insufficient and the methods are relatively limited. This study aims to utilize methods such as Lag Sequential Analysis (LSA) and Epistemic Network Analysis (ENA) to unveil the "black box" of the human-machine collaborative process. In this research, 22 students engaged in collaborative tasks with GAI to refine instructional design schemes within an authentic classroom setting. The results show that the participants significantly improved the quality of instructional design. Leveraging the improvement demonstrated in students' instructional design performance, we categorized them into high- and low-performance groups. Through the analysis of learning behavior, it was observed that the high-performance group adhered to a structured GAI content application framework: "generate → monitor → apply → evaluate." Moreover, they adeptly employed communication strategies emphasizing exercising cognitive agency and actively cultivating a collaborative environment. The conclusions drawn from this research may serve as a reference for a series of practical applications in human-machine collaboration and provide directions for subsequent studies. [ABSTRACT FROM AUTHOR]

The advent of Chat Generative Pre-Trained Transformer (ChatGPT), a conversational artificial intelligence application, has sparked considerable interest and debate within the educational sphere. This narrative article explores the potential implications of ChatGPT on dental education, including its applications, challenges, and ethical considerations. In the realm of dental school admissions, the use of ChatGPT raises questions about academic integrity and the equitable access to resources for applicants. While it may offer benefits in aiding students with writer's block, concerns arise regarding plagiarism and the authenticity of personal statements. Furthermore, its impact on the learning experiences of dental students presents both opportunities and challenges. While ChatGPT can serve as a helpful learning tool, its use may also hinder the development of critical thinking skills and lead to issues of academic dishonesty. In the realm of research, ChatGPT holds promise for facilitating literature analysis, question-answering, and information retrieval. However, its limitations, such as the lack of domain-specific knowledge and potential for bias, must be considered. Ethical concerns surrounding AI-generated content, including misinformation and transparency, necessitate careful regulation and oversight. Looking ahead, the integration of generative AI models into dental education requires collaborative efforts to establish best practices and ethical guidelines. Future research should focus on developing tailored AI models specific to dental education and investigating their long-term effects on learning outcomes and patient care. Ultimately, as educators, it is imperative to embrace technological advancements while ensuring responsible and equitable integration into dental education curricula. [ABSTRACT FROM AUTHOR]

With the advent of readily accessible generative artificial intelligence (GAI), a concern exists within the academic community that research data collected in the context of conducting doctoral dissertation research is authentic. The purpose of the present study was to explore the role of GAI in the production of new research paying particular attention to the use of GAI in collecting new data for a doctoral dissertation. This study employed qualitative methodology examining how GAI, specifically ChatGPT, responded to interview questions from a previously published article by the researchers to determine how closely chatbots mimic responses from the actual study participants. The researchers found that data integrity in qualitative research may be at risk if higher education institutions do not set clear policies and specific parameters for how doctoral research data is obtained and validated in light of GAI. [ABSTRACT FROM AUTHOR]

The use of generative artificial intelligence (Gen-AI) to assist college students in their studies has become a trend. However, there is no academic consensus on whether Gen-AI can enhance the academic achievement of college students. Using a meta-analytic approach, this study aims to investigate the effectiveness of Gen-AI in improving the academic achievement of college students and to explore the effects of different moderating variables. A total of 28 articles (65 independent studies, 1909 participants) met the inclusion criteria for this study. The results showed that Gen-AI significantly improved college students' academic achievement with a medium effect size (Hedges's g = 0.533, 95% CI [0.408,0.659], p <.05). There were within-group differences in the three moderator variables, activity categories, sample size, and generated content, when the generated content was text (g = 0.554, p <.05), and sample size of 21–40 (g = 0.776, p <.05), the use of independent learning styles (g = 0.600, p <.05) had the most significant improvement in college student's academic achievement. The intervention duration, the discipline types, and the assessment tools also had a moderate positive impact on college students' academic achievement, but there were no significant within-group differences in any of the moderating variables. This study provides a theoretical basis and empirical evidence for the scientific application of Gen-AI and the development of educational technology policy. [ABSTRACT FROM AUTHOR]

Microcontrollers are widely used in agriculture, yet most undergraduate agriculture students do not have the programming skills necessary to make use of these devices in their academic programs or careers. However, generative artificial intelligence (AI) chatbots, such as ChatGPT, have the ability to write complex microcontroller programs when properly queried. The study was conducted to determine the effects of undergraduate agriculture students' (n = 22) use of ChatGPT to write a microcontroller program on their programming task performance, self-efficacy, and attitudes toward generative AI. Nine of 11 (81.8%) student pairs were successful in the ChatGPT-assisted programming activity, requiring between one (33.3%) and six (11.1%) queries to develop their programs. The two unsuccessful pairs used either one or two queries and produced somewhat functional programs that did not fully operate as specified. Pre- and posttest surveys indicated significant (p < 0.001) increases in self-efficacy for writing microcontroller programs, for using ChatGPT to write microcontroller programs, and attitudes toward generative AI. This research confirmed that undergraduate agriculture students can successfully use generative AI chatbots to write microcontroller programs and that successful task completion increases student self-efficacy. Further research is needed to determine best practices for using generative AI in teaching and learning microcontroller programming. [ABSTRACT FROM AUTHOR]

The study conducted in the centre of Vietnam in 2023 aims to explore English-majored students’ perceptions towards the application of ChatGPT in developing students’ English language. The research was grounded on the Technology Acceptance Model developed by Davis (1998) and further revised by Shoufan (2023). The quantitative approach was selected for the study with the questionnaire as the instrument. 369 English-majored students who have experienced using ChatGPT in language learning were selected for the study. The findings reveal that although students found it easy to use ChatGPT as well as perceived it as a useful tool for their language learning, they had neutral opinions on its usefulness. Furthermore, students had a high level of attitudes towards the use of ChatGPT in language learning while the negative attitudes towards it were found to be at a low level. While genders were recognised not to have impacts on students’ perceptions, class levels had significant effects on those of students It is highly recommended that teaching strategies, careful monitoring, and a balance between the use of ChatGPT and human interaction should be under consideration for an effective adoption of ChatGPT in language learning and teaching. [ABSTRACT FROM AUTHOR]

Recent developments in Generative Artificial Intelligence (GenAI) have created a paradigm shift in multiple areas of society, and the use of these technologies is likely to become a defining feature of education in coming decades. GenAI offers transformative pedagogical opportunities, while simultaneously posing ethical and academic challenges. Against this backdrop, we outline a practical, simple, and sufficiently comprehensive tool to allow for the integration of GenAI tools into educational assessment: the AI Assessment Scale (AIAS). The AIAS empowers educators to select the appropriate level of GenAI usage in assessments based on the learning outcomes they seek to address. The AIAS offers greater clarity and transparency for students and educators, provides a fair and equitable policy tool for institutions to work with, and offers a nuanced approach which embraces the opportunities of GenAI while recognising that there are instances where such tools may not be pedagogically appropriate or necessary. By adopting a practical, flexible approach that can be implemented quickly, the AIAS can form a much-needed starting point to address the current uncertainty and anxiety regarding GenAI in education. As a secondary objective, we engage with the current literature and advocate for a refocused discourse on GenAI tools in education, one which foregrounds how technologies can help support and enhance teaching and learning, which contrasts with the current focus on GenAI as a facilitator of academic misconduct. [ABSTRACT FROM AUTHOR]

Higher education is currently under a significant transformation due to the emergence of generative artificial intelligence (GenAI) technologies, the hype surrounding GenAI and the increasing influence of educational technology business groups over tertiary education. This commentary, prepared for the Special Issue of the Journal of University Teaching & Learning Practice (JUTLP) on “Enhancing student engagement using Artificial Intelligence (AI) and chatbots,” delves into the complex landscape of opportunities and threats that AI chatbots, including ChatGPT, introduce to the realm of higher education. We argue that while GenAI offers promise in enhancing pedagogy, research, administration, and student support, concerns around academic integrity, labour displacement, embedded biases, environmental sustainability, increased commercialisation, and regulatory gaps necessitate a critical approach. Our commentary advocates for the development of critical AI literacy among educators and students, emphasising the necessity to foster an environment of responsible innovation and informed use of AI. We posit that the successful integration of AI in higher education must be grounded in the principles of ethics, equity, and the prioritisation of educational aims and human values. By offering a critical and nuanced exploration of these issues, our commentary aims to contribute to the ongoing discourse on how higher education institutions can navigate the rise of GenAI, ensuring that technological advancements benefit all stakeholders while upholding core academic values. [ABSTRACT FROM AUTHOR]

This document discusses the use of artificial intelligence (AI), specifically ChatGPT, in English Language Arts (ELA) education. It highlights the potential of AI to support students with disabilities and enhance their literacy skills. The document emphasizes the importance of explicit instruction on how to use AI as a tool and acknowledges that AI is not a substitute for human thinking and learning. It also provides guidelines for instructors on how to incorporate AI tools into their teaching and emphasizes the importance of ethical considerations and responsible use. Additionally, a research paper explores the use of ChatGPT in educational settings and its impact on reading comprehension exercises. [Extracted from the article]

The release of ChatGPT has marked the dawn of a new information revolution that will transform how people communicate and make meaning. However, to date, little is known about the implications of ChatGPT for L2 composition instruction. To address this gap, the present study uses a systematic review design to synthesize available research on the educational potentials of ChatGPT as an instructional assistant, outline the implications of these potentials for L2 writing instruction, and discuss their practical applications. The findings, based on a meta-analysis of 42 research articles, demonstrate that ChatGPT can enhance L2 writing instruction by boosting learners’ motivation, automating instructional tasks, and offering instantaneous, personalized feedback to learners. These findings have important implications for harnessing the instructional potential of generative AI in L2 writing classes. [ABSTRACT FROM AUTHOR]

Via AI video generators, it is possible to create educational videos with humanistic instructors by simply providing a script. The characteristics of video types and features of instructors in videos impact video engagement and, consequently, performance. This study aimed to compare the impact of human instructors and AI-generated instructors in video lectures on video engagement and academic performance. Additionally, the study aimed to examine students’ opinions on both types of videos. Convergent-parallel approach mixed method was used in this study. A total of 108 undergraduate students participated: 48 in the experimental group, 52 in the control group, and eight in the focus group. While the experimental group (AI-generated instructor) and control group (human instructor) watched 10 minutes of two videos each in two weeks, the students in the focus group watched both types of videos with human and AI-generated instructors. Data were collected through the Video Engagement Scale (VES) after the experimental process, and the Academic Performance Test as a pretest and posttest was administered in both groups. The findings of the experimental part revealed that learners’ video engagement was higher in the course with the human instructor compared to the course with the AI-generated instructor. However, the instructor type did not have a significant effect on academic performance. The results based on the qualitative part showed that students thought the AI-generated instructor caused distraction, discomfort, and disconnectedness. However, when the video lesson topic was interesting or when students focused on the video with the intention of learning, these feelings could be ignored. In conclusion, even in today’s conditions, there is no difference in performance between human and AI-generated instructors. As AI technology continues to develop, the difference in engagement is expected to disappear, and AI-generated instructors could be used effectively in video lectures. [ABSTRACT FROM AUTHOR]

This study proposes a hypothetical model combining the unified theory of acceptance and use of technology (UTAUT) with self-determination theory (SDT) to explore design professionals’ behavioral intentions to use artificial intelligence (AI) tools. Moreover, it incorporates job replacement (JR) as a moderating role. Chinese-speaking design professionals in regions influenced by Confucian culture were surveyed. An analysis of 565 valid cases with AMOS (Analysis of Moment Structures) supported the structural model hypothesis. The model explains 52.1% of the variance in behavioral intention to use (BIU), proving its effectiveness in explaining these variances. The results further validate the importance of performance expectancy (PE) over effort expectancy (EE) in influencing BIU. Additionally, it has been shown that the impact on intrinsic motivation (IM) and extrinsic motivation (EM) can be either amplified or diminished by anxiety about JR. For individuals experiencing higher levels of JR anxiety, there is a marked increase in IM. They may perceive adopting AI tools as an opportunity to enhance their skills and job security. Conversely, this anxiety also significantly boosts EM, as the potential for improved efficiency and productivity with AI use becomes a compelling incentive. These findings suggest new paths for academic researchers to explore the psychological impacts of AI on design professionals’ roles. For practitioners, especially in human resources and organizational development, understanding these dynamics can guide the creation of training programs that address job replacement anxiety. [ABSTRACT FROM AUTHOR]

Generally, the Black-Litterman portfolio model relies heavily on the expert investment opinions, where high-quality investment opinions would improve model performance whereas low-quality investment opinions would result in poor model performance. Essentially, expert investment opinions are highly subjective, sourcing from these financial experts' information and knowledge. ChatGPT, as an advanced generative AI model, could extract and analyze information from huge multi-modal data, which is beneficial to build AI investment opinions for Black-Litterman portfolio model. In this study, we construct and analyze the large language model-based Black-Litterman portfolios, ChatGPT-BL and BARD-BL, where the goals of minimum variance and mean-variance trade-off are taken into account. Computational results show that the ChatGPT-based portfolios tend to be conservative, which is suitable for risk-averse investors, while the BARD-based portfolios are aggressive, which is appropriate for risk-seeking investors. Also, the superiority of Black-Litterman model using investor views generating from gradient boosting regression and GJR-GARCH algorithm is illustrated by the efficient frontiers. [ABSTRACT FROM AUTHOR]

The article presents the discussion on concept of the industrial metaverse, a digital reality that mirrors and simulates physical systems to optimize processes and drive sustainability in the chemical process industries. Topics include integration of digital twins, the challenges of implementing high-performance computing; and the importance of upskilling the workforce.

The article "Textile Makerspace: Digital Humanities Meets Craft" explores the intersection of textile craft, storytelling, and data in a unique makerspace at Stanford University. The Textile Makerspace provides a space for various crafts like sewing, knitting, and embroidery, attracting students and staff. The author delves into using textile methods for data visualizations and storytelling, as well as merging weaving with legal advocacy in digital humanities. The article also discusses a petition by the Authors Alliance to expand an exemption for text and data mining, showcasing the potential of computational text analysis through craft-based visualizations. Lastly, a Data Visualization with Textiles course at Stanford encourages interdisciplinary collaboration and experimentation to break traditional modes of scholarship in the humanities. [Extracted from the article]

As artificial intelligence, quantum techniques, and brain neuroscience advance, the educational ecosystem is poised for transformative shifts, triggering a revolution in education. Educational technology’s emphasis is no longer confined to leveraging information technology for educational reform alone; it now actively seeks to optimize and innovate the educational ecosystem, creating a sustainable and virtuous cycle of development. Educational technology will shift from technological care to humanistic care, forming a new “cultural view of educational technology” and entering the post educational technology era. The field of educational technology must move beyond a static mindset centered on information technology application and venture into the realm of an innovative educational ecology. During the construction of this new educational ecology, unique theoretical and practical innovation models should be introduced. It is through this proactive approach that the discipline of educational technology can genuinely demonstrate its value and contribute to the “big education discipline’s” establishment and growth. Currently, at present, attention should be paid to the practice of educational informatization. This entails meticulously summarizing novel insights and experiences in educational informationization and digital transformation, discerning underlying development patterns, and elevating them into innovative theories and methodologies. Special attention should be given to breakthroughs in artificial intelligence, exploring the implications of big data models and generative artificial intelligence for education. Furthermore, there is a necessity to expand both the knowledge system and practical applications within the discipline of educational technology. [ABSTRACT FROM AUTHOR]

Recent advances in Generative Artificial Intelligence are leading to major changes in education, both in the way educators teach and in the way students learn. For example, Generative Artificial Intelligence (GenAI) chatbots, such as ChatGPT, can help students by assisting them in problem solving or supporting them in code development tasks. This article aims precisely to explore the effect of ChatGPT in supporting students with different levels of programming experience in a course on Big Data. A Big Data challenge was carried out during one of the sessions with 31 students from different backgrounds. Overall, the students were able to solve the challenge, and the results of the pre- and post-tests indicate that the students improved their grades, i.e. they learned to solve the programming exercise. This quasi-experimental study shows that ChatGPT can be a valuable tool as an assistant in the field of data science and programming for students learning to program (even for the first time), whether they come from engineering programs or other completely different disciplines. It is important not to forget the role of the professor in guiding the students towards the correct use of these GenAI tools. [ABSTRACT FROM AUTHOR]

This document is an introduction to the third issue of the Journal of Public Affairs Education, which focuses on the evolving dynamics in public affairs education. The articles in this issue cover a diverse range of topics, including violence, corruption, higher education policy, technology, and academic freedom. The goal of this issue is to explore and embody the values of public affairs, such as efficiency, effectiveness, economy, equity, empathy, engagement, and ethics. The articles and book reviews in this issue provide deep insights into the challenges facing public affairs education and offer potential solutions. [Extracted from the article]

The potential benefits and challenges of AI in the workplace are documented with public service facing a particular choice to leverage this tool to better communities. This article explores the use of generative artificial intelligence (AI) tools in public service education. As students prepare to enter public service, they need to gain the skills to manage the advantages and threats of AI. This article explores one attempt to prepare students for the intersection of public service and AI using a human resource management course. This article explores the use of ChatGPT, as a generative AI tool, for both instructors and students across the academic year. We explore the pedagogical strategies involved and share outcomes and lessons learned for future integration of AI in the classroom. [ABSTRACT FROM AUTHOR]

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Purpose: The purpose of the study was to evaluate the impact of generative artificial intelligence (GenAI) on students' learning experiences and perceptions through a master's-level course. The study specifically focused on student engagement, comfort with GenAI and ethical considerations. Design/methodology/approach: The study used an action research methodology employing qualitative data collection methods, including pre- and post-course surveys, reflective assignments, class discussions and a questionnaire. The AI-Ideas, Connections, Extensions (ICE) Framework, combining the ICE Model and AI paradigms, is used to assess students' cognitive engagement with GenAI. Findings: The study revealed that incorporating GenAI in a master's-level instructional design course increased students' comfort with GenAI and their understanding of its ethical implications. The AI-ICE Framework demonstrated most students were at the initial engagement level, with growing awareness of GenAI's limitations and ethical issues. Course reflections highlighted themes of improved teaching strategies, personal growth and the practical challenges of integrating GenAI responsibly. Research limitations/implications: The small sample size poses challenges to the analytical power of the findings, potentially limiting the breadth and applicability of conclusions. This constraint may affect the generalizability of the results, as the participants may not fully represent the broader population of interest. The researchers are mindful of these limitations and suggest caution in interpreting the findings, acknowledging that they may offer more exploratory insights than definitive conclusions. Future research endeavors should aim to recruit a larger cohort to validate and expand upon the initial observations, ensuring a more robust understanding. Originality/value: The study is original in its integration of GenAI into a master's-level instructional design course, assessing both the practical and ethical implications of its use in education. By utilizing the AI-ICE Framework to evaluate students' cognitive engagement and employing action research methodology, the study provides insights into how GenAI influences learning experiences and perceptions. This approach bridges the gap between theoretical understanding and the real-world application of GenAI, offering actionable strategies for its responsible use in educational settings. [ABSTRACT FROM AUTHOR]

Purpose: This exploratory study innovates the pedagogy of undergraduate business research courses by integrating Generative Artificial Intelligence (GAI) tools, guided by human-centered artificial intelligence, social-emotional learning, and authenticity principles. Design/methodology/approach: An insider case study approach was employed to examine an undergraduate business research course where 72 students utilized GAI for coursework. Thematic analysis was applied to their meta-reflective journals. Findings: Students leverage GAI tools as brainstorming partners, co-writers, and co-readers, enhancing research efficiency and comprehension. They exhibit authenticity and human-centered AI principles in their GAI engagement. GAI integration imparts relevant AI skills to students. Research limitations/implications: Future research could explore how teams collectively interact with GAI tools. Practical implications: Incorporating meta-reflections can promote responsible GAI usage and develop students' self-awareness, critical thinking, and ethical engagement. Social implications: Open discussions about social perceptions and emotional responses surrounding GAI use are necessary. Educators can foster a learning environment that nurtures students' holistic development, preparing them for technological challenges while preserving human learning and growth. Originality/value: This study fills a gap in exploring the delivery and outcomes of AI-integrated undergraduate education, prioritizing student perspectives over the prevalent focus on educators' viewpoints. Additionally, it examines the teaching and application of AI for undergraduate research, diverging from current studies that primarily focus on research applications for academics. [ABSTRACT FROM AUTHOR]

Guided by the Technology Acceptance Model, the researchers designed a Google Form survey to explore elementary preservice teachers'(PSTs') perceptions of using Generative AI (GenAI) as part of an authentic literacy methods course activity. Following the activity, responses to a qualitative survey were analyzed to learn about PSTs' experience of using GenAI in developing questions for a read-aloud. Findings indicated that many PSTs perceived GenAI as a useful teaching tool. In addition, they shared their concerns that GenAI may limit creativity and teacher agency. We also found a positive correlation between the use of GenAI in the activity and PSTs' intentions to use GenAI in the future. The study adds to the current literature about TAM with GenAI and underscores the value of GenAI in promoting critical reasoning among PSTs. Pedagogical implications for teacher educators are also discussed. [ABSTRACT FROM AUTHOR]

The main aim of this mixed methods study is to determine the evaluations made by academics on an academic text generated with ChatGPT and get their general views on ChatGPT. The convergent parallel design is utilized where the qualitative and quantitative methods are combined to produce triangulated results. Data were collected via an online form from 45 academics. Academics were asked to read and rate the sample text generated by ChatGPT. The titles "Writing rules," "Language and expression," and "Subject integrity" of the text generated by ChatGPT received the highest ratings from the academics. The qualitative findings obtained were interpreted within the framework of the "Diffusion of Innovations" and "Uses and Gratifications" theories, and the related themes were formed. As a result, the participants were asked about their feelings about using ChatGPT, and the themes of astonishment, pessimism, anxiety, and hopefulness emerged. Participants were asked their opinions about the benefits and negative aspects of using ChatGPT. Themes emerged under the benefits, and negative aspects titles were evaluated. Finally, the other themes that emerged under the satisfaction with ChatGPT and making ChatGPT widespread titles were assessed. [ABSTRACT FROM AUTHOR]

This article discusses the implications of information and communication technologies (ICTs) and artificial intelligence (AI) for preserving cultural heritage. It emphasizes the importance of a culturally sensitive approach to technology adoption and introduces the concept of algorithmic cultural sensitivity. The article explores the potential of AI in replicating and interpreting artworks, while also raising questions about its ability to comprehend cultural significance. It proposes the "cultural heritage through AI" (CHAI) metric as a tool to measure and incentivize the integration of cultural sensitivity into AI systems. The article also discusses the dual nature of digital technology and suggests that algorithmic cultural sensitivity can counteract homogenization and promote inclusiveness and respect for diverse cultures. [Extracted from the article]

The article discusses the issue of copyright protection for AI-generated content and suggests that alternative dispute resolution tactics may be able to address this issue until the law catches up with rapidly developing technology. It highlights cases where the United States Copyright Office and courts have rejected federal copyright protection for AI-generated artwork, emphasizing the requirement of human authorship for copyright protection. The article argues that protecting AI-generated works does not align with the original purpose of copyright law, which is to incentivize human creativity. It suggests that alternative dispute resolution methods, such as mediation and arbitration, may be more suitable for resolving disputes related to AI-generated content. The article also discusses the benefits of early neutral evaluation in providing business-oriented solutions and the importance of ADR in international copyright disputes. It presents ADR as an efficient and effective solution to fill the gap in copyright law related to AI-generated content. [Extracted from the article]

Artificial intelligence (AI) has been evolving since the mid-twentieth-century when luminaries such as Alan Turing, Herbert Simon, and Marvin Minsky began developing rudimentary AI applications. For decades, AI programs remained pretty much in the realm of computer science and experimental game playing. This changed radically in the 2020s when commercial vendors such as OpenAI and Google developed generative AI programs (ChatGPT and Bard) using large language modelling (LLM). As a result, generative AI is now being considered for use in all walks of life, including education. In spring 2023, when ChatGPT burst into the public psyche, twenty-five education students in the author's graduate seminar were invited to participate in a qualitative study using ChatGPT as an essay-writing tool. Fifteen accepted the offer. The purpose in doing this was to give students in this seminar the opportunity to use ChatGPT in a supportive environment and to collect qualitative data via descriptive written evaluation and a focus group to comment on their experiences using ChatGPT. All of these students have master's degrees in education and experience as teachers in New York City schools. Their training and experience give them keen insights into pedagogical practice making them ideally suited to evaluate ChatGPT as an essay-writing tool. This article reports on the results of this study. Key findings indicate that the vast majority of these students had a good experience in using ChatGPT for their essays. Many, especially the secondary school teachers, would use it in their own classes. [ABSTRACT FROM AUTHOR]

Despite widespread discussions, insights into GenAI's impact on education have been limited because early studies have often been narrow in scope and focused on specific contexts. Therefore, the purpose of this study is to explore and analyze the volume, engagement, and content of initial reactions to one leading GenAI tool, ChatGPT. Specifically, we collected and analyzed public online discussions of ChatGPT in the first four months following the tool's release. We collected 345 posts and 6,463 comments about ChatGPT from 25 education-focused subreddits. We analyzed the volume, engagement, and content of ChatGPT discussions through descriptive statistics and natural language processing techniques. Findings show relatively low volume of ChatGPT discussions, unevenly spread across education-related subreddits--with the majority of the discussions occurring in two subreddits, while six subreddits did not have any discussions. Despite this, the level of engagement within ChatGPT posts was substantial; for instance, a ChatGPT post hosted a median of 15 comments, and these comments were lengthy, indicating rich engagement rather than superficial. The content of ChatGPT discussions across the six largest education-related subreddits differed in the degree of analytical thinking and emotional tone even while sharing a predominant focus on students and AI. Diverse reactions to and perspectives on GenAI--observed from varied levels of volume, engagement, and content of ChatGPT across educational-related subreddits--highlights how diverse educational stakeholders reacted to GenAI differently, offering insights into how to explore, analyze, and comprehend the spread and adoption of technological innovation in education. [ABSTRACT FROM AUTHOR]

The arrival of generative artificial intelligence (AI) is fundamentally different from prior technologies used in educational settings. Educators and researchers of online, blended, and in-person learning are still coming to grips with how to employ current AI technologies in the learning experience, let alone understanding the potential consequences that future and unknown developments in AI will produce. Despite potential risks, AI may revolutionize previous models of teaching and learning, and perhaps create opportunities to realize progressive educational goals. Given the longstanding tradition of using philosophy to examine questions surrounding ethics, ontology, technology, and education, the purpose of this critical reflection paper is to draw from prominent philosophers across these disciplines to address the question: how can AI be employed in future educational contexts in a humanizing and ethical manner? Drawing from the work of Gunther Anders, Michel Foucault, Paolo Freire, Benjamin Bloom, and Hannah Arendt, we propose a framework for assessing the use and ethics of AI in modern education contexts regarding human versus AI generated textual and multimodal content, and the broader political, social, and cultural implications. We conclude with applied examples of the framework and implications for future research and practice. [ABSTRACT FROM AUTHOR]

Generative AI, much like the Force in Star Wars, wields significant power, capable of immense good or potential harm depending on its application. This editorial indicates the dual nature of generative AI within educational contexts, drawing parallels to the light and dark sides of the Force. Generative AI's proficiency in language manipulation positions it as a transformative tool in education, yet its influence must be managed wisely. The evolution of AI mirrors the advent of personal computers, suggesting a future where individuals may have personal AI assistants tailored to their needs. This speculative future poses critical questions about the balance of force between humans and AI. This editorial along with articles published in the special issue urge critical reflection on our current path and the decisions that will shape the future of AI in education. [ABSTRACT FROM AUTHOR]

This research delves into the factors influencing the adoption of ChatGPT, a sophisticated AI-based chatbot, among Generation Z members in Croatia. Employing an extended UTAUT2 model, the impact of various factors on the behavioral intention to use ChatGPT is explored. The study included 694 Generation Z participants, and data were collected through an online survey featuring self-reporting questions. The analysis utilized statistical software packages for performing both confirmatory and exploratory factor analyses, in addition to hierarchical linear regression. Key findings reveal that performance expectancy, social influence, hedonic motivation, habit, and personal innovativeness significantly influence the behavioral intention to use ChatGPT. However, effort expectancy, facilitating conditions, and price value do not exhibit a significant impact. Notably, the study excludes the use behavior factor due to multicollinearity issues with behavioral intention. While the research does not focus on moderating factors, it reports that the adapted UTAUT2 model explains 65% of the variance in the adoption of ChatGPT by Generation Z users. [ABSTRACT FROM AUTHOR]