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This article seeks to explore the convergent validity of (and thus the consistency between) a few popular generative artificial intelligence (AI) robots in evaluating popular mobile learning apps' usability. The three robots adopted in the study were Microsoft Copilot, Google PaLM, and Meta Llama, which were individually instructed to accord rating scores to the eight major usability dimensions, namely, (1) content/course quality, (2) pedagogical design, (3) learner support, (4) technology infrastructure, (5) social interaction, (6) learner engagement, (7) instructor support, and (8) cost-effectiveness of 17 currently most popular mobile learning apps. For each of the three robots, the minimum, the maximum, the range, and the standard deviation of the rating scores for each of the eight dimensions were computed across all the mobile learning apps. The rating score difference for each of the eight dimensions between any pair of the above three robots was calculated for each app. The mean of the absolute value, the minimum, the maximum, the range, and the standard deviation of the differences for each dimensions between each pair of robots were calculated across all the apps. A paired sample t-test was then applied to each dimension for the rating score difference between each robot pair over all the apps. Finally, Cronbach's coefficient alpha of the rating scores was computed for each of the eight dimensions between all the three robots across all the apps. The computational results were to reveal whether the three robots awarded discrimination in evaluating each dimension across the apps, whether each robot, with respect to any other robot, erratically and/or systematically overrate or underrate any dimension over the apps, and whether there was high convergent validity of (and thus consistency between) the three robots in evaluating each dimension across the apps. Among other auxiliary results, it was revealed that the convergent validity of (and the consistency between) the three robots was marginally acceptable only in evaluating mobile learning apps' dimension of (1) content/course quality but not at all in the dimensions (2) pedagogical design, (3) learner support, (4) technology infrastructure, (5) social interaction, (6) learner engagement, (7) instructor support, and (8) cost-effectiveness. [For the full proceedings, see ED659933.]

This paper explores the concept of self-efficacy and its impact on individual performance on a mobile learning application. Self-efficacy refers to one's belief in their ability to achieve their goals and is a key factor in everyday life. To investigate the relationship between self-efficacy and performance, we conducted an experiment with 104 participants, which consisted of two parts. First, we evaluated their self-efficacy levels using a survey designed to assess their perceived self-efficacy levels before and after their tests. Second, we asked participants to pilot a drone in a virtual environment and complete a series of races as quickly as possible. Our findings demonstrate that self-efficacy does indeed affect the individual performance, as we observed a clear correlation between self-efficacy levels and task completion times. [For the full proceedings, see ED659933.]

There is a growing concern that AI is likely to replace the work done face to face in the classroom by teachers. The concerns also extend to the students use of AI to complete assignments which could impact on their grades either positive or negative and in some cases, when a student work is detected with high AI the work could be classified as plagiarism if AI usage is not declared. On another note, there are increasing debates about the use of AI as a valid tool to support work completed by human beings. Whatever maybe the growing concerns, many researchers have argued that AI is not likely to replace education jobs such as teaching and learning done by teachers and education administrators. The purpose of this paper is to explore debates around the use of AI in education, mostly in teaching and learning and assessment of students university misconduct policy. Teachers work and the link to the opinions on integrating AI in the classroom are illuminated by empirical evidence gathered via interviews. A lot of educators respond to AI in different ways. Some of the debates falls around AI as God of the admin work and assessment of students s sent tools that can help reduce some work such as helping with multiple choice questions, on the other hand, some students have been penalised and in some cases failed their work due to use of AI in completing their assignments without acknowledging the use. In addition, others have argued that AI has come to replace the work done by teachers and are anxious about AI in education jobs done by teachers, hence would not bear the idea for classroom teaching and learning. [For the complete Volume 22 proceedings, see ED656158.]

Eight scholarly papers and seven abstracts comprise the content of the twenty-third annual Brick & Click Libraries Conference, held annually at Northwest Missouri State University in Maryville, Missouri. The 2023 paper and abstract titles include: (1) The Reliability and Usability of ChatGPT for Library Metadata (Jenny Bodenhamer); (2) A Balancing Act in the Archives: Increasing Access to the Great Plains Black History Museum Collections (Wendy Guerra and Lori Schwartz); (3) Developing Info Students Where They Are: Personalizing Instruction to Increase Literacy Skills to Meet Engagement (Jorge A. León); (4) Empowering Undergraduates: Building Confidence in Primary Source Literacy (Jaycie Vos and Jess Cruz); (5) Quest for the Best: An Info Lit Strategy for First Year Seminars (Stephanie Hallam, Mary Bangert, and Michael Bezushko); (6) Are We Putting Our Values into Practice? Chat Reference Assessment (Mardi Mahaffy); (7) A Pilot Workshop on AI Art and Libraries at the University of Mississippi (Alex Watson); (8) New Expansions of Open Access to Benefit Research and Researchers (Barbara Pope); (9) Zettelkasten Note-Taking in Zotero for Grounded Writing (Rachel Brekhus); (10) Building Community: Library Leadership of a Common Book Program (Jill Becker); (11) Digital Media and Innovation Lab: A Must Have for Academic Libraries (Navadeep Khanal and Joseph Sabo); (12) Digital Libraries as Digital Third Place: Virtual Programming in the Age of Loneliness (Craig Finlay and Jenny Haddon); (13) Community Engagement: Academic and School Library Partnerships (Melissa Dennis); (14) Launching a Ticketing System With Asana (Hong Li); and (15) Meeting the Needs of Student Parents (Sarah Hebert). [For the 2022 proceedings, see ED623765.]

This paper presents the best practices used by institutional review boards (IRBs) and human research protections programs (HRPPs) to prepare online graduate student investigators for human research protections specific to research within online graduate degree programs or where research supervisors are not proximal to graduate student investigators and their research protocols. In recent years, advances in artificial intelligence (AI), machine learning (ML), and other data mining/scraping forms have adversely impacted individual privacy and the unintended sharing of personally identifiable information (PII). With this growth of ubiquitous digital technologies, such as AI, ML, and data mining/scraping, used across online graduate degree programs, specialized training and preparation are needed to best prepare graduate student researchers for human research protections involving data with PII. Implications for IRBs and HRPPs are also addressed in this rapidly evolving climate, with recommendations for the design of online graduate degree programs that include graduate research and the best strategies to prepare online graduate student investigators for human research protections. [This paper was published in: "1st Annual Virtual Fall National Conference on Creativity, Innovation, and Technology (NCCiT) Proceedings," November 15-16, 2023, pp. 84-108.]

Platform-enabled research services will control, manage, and measure learner experiences within that platform. In this paper, we consider the need for research services that examine learner experiences "outside" the platform. For example, we describe an effort to conduct an experiment on peer assessment in a college writing course, where Terracotta (a research service within the learning management system) randomly assigned students to either (1) write peer assessments themselves, (2) use a generative AI tool to provide the feedback, or (3) build upon and improve the feedback provided by generative AI. This research effort was not successful, and it stands as an example of the limitations of current platform-enabled research services, and the need for infrastructure to support research beyond existing platforms.

With the approach of constant changes and quality assurance in education, we have reached an optimum that no longer justifies all further investments in such changes, as the results of these investments are (and will be) minimal and insufficient. We have reached a stage where we must shift from evolution to revolution, from constant changes in education to its complete transformation. Here, we must point out that we must reverse the flow of systemic changes from the dehumanization of society as that in Industry 4.0 or, in a slightly softer form, the Japanese vision of Society 5.0. This reverse flow offers us the re-humanization of society's development and it can be called Society 6.0 or, historically, also Society 1.1 (back to the past, to the first industrial revolution). [For the full proceedings, see ED629086.]

With the rapid advancement of technology, education is undergoing a transformational change. Chatbots have become increasingly popular in recent years and are being utilized as teaching assistants to support teachers and students in various ways. However, little research has been done on the skills required by teachers to prepare curriculum content using chatbots. The research aims to identify the skills teachers need to prepare curriculum content with chatbots. It examines the fundamental and cognitive skills individuals need to interpret content generated by chatbots and explores the difference between self-assessment and evaluator-based assessment. Fifty-eight third-year students, pre-service teachers, in the Elementary education program attempted to write a lesson plan using ChatGPT and completed a questionnaire to assess the skills required. Their communication with the chatbot as well as their prepared lesson plans were reviewed by an evaluator who rated the skills of the participating pre-service teachers. Results indicate that preservice teachers tend to overestimate their skills required to interpret chatbot-generated content compared to the evaluator's ratings. Such discrepancies could lead to inaccurate or incomplete assessments of their skills, which could hinder their potential for growth and development. [For the full proceedings, see ED629086.]

There were two projects at the National Defence University of Finland (NDU), which both ended by the end of 2022. One of them tried to find the answers to the main question: How artificial intelligence (AI) could be used to improve learning, teaching, and planning? The other tried to find the answer to the main question: What new skills do officers need when artificial intelligence is coming? We did literature reviews and found out that intelligent technology combined with data analytics can offer several improvements to traditional classroom teaching. From literature reviews, we also found some new skills that officers might need to be able to handle AI-based technologies. This is a position paper presenting the arguable opinions of the writers. We have found lots of benefits that the use of intelligent learning technology can bring, mainly by supporting individual learning paths. There is also an obvious need for AI officers who should have a deeper understanding of the AI-supported technology than normal officers. This project and some other similar projects have raised a lot of discussions, one seminar series about artificial intelligence and we do have some trained AI officers as well. [For the full proceedings, see ED629086.]

This commentary was written by ChatGPT, an artificial intelligence language model developed by OpenAI. It was conceived by the first author as a test for how the advent of predictive language modeling will create opportunities and challenges for researchers and teachers in mathematics education. The paper consists of a commentary that was written by ChatGPT, followed by a reflection written by the authors that explains how the model was prompted to generate the text and how we worked with ChatGPT to validate and edit the text that was produced. We consider the implications of models like ChatGPT on the future of academic work. [For the complete proceedings, see ED658295.]

The expanding domain of Artificial Intelligence (AI) offers a diverse array of educational applications and tools. However, the scholarly exploration of AI's suitability for enhancing English as a Foreign Language (EFL) instruction at the university level remains notably limited. This research gap impedes educators from fully harnessing AI's pedagogical potential. Given the inclusion of linguistic and literary disciplines in preservice EFL teacher training in Slovakia, it is increasingly imperative for educators to acquaint themselves with various AI tools, enabling the development of effective methodologies for enhancing EFL teaching and learning. Integrating AI into teacher training programs equips future EFL educators with essential skills for 21st-century classrooms and meets the evolving needs of digitally proficient students. This paper aims to provide a concise yet comprehensive overview of AI's relevance to pre-service EFL teacher training, encompassing linguistic and literary domains, by categorising six prominent AI forms: a) Natural Language Processing (NLP) Tools, b) Content Creation and Personalisation tools, c) Content Recommendation Systems, d) Emotion and Sentiment Analysers, e) Text Summarisation and Analysis tools, and f) Chatbots and Virtual Assistants. Furthermore, it highlights the research gap in AI's implementation in EFL education and emphasises the need to explore pedagogical and ethical implications while outlining future research directions to enhance our understanding of this dynamic field. [For the full proceedings, see ED652261.]

In today's technology, there are rapid advances in the field of artificial intelligence. With the increasing involvement of artificial intelligence in daily activities, great changes are taking place in our habits. At this point, the necessity of educating students in accordance with the age of artificial intelligence emerges. Students' acquaintance with current technologies requires that the education provided is up to date. From these developments artificial intelligence, and its effects on society should be conveyed to students. The aim of the study is to implement the curriculum developed for middle school students to learn about the ethical dimension of artificial intelligence and to reveal students' views on the subject. Within the framework of the Artificial Intelligence and Ethics curriculum, 25 sixth grade students were trained. At the end of the training, a semistructured interview form was applied to the students. Students' views on the ethical dimension of artificial intelligence were revealed. As a result, education has been contributed with an artificial intelligence and ethics curriculum suitable for middle school students. In general, the approach of including the ethical dimension of artificial intelligence in education shows that middle school students can evaluate artificial intelligence as a personal and social issue beyond just having knowledge about its functioning. [For the full proceedings, see ED652261.]

The Community Learning and Service Partnership (CLASP) being named the recipient of the Malcolm Knowles Award prompts retrospection and raises the question of what to present at the American Association for Adult and Continuing Education (AAACE) conference. What is the overarching narrative of CLASP? Given the international and intergenerational nature of the program, anecdotes abound--stories of transformation, rich relationships, and shared achievements; but what is the "metastory"? An unfinished, in-house film made years ago wherein program participants and observers spoke freely on camera may serve. From a detached perspective, we turn to artificial intelligence in the form of a qualitative data analytics program. This paper reports the results of that media analysis and conference attendees' reactions to the film. [For the full proceedings, see ED648717.]

This book contains a compilation of papers presented at the International Psychological Applications Conference and Trends (InPACT) 2023, organized by the World Institute for Advanced Research and Science (WIARS), held in International Psychological Applications Conference and Trends (InPACT) 2023, held in Lisbon, Portugal, from 22 to 24 of April 2023. he goal of understanding individuals and groups (mental functions and behavioral standpoints), from this academic and practical scientific discipline, aims ultimately to benefit society. The International Conference seeks to provide some answers and explore the several areas within the Psychology field, new developments in studies and proposals for future scientific projects. The goal is to offer a worldwide connection between psychologists, researchers and lecturers, from a wide range of academic fields, interested in exploring and giving their contribution in psychological issues. We take pride in having been able to connect and bring together academics, scholars, practitioners and others interested in a field that is fertile in new perspectives, ideas and knowledge. We counted on an extensive variety of contributors and presenters, which can supplement the view of the human essence and behavior, showing the impact of their different personal, academic and cultural experiences. This is, certainly, one of the reasons there are several nationalities and cultures represented, inspiring multi-disciplinary collaborative links, fomenting intellectual encounters and development. InPACT 2023 received 548 submissions, from more than 39 different countries all over the world, reviewed by a double-blind process. Submissions were prepared to take the form of Oral Presentations, Posters, Virtual Presentations and Workshops. 192 submissions (overall, 35% acceptance rate) were accepted for presentation at the conference.

ASCUE, the Association Supporting Computer Users in Education, is a group of people interested in small college computing issues. It is a blend of people from all over the country who use computers in their teaching, academic support, and administrative support functions. ASCUE has a strong tradition of bringing its members together to pool their resources to help each other. ASCUE continues the tradition of sharing through its national conference held every year in June, its conference proceedings, and its newsletter. The 55th annual conference is about returning to the classroom after an abrupt shift to online work and instruction demands a reimagining of how and why technology is incorporated in education. This year's conference highlights work that encourages empathy, equity, and intentionality when administering, supporting, and integrating technical components in the classroom. [For the full paper, "Recruiting the Next Generation of Information Technology Professionals," see ED629427.]

Understanding a student's problem-solving strategy can have a significant impact on effective math learning using Intelligent Tutoring Systems (ITSs) and Adaptive Instructional Systems (AISs). For instance, the ITS/AIS can better personalize itself to correct specific misconceptions that are indicated by incorrect strategies, specific problems can be designed to improve strategies and frustration can be minimized by adapting to a student's natural way of thinking rather than trying to fit a standard strategy for all. While it may be possible for human experts to identify strategies manually in classroom settings with sufficient student interaction, it is not possible to scale this up to big data. Therefore, we leverage advances in Machine Learning and AI methods to perform scalable strategy prediction that is also fair to students at all skill levels. Specifically, we develop an embedding called MVec where we learn a representation based on the mastery of students. We then cluster these embeddings with a non-parametric clustering method where we progressively learn clusters such that we group together instances that have approximately symmetrical strategies. The strategy prediction model is trained on instances sampled from these clusters. This ensures that we train the model over diverse strategies and also that strategies from a particular group do not bias the DNN model, thus allowing it to optimize its parameters over all groups. Using real world large-scale student interaction datasets from MATHia, we implement our approach using transformers and Node2Vec for learning the mastery embeddings and LSTMs for predicting strategies. We show that our approach can scale up to achieve high accuracy by training on a small sample of a large dataset and also has predictive equality, i.e., it can predict strategies equally well for learners at diverse skill levels. [For the complete proceedings, see ED630829.]

Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide teachers' attention to students most in need. The input to the model includes snapshots of the student's evolving software code and additional meta-features. The model combines a transformerbased neural architecture for embedding students' code in programming language space with a time-aware LSTM for representing the evolving code snapshots. We evaluate our approach with data obtained from two Java development environments created for beginner programmers. We focused on common programming errors which differ in their difficulty and whether they can be uniquely identified during compilation. Our deep language model was able to outperform several baseline models that use an alternative embedding method or do not consider how the programmer's code changes over time. Our results demonstrate the added value of utilizing multiple code snapshots to predict bug-fix-time using deep language models for programming. [For the complete proceedings, see ED630829.]

With the recent release of Chat-GPT by OpenAI, the automated text generation capabilities of GPT-3 are seen as transformative and potentially systemically disruptive for higher education. While the impact on teaching and learning practices is still unknown, it is apparent that alongside risks these tools offer the potential to augment human intelligence (intelligence augmentation, or IA). However, strategies for such IA, involving partnership of tool-human, will be needed to support learning. In the context of writing, an investigation of potential approaches is needed given empirical data and studies are currently limited. We introduce a novel visual representation CoAuthorViz to examine keystroke logs from a writing assistant where writers interacted with GPT-3 writing suggestions to co-write with the machine. We demonstrate the use of our visualization by exemplifying different kinds of writing behaviour from users writing with GPT-3 support and derive metrics such as their usage of GPT-3 suggestions in relation to overall writing quality indicators. We also release the materials open source to further progress our understanding of desirable user behaviour when working with such state-of-the-art AI tools. [For the complete proceedings, see ED630829.]

Automated Essay Scoring (AES) tools aim to improve the efficiency and consistency of essay scoring by using machine learning algorithms. In the existing research work on this topic, most researchers agree that human-automated score agreement remains the benchmark for assessing the accuracy of machine-generated scores. To measure the performance of AES models, the Quadratic Weighted Kappa (QWK) is commonly used as the evaluation metric. However, we have identified several limitations of using QWK as the sole metric for evaluating AES model performance. These limitations include its sensitivity to the rating scale, the potential for the so-called "kappa paradox" to occur, the impact of prevalence, the impact of the position of agreements in the diagonal agreement matrix, and its limitation in handling a large number of raters. Our findings suggest that relying solely on QWK as the evaluation metric for AES performance may not be sufficient. We further discuss insights into additional metrics to comprehensively evaluate the performance and accuracy of AES models. [For the complete proceedings, see ED630829.]

We explore multi-modal machine learning-based approaches (facial expression recognition, auditory emotion recognition, and text sentiment analysis) to identify "negative moments" of teacher-student interaction during classroom teaching. Our analyses on a large (957 videos, each 20min) dataset of classroom observations suggest that: (1) Negative moments occur sparsely and are laborious to find by manually watching videos from start to finish; (2) Contemporary machine perception tools for emotion, speech, and text sentiment analysis show only limited ability to capture the diverse manifestations of classroom negativity in a fully automatic way; (3) Semi-automatic procedures that combine machine perception with human annotation may hold more promise for finding authentic moments of classroom negativity; Finally, (4) even short 10 sec negative moments contain rich structure in terms of the actions and behaviors that they comprise. [For the complete proceedings, see ED630829.]

In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design and which is based on the explainable k-nearest neighbor algorithm, recommends a set of courses that have been passed by the majority of the student's nearest neighbors who have completed their studies. The present evaluation is based on the data of students from three different study programs. One result is that the recommendations do lower the dropout risk. We also discovered that while the recommended courses differed from those taken by students who dropped out, they matched quite well with courses taken by students who completed the degree program. Although the course recommender system targets primarily students at risk, students doing well could use it. Furthermore, we found that the number of recommended courses for struggling students is less than the number of courses they actually enrolled in. This suggests that the recommendations given indicate a different and hopefully feasible path through the study program for students at risk of dropping out. [For the complete proceedings, see ED630829.]

Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1) generate learning curves following the power law of practice; and (2) are predictive of response correctness. We train a neural architecture (named KC-Finder) that classifies the correctness of student code submissions and captures problem-KC relationships. Our evaluation on data from 351 students in an introductory Java course shows that the learned KCs can generate reasonable learning curves and predict code submission correctness. At the same time, some KCs can be interpreted to identify programming skills. We compare the learning curves described by our model to four baselines, showing that: (1) identifying KCs with naive methods is a difficult task; and (2) our learning curves exhibit a substantially better curve fit. Our work represents a first step in solving the data-driven KC discovery problem in computing education. [For the complete proceedings, see ED630829.]

Prediction of student performance in introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help students and instructors gain insights into students' different programming behaviors and problem-solving strategies that can lead to good or poor performance. This study develops an explainable model that predicts students' performance based on programming assignment submission information. We extract different data-driven features from students' programming submissions and employ a stacked ensemble model to predict students' final exam grades. We use SHAP, a game-theory-based framework, to explain the model's predictions to help the stakeholders understand the impact of different programming behaviors on students' success. Moreover, we analyze the impact of important features and utilize a combination of descriptive statistics and mixture models to identify different profiles of students based on their problem-solving patterns to bolster explainability. The experimental results suggest that our model significantly outperforms other Machine Learning models, including KNN, SVM, XGBoost, Bagging, Boosting, and Linear regression. Our explainable and transparent model can help explain students' common problem-solving patterns in relationship with their level of expertise resulting in effective intervention and adaptive support to students. [For the complete proceedings, see ED630829.]

Teachers often rely on the use of a range of open-ended problems to assess students' understanding of mathematical concepts. Beyond traditional conceptions of student open-ended work, commonly in the form of textual short-answer or essay responses, the use of figures, tables, number lines, graphs, and pictographs are other examples of open-ended work common in mathematics. While recent developments in areas of natural language processing and machine learning have led to automated methods to score student open-ended work, these methods have largely been limited to textual answers. Several computer-based learning systems allow students to take pictures of hand-written work and include such images within their answers to open-ended questions. With that, however, there are few-to-no existing solutions that support the auto-scoring of student hand-written or drawn answers to questions. In this work, we build upon an existing method for auto-scoring textual student answers and explore the use of OpenAI/CLIP, a deep learning embedding method designed to represent both images and text, as well as Optical Character Recognition (OCR) to improve model performance. We evaluate the performance of our method on a dataset of student open-responses that contains both text- and image-based responses, and find a reduction of model error in the presence of images when controlling for other answer-level features. [For the complete proceedings, see ED630829.]

Massive Open Online Courses (MOOCs) make high-quality learning accessible to students from all over the world. On the other hand, they are known to exhibit low student performance and high dropout rates. Early prediction of student performance in MOOCs can help teachers intervene in time in order to improve learners' future performance. This is particularly important in healthcare courses, given the acute shortages of healthcare staff and the urgent need to train data-literate experts in the healthcare field. In this paper, we analysed a health data science MOOC taken by over 3,000 students. We developed a novel three-step pipeline to predict student performance in the early stages of the course. In the first step, we inferred the transitions between students' low-level actions from their clickstream interactions. In the second step, the transitions were fed into Artificial Neural Network (ANN) that predicted student performance. In the final step, we used two explanation methods to interpret the ANN result. Using this approach, we were able to predict learners' final performance in the course with an AUC ranging from 83% to 91%. We found that students who interacted predominately with lab, project, and discussion materials outperformed students who interacted predominately with lectures and quizzes. We used the DiCE counterfactual method to automatically suggest simple changes to the learning behaviour of low- and moderate-performance students in the course that could potentially improve their performance. Our method can be used by instructors to help identify and support struggling students during the course. [For the complete proceedings, see ED630829.]

We propose an innovative, effective, and data-agnostic method to train a deep-neural network model with an extremely small training dataset, called VELR (Voting-based Ensemble Learning with Rejection). In educational research and practice, providing valid labels for a sufficient amount of data to be used for supervised learning can be very costly and often impractical. The shortage of training data often results in deep neural networks being overfitting. There are many methods to avoid overfitting such as data augmentation and regularization. Though, data augmentation is considerably data dependent and does not usually work well for natural language processing tasks. Moreover, regularization is often quite task specific and costly. To address this issue, we propose an ensemble of overfitting models with uncertainty-based rejection. We hypothesize that misclassification can be identified by estimating the distribution of the class-posterior probability P(y|x) as a random variable. The proposed VELR method is data independent, and it does not require changes to the model structure or the re-training of the model. Empirical studies demonstrated that VELR achieved classification accuracy of 0.7 with only 200 samples per class on the CIFAR-10 dataset, but 75% of input samples were rejected. VELR was also applied to a question generation task using a BERT language model with only 350 training data points, which resulted in generating questions that are indistinguishable from human-generated questions. The paper concludes that VELR has potential applications to a broad range of real-world problems where misclassification is very costly, which is quite common in the educational domain. [For the complete proceedings, see ED630829.]

Vocabulary proficiency diagnosis plays an important role in the field of language learning, which aims to identify the level of vocabulary knowledge of a learner through his or her learning process periodically, and can be used to provide personalized materials and feedback in language-learning applications. Traditional approaches are widely applied for modeling knowledge in science or mathematics, where skills or knowledge concepts are well-defined and easy to associate with each item. However, only a handful of works focus on defining knowledge concepts and skills using linguistic characteristics for language knowledge proficiency diagnosis. In addressing this, we propose a framework for vocabulary proficiency diagnosis based on neural networks. Specifically, we propose a series of methods based on our framework that uses different linguistic features to define skills and knowledge concepts in the context of the language learning task. Experimental results on a real-world second-language learning dataset demonstrate the effectiveness and interpretability of our framework. We also provide empirical evidence with ablation testing to prove that our knowledge concept and skill definitions are reasonable and critical to the performance of our model. [For the complete proceedings, see ED630829.]

The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for amplifying human potential." Not all students or seekers of knowledge receive the education necessary to help them realize their full potential, be it due to a lack of resources or lack of access to high quality teaching. The dearth in high-quality educational content, teaching aids, and methodologies, and non-availability of objective feedback on how they could become better teachers, deprive our teachers from achieving their full potential. The administrators and policy makers lack tools for making optimal decisions such as optimal class sizes, class composition, and course sequencing. All these handicap the nations, particularly the economically emergent ones, who recognize the centrality of education for their growth. EDM-2023 has striven to focus on concepts, principles, and techniques mined from educational data for amplifying the potential of all the stakeholders in the education system. The spotlights of EDM-2023 include: (1) Five keynote talks by outstanding researchers of eminence; (2) A plenary Test of Time award talk and a Banquet talk; (3) Five tutorials (foundational as well as advanced); (4) Four thought provoking panels on contemporary themes; (5) Peer reviewed technical paper and poster presentations; (6) Doctoral students consortium; and (7) An enchanting cultural programme. [Individual papers are indexed in ERIC.]

The International Conference on Academic Studies in Technology and Education (ICASTE) is set to take place at Amara Premier Palace Hotel in Antalya, Turkey, from November 16-19, 2023. Organized by the International Society for Research in Education and Science (ISRES) and the International Society for Academic Research in Science, Technology, and Education (ARSTE), the conference promises a diverse range of topics in the realm of education and technology. Highlighted presentations include discussions on the role of AI assistants and chatbots in higher education, the application of gamification to enhance student engagement and learning outcomes, and a systematic review of literature on the teaching of Nature of Science (NOS) based on the Family Resemblance Approach. Other notable topics cover issues such as teachers' organizational alienation and emotional labor, upskilling higher education engineering teachers for online creativity teaching, challenges in forming artistic-aesthetic culture in the virtual environment era, and the ethical considerations in education within the digital age. Further presentations delve into global concerns within sustainable development in science education, the significance of family as a fundamental principle in sustainable development, and the development of a smart wheelchair for the autonomous movement of disabled individuals. The conference also explores emerging trends in international teaching partnerships, the use of virtual laboratories in science education, and the integration of robotics and automation to enhance warehouse efficiency among logistics operators. Additionally, there are discussions on sanogenic reflection for emotional well-being, small group conversation structures in science education, risk management as part of sustainable technological and industrial development, features of adolescent reflection with different character accentuations, and the implementation of the ADDIE model for material design in English language teaching. Moreover, the importance of creative drama in science education, innovative practices for environmental awareness and action in sustainability education, and the incorporation of artificial intelligence in STEM education are covered. The conference concludes with presentations on English conversational learning in a first middle school-based boarding school, the reflective and intuitive aspects in design, and the support of pre-service ESOL teachers' critical language awareness through dialectical variation. The event promises a comprehensive exploration of current issues and innovations at the intersection of technology and education. [This conference was organized by the International Society for Academic Research in Science, Technology, and Education (ARSTE).]

Mobile learning or mLearning has become an essential tool in many fields in this digital era, among the ones educational training deserves special attention, that is, applied to both basic and higher education towards active, flexible, effective high-quality and continuous learning. However, despite the advances in Natural Language Processing techniques, mLearning still does not take full advantage of the great potential of the latter techniques. It is the case of the transformer language models distributed in chatbot form. Accordingly, this work presents a mLearning application based on OpenAI GPT-3 model, which enables user-friendly interactions. The proposed system was tested in two evaluation scenarios, and the experimental results endorse its appropriateness toward learning experience enhancement. [For the full proceedings, see ED639391.]

Artificial intelligence (AI) technology is already challenging a variety of societal areas, including education. It is transforming education to data driven. AI-enhanced technologies in education (abbreviated AIinED) will have a significant role in changing the teaching and learning methods, as well as impacting the behavior and organization of the educational system. It is considered that the AIinED will change the paradigm of education in the future. And yet, AIinED is still more in the lab than being practically implemented in education and training. We consider three major players in the implementation of AIinED -- students, teachers, and society. All three can benefit from AIinED and at the same time be a potential target of the risks that AIinED brings along with its promises -- may be one of the reasons why main stakeholders (UNESCO, EC etc.) have been developing guidelines and recommendations for ethical use of AIinED. The literature shows that the center of AIinED system will be the student, but we consider the student not only as a target but also as a source of ideas for AIinED development with the potential to accelerate the process of adoption of AIinED. Hence, one of the big questions should be how the students foresee the role of artificial intelligence in education. To initiate such a question, though, it is important to know the level of understanding among the students about what and where artificial intelligence is. There are three major aspects that AIinED must be considered accordingly -- technological, lawful, and ethical. This paper presents the results of a study on high school students' understanding of AI technologies and their attitudes to their application in education. A survey was used as a tool to elaborate. The conceptual model of the research was developed on the basis of established theories linking attitudes to behavior and the acceptance of artificial intelligence technologies in education. Each element of this concept is explored with a different part of the questionnaire, which contains a total of 12 questions (some of which with sub-questions). The survey was elaborated online within October-November 2021. A link to the questionnaire in Bulgarian was provided to 178 high and vocational high schools educating students aged 14-19 (grades 8-12) across the country (Bulgaria). 766 students submitted their replies through the online survey form. Descriptive statistics and analysis of the frequencies of the respondents' opinions were made based on the data. The results show that the students participating in the survey:(a) understand the essence of AI technologies; (b) they are convinced of the usefulness of the application of artificial intelligence technologies in their daily activities and strongly believe that it improves it; (c) are not entirely clear about the benefits of artificial intelligence enhanced technologies in learning and teaching; (d) do not demonstrate sufficient knowledge and understanding of the necessity of ethical use of AI technologies in education; The latter reduces the positive influence of the perceived usefulness of artificial intelligence technologies in the learning process on students' attitudes. [For the full proceedings, see ED639391.]

In order to keep up with the speed of science and technology in today's conditions, it is important for human beings to constantly renew themselves and adapt to the changing world and living conditions. In this content, education plays a significant role, and art education, in particular, hold a pioneering role in nurturing civilized individuals. Art education, which is considered important at a young age, takes on the locomotive task of developing a child's creative power and integrating the knowledge and skills they acquire into real-life applications by incorporating current techniques and tools. The importance of drawing in a child's development can be expressed by its role as a means for perceiving the external world and self-expression. Studies also demonstrate that children utilize the knowledge and skills they acquire on touch screens in their real lives. Drawing applications with fun themes for children attract attention. However, these applications are generally designed for entertainment purpose and targeted towards children who can read and write. The lack of an educational software that provides audio guidance and teaches drawing with the support of artificial intelligence has led to the development of the 'Artificial Intelligence-Assisted Directed Drawing Technique' as identified in the literature research. This technique supports a child's creativity, enhances their fine motor skills, increases their digital literacy, and provides an enjoyable learning experience. Additionally, it enables children to create original drawings and express their ideas while guiding their progress and supporting their learning process. The artificial intelligence-assisted directed drawing application is believed to contribute to the development of a child's self-drawing skills, beneficial use of technological devices like tablets by preschoolers, and increasing the interest and talent of future generations in artistic activities. [This paper was published in: "EJER Congress 2023 International Eurasian Educational Research Congress Conference Proceedings," Ani Publishing, 2023, pp. 511-519.]

This article intends to investigate the consistency between a few popular generative AI robots in the evaluation of massive open online course (MOOC) platforms. The four robots experimented with in the study were Claude+, GPT-4, Sage, and Dragonfly, which were tasked with awarding rating scores to the eight major dimensions, namely (1) content/course quality, (2) pedagogical design, (3) learner support, (4) technology infrastructure, (5) social interaction, (6) learner engagement, (7) instructor support, and (8) cost-effectiveness, of the 31 currently very popular MOOC platforms. Only Claude+'s and Dragonfly's rating scores turned out to be amenable to statistical analysis. For each of the two robots, the minimum, the maximum, the range, and the standard deviation of the rating scores for each of the eight dimensions were computed across all the 31 MOOC platforms. The rating score difference for each of the eight dimensions between the two robots was calculated for each platform. The mean of the absolute value, the minimum, the maximum, the range, and the standard deviation of the differences for each dimension between the two robots were calculated across all platforms. A paired sample t-test was then applied to each dimension for the rating score difference between the two robots over all the platforms. Finally, a correlation coefficient of the rating scores was computed for each of the eight dimensions between the two robots across all the MOOC platforms. The computational results were to reveal whether the two robots awarded discrimination in evaluating each dimension across the platforms, whether any of the two robots systematically underrated or overrated any dimension with respect to the other robot, and whether there was consistency between the two robots in evaluating each dimension across the platforms. It was found that discrimination was prominent in the evaluation of all dimensions save Dragonfly's rating of the dimensions learner support, technology infrastructure, and instructor support, Claude+ systematically underrated all dimensions (p < 0.000 < 0.05) compared with Dragonfly except for the dimension cost-effectiveness, which Claude+ systematically overrated (p = 0.003 < 0.05), and the evaluation by the two robots was consistent only for the dimensions content/course quality, pedagogical design, and learner engagement with the correlation coefficients ranging from 0.445 to 0.632 (p from 0.000 to 0.012 < 0.05). Consistency implies at least the partial trustworthiness of the evaluation of these MOOC platforms by either of these two popular generative AI robots based on the analogous concept of convergent validity for an operationalized instrument to measure an abstract construct. [For the full proceedings, see ED636095.]

Although information technology (ICT) education is being strengthened based on the national context, there are reports suggesting a decline in young people's communication skills. This phenomenon can be attributed to the rapid development of informatisation, which includes the diversification and spread of information tools, as well as the prevalence of nonverbal communication, such as pictograms in social networking services. In addition, the COVID-19 pandemic has drastically reduced face-to-face communication opportunities, making interactive communication in on-demand classes challenging. Even in assignments and short tests completed during class, many instances of content being copied and pasted from the web or written in a disorganized manner have been observed. For instance, students entering ICT-related careers, particularly those graduating from the faculty of informatics, must possess the ability to communicate with engineers and clients while implementing ICT advancements. Alongside programming skills, strong communication abilities are essential. Moreover, the emergence of generative artificial intelligence (AI) tools, such as ChatGPT and Bing AI, has considerably diminished the opportunities for independent thinking. In the current era of enhanced ICT education, AI, and IoT, the Faculty of Informatics at the Kanagawa Institute of Technology has been engaged in discussions regarding learning materials that aim to strengthen students' ability to think and communicate in their own words, preventing the mechanisation of individuals. This paper presents the development and implementation of learning materials designed to enhance students' verbal communication skills through the description and re-production of mathematical graphs. [For the full proceedings, see ED636095.]

This paper delves into statistical disparities between human-written and ChatGPT-generated texts, utilizing an analysis of Shannon's equitability values, and token frequency. Our findings indicate that Shannon's equitability can potentially be a differentiating factor between texts produced by humans and those generated by ChatGPT. Additionally, we uncover substantial distinctions when studying the most frequent tokens. [For the full proceedings, see ED636095.]

Due to the advances of artificial intelligence (AI) and natural language processing, new AI-powered writing tools have emerged. They can be used by students among other things for text translation, to improve spelling or to generate new texts. In academic writing, AI-powered writing tools are posing challenges but also opportunities for teaching and learning. It is an open question in which way to sensibly deal with these tools. To address the issue, this paper investigates, what interests different stakeholders (students, lecturers, university administration) pursue in relation to AI-powered writing tools. Building on this, tensions between different stakeholders are identified and (teaching) strategies proposed to deal with these tensions. To discuss the findings in light of recent developments around ChatGPT, semi-structured expert interviews were conducted in April 2023 with five academic writing lecturers at the University of St. Gallen. The results suggest that as writing tools become more and more powerful, the need for strategies to ensure their reasonable and transparent use also increases. [For the full proceedings, see ED636095.]

This paper proposes a didactic design that is centered around an immersive, multilinear narrative in virtual reality as a means of illustrating human life on the edge of technological singularity. It explores the potential of narrative scenarios to trigger a discourse from users' perspective. Affective Computing is taken as a use case. It is a subfield of AI that focuses on identifying, understanding, and appropriately responding to human emotions. Its goal is to create more personalized and emotionally engaging human-machine interactions. To explore what life might be like if an emotionally intelligent AI became our best friend, a multilinear scenario was created. This scenario takes the reader through different stages of the protagonist's life, starting from the first day of secondary school and ending with the loss of a loved one in midlife. The systematic approach to create and validate the multilinear interactive scenario is described and the results of an experiment with 164 participants are presented. The ultimate goal is the application of this approach for educational purposes regarding ethical thinking and responsible innovations. [For the full proceedings, see ED636095.]

This quasi-experimental study seeks to improve the conceptual quality of summary essays by comparing two conditions, essay prompts with or without a list of 13 broad concepts, the concepts were selected across a continuum of the 100 most frequent words in the lesson materials. It is anticipated that only the most central concepts will be used as "anchors" when writing. Participants (n = 90) in an Architectural Engineering undergraduate course read the assigned lesson textbook chapter and attended lectures and labs, then in a final lab session were asked to write a 300-word summary of the lesson content. Data consists of the essays converted to networks and the end-of-unit multiple choice test. Compared to the expert network benchmark, the essay networks of those receiving the broad concepts in the writing prompt were not significantly different from those who did not receive these concepts. However those receiving the broad concepts were significantly more like peer essay networks (mental model convergence) and like the networks of the two PowerPoint lectures but neither were like the textbook chapter. Further, those receiving the broad concepts performed significantly better on the end-of-unit test than those not receiving the concepts. Term frequency analysis of the essays indicates as expected that the most network-central concepts had a greater frequency in essays, the other terms frequencies were remarkably the same for both the terms and no terms groups, suggesting a similar underlying conceptual mental model of this lesson content. To further explore the influence of anchoring concepts in summary writing prompts, essays were generated with the same two summary writing prompts using OpenAI (ChatGPT) and Google Bard, plus a new prompt that used the 13 most central concepts from the expert's network. The quality of the essay networks for both AI systems were equivalent to the students' essay networks for the broad concepts and for the no concept treatments. However, the AI essays derived with the 13 most central concepts were significantly better (more like the expert network) than the students and AI essays derived with broad concepts or no concepts treatments. In addition, Bard and OpenAI used several of the same concepts at a higher frequency than the students suggesting that the two AI systems have more similar knowledge graphs of this content. In sum, adding 13 broad conceptual terms to a summary writing prompt improved both structural and declarative knowledge outcomes, but adding 13 most central concepts may be even better. More research is needed to understand how including concepts and other terms in a writing prompt influences students' essay conceptual structure and subsequent test performance. [For the full proceedings, see ED636095.]

The spread of Artificial Intelligence (AI) has been recently generating worries among teachers and educators about the validity of assessment when students make use of AI tools to solve tasks. To tackle this issue, we propose mathematical problem solving activities to be carried out with the aid of ChatGPT, showing how problem solving and critical thinking continue to be pivotal in solving mathematical problems, even if this is performed with the aid of AI. After discussing theoretical frameworks on strategies of problem solving and phases of the critical thinking process, we present six problems of combinatorics that we submitted to ChatGPT. We also asked 40 university students to solve the six problems in group with the aid of ChatGPT during an international module on Problem Solving and Critical Thinking and collected the tutors' observations about the activities. Analyzing ChatGPT solutions and tutors' reflections, we show that the proposed activity requires problem solving and critical thinking to be accomplished. The results corroborate the idea that, instead of limiting the use of AI in education, it is possible to integrate it within learning and assessment to achieve the learning goals. [For the full proceedings, see ED636095.]

ChatGPT and Bard, two chatbots powered by Large Language Models (LLMs), are propelling the educational sector towards a new era of instructional innovation. Within this educational paradigm, the present investigation conducts a comparative analysis of these groundbreaking chatbots, scrutinizing their distinct operational characteristics and applications as depicted in current scholarly discourse. ChatGPT emerges as an exemplary tool in task-oriented textual interactions, while Bard brandishes unique features such as Text-To-Speech (TTS) functionality, which enhances accessibility and inclusive education, as well as integration with Google Workspace applications. This research critically examines their utilization in various spheres such as pedagogy, academic research, Massive Open Online Courses (MOOCs), mathematics, and software programming. Findings accentuate ChatGPT's superior efficacy in content drafting, code generation, language translation, and providing clinically precise responses, notwithstanding Bard's significant potential encapsulated in its exclusive features. Furthermore, the study traverses' crucial ethical aspects, including privacy concerns and inherent bias, underscoring the profound implications of these Artificial Intelligence (AI) technologies on literature and advocating against the indiscriminate reliance on such models. [For the full proceedings, see ED636095.]

The exponential growth of scholarly publications in recent years has presented a daunting challenge for researchers to keep track of relevant articles within their research field. To address this issue, we examined the capabilities of InfraNodus, an AI-Powered text network analysis platform. InfraNodus promises to provide insights into any discourse, uncover blind spots, and enhance a scholar's perspective by representing text as a network graph with relevant topical clusters and their relations. To understand the tools' effectiveness in analyzing scholarly articles, we used a set of 15 abstracts and 15 full papers. Our findings revealed that InfraNodus could indeed create topical clusters and meaningful patterns from abstracts, but its generated questions and summaries lacked relevance and coherence with the content. A deeper understanding of how the AI operates within the tool would benefit researchers seeking to optimize their literature review processes. [For the full proceedings, see ED636095.]

This paper provides a possible strategy for integrating large language artificial intelligence models (LLMs) in supporting students' education in artistic or design activities. We outline the methodological foundations concerning the integration of CHATGPT LLM in the educational approach aimed at enhancing artistic conception and design ideation. We also present the knowledge and system architecture for integrating LLM in the °'°Kobi system. Finally, this paper discusses some relevant aspects concerning the system's application in a real educational context and briefly reports its preliminary assessment. [For the full proceedings, see ED636095.]

In this study, the authors have developed a web-based programming exercise system currently implemented in classrooms. This system not only provides students with a web-based programming environment but also tracks the time spent on exercises, logging operations such as program editing, building, execution, and testing. Additionally, it records their results. For educators, the system offers insights into each student's progress, the evolution of their source code, and the instances of errors. While teachers find these functions beneficial, the method of providing feedback to students needs improvement. Immediate feedback is proven to be more effective for student learning. If the final course score could be predicted based on early data (e.g., from the 1st or 2nd week), students could adapt their study strategies accordingly. This paper demonstrates that one can predict the final score using the system's operational logs from the initial phases of the course. Furthermore, the score predictions can be revised weekly based on new class logs. We also explore the potential of offering tailored advice to students to enhance their final score. [For the full proceedings, see ED636095.]

The sudden, unexpected breakthrough in the intelligence shown by machines, as a wished for, but very disruptive element, will shape the future of our civilisation and Humans as individuals and collectives. The extreme drive towards commercialisation of newest developments already led to an extremely wide spread of Machine Intelligence Assistants (and chatbots), with plans of many companies to include them into everything they produce. This puts Humans in a very precarious situation, specifically regarding ethics and morale, trustworthiness and confidence. Suddenly we found ourselves in a situation that Education has to be extended to cater for two types of intelligences: the Humans and the Machines. On the Machine side, it has been shown that it is very difficult to obtain a trustworthy and highly ethical non-biased intelligence. The present day approach of "training" the "models" must be overcome by the realisation that MI is based on collected Human knowledge, but initially "trained" without any regard to the order of learning, which directly influences the initial alignment of the emerging intelligence, the same way learning does in Humans. On the Human side, it is getting obvious that this disruptive development was generally completely unexpected, and no educational preparation was ever envisioned for this situation. However, between others, a good example of possible positive cooperation of Humans and Machines, which necessitates proper MI ethics, is Democratisation of Academic Publishing, where, based on blockchain trustworthiness, Open Access publishing is done in such a way that all stakeholders in the process get appropriate recognition and reward. The use of well educated Ethical Machine Intelligence in this process of management of an enormous amount of academic work and peer reviews will enable academic education and scientific development to be ethical, transparent, fair, trustworthy and accessible to all authors and readers throughout their life. [For the full proceedings, see ED654100.]

This paper presents a mobile application aimed at enhancing the financial literacy of college students by monitoring their spending patterns and promoting better decision-making. The application is developed using the agile methodology with Android Studio and Flutter as development tools and Firebase as a database. The app is divided into sub-applications, with the home page serving as the program's integration point, displaying a summary of the user's financial progress. The app generates valuable insights into the user's current and future financial success, utilizing data analytics and machine learning to provide detailed and summary insights into the user's financial progress. The machine-learning algorithm used in this app is linear regression, which predicts the user's income and expenses for the upcoming month based on their historical spending data. In addition, the app highlights deals and student discounts in the user's vicinity and links to financial articles that promote better financial planning and decision-making. By promoting responsible spending habits and providing valuable financial insights, this mobile application aims to help students become financially literate and make informed financial decisions for future. [For the full proceedings, see ED654100.]

Three categories of audio recordings, a web service platform, and commercial software were involved in this analysis process. The three categories of audio recordings included eight filtered student recordings, an audio recording provided by a Mandarin instructor from University Malaysia Sabah, and an audio recording generated by Text-to-Speech MP3, available at https://ttsmp3.com/. All ten recordings will be uploaded into the NCH WavePad Sound Editor to build a spectrum image. This will allow us to compare and identify differences in tone reading, emotion reading, etc. You can download the NCH WavePad Sound Editor from https://www.nch.com.au/WavePad/index.html. This information is beneficial for all Mandarin language beginners and their teachers, as it can be used for comparison purposes. With this knowledge, they can further undertake a pilot study. This study provides a foundation for investigating differences in human and machine reading from multiple perspectives. Furthermore, this dataset can be used to analyze reading speed. It serves as a crucial starting point for spectrum, voice wave, and reader behavior research. [For the full proceedings, see ED654100.]

Artificial intelligence has potentiated changes in higher education. In this position paper, we propose to discuss dimensions that shape higher education and that can be transformed by the mobilization of Artificial Intelligence: teaching-learning processes (from the perspectives of scholars and students), research (from the perspective of professional researchers, students and advisors), management of organizations (from the perspective of managers, subordinates and students), and the relationship with the outside (the look from inside to outside, as well as from outside to inside). To this end, a document collection and analysis was carried out to support our argument about the new challenges and the potential that emerges from the existence and application of artificial intelligence in higher education. As this change, like any transformation, will have positive and negative aspects, some implications of both a theoretical and more practical nature of these challenges for academics, students and other stakeholders in higher education institutions are also discussed. [For the full proceedings, see ED654100.]

The effective anti-corruption education requires careful understanding of the teaching-learning context. At the different stages of the designing an anti-corruption focuses course, we need to consider factors related to target learning group as well as the respective context in which they are. Learning style versatility is an important factor that needs to be analyzed for an effective and outcome linked course in Anti-Corruption. The emergence of artificial intelligence provides significant opportunities in improving the outcome-focused course design. Automating the identification of learning style versatility of target learning group can simplify the process of customizing the course contents, pedagogy and assessment framework for the given learning group. The article is conceptual in nature and provides a generic framework for applying artificial intelligence (AI) in developing the anti-corruption course, tailored to the target learning group characteristics. The suggested framework can be adapted under the different teaching-learnings contexts, as per the need. The anti-corruption is an extended and applied area of ethics and, therefore, the framework can also be extended in developing course modules related to other similar themes. [For the full proceedings, see ED654100.]

The last few years have witnessed significant integration of technology, particularly Information and Communication Technology (ICT) in both content as well as delivery of business courses in higher education. On the one side, the contents related to technological applications are increasingly expanding the space in business education curriculum while on the other hand the mode of delivery has also been significantly influenced. The teaching of business ethics involves a number of distinct issues, often challenging the educators to plan and deliver the course effectively. The current chapter, which is based on review of literature as well as authors' own experiences in teaching business ethics in university courses, critically examines the role of technology as enabler of business ethics course delivery. The Chapter also provides a framework of how the technology can be used to link the teaching, research and participative community action in enhancing the relevance of business ethics teaching in a given context. The chapter provides conclusions for deans, course leaders and faculty members in order to manage change smoothly. [For the full proceedings, see ED654100.]

The integration of ChatGPT in Personal Learning Environments (PLEs) has emerged as a promising approach to personalized learning in tertiary education. ChatGPT is believed to have the potential to transform traditional higher education into a more personalized, quality-driven, and student-centered learning experience that fosters critical thinking, self-regulated learning, and creativity. While recent studies have highlighted the potential benefits of ChatGPT in enhancing personalized learning experiences, there are several risks and challenges that need to be addressed. This paper reviews relevant literature on ChatGPT and PLEs and identifies key risks and challenges associated with their integration, including ethical concerns, data privacy, technical issues, and user acceptance. Meanwhile, the paper also proposes ways and thoughts for the future implementation of ChatGPT in PLEs. The paper concludes that ChatGPT has significant potential to facilitate a new round of educational revolution which pushes educators to reconsider why to teach, how to teach, and what to teach. [For the full proceedings, see ED654100.]