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For the forty-fifth time, the Association for Educational Communications and Technology (AECT) is sponsoring the publication of these Proceedings. Papers published in this volume were presented online and onsite during the annual AECT Convention. The Proceedings of AECT's Convention are published in two volumes. Volume #1 contains papers dealing primarily with research and development topics. Papers dealing with the practice of instructional technology including instruction and training issues are contained in Volume #2. This year, both volumes are included in one document.

For the forty-fourth time, the Association for Educational Communications and Technology (AECT) is sponsoring the publication of these Proceedings. Papers published in this volume were presented online and onsite during the annual AECT Convention. Volume 1 contains papers dealing primarily with research and development topics. Papers dealing with the practice of instructional technology including instruction and training issues are contained in Volume 2. [For volume 2, see ED617429.]

For the forty-third time, the Association for Educational Communications and Technology (AECT) is sponsoring the publication of these Proceedings. Papers published in this volume were presented online during the annual AECT Convention. Volume 2 contains 15 papers dealing the practice of instructional technology including instruction and training issues. Papers dealing primarily with research and development are contained in Volume 1. [For Volume 1, see ED617421.]

For the forty-second time, the Association for Educational Communications and Technology (AECT) is sponsoring the publication of these Proceedings. Papers published in this volume were presented at the annual AECT Convention in Las Vegas, Nevada. The Proceedings of AECT's Convention are published in two volumes. Volume 1 contains 37 papers dealing primarily with research and development topics. Papers dealing with the practice of instructional technology including instruction and training issues are contained in Volume 2. [For Volume 2, see ED609417.]

For the forty-second time, the Association for Educational Communications and Technology (AECT) is sponsoring the publication of these Proceedings. Papers published in this volume were presented at the annual AECT Convention in Las Vegas, Nevada. The Proceedings of AECT's Convention are published in two volumes. Volume 1 contains papers dealing primarily with research and development topics. Twenty-three papers dealing with the practice of instructional technology including instruction and training issues are contained in Volume 2. [For Volume 1, see ED609416.]

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.]

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.]

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.]

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.]

The growth of Artificial Intelligence (AI) chatbots has created a great deal of discussion in the education community. While many have gravitated towards the ability of these bots to make learning more interactive, others have grave concerns that student created essays, long used as a means of assessing the subject comprehension of students, may be at risk. The bot's ability to quickly create high quality papers, sometimes complete with reference material, has led to concern that these programs will make students too reliant on their ability and not develop the critical thinking skills necessary to succeed. The rise in these applications has led to the need for the development of detection programs that are able to read the students submitted work and return an accurate estimation of if the paper is human or computer created. These detection programs use natural language processing's (NLP) ideas of perplexity, or randomness of the text, and burstiness, or the tendency for certain words and phrases to appear together, plus sophisticated algorithms to compare the essays to preexisting literature to generate an accurate estimation on the likely author of the paper. The use of these systems has been found to be highly effective in reducing plagiarism among students, however concerns have been raised about the limitations of these systems. False positives, false negatives, and cross language identification are three areas of concern amongst faculty and have led to reduced usage of the detection engines. Despite the limitations however, these systems are a valuable tool for educational institutions to maintain academic integrity and ensure that students are submitting original work. [For the full proceedings, see ED656038.]

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 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.]

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.]

The paper describes the use of expert systems technology in translating test and observational data into objectives for Individualized Education Programs (IEPs) with handicapped students. The Math Test Interpreter (MTI) is designed to combine student information, results from the Key Math Diagnostic Arithmetic Test and additional program generated criterion referenced test data to produce a prescription in mathematics. The Behavior Consultant (BC) program applies the expert system approach to classroom behavior problems and features two videodisc components. Examples of a typical consultation with each of the expert systems illustrate their factual and heuristic rules and their use of backchaining to work from hypothesized conclusions to known facts. Possible system outcomes are delineated, including situations of inadequate information and development of objectives for IEPs. The paper concludes with a note on the implications of appropriate, clearly stated objectives for the education of handicapped students. (CL)

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.]

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.]

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.]

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.]

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.]

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.]