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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.]

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.]

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.

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.]

Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide explanations for many examples typically used in a programming class. In this paper, we assess the feasibility of using LLMs to generate code explanations for passive and active example exploration systems. To achieve this goal, we compare the code explanations generated by chatGPT with the explanations generated by both experts and students. [This paper was published in: "Proceedings of Machine Learning Research" (2024).]

Intelligent tutoring systems (ITS) can help students learn successfully, yet little work has explored the role of caregivers in shaping that success. Past interventions to support caregivers in supporting their child's homework have been largely disjunct from educational technology. The paper presents prototyping design research with nine middle school caregivers. We ask: (1) what are caregivers' preferences for different prototypes incorporating data-driven recommendations into their math homework support? Integrating caregivers' preferences, we then ask: (2) what are caregivers' perceptions when interacting with a prototype of an intelligent chatbot tool to support students' homework? We found caregivers reported feeling comfortable integrating AI into their practices and appreciated chat-based support for understanding content and effective ITS use. Our results highlight the affordances of ITS data and AI to assist caregivers who would otherwise not be able to support their child's homework, paving the way for more effective and equitable mathematics learning. [This paper will be published in the ISLS2024 proceedings.]

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.]

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 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.]

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.]

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.]

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.]

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.]

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.]

Today more than ever before, we have access to new technologies which provide unforeseen opportunities for educators to pursue new innovations in online education. Pursuing innovation is a complex process! It starts with an idea, but that needs to be coupled with the right team of experts willing to take big risks and put in the hard work to build something new. An instructional design team was empowered to reimagine an Introduction to Sociology university course as a Game-Based Learning (GBL) experience utilizing cutting edge Virtual Reality (VR) technology. The result was a innovate collaborative process that resulted in a brand-new type of learning based in Game theory, Method of Loci, and VR Immersion Simulations to promote deeper retention of core concepts. The team deconstructed the way that university courses operated, in order to rebuild the educational process in a whole new, learner-centric manner. In addition to a review of the build process, this paper will explore the results of in-course surveys completed by student participants. [For the full proceedings, see ED656038.]

The unprecedented growth in the use of AI and its related technologies will put a tremendous stress on US institutions to produce the required number of technologically prepared workers to fill critically important job openings. In the US, low-income and URM students participate less vigorously in STEM-related fields; the problem is even more serious in post-baccalaureate level degrees. To address the future needs of the nation, we must increase the number of low-income students in STEM, with special attention to AI related technologies, to fill the millions of technology job openings. This paper will report on the impact of a NSF SSTEM project in which we combined (a) a mentorship model for talented, low-income students to develop a sense of self-efficacy and belongingness along with (b) a model of curricular and co-curricular supports (e.g., including engagement with AI technologies and research) and (c) limited financial assistance, all of which have increased the low-income student success in completing both their BS degree in engineering and their MS degree in AI, and addressing a national need. [For the full proceedings, see ED656038.]

Assessing students' answers and in particular natural language answers is a crucial challenge in the field of education. Advances in transformer-based models such as Large Language Models (LLMs), have led to significant progress in various natural language tasks. Nevertheless, amidst the growing trend of evaluating LLMs across diverse tasks, evaluating LLMs in the realm of automated answer assessment has not received much attention. To address this gap, we explore the potential of using LLMs for automated assessment of student's short and open-ended answers in program comprehension tasks. Particularly, we use LLMs to compare students' explanations with expert explanations in the context of line-by-line explanations of computer programs. For comparison purposes, we assess both decoder-only Large Language Models (LLMs) and encoder-based Semantic Textual Similarity (STS) models in the context of assessing the correctness of students' explanation of computer code. Our findings indicate that decoder-only LLMs, when prompted in few-shot and chain-of-thought setting perform comparable to fine-tuned encoder-based models in evaluating students' short answers in the programming domain. [[This paper was published in: "Proceedings of Machine Learning Research" (2024).]

The process of generating challenging and appropriate distractors for multiple-choice questions is a complex and time-consuming task. Existing methods for an automated generation have limitations in proposing challenging distractors, or they fail to effectively filter out incorrect choices that closely resemble the correct answer, share synonymous meanings, or imply the same information. To overcome these challenges, we propose a comprehensive toolkit that integrates various approaches for generating distractors, including leveraging a general knowledge base and employing a T5 LLM. Additionally, we introduce a novel strategy that utilizes natural language inference to increase the accuracy of the generated distractors by removing confusing options. Our models have zero-shot capabilities and achieve good results on the DGen dataset; moreover, the models were fine-tuned and outperformed state-of-the-art methods on the considered dataset. To further extend the analysis, we introduce human annotations with scores for 100 test questions with 1085 distractors in total. The evaluations indicated that our generated options are of high quality, surpass all previous automated methods, and are on par with the ground truth of human-defined alternatives. [This paper was published in: "AIED 2024, LNAI 14830," edited by A. M. Olney et al., Springer Nature Switzerland, 2024, pp. 242-50.]

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)

Images in children's books convey messages about society and the roles that people play in it. Understanding these messages requires systematic measurement of who is represented. Computer vision face detection tools can provide such measurements; however, state-of-the-art face detection models were trained with photographs, and 80\% of images in children's books are illustrated; thus existing methods both misclassify and miss classifying many faces. In this paper, we introduce a new approach to analyze images using AI tools, resulting in data that can assess representation of race, gender, and age in both illustrations and photographs in children's books. We make four primary contributions to the fields of deep learning and social sciences: (1) We curate an original face detection data set (IllusFace 1.0) by manually labeling 5,403 illustrated faces with bounding boxes. (2) We train two AutoML-based face detection models for illustrations: (i) using IllusFace 1.0 (FDAI); (ii) using iCartoon, a publicly available data set (FDAI_iC), each optimized for illustrated images, detecting 2.5 times more faces in our testing data than the established face detector using Google Vision (FDGV). (3) We curate a data set of the race, gender, and age of 980 faces manually labeled by three different raters (CBFeatures 1.0). (4) We train an AutoML feature classification model (FCA) using CBFeatures 1.0. We compare FCA with the performance of another AutoML model that we trained on UTKFace, a public data set (FCA_UTK) and of an established model using FairFace (FCF). Finally, we examine distributions of character identities over the last century across the models. We find that FCA is 34% more accurate than FCF in its race predictions. These contributions provide tools to educators, caregivers, and curriculum developers to assess the representation contained in children's content. [This paper was published in: "Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)," 2022, pp. 462-471.]

The context for this paper is the "Synthetic Training Environment Experiential Learning -- Readiness" (STEEL-R) project [1], which aims to estimate individual and team competence using data collected from synthetic, semi-synthetic, and live scenario-based training exercises. In STEEL-R, the "Generalized Intelligent Framework for Tutoring" (GIFT) orchestrates scenario sessions and reports data as experience API (xAPI)statements. These statements are translated into assertions about individual and team competencies by the "Competency and Skills System" (CaSS). Mathematical models use these assertions to estimate the competency states of trainees. This information is displayed in a dashboard that enables users to explore progression over time and informs decisions concerning advancement to the next training phase and which skills to address. To test, tune, and demo STEEL-R, more data was needed than was available from real-world training exercises. Since the raw data used to estimate competencies are captured in xAPI statements, a component called DATASIM was added. DATASIM simulated training sessions by generating xAPI statements that conformed to a STEEL-R "xAPI Profile." This facilitated testing of STEEL-R and was used to create a demo that highlighted the ability to map data from multiple training systems to a single competency framework and to generate a display that team leaders can use to personalize and optimize training across multiple training modalities. This paper gives an overview of STEEL-R, its architecture, and the features that enabled the use of artificial data. The paper explains how xAPI statements are converted to assertions and how these are used to estimate trainee competency. This is followed by a section on xAPI Profiles and on the xAPI Profile used in STEEL-R. The paper then discusses how artificial data were generated and the challenges of modeling longitudinal development and team in these data. The paper ends with a section on future research. [For the full proceedings, see ED623995.]

The 63 papers in this collection include two keynote addresses: "Patient Simulation Using Interactive Video: An Application" (Joseph V. Henderson), and "Intelligent Tutoring Systems: Practice Opportunities and Explanatory Models" (Alan Lesgold). The remaining papers are grouped under five topics: (1) Artificial Intelligence, including intelligent computer assisted learning, problem solving, artificial intelligence, and programming (15 papers); (2) Delivery Systems, including distance learning, communications, and hardware (9 papers); (3) Developments, including interactive video, simulation, authoring, computer managed learning, and computer based training (12 papers); (4) Research/Evaluation and Future Directions, including research, policy/planning, and philosophical aspects (21 papers); and (5) Software Tutorials, including computer assisted learning tools and commercial product applications (4 papers). The text is supplemented by various figures, and references are provided for each paper. (EW)

Modern language learning applications have become 'smarter' and 'intelligent' by including Artificial Intelligence (AI) and Machine Learning (ML) technologies to collect different kinds of data. This data can be used for analysis on a microscopic and/or macroscopic level to provide granulation of knowledge. We analyzed 1,213 French language learner data over a 30-day period, publicly available from Duolingo, to compare the progression of individual learners (microscopic granularity) and large groups of learners (macroscopic granularity). Using network modeling, we compared patterns of individual learners against one another and that of a learning community and determined what groups of learners typically practice across communities. Preliminary results suggest how applications for L2 learning can be designed to create an optimal path for learning. [For the complete volume, "Intelligent CALL, Granular Systems and Learner Data: Short Papers from EUROCALL 2022 (30th, Reykjavik, Iceland, August 17-19, 2022)," see ED624779.]

The use of chatbots in language learning has been on the rise. In recent Computer-Assisted Language Learning (CALL) research, there is a consensus that rule-based, scripted voice systems are optimal for language learning. Such systems integrate well into instructed language learning in that interactions with the user are predictable and controlled. Open, AI-based voice systems (such as in personal assistants like Siri) do not provide that degree of task-oriented learning. However, the argument is made here that they have the potential to provide open-ended conversational practice and language development which aligns with an ecological, usage-based perspective on language development. [For the complete volume, "Intelligent CALL, Granular Systems and Learner Data: Short Papers from EUROCALL 2022 (30th, Reykjavik, Iceland, August 17-19, 2022)," see ED624779.]

The use of computers in higher education teaching programs is discussed in 16 papers and reports. Applications of computers in teaching particular subjects including prehistory and anthropology, mathematics, Hindi, plant science, chemistry, language, medicine, drawing, statistics, and engineering are discussed in 10 of the contributions. The other papers address attitudes and barriers to the use of computing in teaching and learning, recent developments in hardware applicable to computer assisted instruction, interactive graphics and image displays, and artificial intelligence. A 105-item bibliography is included. (CHC)

The role of artificial intelligence expert systems in administrative issues in special education is examined. Mandate Consultant (MC) is one such system designed to provide a second opinion on the consistency of school officials' actions in implementing the Individualized Education Program team process. MC employs rules based on the Education For All Handicapped Children Act. Examples of a typical MC consultation illustrate how rules are used to determine which questions to ask and which conclusion to infer. The ways in which MC uses backchaining to determine if rules succeed or fail are explained and examples cited. Outcomes of the consultation are noted, as are additional features such as the opportunity to query the program at any point in the consultation regarding its immediate conclusions. (CL)

These proceedings contain the papers of the International Conference e-Learning 2014, which was organised by the International Association for Development of the Information Society and is part of the Multi Conference on Computer Science and Information Systems (Lisbon, Portugal July 15-19, 2014). The e-Learning 2014 conference aims to address the main issues of concern within e-Learning. This conference covered technical as well as the non-technical aspects of e-Learning under seven main areas: Organisational Strategy and Management Issues; Technological Issues; e-Learning Curriculum Development Issues; Instructional Design Issues; e-Learning Delivery Issues; e-Learning Research Methods and Approaches; e-Skills and Information Literacy for Learning. The conference included the Keynote Lecture: "Moving Higher Education Forward in the Digital Age: Realising a Digital Strategy," by Neil Morris, Professor of Educational Technology, Innovation and Change and Director of Digital Learning, University of Leeds, UK. Papers in these proceedings include: (1) Culture, Gender and Technology-Enhanced Learning: Female and Male Students' Perceptions Across Three Continents, Thomas Richter and Asta Zelenkauskaite; (2) IPads In Learning: The Web of Change Bente Meyer; (3) A Blended Approach to Canadian First Nations Education, Martin Sacher, Mavis Sacher and Norman Vaughan; (4) A Storytelling Learning Model For Legal Education, Nicola Capuano, Carmen De Maio, Angelo Gaeta, Giuseppina Rita Mangione, Saverio Salerno and Eleonora Fratesi; (5) Acceptance and Success Factors for M-Learning of ERP Systems Curricula, Brenda Scholtz and Mando Kapeso; (6) Self-Regulation Competence in Music Education, Luca Andrea Ludovico and Giuseppina Rita Mangione; (7) Time-Decayed User Profile for Second Language Vocabulary Learning System, Li Li and Xiao Wei; (8) E-Learning Trends and Hypes in Academic Teaching: Methodology and Findings of a Trend Study, Helge Fischer, Linda Heise, Matthias Heinz, Kathrin Moebius and Thomas Koehler; (9) Proof of Economic Viability of Blended Learning Business Models, Carsten Druhmann and Gregor Hohenberg; (10) Does Satellite Television Program Satisfy Ethiopian Secondary School Students? Sung-Wan Kim and Gebeyehu Bogale; (11) Organisation and Management of a Complete Bachelor Degree Offered Online at the University of Milan for Ten Years, Manuela Milani, Sabrina Papini, Daniela Scaccia and Nello Scarabottolo; (12) Structural Relationships between Variables of Elementary School Students' Intention of Accepting Digital Textbooks, Young Ju Joo, Sunyoung Joung, Se-Bin Choi, Eugene Lim and Kyung Yi Go; (13) Dynamic Fuzzy Logic-Based Quality of Interaction within Blended-Learning: The Rare and Contemporary Dance Cases, Sofia B. Dias, José A. Diniz and Leontios J. Hadjileontiadis; (14) Do English Listening Outcome and Cognitive Load Change for Different Media Delivery Modes in U-Learning?, Chi-Cheng Chang, Hao Lei and Ju-Shih Tseng; (15) The Use of ELGG Social Networking Tool for Students' Project Peer-Review Activity, Ana Coric Samardzija and Goran Bubas; (16) Educational Multimedia Profiling Recommendations for Device-Aware Adaptive Mobile Learning, Arghir-Nicolae Moldovan, Ioana Ghergulescu and Cristina Hava Muntean; (17) Inside, Outside, Upside Down: New Directions in Online Teaching and Learning, Lena Paulo Kushnir and Kenneth C. Berry; (18) A Study on the Methods of Assessment and Strategy of Knowledge Sharing in Computer Course, Pat P. W. Chan; (19) Using Agent-Based Technologies to Enhance Learning in Educational Games, Ogar Ofut Tumenayu, Olga Shabalina, Valeriy Kamaev and Alexander Davtyan; (20) Designing a Culturally Sensitive Wiki Space for Developing Chinese Students' Media Literacy, Daria Mezentceva; (21) Shared Cognition Facilitated by Teacher Use of Interactive Whiteboard Technologies, Christine Redman and John Vincent; (22) Modeling Pedagogy for Teachers Transitioning to the Virtual Classroom, Michael J. Canuel and Beverley J. White; (23) The Effectiveness of SDMS in the Development of E-Learning Systems in South Africa, Kobus van Aswegen, Magda Huisman and Estelle Taylor; (24) Online Learning Behaviors for Radiology Interns Based on Association Rules and Clustering Technique, Hsing-Shun Chen and Chuen-He Liou; (25) The Use of SDMS in Developing E-Learning Systems in South Africa, Estelle Taylor, Kobus van Aswegen and Magda Huisman; (26) Assessment of the Use of Online Comunities to Integrate Educational Processes Development Teams: An Experience in Popular Health Education in Brazil, Elomar Castilho Barilli, Stenio de Freitas Barretto, Carla Moura Lima and Marco Antonio Menezes; (27) Stereo Orthogonal Axonometric Perspective for the Teaching of Descriptive Geometry, José Geraldo Franco Méxas, Karla Bastos Guedes and Ronaldo da Silva Tavares; (28) Delivery of E-Learning through Social Learning Networks, Georgios A. Dafoulas and Azam Shokri; (29) The Implementation of Web 2.0 Technology for Information Literacy Instruction in Thai University Libraries, Oranuch Sawetrattanasatian; (30) Designing Educational Social Machines for Effective Feedback, Matthew Yee-King, Maria Krivenski, Harry Brenton, Andreu Grimalt-Reynes and Mark d'Inverno; (31) A Support System for Error Correction Questions in Programming Education, Yoshinari Hachisu and Atsushi Yoshida; (32) A Platform for Learning Internet of Things, Zorica Bogdanovic, Konstantin Simic, Miloš Milutinovic, Božidar Radenkovic and Marijana Despotovic-Zrakic, (33) Dealing with Malfunction: Locus of Control in Web-Conferencing, Michael Klebl; (34) Copyright and Creative Commons License: Can Educators Gain Benefits in the Digital Age? (Wariya Lamlert); (35) The Curriculum Design and Development in MOOCs Environment (Fei Li, Jing Du and Bin Li); (36) Stakeholders Influence in Maltese Tourism Higher Education Curriculum Development (Simon Caruana and Lydia Lau); (37) Online Social Networks and Computer Skills of University Students (Maria Potes Barbas, Gabriel Valerio, María Del Carmen Rodríguez-Martínez, Dagoberto José Herrera-Murillo and Ana María Belmonte-Jiménez); (38) Implementation of Artificial Intelligence Assessment in Engineering Laboratory Education (Maria Samarakou, Emmanouil D. Fylladitakis, Pantelis Prentakis and Spyros Athineos); (39) An Exploration of the Attitude and Learning Effectiveness of Business College Students towards Game Based Learning (Chiung-Sui Chang, Ya-Ping Huang and Fei-Ling Chien); (40) Application of E-Learning Technologies to Study a School Subject (Nadia Herbst and Elias Oupa Mashile); (41) Possibilities of Implementation of Small Business Check-Up Methodology in Comparative Analysis of Secondary Schools and Universities in Slovakia (Katarína Štofková, Ivan Strícek and Jana Štofková); (42) Digging the Virtual Past (Panagiota Polymeropoulou); (43) Technology Acceptance of E-Learning within a Blended Vocational Course in West Africa (Ashwin Mehta); (44) Development of an E-Learning Platform for Vocational Education Systems in Germany (Andreas Schober, Frederik Müller, Sabine Linden, Martha Klois and Bernd Künne); (45) Facebook Mediated Interaction and learning in Distance Learning at Makerere University (Godfrey Mayende, Paul Birevu Muyinda, Ghislain Maurice Norbert Isabwe, Michael Walimbwa and Samuel Ndeda Siminyu); (46) Assessing the Purpose and Importance University Students Attribute to Current ICT Applications (Maurice Digiuseppe and Elita Partosoedarso); (47) E-Learning System for Design and Construction of Amplifier Using Transistors (Atsushi Takemura); (48) Technology, Gender Attitude, and Software, among Middle School Math Instructors (Godwin N. Okeke); (49) Structuring Long-Term Faculty Training According to Needs Exhibited by Students' Written Comments in Course Evaluations (Robert Fulkerth); (50) Integration of PBL Methodologies into Online Learning Courses and Programs (Roland Van Oostveen, Elizabeth Childs, Kathleen Flynn and Jessica Clarkson); (51) Improving Teacher-Student Contact in a Campus Through a Location-Based Mobile Application (Vítor Manuel Ferreira and Fernando Ramos); (52) Incorporating Collaborative, Interactive Experiences into a Technology-Facilitated Professional Learning Network for Pre-Service Science Teachers (Seamus Delaney and Christine Redman); (53) The Efficiency of E-Learning Activities in Training Mentor Teachers (Laura Serbanescu and Sorina Chircu); (54) Development of an IOS App Using Situated Learning, Communities of Practice, and Augmented Reality for Autism Spectrum Disorder (Jessica Clarkson); (55) Using Case-Based Reasoning to Improve the Quality of Feedback Provided by Automated Grading Systems (Angelo Kyrilov and David C. Noelle); (56) International Multidisciplinary Learning: An Account of a Collaborative Effort among Three Higher Education Institutions (Paul S. H. Poh, Robby Soetanto, Stephen Austin and Zulkifar A. Adamu); (57) Interactive Learning to Stimulate the Brain's Visual Center and to Enhance Memory Retention (Yang H. Yun, Philip A. Allen, Kritsakorn Chaumpanich and Yingcai Xiao); (58) How Digital Technologies, Blended Learning and MOOCs Will Impact the Future of Higher Education (Neil P. Morris); (59) Factors Influencing the Acceptance of E-Learning Adoption in Libya's Higher Education Institutions (Mahfoud Benghet and Markus Helfert); (60) Motivation as a Method of Controlling the Social Subject Self-Learning (Andrey V. Isaev, Alla G. Kravets and Ludmila A. Isaeva); (61) Designing Environment for Teaching Internet of Things (Konstantin Simic, Vladimir Vujin, Aleksandra Labus, Ðorde Stepanic and Mladen Stevanovic); (62) Fostering Critical Thinking Skills in Students with Learning Disabilities through Online Problem-Based Learning (Kathleen Flynn); and (63) A System for the Automatic Assembly of Test Questions Using a NO-SQL Database (Sanggyu Shin and Hiroshi Hashimoto). Luís Rodrigues is an associate editor of the proceedings. Individual papers contain references. An author index is included.

This report summarizes and interprets the discussions at a seminar on artificial intelligence (AI) training domains and knowledge representations which was sponsored by the United Kingdom Training Commission. The following broad areas are addressed: (1) the context, process, and diversity of requirements of training and training needs; (2) defining AI, expert systems, and prospects in AI; (3) the origins, recent approaches, and current research directions in the use of computers to enhance learning; and (4) AI applications in training. Implications for the Training Commission are then considered. A 9-item annotated bibliography is included, and an update of the Training Commission's program and a list of seminar participants are appended. Other publications of the Economic and Social Research Council relating to information technology and education are listed, along with ordering information. (MES)

Some of the 12 conference papers presented in this proceedings focus on the present and potential capabilities of artificial intelligence and expert systems as they relate to a wide range of library applications, including descriptive cataloging, technical services, collection development, subject indexing, reference services, database searching, and document delivery. Other papers deal with the underlying design issues of knowledge representation and natural language processing. The papers are; (1) "Artificial Intelligence: What Will They Think of Next?" (Douglas P. Metzler); (2) "Technical Services Processes as Models for Assessing Expert System Suitability and Benefits" (Charles Fenly); (3) "Automated Cataloging: Implications for Libraries and Patrons" (Stuart Weibel); (4)"Interactive Knowledge-Based Systems for Improved Subject Analysis and Retrieval" (Susanne M. Humphrey); (5) "Reference Expert Systems: Foundations in Reference Theory" (James R. Parrott); (6) "Expert Systems at the National Agricultural Library: Past, Present, and Future" (Samuel T. Waters); (7) "User Models for Information Systems: Prospects and Problems" (Christine L. Borgman and Yolanda I. Plute); (8) "Natural Language Processing: Current Status for Libraries" (Amy Warner); (9) "Knowledge Representation in Artificial Intelligence" (Irene L. Travis); (10) "Intelligent Interfaces to Online Databases" (Brian C. Vickery); (11) "Expert Systems in Document Delivery: The Feasibility of Learning Capabilities" (Jaime Pontigo, Ezequiel Tovar-Reyes, Guillermo Rodriquez, and Sergio Ortiz-Gama); and (12) "Walking Your Talk: Why Information Managers Are Not High Tech" (W. David Penniman). An index and brief author biographies conclude the volume; chapters include references. (KRN)

Artificial intelligence (AI) education is still in the exploratory stage for K-12 schools. There is a serious lack of studies that informed schools teachers about AI curriculum design. Accordingly, this paper presented an AI curriculum and examined whether the curriculum improves students' perceived AI knowledge, attitudes, and motivation towards AI, as well as caters to students with different genders and academic achievement. It involved 64 grade 8-9 students. Results show that in the AI curriculum, 1) the students' self-report indicated that they became more competent, developed more positive attitude and higher intrinsic motivation to learn AI, 2) there were insignificant differences between girl and boys, and 3) there were almost no significant differences between high and low achieving students.

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.]

The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions (Pittsburgh 2010, Cordoba 2009 and Montreal 2008), and a series of workshops within the AAAI, AIED, EC-TEL, ICALT, ITS, and UM conferences. The increase of e-learning resources such as interactive learning environments, learning management systems, intelligent tutoring systems, and hypermedia systems, as well as the establishment of state databases of student test scores, has created large repositories of data that can be explored to understand how students learn. The EDM conference focuses on data mining techniques for using these data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. This publication presents the following papers: (1) Social Information Discovery (Barry Smyth); (2) On exploration and mining of data in educational practice (Erik-Jan van der Linden, Martijn Wijffelaars, Thomas Lammers); (3) EDM and the 4th Paradigm of Scientific Discovery--Reflections on the 2010 KDD Cup Competition (John Stamper); (4) Factorization Models for Forecasting Student Performance (Nguyen Thai-Nghe, Tomas Horvath and Lars Schmidt-Thieme); (5) Analyzing Participation of Students in Online Courses Using Social Network Analysis Techniques (Reihaneh Rabbany Khorasgani, Mansoureh Takaffoli and Osmar Zaiane); (6) A Machine Learning Approach for Automatic Student Model Discovery (Nan Li, Noboru Matsuda, William W. Cohen and Kenneth R. Koedinger); (7) Conditions for effectively deriving a Q-Matrix from data with Non-negative Matrix Factorization (Michel C. Desmarais); (8) Student Translations of Natural Language into Logic: The Grade Grinder Translation Corpus Release 1.0 (Dave Barker-Plummer, Richard Cox and Robert Dale); (9) Instructional Factors Analysis: A Cognitive Model For Multiple Instructional Interventions (Min Chi, Kenneth Koedinger, Geoff Gordon, Pamela Jordan and Kurt Vanlehn); (10) The Simple Location Heuristic is Better at Predicting Students Changes in Error Rate Over Time Compared to the Simple Temporal Heuristic (A.F. Nwaigwe and K.R. Koedinger); (11) Items, skills, and transfer models: which really matters for student modeling? (Y. Gong and J.E. Beck); (12) Avoiding Problem Selection Thrashing with Conjunctive Knowledge Tracing (K.R. Koedinger, P.I. Pavlik Jr., J. Stamper, T. Nixon and S. Ritter); (13) Less is More: Improving the Speed and Prediction Power of Knowledge Tracing by Using Less Data (Bahador Nooraei, Zachary Pardos, Neil T. Heffernan and Ryan S.J.D. Baker); (14) Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop (R. Martinez Maldonado, K. Yacef, Judy Kay, A. Kharrufa and A. Al-Qaraghuli); (15) Acquiring Item Difficulty Estimates: a Collaborative Effort of Data and Judgment (K. Wauters, P. Desmet and W. Van Den Noortgate); (16) Spectral Clustering in Educational Data Mining (Shubhendu Trivedi, Zachary A. Pardos, Gabor Sarkozy and Neil T. Heffernan); (17) Does Time Matter? Modeling the Effect of Time with Bayesian Knowledge Tracing (Yumeng Qiu, Yingmei Qi, Hanyuan Lu, Zachary Pardos and Neil Heffernan); (18) Learning classifiers from a relational database of tutor logs (Jack Mostow, Jose Gonzalez-Brenes and Bao Hong Tan); (19) A Framework for Capturing Distinguishing User Interaction Behaviors in Novel Interfaces (S. Kardan and C. Conati); (20) How to Classify Tutorial Dialogue? Comparing Feature Vectors vs. Sequences (Jose Gonzalez-Brenes, Jack Mostow and Weisi Duan); (21) Automatically Detecting a Students Preparation for Future Learning: Help Use is Key (Ryan S.J.D. Baker, Sujith M. Gowda and Albert T. Corbett); (22) Ensembling Predictions of Student Post-Test Scores for an Intelligent Tutoring System (Zachary A. Pardos, Sujith M. Gowda, Ryan S.J.D. Baker and Neil T. Heffernan); (23) Improving Models of Slipping, Guessing, and Moment-By-Moment Learning with Estimates of Skill Difficulty (Sujith M. Gowda, Jonathan P. Rowe, Ryan S.J.D. Baker, Min Chi and Kenneth R. Koedinger); (24) A Method for Finding Prerequisites Within a Curriculum (Annalies Vuong, Tristan Nixon and Brendon Towle); (25) Estimating Prerequisite Structure From Noisy Data (Emma Brunskill); (26) What can closed sets of students and their marks say? (Dmitry Ignatov, Serafima Mamedova, Nikita Romashkin, and Ivan Shamshurin); (27) How university entrants are choosing their department? Mining of university admission process with FCA taxonomies (Nikita Romashkin, Dmitry Ignatov and Elena Kolotova); (28) What's an Expert? Using learning analytics to identify emergent markers of expertise through automated speech, sentiment and sketch analysis (Marcelo Worsley and Paulo Blikstein); (29) Using Logistic Regression to Trace Multiple Subskills in a Dynamic Bayes Net (Yanbo Xu and Jack Mostow); (30) Monitoring Learners Proficiency: Weight Adaptation in the Elo Rating System (K. Wauters, P. Desmet and W. Van Den Noortgate); (31) Modeling students activity in online discussion forums: a strategy based on time series and agglomerative hierarchical clustering (G. Cobo, D. Garcia, E. Santamaria, J.A. Moran, J. Melenchon and C. Monzo); (32) Prediction of Perceived Disorientation in Online Learning Environment with Random Forest Regression (Gokhan Akcapinar, Erdal Cosgun and Arif Altun); (33) Analysing Student Spatial Deployment in a Computer Laboratory (Vladimir Ivancevic, Milan Celikovic and Ivan Lukovic); (34) Predicting School Failure Using Data Mining (C. Marquez-Vera, C. Romero and S. Ventura); (35) A Dynamical System Model of Microgenetic Changes in Performance, Efficacy, Strategy Use and Value during Vocabulary Learning (P. Pavlik Jr. and S. Wu); (36) Desperately Seeking Subscripts: Towards Automated Model Parameterization (J. Mostow, Y. Xu and M. Munna); (37) Automatic Generation of Proof Problems in Deductive Logic (B. Mostafavi, T. Barnes and M. Croy); (38) Comparison of Traditional Assessment with Dynamic Testing in a Tutoring System (Mingyu Feng, Neil T. Heffernan, Zachary A. Pardos and Cristina Heffernan); (39) Evaluating a Bayesian Student Model of Decimal Misconceptions (G. Goguadze, S. Sosnovsky, S. Isotani and B. Mclaren); (40) Exploring user data from a game-like math tutor: a case study in causal modeling (D. Rai and J. E. Beck); (41) Goal Orientation and Changes of Carelessness over Consecutive Trials in Science Inquiry (A. Hershkovitz, R.S.J.D. Baker, J. Gobert and M. Wixon); (42) Towards improvements on domain-independent measurements for collaborative assessment (Antonio R. Anaya and Jesus G. Boticario); (43) A Java desktop tool for mining Moodle data (R. Pedraza-Perez, C. Romero and S. Ventura); (44) Using data mining in a recommender system to search for learning objects in repositories (A. Zapata-Gonzalez, V.H. Menendez, M.E. Prieto-Mendez and C. Romero); (45) E-learning Web Miner: A data mining application to help instructors involved in virtual courses (Diego Garcia-Saiz and M.E. Zorrilla Pantaleon); (46) Computerized Coding System for Life Narratives to Assess Students' Personality Adaption (Q. He, B.P. Veldkamp and G.J. Westerhof); (47) Partially Observable Sequential Decision Making for Problem Selection in an Intelligent Tutoring System (Emma Brunskill and Stuart Russell); (48) Mining Teaching Behaviors from Pedagogical Surveys (J. Barracosa and C. Antunes); (49) Variable Construction and Causal Modeling of Online Education Messaging Data: Initial Results (S. Fancsali); (50) The Hospital Classrooms Environments Challenge (Carina Gonzalez and Pedro A. Toledo); (51) Combining study of complex network and text mining analysis to understand growth mechanism of communities on SNS (Osamu Yamakawa, Takahiro Tagawa, Hitoshi Inoue, Koichi Yastake and Takahiro Sumiya); (52) Logistic Regression in a Dynamic Bayes Net Models Multiple Subskills Better! (Yanbo Xu and Jack Mostow); (53) Studying problem-solving strategies in the early stages of learning programming (E. Cambranes-Martinez and J. Good); (54) Brick: Mining Pedagogically Interesting Sequential Patterns (Anjo Anjewierden, Hannie Gijlers, Nadira Saab and Robert De Hoog); (55) Intelligent evaluation of social knowledge building using conceptual maps with MLN (L. Moreno, C.S. Gonzalez, R. Estevez and B. Popescu); (56) Identifying Influence Factors on Students Success by Subgroup Discovery (F. Lemmerich, M. Ifland and F. Puppe); (57) Analyzing University Data for Determining Student Profiles and Predicting Performance (D. Kabakchieva, K. Stefanova and V. Kisimov); (58) The EDM Vis Tool (Matthew Johnson, Michael Eagle, Leena Joseph and Tiffany Barnes); (59) Towards Modeling Forgetting and Relearning in ITS: Preliminary Analysis of ARRS Data (Y. Wang and N.T. Heffernan); (60) Quality Control and Data Mining Techniques Applied to Monitoring Scaled Scores (A.A. Von Davier); (61) eLAT: An Exploratory Learning Analytics Tool for Reflection and Iterative Improvement of Technology Enhanced Learning (A.L. Dyckhoff, D. Zielke, M.A. Chatti and U. Schroeder); (62) Predicting graduate-level performance from undergraduate achievements (J. Zimmermann, K.H. Brodersen, J.-P. Pellet, E. August and J.M. Buhmann); (63) Mining Assessment and Teaching Evaluation Data of Regular and Advanced Stream Students (Irena Koprinska); (64) Investigating Usage of Resources in LMS with Specific Association Rules (A. Merceron); (65) Towards Parameter-Free Data Mining: Mining Educational Data with "yacaree" (Jose L. Balcazar, Diego Garcia-Saiz and Marta E. Zorrilla); (66) Factors Impacting Novice Code Comprehension in a Tutor for Introductory Computer Science (Leigh Ann Sudol-DeLyser and Jonathan Steinhart); (67) Investigating the Transitions between Learning and Non-learning Activities as Students Learn Online (P.S. Inventado, R. Legaspi, M. Suarez and M. Numao); (68) Learning parameters for a knowledge diagnostic tools in orthopedic surgery (S. Lalle and V. Luengo); (69) Problem Response Theory and its Application for Tutoring (P. Jarusek and R. Pelanek); and (70) Towards Better Understanding of Transfer in Cognitive Models of Practice (Michael V. Yudelson, Philip I. Pavlik, Jr. and Kenneth R. Koedinger). Individual papers contain tables, figures, footnotes and references.