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James W. Drisko – Journal of Teaching in Social Work, 2025
The rise of AI generated texts offers promise but creates new challenges for social work teaching. A recent survey found that 89% of higher education students used AI on their homework. AI generated text may be difficult to distinguish from a student's own work, yet are being submitted as the student's own work. This poses new challenges to…
Descriptors: Plagiarism, Social Work, Counselor Training, Artificial Intelligence
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Michael E. Ellis; K. Mike Casey; Geoffrey Hill – Decision Sciences Journal of Innovative Education, 2024
Large Language Model (LLM) artificial intelligence tools present a unique challenge for educators who teach programming languages. While LLMs like ChatGPT have been well documented for their ability to complete exams and create prose, there is a noticeable lack of research into their ability to solve problems using high-level programming…
Descriptors: Artificial Intelligence, Programming Languages, Programming, Homework
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Conrad Borchers; Jeroen Ooge; Cindy Peng; Vincent Aleven – Grantee Submission, 2025
Personalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials. We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Learner Controlled Instruction, Learning Analytics
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Omar Albaloul; Risto Marttinen; Chad Killian – Journal of Physical Education, Recreation & Dance, 2024
The recent emergence of artificial intelligence (AI) tools has significantly influenced different fields, including education. One notable example is ChatGPT, an AI-driven large language model (LLM) developed by OpenAI. This tool holds potential for supporting both teachers and students in the teaching and learning process. While some fields of…
Descriptors: Physical Education, Artificial Intelligence, Natural Language Processing, Man Machine Systems
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Devika Venugopalan; Ziwen Yan; Conrad Borchers; Jionghao Lin; Vincent Aleven – Grantee Submission, 2025
Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning…
Descriptors: Homework, Computational Linguistics, Teaching Methods, Learning Analytics
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Ha Tien Nguyen; Conrad Borchers; Meng Xia; Vincent Aleven – Grantee Submission, 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…
Descriptors: Middle School Mathematics, Intelligent Tutoring Systems, Caregivers, Caregiver Attitudes
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Lonneke Boels; Enrique Garcia Moreno-Esteva; Arthur Bakker; Paul Drijvers – International Journal of Artificial Intelligence in Education, 2024
As a first step toward automatic feedback based on students' strategies for solving histogram tasks we investigated how strategy recognition can be automated based on students' gazes. A previous study showed how students' task-specific strategies can be inferred from their gazes. The research question addressed in the present article is how data…
Descriptors: Eye Movements, Learning Strategies, Problem Solving, Automation
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Melanie M. Cooper; Michael W. Klymkowsky – Journal of Chemical Education, 2024
The use of large language model Generative AI (GenAI) systems by students and instructors is increasing rapidly, and there is little choice but to adapt to this new situation. Many, but not all, students are using GenAI for homework and assignments, which means that we need to provide equitable access for all students to AI systems that can…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Software, Homework
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John Pace; John Hansen; John Stewart – Physical Review Physics Education Research, 2024
Machine learning models were constructed to predict student performance in an introductory mechanics class at a large land-grant university in the United States using data from 2061 students. Students were classified as either being at risk of failing the course (earning a D or F) or not at risk (earning an A, B, or C). The models focused on…
Descriptors: Artificial Intelligence, Identification, At Risk Students, Physics
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Lionel Mew; William H. Money – Information Systems Education Journal, 2024
Since it was released on November 30, 2022, ChatGPT has offered numerous opportunities for higher education professors to improve their course offerings. However, not all information provided by the application is accurate. The application has been known to yield highly inaccurate information with high confidence. Yet, with that knowledge, ChatGPT…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Library Research