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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
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

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
Fabio Spano; Adam Kardos; Craig Dennis Howard – International Journal of Designs for Learning, 2025
This design case presents a gamified dictionary learning intervention for away-from-school learning. Intended to be mobile, the game features AI speech recognition for practicing English words and phrases. We also introduce a second feature--an integrated teacher dashboard that addresses issues in traditional homework--a lack of immediate feedback…
Descriptors: Gamification, Dictionaries, Artificial Intelligence, English (Second Language)
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

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
Amy Chen Kulesa; Marisa Mission; Michelle Croft; Mary K. Wells – Bellwether, 2025
Generative artificial intelligence (GenAI) is a tool that promises efficiency and customization for teachers and students alike but also carries risks of dependency and detachment. This report aims to explore new questions for educators, system leaders, and tool developers such as how much cognitive effort AI should alleviate, how much it should…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Change, Cognitive Processes

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
Mingyu Feng; Neil Heffernan; Kelly Collins; Cristina Heffernan; Robert F. Murphy – Grantee Submission, 2023
Math performance continues to be an important focus for improvement. The most recent National Report Card in the U.S. suggested student math scores declined in the past two years possibly due to COVID-19 pandemic and related school closures. We report on the implementation of a math homework program that leverages AI-based one-to-one technology,…
Descriptors: Homework, Artificial Intelligence, Computer Assisted Instruction, Feedback (Response)
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
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
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
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
Yifan Lu; K. Supriya; Shanna Shaked; Elizabeth H. Simmons; Alexander Kusenko – Physical Review Physics Education Research, 2025
Inequities in student access to trigonometry and calculus are often associated with racial and socioeconomic privilege, and often influence introductory physics course performance. To mitigate these disparities in student preparedness, we developed a two-pronged intervention consisting of (1) incentivized supplemental math assignments and (2)…
Descriptors: Mathematics Instruction, Teaching Methods, Academic Achievement, Science Instruction
Aydogdu, Seyhmus – Education and Information Technologies, 2020
Prediction of student performance is one of the most important subjects of educational data mining. Artificial neural networks are seen to be an effective tool in predicting student performance in e-learning environments. In the studies carried out with artificial neural networks, performance predictions based on student scores are generally made,…
Descriptors: Prediction, Academic Achievement, Electronic Learning, Artificial Intelligence
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