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Gyuhun Jung; Markel Sanz Ausin; Tiffany Barnes; Min Chi – International Educational Data Mining Society, 2024
We presented two empirical studies to assess the efficacy of two Deep Reinforcement Learning (DRL) frameworks on two distinct Intelligent Tutoring Systems (ITSs) to exploring the impact of Worked Example (WE) and Problem Solving (PS) on student learning. The first study was conducted on a probability tutor where we applied a classic DRL to induce…
Descriptors: Intelligent Tutoring Systems, Problem Solving, Artificial Intelligence, Teaching Methods
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
Md. Mirajul Islam; Xi Yang; John Hostetter; Adittya Soukarjya Saha; Min Chi – International Educational Data Mining Society, 2024
A key challenge in e-learning environments like Intelligent Tutoring Systems (ITSs) is to induce effective pedagogical policies efficiently. While Deep Reinforcement Learning (DRL) often suffers from "sample inefficiency" and "reward function" design difficulty, Apprenticeship Learning (AL) algorithms can overcome them.…
Descriptors: Electronic Learning, Intelligent Tutoring Systems, Teaching Methods, Algorithms
Betty Exintaris; Nilushi Karunaratne; Elizabeth Yuriev – Journal of Chemical Education, 2023
Successful problem solving is a complex process that requires content knowledge, process skills, developed critical thinking, metacognitive awareness, and deep conceptual reasoning. Teaching approaches to support students developing problem-solving skills include worked examples, metacognitive and instructional scaffolding, and variations of these…
Descriptors: College Bound Students, Problem Solving, Metacognition, Scaffolding (Teaching Technique)
Giulia Polverini; Bor Gregorcic – Physical Review Physics Education Research, 2024
The well-known artificial intelligence-based chatbot ChatGPT-4 has become able to process image data as input in October 2023. We investigated its performance on the test of understanding graphs in kinematics to inform the physics education community of the current potential of using ChatGPT in the education process, particularly on tasks that…
Descriptors: Computer Software, Artificial Intelligence, Visual Impairments, Graphs
Murphy, Michelle Pauley; Hung, Woei – TechTrends: Linking Research and Practice to Improve Learning, 2023
One hundred years ago, Paul Weiss and Ludwig von Bertalanffy independently proposed that living organisms interact with their environment through systems. In the century that has followed, systems thinking and modeling have grown in tandem with discovery of the vast complexity of the universe at microscopic through astronomic levels. As our…
Descriptors: Systems Approach, Cognitive Processes, Artificial Intelligence, Learning Processes
Joalise Janse van Rensburg – Discover Education, 2024
The ability to think critically is an important and valuable skill that students should develop to successfully solve problems. The process of writing requires critical thinking (CT), and the subsequent piece of text can be viewed as a product of CT. One of the strategies educators may use to develop CT is modelling. Given ChatGPT's ability to…
Descriptors: Critical Thinking, Writing Instruction, Computer Software, Artificial Intelligence
Suna-Seyma Uçar; Inigo Lopez-Gazpio; Josu Lopez-Gazpio – Education and Information Technologies, 2025
Recent advancements in large language models (LLMs) have shown potential in enhancing educational practices, particularly in technology-assisted learning environments. This study critically evaluates the reasoning capabilities of LLMs, such as ChatGPT, within the context of chemistry education. We designed targeted adversarial prompts that…
Descriptors: Abstract Reasoning, Thinking Skills, Artificial Intelligence, Technology Uses in Education
Matsuda, Noboru – International Journal of Artificial Intelligence in Education, 2022
This paper demonstrates that a teachable agent (TA) can play a dual role in an online learning environment (OLE) for learning by teaching--the teachable agent working as a synthetic peer for students to learn by teaching and as an interactive tool for cognitive task analysis when authoring an OLE for learning by teaching. We have developed an OLE…
Descriptors: Artificial Intelligence, Teaching Methods, Intelligent Tutoring Systems, Feedback (Response)
Michael E. Robbins; Gabriel J. DiQuattro; Eric W. Burkholder – Physical Review Physics Education Research, 2025
[This paper is part of the Focused Collection in Investigating and Improving Quantum Education through Research.] One of the greatest weaknesses of physics education research is the paucity of research on graduate education. While there are a growing number of investigations of graduate student degree progress and admissions, there are very few…
Descriptors: Science Education, College Science, Science Instruction, Teaching Methods
John Jairo Jaramillo; Andrés Chiappe – Prospects, 2024
This article describes a systematic literature review focused on identifying substantial drawbacks in the current curricula and on the challenges to developing AI-driven curricula. One hundred thirty articles were read in depth and qualitatively analyzed. The results suggest that educational stakeholders should integrate AI into the curriculum…
Descriptors: Trend Analysis, Interdisciplinary Approach, Problem Solving, Futures (of Society)
Chih-Hung Wu; Ting-Sheng Weng; Chih-Hsing Liu – Educational Technology & Society, 2025
With the growing attention directed towards ChatGPT and its applications in education, this study explored its impact on various variables pertaining to student learning. Specifically, an integrated theoretical framework was used to investigate the factors that influence student problem-solving and critical thinking abilities when using ChatGPT…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Learning Motivation
Frances A. Stott; David M. Stott – Advances in Accounting Education: Teaching and Curriculum Innovations, 2023
The chapter explores the possibilities related to using ChapGPT in tax education. Specifically, we address how tax has historically been taught in higher education and how the use of new and developing artificial intelligence (AI) tools such as ChatGPT can enhance this process going forward. The key takeaway is that AI tools are just that --…
Descriptors: Accounting, Teaching Methods, Artificial Intelligence, Business Education
Jian-Hong Ye; Mengmeng Zhang; Weiguaju Nong; Li Wang; Xiantong Yang – Education and Information Technologies, 2025
ChatGPT, as an example of generative artificial intelligence, possesses high-level conversational and problem-solving capabilities supported by powerful computational models and big data. However, the powerful performance of ChatGPT might enhance learner dependency. Although it has not yet been confirmed, many teachers and scholars are also…
Descriptors: Artificial Intelligence, College Students, Problem Solving, Student Attitudes
Judith Galezer; Smadar Szekely – Informatics in Education, 2024
Spark, one of the products offered by MyQ (formerly Plethora), is a game-based platform meticulously designed to introduce students to the foundational concepts of computer science. By navigating through logical challenges, users delve into topics like abstraction, loops, and graph patterns. Setting itself apart from its counterparts, Spark boasts…
Descriptors: Learning Management Systems, Game Based Learning, Computer Science Education, Teaching Methods

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