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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
Hsiao-Ping Hsu – TechTrends: Linking Research and Practice to Improve Learning, 2025
The advancement of large language model-based generative artificial intelligence (LLM-based GenAI) has sparked significant interest in its potential to address challenges in computational thinking (CT) education. CT, a critical problem-solving approach in the digital age, encompasses elements such as abstraction, iteration, and generalisation.…
Descriptors: Programming, Prompting, Computation, Thinking Skills
John Paul P. Miranda; Jaymark A. Yambao – Journal of Education and Learning (EduLearn), 2025
This study explores the novice programmers' intention to use chat generative pretrained transformer (ChatGPT) for programming tasks with emphasis on performance expectancy (PE), risk-reward appraisal (RRA), and decision-making (DM). Utilizing partial least squares structural equation modeling (PLS-SEM) and a sample of 413 novice programmers, the…
Descriptors: Novices, Employees, Programming, Artificial Intelligence
Umar Alkafaween; Ibrahim Albluwi; Paul Denny – Journal of Computer Assisted Learning, 2025
Background: Automatically graded programming assignments provide instant feedback to students and significantly reduce manual grading time for instructors. However, creating comprehensive suites of test cases for programming problems within automatic graders can be time-consuming and complex. The effort needed to define test suites may deter some…
Descriptors: Automation, Grading, Introductory Courses, Programming
A Comparison of Generative AI Solutions and Textbook Solutions in an Introductory Programming Course
Ernst Bekkering; Patrick Harrington – Information Systems Education Journal, 2025
Generative AI has recently gained the ability to generate computer code. This development is bound to affect how computer programming is taught in higher education. We used past programming assignments and solutions for textbook exercises in our introductory programming class to analyze how accurately one of the leading models, ChatGPT, generates…
Descriptors: Higher Education, Artificial Intelligence, Programming, Textbook Evaluation
Michelle Pauley Murphy; Woei Hung – TechTrends: Linking Research and Practice to Improve Learning, 2024
Constructing a consensus problem space from extensive qualitative data for an ill-structured real-life problem and expressing the result to a broader audience is challenging. To effectively communicate a complex problem space, visualization of that problem space must elucidate inter-causal relationships among the problem variables. In this…
Descriptors: Information Retrieval, Data Analysis, Pattern Recognition, Artificial Intelligence
Jian Liao; Linrong Zhong; Longting Zhe; Handan Xu; Ming Liu; Tao Xie – IEEE Transactions on Learning Technologies, 2024
ChatGPT has received considerable attention in education, particularly in programming education because of its capabilities in automated code generation and program repairing and scoring. However, few empirical studies have investigated the use of ChatGPT to customize a learning system for scaffolding students' computational thinking. Therefore,…
Descriptors: Scaffolding (Teaching Technique), Thinking Skills, Computation, Artificial Intelligence
Saso Koceski; Natasa Koceska; Limonka Koceva Lazarova; Marija Miteva; Biljana Zlatanovska – Journal of Technology and Science Education, 2025
This study aims to evaluate ChatGPT's capabilities in certain numerical analysis problem: solving ordinary differential equations. The methodology which is developed in order to conduct this research takes into account the following mathematical abilities (defined according to National Centre for Education Statistics): Conceptual Understanding,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Number Concepts, Problem Solving
Tarattakan Pachumwon; Thada Jantakoon; Rukthin Laoha – Higher Education Studies, 2025
This study introduces CAILE, a design thinking-driven conceptual framework for a Creative AI Learning Environment, designed to enhance programming skills. Evaluates clarity, appropriateness, and feasibility through expert judgment. Phase 1 synthesized 34 peer-reviewed studies (2019-2025) to articulate CAILE's structure across three layers: Inputs…
Descriptors: Creativity, Artificial Intelligence, Technology Uses in Education, Programming
Siran Li; Jiangyue Liu; Qianyan Dong – Australasian Journal of Educational Technology, 2025
Recent advancements in generative artificial intelligence (GenAI) have drawn significant attention from educators and researchers. However, its effects on learners' programming performance, self-efficacy and learning processes remain inconclusive, while the mechanisms underlying its efficiency-enhancing potential are underexplored. This study…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Science Education, Programming
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)
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
Xin Gong; Zhixia Li; Ailing Qiao – Education and Information Technologies, 2025
Feedback is crucial during programming problem solving, but context often lacks critical and difference. Generative artificial intelligence dialogic feedback (GenAIDF) has the potential to enhance learners' experience through dialogue, but its effectiveness remains sufficiently underexplored in empirical research. This study employed a rigorous…
Descriptors: Artificial Intelligence, Technology Uses in Education, Dialogs (Language), Feedback (Response)
Gang Zhao; Lijun Yang; Biling Hu; Jing Wang – Journal of Educational Computing Research, 2025
Human-computer collaboration is an effective way to learn programming courses. However, most existing human-computer collaborative programming learning is supported by traditional computers with a relatively low level of personalized interaction, which greatly limits the efficiency of students' efficiency of programming learning and development of…
Descriptors: Artificial Intelligence, Man Machine Systems, Programming, Learning Strategies
Hao-Yue Jin; Maria Cutumisu – Education and Information Technologies, 2024
Computational thinking (CT) is considered to be a critical problem-solving toolkit in the development of every student in the digital twenty-first century. Thus, it is believed that the integration of deeper learning in CT education is an approach to help students transfer their CT skills beyond the classroom. Few literature reviews have mapped…
Descriptors: Computation, Thinking Skills, Problem Solving, Artificial Intelligence

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