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Showing 1 to 15 of 93 results Save | Export
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Jihae Suh; Kyuhan Lee; Jaehwan Lee – Education and Information Technologies, 2025
Artificial Intelligence (AI) has rapidly emerged as a powerful tool with the potential to enhance learning environments. However, effective use of new technologies in education requires a good understanding of the technology and good design for its use. Generative AI such as ChatGPT requires particularly well-designed instructions due to its ease…
Descriptors: Programming, Computer Science Education, Artificial Intelligence, Technology Uses in Education
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Guangrui Fan; Dandan Liu; Rui Zhang; Lihu Pan – International Journal of STEM Education, 2025
Purpose: This study investigates the impact of AI-assisted pair programming on undergraduate students' intrinsic motivation, programming anxiety, and performance, relative to both human-human pair programming and individual programming approaches. Methods: A quasi-experimental design was conducted over two academic years (2023-2024) with 234…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Programming
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Fein, Benedikt; Graßl, Isabella; Beck, Florian; Fraser, Gordon – International Educational Data Mining Society, 2022
The recent trend of embedding source code for machine learning applications also enables new opportunities in learning analytics in programming education, but which code embedding approach is most suitable for learning analytics remains an open question. A common approach to embedding source code lies in extracting syntactic information from a…
Descriptors: Artificial Intelligence, Learning Analytics, Programming, Programming Languages
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Jinshui Wang; Shuguang Chen; Zhengyi Tang; Pengchen Lin; Yupeng Wang – Education and Information Technologies, 2025
Mastering SQL programming skills is fundamental in computer science education, and Online Judging Systems (OJS) play a critical role in automatically assessing SQL codes, improving the accuracy and efficiency of evaluations. However, these systems are vulnerable to manipulation by students who can submit "cheating codes" that pass the…
Descriptors: Programming, Computer Science Education, Cheating, Computer Assisted Testing
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Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2023
This paper systematically explores how Large Language Models (LLMs) generate explanations of code examples of the type used in intro-to-programming courses. As we show, the nature of code explanations generated by LLMs varies considerably based on the wording of the prompt, the target code examples being explained, the programming language, the…
Descriptors: Computational Linguistics, Programming, Computer Science Education, Programming Languages
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Boxuan Ma; Li Chen; Shin’ichi Konomi – International Association for Development of the Information Society, 2024
Generative artificial intelligence (AI) tools like ChatGPT are becoming increasingly common in educational settings, especially in programming education. However, the impact of these tools on the learning process, student performance, and best practices for their integration remains underexplored. This study examines student experiences and…
Descriptors: Artificial Intelligence, Computer Science Education, Programming, Computer Uses in Education
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Anna Y. Q. Huang; Cheng-Yan Lin; Sheng-Yi Su; Stephen J. H. Yang – British Journal of Educational Technology, 2025
Programming education often imposes a high cognitive burden on novice programmers, requiring them to master syntax, logic, and problem-solving while simultaneously managing debugging tasks. Prior knowledge is a critical factor influencing programming learning performance. A lack of foundational knowledge limits students' self-regulated learning…
Descriptors: Artificial Intelligence, Technology Uses in Education, Coding, Programming
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Muhammad Fawad Akbar Khan; Max Ramsdell; Erik Falor; Hamid Karimi – International Educational Data Mining Society, 2024
This paper undertakes a thorough evaluation of ChatGPT's code generation capabilities, contrasting them with those of human programmers from both educational and software engineering standpoints. The emphasis is placed on elucidating its importance in these intertwined domains. To facilitate a robust analysis, we curated a novel dataset comprising…
Descriptors: Artificial Intelligence, Automation, Computer Science Education, Programming
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Neil C. C. Brown; Pierre Weill-Tessier; Juho Leinonen; Paul Denny; Michael Kölling – ACM Transactions on Computing Education, 2025
Motivation: Students learning to program often reach states where they are stuck and can make no forward progress--but this may be outside the classroom where no instructor is available to help. In this situation, an automatically generated next-step hint can help them make forward progress and support their learning. It is important to know what…
Descriptors: Artificial Intelligence, Programming, Novices, Technology Uses in Education
Diana Franklin; Paul Denny; David A. Gonzalez-Maldonado; Minh Tran – Cambridge University Press & Assessment, 2025
Generative AI is a disruptive technology that has the potential to transform many aspects of how computer science is taught. Like previous innovations such as high-level programming languages and block-based programming languages, generative AI lowers the technical expertise necessary to create working programs, bringing the power of computation…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Science Education, Expertise
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Ishaya Gambo; Faith-Jane Abegunde; Omobola Gambo; Roseline Oluwaseun Ogundokun; Akinbowale Natheniel Babatunde; Cheng-Chi Lee – Education and Information Technologies, 2025
The current educational system relies heavily on manual grading, posing challenges such as delayed feedback and grading inaccuracies. Automated grading tools (AGTs) offer solutions but come with limitations. To address this, "GRAD-AI" is introduced, an advanced AGT that combines automation with teacher involvement for precise grading,…
Descriptors: Automation, Grading, Artificial Intelligence, Computer Assisted Testing
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Lokkila, Erno; Christopoulos, Athanasios; Laakso, Mikko-Jussi – Informatics in Education, 2023
Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student…
Descriptors: Prior Learning, Programming, Computer Science Education, Markov Processes
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Cindy Royal – Journalism and Mass Communication Educator, 2025
Artificial intelligence (AI) has taken the forefront in discussions of the future of media and education. Although there are valid concerns, AI has the potential to be useful in learning new skills, particularly those related to computer programming. This case study depicts the ways AI was introduced to assist in teaching coding, specifically in a…
Descriptors: Artificial Intelligence, Coding, Programming, Computer Science Education
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Zifeng Liu; Wanli Xing; Xinyue Jiao; Chenglu Li; Wangda Zhu – Education and Information Technologies, 2025
The ability of large language models (LLMs) to generate code has raised concerns in computer science education, as students may use tools like ChatGPT for programming assignments. While much research has focused on higher education, especially for languages like Java and Python, little attention has been given to K-12 settings, particularly for…
Descriptors: High School Students, Coding, Artificial Intelligence, Electronic Learning
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Maciej Pankiewicz; Yang Shi; Ryan S. Baker – International Educational Data Mining Society, 2025
Knowledge Tracing (KT) models predicting student performance in intelligent tutoring systems have been successfully deployed in several educational domains. However, their usage in open-ended programming problems poses multiple challenges due to the complexity of the programming code and a complex interplay between syntax and logic requirements…
Descriptors: Algorithms, Artificial Intelligence, Models, Intelligent Tutoring Systems
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