An Automatic Code Generation Tool Using Generative Artificial Intelligence for Element Fill-in-the-Blank Problems in a Java Programming Learning Assistant System

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Опубликовано в::Electronics vol. 14, no. 11 (2025), p. 2261
Главный автор: Zhu Zihao
Другие авторы: Funabiki Nobuo, Mustika, Mentari, Aung Soe Thandar, Wen-Chung, Kao, Yi-Fang, Lee
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MDPI AG
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100 1 |a Zhu Zihao  |u Department of Information and Communication Systems, Okayama University, Okayama 700-8530, Japan; pqt85hm5@s.okayama-u.ac.jp (M.M.); soethandar@s.okayama-u.ac.jp (S.T.A.) 
245 1 |a An Automatic Code Generation Tool Using Generative Artificial Intelligence for Element Fill-in-the-Blank Problems in a Java Programming Learning Assistant System 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a Presently, Java is a fundamental object-oriented programming language that can be mastered by any student in information technology or computer science. To assist both teachers and students, we developed the Java Programming Learning Assistant System (JPLAS). It offers several types of practice problems with different levels and learning goals for step-by-step self-study, where any answer is automatically marked in the system. One challenge for teachers that is addressed with JPLAS is the generation of proper exercise problems that meet learning requirements. We implemented programs for generating new problems from given source codes, as collecting and evaluating suitable codes remains time-consuming. In this paper, we present an automatic code generation tool using generative AI to solve this challenge. Prompt engineering is used to help generate an appropriate source code, and the quality is controlled by optimizing the prompt based on the outputs. For applications in JPLAS, we implement a web application system to automatically generate an element fill-in-the-blank problem (EFP) in JPLAS. For evaluation, we select the element fill-in-the-blank problem (EFP) as the target type in JPLAS and generate several instances using this tool. The results confirm the validity and effectiveness of the proposed method. 
610 4 |a Hangzhou DeepSeek Artificial Intelligence Co Ltd 
653 |a Object oriented programming 
653 |a Problem solving 
653 |a Java 
653 |a Source code 
653 |a Teaching methods 
653 |a Learning disabilities 
653 |a Syntax 
653 |a Applications programs 
653 |a Trends 
653 |a Curricula 
653 |a Student participation 
653 |a Generative artificial intelligence 
653 |a Prompt engineering 
653 |a Visualization 
653 |a Teachers 
653 |a Chatbots 
653 |a Motivation 
653 |a Programming languages 
653 |a Embedded systems 
653 |a Learning 
653 |a Personalized learning 
653 |a Design 
653 |a Object-oriented languages 
653 |a Gamification 
653 |a Education 
700 1 |a Funabiki Nobuo  |u Department of Information and Communication Systems, Okayama University, Okayama 700-8530, Japan; pqt85hm5@s.okayama-u.ac.jp (M.M.); soethandar@s.okayama-u.ac.jp (S.T.A.) 
700 1 |a Mustika, Mentari  |u Department of Information and Communication Systems, Okayama University, Okayama 700-8530, Japan; pqt85hm5@s.okayama-u.ac.jp (M.M.); soethandar@s.okayama-u.ac.jp (S.T.A.) 
700 1 |a Aung Soe Thandar  |u Department of Information and Communication Systems, Okayama University, Okayama 700-8530, Japan; pqt85hm5@s.okayama-u.ac.jp (M.M.); soethandar@s.okayama-u.ac.jp (S.T.A.) 
700 1 |a Wen-Chung, Kao  |u Department of Electrical Engineering, National Taiwan Normal University, Taipei 10610, Taiwan; jungkao@ntnu.edu.tw 
700 1 |a Yi-Fang, Lee  |u Department of Industrial Education, National Taiwan Normal University, Taipei 10610, Taiwan; ivana@ntnu.edu.tw 
773 0 |t Electronics  |g vol. 14, no. 11 (2025), p. 2261 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
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