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Generative Artificial Intelligence in Computer Programming: Does It Enhance Learning, Motivation, and the Learning Environment?
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DOI：10.1109/ACCESS.2025.3532883.png)
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摘要
En 中文
Generative artificial intelligence (GenAI) is emerging as a transformative technology in higher education, particularly in programming instruction. However, its impact on learning, motivation, and the educational environment must still be fully understood. This study aims to determine the capacity of GenAI to generate effective computer programming learning in STEM university students, comparing it with active learning methods based on video. An experiment was conducted with 40 computer engineering students divided into two groups: one using GenAI (Google Gemini 1.5) and another employing educational videos. Pre- and post-tests of knowledge and the Intrinsic Motivation Inventory (IMI) were applied to evaluate learning, intrinsic motivation, and the learning environment. No significant differences in learning were found between the groups. However, GenAI significantly increased perceived autonomy and reduced perceived effort and pressure, while video-based learning significantly improved perceived competence. These findings suggest that both methods seem to motivate in diverse ways and that they could complement each other in an integrated teaching approach, offering new perspectives for designing programming learning environments in higher education.
Keyword:
Programming profession
Education
Learning (artificial intelligence)
Generative AI
Codes
Artificial intelligence
Focusing
Active learning
Ethics
Chatbots
Generative artificial intelligence
programming education
intrinsic motivation
active learning
engineering programming
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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