政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/158683

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/158683

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题名:	生成式AI輔助工具應用於高中Python程式設計教學對於學生學習成效、自我效能、運算思維與學習焦慮之影響研究 The Effects of Integrating Generative AI-Assisted Tools into High School Python Programming Instruction on Students’ Learning Performance, Self-Efficacy, Computational Thinking, and Learning Anxiety
作者:	陳顗元 Chen, Yi-Yuan
贡献者:	陳志銘 Chen, Chih-Ming 陳顗元 Chen, Yi-Yuan
关键词:	生成式人工智慧程式設計教學 AI 助手學習成效自我效能運算思維學習焦慮高中資訊教育 Generative Artificial Intelligence Programming Instruction AI Assistant Learning Achievement Self-Efficacy Computational Thinking Learning Anxiety High School Informatics Education
日期:	2025
上传时间:	2025-08-04 15:01:31 (UTC+8)
摘要:	隨著生成式人工智慧（Generative AI）技術的快速發展，AI 在教育領域中的應用已日益普及，尤其在程式設計教學方面的應用具有極大的發展潛力。高中程式設計課程長期以來面臨學習門檻高、個別差異大與教師負擔重等挑戰，傳統教學模式難以兼顧學生的多元學習需求。為回應此一困境，本研究發展以 ChatGPT API 為基礎之「程式設計 AI 助手」，作為高中 Python程式設計課程的輔助學習工具，探討相較於傳統教師講授教學法，其作為傳統教師講授教學法外加之輔助學習工具，對於學生的程式設計學習成效、自我效能、運算思維，以及程式設計焦慮的影響，也進一步探討其對於高低不同程式設計先備能力學生的程式設計學習成效、自我效能、運算思維，以及程式設計焦慮影響。本研究採準實驗研究設計，以新竹縣某高中一年級兩個班級（共67人）為研究對象，兩組皆由同一位教師進行授課，本研究將兩個班級隨機分派為實驗組（教師講授、AI助手）與控制組（僅教師講授），並進行歷時十一週之教學。研究結果發現，實驗組與控制組在教學實驗進行前的程式設計先備能力、自我效能、運算思維能力相近，不具有顯著差異。在教學實驗進行後，實驗組學生在程式設計學習成效、自我效能、運算思維上均顯著優於控制組學生，但是在程式設計學習焦慮上不具有顯著的差異。此外，高先備程式設計能力的實驗組學生在程式設計學習成效及自我效能上，均顯著優於高先備程式設計能力的控制組學生，但是在運算思維與程式設計學習焦慮上則不具有顯著的差異；而低先備程式設計能力的兩組學習者，在學習成效、自我效能、運算思維與程式設計學習焦慮上，則不具有顯著的差異，顯示程式設計AI助手對於低先備程式設計能力學生的輔助效果有限。最後基於本研究之研究結果，提出未來可延長教學期程並深化課程內容，結合縱貫追蹤設計，來持續觀察AI助手對於學生程式設計之高階認知與長期成長的影響。此外，應擴大研究對象至資訊科或高動機學習族群，並發展針對於低先備程式設計能力學生的個別化補救教學模式，以提升其學習動機與突破其學習瓶頸。同時，持續優化程式設計AI助手互動介面與回饋設計，並推動教師與AI助手之協作教學模式，以補足情感支持之不足。再則，在系統層面則可導入外部記憶及多代理AI助手架構，提升回應精準度與知識支持，以進一步發揮程式設計AI助手於教學現場之最大效益。 With the rapid development of Generative Artificial Intelligence (Generative AI) technologies, AI applications in the field of education have become increasingly widespread, especially showing tremendous potential for development in programming instruction. High school programming courses have long faced challenges such as high learning barriers, large individual differences, and heavy teacher workload. Traditional instructional models often struggle to meet the diverse learning needs of students. In response to these issues, this study developed a “Programming AI Assistant” based on the ChatGPT API as a supplementary learning tool for high school Python programming courses. The research explored, compared with traditional lecture-based instruction, the effects of using the AI assistant—added to regular classroom teaching—on students’ programming learning achievement, self-efficacy, computational thinking, and programming anxiety. Further, the study investigated its impact on students with different levels of prior programming ability. This study adopted a quasi-experimental research design. Two first-year classes from a high school in Hsinchu County (a total of 67 students) were selected as research subjects. Both classes were taught by the same teacher and randomly assigned to the experimental group (teacher instruction plus AI assistant) or the control group (teacher instruction only) for eleven weeks. Results showed that, before the intervention, the experimental and control groups did not differ significantly in programming prior knowledge, self-efficacy, or computational thinking. After the intervention, the experimental group demonstrated significantly greater improvements than the control group in programming learning achievement, self-efficacy, and computational thinking, but not in programming anxiety. Further analysis revealed that among students with higher prior programming ability, those in the experimental group outperformed those in the control group in both programming achievement and self-efficacy, but there were no significant differences in computational thinking and programming anxiety. Among students with lower prior programming ability, there were no significant differences between the two groups in any of the measured outcomes, indicating that the support provided by the AI assistant was limited for students with weaker foundations. Based on these findings, this study suggests that future research could extend the instructional period and deepen course content, using longitudinal designs to continuously observe the effects of AI assistants on students’ advanced cognitive development and long-term growth in programming. It is also recommended to broaden the research population to include information science or highly motivated student groups, and to develop individualized remedial instruction models for students with lower prior programming ability, to enhance their motivation and help them overcome learning bottlenecks. Furthermore, continuous optimization of the interaction interface and feedback design of the programming AI assistant is needed, as well as the promotion of collaborative teaching models involving both teachers and AI assistants, to address the lack of emotional support. Furthermore, at the system level, external memory modules and multi-agent AI assistant architectures can be incorporated to enhance response accuracy and knowledge support, thereby further maximizing the effectiveness of programming AI assistants in classroom settings.
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