- 學位論文
生成式 AI 對於國中小學生學習動機與生成式AI應用課程參與意圖之因素的影響
Exploring the Impact of Generative AI on Learning Motivation and Factors Influencing Students’ Intention of Participating in Generative AI Integrated Courses
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摘要
2023年被譽為生成式AI元年，教育相關應用蓬勃發展。在2023年初，澳洲僅不到20%學生使用生成式AI工具，隨後亞非國家達到40%，印尼更高達90%。亞太地區多國政府因應此趨勢提出相關政策和指引。本研究探討生成式AI應用課程對學生學習動機及參與意圖的影響是否隨使用率提升而增加。 採用社會認知理論及計畫行為理論的整合模型，本研究探討課程相關變數和學生感受相關變數對學習動機及生成式AI應用課程參與意圖的影響，共回收96份有效問卷。 研究結果顯示：課程滿意度、生成式AI素養、學習動機和教師支持顯著影響學生參與意圖。生成式AI自我效能和教師支持顯著影響學習動機。自我效能感和教師支持的提升能激發學習動機，進而促進課程參與意圖。生成式AI素養的提升能增加參與意圖，創造更多學習機會，獲得更多教師支持、提升自我效能，最終提升學習動機。 然而，部分假設不成立，如課程滿意度對學習動機、生成式AI自我效能對參與意圖、生成式AI素養對學習動機的直接影響均不顯著。代表學習動機和參與意圖的影響因素可能比預期更複雜。 本研究提供了影響學習動機和參與意圖因素的實證結果，為生成式AI課程教師和教育工作者提供課程設計建議，有助於鼓勵學生參與生成式AI應用課程，並為生成式AI教育研究相關實證基礎。
並列摘要
The year 2023 is seen as the start of the generative AI era, especially in education. At the beginning of 2023, less than 20% of students in Australia were using generative AI tools. Later, the usage in Asia and Africa grew to 40%, with Indonesia reaching 90%. Many governments in the Asia-Pacific region have responded to this trend by creating new policies and guidelines. This study aims to understand whether the impact of generative AI application courses on students' learning motivation and participation intention increases with the rise in usage rate. The study employed an integrated model of Social Cognitive Theory and the Theory of Planned Behavior to examine how course-related factors and student perceptions impact learning motivation and the intention to participate in generative AI courses. A total of 96 valid questionnaires were collected. The results show that course satisfaction, generative AI literacy, learning motivation, and teacher support significantly affect students' intention to participate. Self-efficacy in using generative AI and teacher support greatly influence learning motivation. Improving students' self-efficacy in using generative AI and teacher support can boost learning motivation, which then enhances their intention to participate. Also, better generative AI literacy can lead to more participation intention, creating more learning opportunities, gaining more teacher support, boosting students' self-efficacy, and ultimately increasing learning motivation. However, some hypotheses were not supported, such as the direct effects of course satisfaction on learning motivation, generative AI self-efficacy on participation intention, and generative AI literacy on learning motivation were not significant. This suggests that the factors influencing learning motivation and participation intention may be more complex than expected. This study provides empirical results on factors influencing learning motivation and participation intention, offering course design suggestions for teachers and educators of generative AI courses. It helps encourage student participation in generative AI application courses and contributes to the empirical foundation for research in generative AI education.
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