生成式AI賦能的回饋機器人用於改善學習成效

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姓名

蘇聖益(Sheng-Yi Su) 查詢紙本館藏

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資訊工程學系

論文名稱

生成式AI賦能的回饋機器人用於改善學習成效
(Improving Learning Achievement with a Generative AI-Enabled Feedback Chatbot)

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至系統瀏覽論文 (2029-8-1以後開放)

摘要(中)

本研究介紹了PyFeedbacker，一款結合了先進的生成式AI技術和機器人功
能的教育工具。PyFeedbacker的核心特色在於其能夠提供針對性的概念回饋與程
式設計回饋，概念回饋透過生成式AI比對範例總結與學生總結，提供關鍵字與
概念提示，幫助學生進一步完善其概念，並生成針對性回饋，從而精確提供學生
所需的概念回饋；程式設計回饋能回答學生在程式設計實作過程中的提問，
PyFeedbacker 能分析學生的程式碼，並提供錯誤診斷與改進建議作為程式設計回
饋，從而使學生在程式設計的學習過程中能更快地識別和修正問題，進而提高解
決問題的能力。本研究展示了生成式AI技術在教育應用上的可能性，有望在教
育回饋產生上減少對於教師、助教的依賴，進而改變教育資源的配置方式。本研
究詳細闡述了PyFeedbacker的系統架構設計、生成式AI技術的整合過程，以及
其在教育實踐中的實驗設計及成果。在實證研究方面，我們在大學的Python 程
式設計課程中對 PyFeedbacker 進行了實驗，以評估其在提高學生學習成效和激
發學習動機、學習策略方面的效果。實驗結果顯示，學生在使用PyFeedbacker進
行學習時，不僅學習成效得到提升，學習動機與策略也隨之增強。本研究不僅證
實了PyFeedbacker 在教育領域的應用價值，也展示了生成式AI技術與教育整合
的潛力及生成式AI-人類協作模式的實際應用。

摘要(英)

This study introduces PyFeedbacker, an educational tool that combines advanced
generative AI technology with robotic functions. The core feature of PyFeedbacker is
its ability to provide targeted conceptual feedback and programming feedback.
Conceptual feedback offers hints related to concepts and keywords to help students
improve their understanding. This feature uses generative AI to compare examples with
student summaries and generates corresponding feedback. Programming feedback
addresses students′ questions during programming exercises. PyFeedbacker analyzes
students′ code, provides error diagnosis, and offers improvement suggestions, enabling
students to quickly identify and correct issues, thus enhancing their problem-solving
skills. The study demonstrates the potential of generative AI technology in educational
applications, promising to reduce reliance on teachers and teaching assistants for
feedback, and potentially changing the allocation of educational resources. The
research details the system architecture design of PyFeedbacker, the integration process
of generative AI technology, and the experimental design and outcomes in educational
practice. In the empirical research, we conducted experiments with PyFeedbacker in a
university Python programming course to evaluate its effectiveness in improving
students′ learning outcomes and motivating learning strategies. The results showed that
students not only improved their learning outcomes but also enhanced their motivation
and strategies when using PyFeedbacker. This study not only confirms the value of
PyFeedbacker in the field of education but also showcases the potential of integrating
generative AI technology with education and the practical application of the generative
AI-human collaboration model.

關鍵字(中)

★ 生成式AI
★ 機器人
★ 概念回饋
★ 程式設計回饋
★ 學習成效

關鍵字(英)

★ generative AI
★ robot
★ concept feedback
★ programming feedback
★ learning achievement

論文目次

目錄
摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
1 緒論 1
2 文獻探討 4
2.1 聊天機器人在教育上的應用 4
2.2 生成式AI 6
2.3 總結寫作 7
2.4 輔助程式設計 8
3 系統開發 9
3.1 PyFeedbacker 介面介紹 9
3.2 基於生成式AI的概念回饋功能 10
3.2.1 概念回饋系統架構 13
3.2.2 生成範例內容流程 14
3.2.3 概念回饋內容 16
3.2.4 概念回饋內容驗證方式 18
3.3 基於生成式AI的程式設計回饋功能 19
3.3.1 程式設計回饋系統架構 20
3.3.2 程式設計回饋內容 22
3.3.3 程式設計回饋內容驗證方式 23
4 方法論 24
4.1 參與者 24
4.2 學習環境介紹 25
4.3 實驗設計與課程活動 27
4.4 評量工具 29
5 實驗結果 34
5.1 生成式AI賦能的回饋機器人是否能提升學習成績? (RQ1) 34
5.2 生成式AI賦能的回饋機器人是否能改善學習動機與學習策略? (RQ2) 35
6 討論 39
7 限制與未來研究 40
8 參考文獻 41

參考文獻

8 參考文獻
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指導教授

楊鎮華(Stephen J.H. Yang)

審核日期

2024-7-10

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