學習問題拆解思維能力以提升人類與生成式人工智慧協作開發的程式碼品質

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張文耀(Wen-Yao Chang) 查詢紙本館藏

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論文名稱

學習問題拆解思維能力以提升人類與生成式人工智慧協作開發的程式碼品質
(Learning Problem Decomposition Skills to Enhance Code Quality in Human-AI Collaborative Coding)

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至系統瀏覽論文 (2029-7-17以後開放)

摘要(中)

隨著生成式人工智慧（Generative Artificial Intelligence, GAI）技術的飛速發展，軟體開發模式正經歷一場深刻的變革。從傳統的個人開發模式逐步轉向人機協作開發（Human-AI Collaborative Coding）模式，這一轉變迫使開發者必須掌握新的能力。許多研究指出，生成式 AI 在處理簡單且重複性高的任務（例如程式碼自動生成）上展現卓越的性能。因此，在人機協作開發中，為了最大化人工智慧的效能，開發者應學會將複雜問題盡可能拆解為更小、更明確定義的模組，使得人工智慧更容易生成正確的答案。接著分解其提示任務，以促進人工智慧達到最佳性能。這凸顯了問題拆解能力的重要性。

本研究聚焦於探討開發者在人機協作開發中，Decomposition思維能力的重要性，並提出提示模板學習方法。透過引導學習者進行程式模組和任務拆解的訓練，旨在培養學習者的Decomposition思維能力，以提升人機協作開發的效果。本研究設計了一個學習系統，並進行了一項包含35名參與者、持續七週的學習實驗。研究結果顯示，學習者在採用提示模板學習方法後，在程式碼的模組化與可讀性方面達到了最佳的效果。參與者的反饋同樣表明，此學習方法能顯著提升程式碼品質。

摘要(英)

With the rapid advancement of Generative Artificial Intelligence (GAI) technology, software development paradigms are undergoing profound transformations. Moving from traditional individual coding practices to Human-AI Collaborative Coding, this shift necessitates new capabilities among developers. Numerous studies have highlighted the exceptional performance of generative AI in handling simple and repetitive tasks, such as automatic code generation. Thus, in Human-AI collaborative coding, to maximize the efficiency of artificial intelligence, developers should learn to decompose complex problems into smaller, more clearly defined modules, enabling AI to generate more accurate solutions. Furthermore, refining prompt tasks is essential to optimize AI performance, highlighting the importance of decomposition skills.

This study focuses on the significance of Decomposition thinking abilities in Human-AI collaborative coding and introduces a prompt template learning approach. By guiding learners through the training of program modularization and task decomposition, this approach aims to cultivate Decomposition thinking skills, thereby enhancing the effectiveness of Human-AI collaborative development. The study designed a learning system and conducted a seven-week experiment involving 35 participants. Results indicate that learners achieved optimal outcomes in code modularization and readability after adopting the prompt template learning method. Feedback from participants also demonstrates that this method significantly improves code quality.

關鍵字(中)

★ 生成式人工智慧
★ 人機協作
★ 運算思維
★ 問題拆解
★ 提示工程
★ 電腦科學教育

關鍵字(英)

★ Generative Artificial Intelligence
★ Human-AI Collaboration
★ Computational Thinking
★ Problem Decomposition
★ Prompt Engineering
★ Computer Science Education

論文目次

摘要 xi
Abstract xiii
目錄 xv

一、緒論
1.1 研究背景 1
1.2 研究目標 3
1.3 研究問題與對策 3

二、相關研究
2.1 人機協作開發 5
2.2 生成式 AI 背景下的資訊教育 6
2.3 Decomposition 思維的學習 7

三、學習方法與實作
3.1 學習方法 9
3.2 系統介面 12
3.2.1 主介面 12
3.2.2 創建對話介面 13
3.2.3 聊天介面 14
3.2.4 新增提示介面 15
3.3 學習流程 - 以開發圖書管理系統為例 17
3.3.1 步驟一：閱讀題目 17
3.3.2 步驟二：創建聊天室 18
3.3.3 步驟三：與 AI 助手互動 19
3.3.4 步驟四：透過提示模板新增提示詞 20
3.3.5 步驟五：AI 生成回覆 21
3.4 系統實作 22
3.4.1 系統概述 22
3.4.2 Nginx 反向代理伺服器 22
3.4.3 Vue 前端伺服器 23
3.4.4 MySQL 資料庫 24
3.4.5 Express 後端伺服器 25

四、實驗設計
4.1 實驗對象 27
4.2 實驗流程 27
4.3 學習內容 29
4.4 實驗評估 32
4.4.1 前測驗與兩次後測驗 32
4.4.2 評估標準 32

五、實驗結果與討論
5.1 描述統計分析結果 35
5.2 學習成效分析工具 36
5.2.1 常態分布檢定 36
5.2.2 變異數同質性檢定 37
5.2.3 Mauchly’s 球形性檢定 37
5.2.4 單因子相依變異數分析 38
5.3 提示模板學習系統聊天次數與第二次後測驗成績的相關性分析 40
5.4 受試者回饋 41
5.5 受試者提示行為及程式碼分析 42
5.5.1 受試者提示行為分析 42
5.5.2 受試者程式碼分析 44
5.6 實驗結果討論 47
5.7 研究限制 48

六、結論與未來展望
6.1 結論 49
6.2 未來展望 50

參考文獻 51

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指導教授

莊永裕(Yung-Yu Zhuang)

審核日期

2024-7-18

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