自主學習（Self-Regulated Learning, SRL）在台灣教育系統中日益受到重視。自主學習強調學生在學習過程中的自我控制與調整能力，旨在培養學生的批判性思維、創新能力及終身學習技能；然而實施自主學習面臨的挑戰之一是如何有效地提供適性化的指導和學習資源，以適應每個學生的不同需求和學習進度，尤其當利用大型語言模型融入自主學習時，不同的提示策略所產出的回應品質是值得討論的議題。
本論文旨在探討不同提示策略對大型語言模型在自主學習環境中的回答品質的影響。我們通過一系列實驗，比較了無策略、搜尋增強生成策略、鏈式提示策略及鏈式提示搭配搜尋增強生成策略四種方法，對其在回答忠實度、答案相關性及反問任務中的表現進行了詳細分析。
實驗結果顯示，搜尋增強生成策略在確保生成答案的忠實度和與知識文本的一致性方面具有顯著優勢；鏈式提示策略則在反問任務具有顯著的幫助。綜合實驗結果得知應用鏈式提示搭配搜尋增強策略能顯著提升大型語言模型的回答品質，特別是在需要高忠實度和高相關性的回答任務中表現優異。未來的研究建議可進一步優化提示策略，提升模型的穩定性和普適性，以更廣泛地應用於教育領域。

Self-Regulated Learning (SRL) is increasingly valued in Taiwan's education system, emphasizing students' ability to control and adjust their learning processes to cultivate critical thinking, innovation, and lifelong learning skills. One challenge in implementing SRL is providing personalized guidance and resources to meet individual student needs, especially when using large language models. The quality of responses generated by different prompting strategies is a key issue.
This thesis explores the impact of various prompting strategies on the response quality of large language models in SRL environments. We compared four methods: no strategy, search-enhanced generation, chain-of-thought prompting, and a combination of chain-of-thought prompting with search-enhanced generation. Our experiments analyzed response fidelity, answer relevance, and reverse questioning tasks.
Results showed that the search-enhanced generation strategy ensured high fidelity and consistency with knowledge texts, while chain-of-thought prompting was beneficial for reverse questioning. Combining both strategies significantly improved response quality, especially in tasks requiring high fidelity and relevance. Future research should focus on optimizing these strategies to enhance model stability and applicability in education.

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