近年來，隨著人工智慧技術（如大型語言模型）發展，教育領域迎來新教學模式，但較少研究將認知負荷理論結合人工智慧學習工具設計。本研究探討將認知負荷理論中的「示例效應」應用於大型語言模型學習回饋工具，對學習者回饋接受度、人工智慧素養及學習成效的影響。研究針對傳統回饋在個性化與效率上不足，以及人工智慧工具使用中學習者接受度與認知負荷管理挑戰，提出結合示例效應的回饋設計方案。
本研究採取對照實驗設計，招募未修習資料結構排序演算法課程之大專生作為實驗對象。實驗組於回饋中納入完整示例步驟，對照組僅提供錯誤更正；透過線上系統進行學習活動，並以問卷測量外部認知負荷、增生認知負荷、回饋接受度、人工智慧素養、認知學習成效與實際學習成效，共回收122份有效問卷，以獨立樣本T檢定及偏最小平方結構方程模型分析。
研究結果證實在大型語言模型學習回饋工具的設計中，應用示例效應能有效改善學習者對工作記憶的調控，也有助於培養人工智慧素養、使他們更能接受學習回饋，並顯著促進實際與認知的學習成果。此外，研究也驗證了外部認知負荷會對增生認知負荷、回饋接受度，以及學習成效產生負面影響，增生認知負荷則會對回饋接受度、人工智慧素養和認知學習成效產生正面影響。其中人工智慧素養能提升回饋接受度，而回饋接受度會顯著影響認知學習成效。
本研究不僅填補了大型語言模型學習回饋工具中示例效應應用的學術空白，亦結合認知負荷理論提出實證證據，為人工智慧學習回饋工具之設計與實踐提供具體建議：透過示例效應可有效控管認知負荷、提升回饋接受度與人工智慧素養，進而促進學習成效的全面提升。

In recent years, AI technologies like large language models have transformed education. Yet few studies integrate Cognitive Load Theory into AI-based feedback tools. Addressing personalization and efficiency shortcomings of traditional feedback methods and challenges in AI-tool acceptance and cognitive-load management, this study examines how embedding the “worked example effect” in an LLM feedback tool affects learners’ feedback receptivity, AI literacy, and learning outcomes.
We conducted a controlled experiment with college students who had not taken a data structures course. Participants engaged in a series of programming exercises online, receiving feedback immediately after each attempt. The experimental group received fully worked solution steps, while the control group received only error-correction feedback. A total of 122 valid responses were analyzed. We measured extraneous and germane cognitive load, feedback receptivity, AI literacy, cognitive learning outcomes, and actual performance. Analyses included independent-samples t-tests and PLS-SEM.
The results confirm that applying the worked example effect in designing feedback tools powered by large language models supports learners in managing working memory, fosters AI literacy, increases receptivity to feedback, and significantly enhances both cognitive and actual learning outcomes. This study also shows that extraneous cognitive load negatively impacts germane load, feedback receptivity, and learning performance, while germane load positively influences receptivity, AI literacy, and cognitive learning. Notably, enhanced AI literacy promotes greater feedback receptivity, which in turn significantly predicts cognitive learning gains.
This study not only fills a research gap in applying the worked example effect within LLM-based feedback tools but also provides empirical evidence grounded in Cognitive Load Theory. We offer practical design recommendations: integrating worked examples can help regulate cognitive load, enhance AI literacy and feedback receptivity, and ultimately drive holistic improvements in learning outcomes.

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