近年來，生成式人工智慧逐漸應用於英語教學領域，相關研究指出此類工具在支援英語字彙學習方面具有潛力。其中，ChatGPT 因能即時回應學習者需求並提供多元語言輸出形式而受到關注。然而，現有研究多著重於學習成效，對於學習者在使用人工智慧輔助學習時，自我調控策略的發展與調整仍缺乏深入探討。
為彌補此研究缺口，本研究採質性個案研究法，探討三名高成就臺灣高中生在教師引導下運用 ChatGPT 進行英語字彙學習的自我調控歷程。研究以「透過資訊與通訊科技的自我調控字彙學習（SRLvocICT）」架構為理論基礎，進行為期四週的教師引導式 AI 輔助學習介入，分析學生在使用前、使用中與使用後三個階段中，其投入控制、後設認知控制、情意控制、資源控制與社會控制策略的變化，並探討學生對此學習方式的觀感與挑戰。
研究結果顯示，受試者皆展現高度且穩定的自我調控能力；在教師引導下，AI 的使用主要強化既有策略，而非促使策略轉換。學習者在後設認知監控與資源協調方面表現更為成熟，能以精確的提示詞與 ChatGPT 互動，並主動查證生成內容。整體而言，學生肯定 ChatGPT 的輔助價值，但亦認為其生成內容需經驗證。本研究將提示工程視為後設認知監控的具體展現，並討論其對 AI 輔助學習任務設計與教學支援之啟示。

In recent years, research has increasingly examined the use of generative AI (Gen AI) in ELT, showing that AI applications can support vocabulary acquisition. Among GenAI tools, ChatGPT has attracted attention for its potential to facilitate vocabulary learning. However, little is known about how learners develop and adjust self-regulated strategies when using AI tools. To address this gap, this qualitative case study investigated self-regulated vocabulary learning behaviors among three high-achieving Taiwanese high school students engaged in guided practice with ChatGPT. Drawing on the SRLvocICT framework, the study traced changes in students’ commitment, metacognitive, affective, resource, and social control strategies across the pre-, during-, and post-use phases of a four-week guided AI-assisted vocabulary learning intervention, and examined students’ perceptions and challenges.
Findings indicate that participants maintained strong self-regulation and that guided AI use reinforced rather than transformed existing routines. Metacognitive monitoring and resource coordination were strengthened through tighter prompting, systematic verification, and integration of AI-generated output with authoritative sources. Commitment shifted from vocabulary quantity to accurate application in sentences and short texts. Affective control remained steady, while social control showed limited change without explicit task design. Students viewed ChatGPT as useful yet provisional, preferring single-instruction prompts and organized outputs such as tables, short quizzes, sentence rewrites, and brief dialogues. Challenges such as misinterpretation, information overload, and limited depth of use were addressed through prompt refinement, verification, and quick retries. The study positions prompt engineering as an indicator of metacognitive monitoring and discusses implications for task design and instructional support.

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