論文原著：Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performanceDoi：10.1111/bjet.13544中文摘要：隨著科技與教育創新的持續發展，現今的學習者能從教師、同儕、教育科技，乃至近來的生成式人工智慧（如 ChatGPT）等代理者獲得多元支持。特別是，人機協作與混合智慧（Hybrid Intelligence）在學習中的應用，已成為學術界日益關注的議題。然而，「混合智慧」這一概念仍處於萌芽階段，學習者如何與 AI、人類專家與智慧學習系統建立共生關係、從中受益，仍不甚明朗。這一新興概念亦缺乏以實證研究為基礎，對其運作機制與學習後果的深入理解。為了填補這一研究空白，我們進行了一項隨機實驗研究，比較在寫作任務中接受不同代理者支持的學習者，在學習動機、自我調節學習過程與學習表現上的差異。受試者共117名大學生，分別接受 ChatGPT（即 AI 組）、與人類專家對談、使用寫作分析工具、或無額外工具等四種支持。我們收集並分析了他們的多通道學習資料、表現結果與動機指標。研究結果顯示： 1. 接受不同學習支持的學習者，在任務後的內在動機上沒有顯著差異； 2. 各組學習者在自我調節學習過程中的頻率與順序存在顯著差異； 3. 雖然 ChatGPT 組在作文分數提升上表現較佳，但其知識獲得與遷移並無顯著差異。我們的研究發現，即使動機相同，不同的學習支持仍導致學習過程差異，進而影響學習表現。值得注意的是，像 ChatGPT 這樣的 AI 技術可能促使學習者對技術過度依賴，並潛在地引發「後設認知懶惰」（metacognitive laziness）。總結而言，了解並善用不同代理者在學習中的優勢與限制，對於未來混合智慧的發展至關重要。英文摘要：With the continuous development of techno- logical and educational innovation, learners nowadays can obtain a variety of supports from agents such as teachers, peers, education technologies, and recently, generative artificial intelligence such as ChatGPT. In particular, there has been a surge of academic interest in human-­ AI collaboration and hybrid intelligence in learning. The concept of hybrid intelligence is still at a nascent stage, and how learners can benefit from a symbiotic relationship with various agents such as AI, human experts and intelligent learning systems is still unknown. The emerging concept of hybrid intelligence also lacks deep insights and understanding of the mechanisms and consequences of hybrid human-­ AI learning based on strong empirical research. In order to address this gap, we conducted a randomised experimental study and compared learners' motivations, self-­regulated learning processes and learning performances on a writing task among different groups who had support from different agents, that is, ChatGPT (also referred to as the AI group), chat with a human expert, writing analytics tools, and no extra tool. A total of 117 university students were recruited, and their multi-channel learning, performance and motivation data were collected and analysed. The results revealed that: (1) learners who received different learning support showed no difference in post-­ task intrinsic motivation; (2) there were significant differences in the frequency and sequences of the self-­regulated learning processes among groups; (3) ChatGPT group outperformed in the essay score improvement but their knowledge gain and transfer were not significantly different. Our research found that in the absence of differences in motivation, learners with different supports still exhibited different self-­ regulated learning processes, ultimately leading to differentiated performance. What is particularly note-worthy is that AI technologies such as ChatGPT may promote learners' dependence on technology and potentially trigger “metacognitive laziness”. In conclusion, understanding and leveraging the respective strengths and weaknesses of different agents in learning is critical in the field of future hybrid intelligence.