# Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance arXiv: https://arxiv.org/abs/2412.09315 ยท HTML: https://mlsanity.com/paper/2412.09315/ ## Key facts - Cited by 58 papers in the map (data through 14 Oct 2026). - Builds on 1 of its 79 references that are in the map. - First posted on arXiv 12 Dec 2024. ## Builds on (most cited first, 1 of 1) - [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018) ## Built on by (most cited first, 30 of 58) - [Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study](https://mlsanity.com/paper/2504.05570/) (2025) - [Evaluating GPT- and Reasoning-based Large Language Models on Physics Olympiad Problems: Surpassing Human Performance and Implications for Educational Assessment](https://mlsanity.com/paper/2505.09438/) (2025) - [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025) - [How Large Language Models Are Changing MOOC Essay Answers: A Comparison of Pre- and Post-LLM Responses](https://mlsanity.com/paper/2504.13038/) (2025) - [LLM-Generated Feedback Supports Learning If Learners Choose to Use It](https://mlsanity.com/paper/2506.17006/) (2025) - [Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks](https://mlsanity.com/paper/2603.05801/) (2026) - [When Autonomy Breaks: The Hidden Existential Risk of AI](https://mlsanity.com/paper/2503.22151/) (2025) - [Scaffolding Metacognition in Programming Education: Understanding Student-AI Interactions and Design Implications](https://mlsanity.com/paper/2511.04144/) (2025) - [Mitigating "Epistemic Debt" in Generative AI-Scaffolded Novice Programming using Metacognitive Scripts](https://mlsanity.com/paper/2602.20206/) (2026) - [Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programming](https://mlsanity.com/paper/2604.27134/) (2026) - [Distinguishing performance gains from learning when using generative AI](https://mlsanity.com/paper/2605.13731/) (2026) - [JELAI: Integrating AI and Learning Analytics in Jupyter Notebooks](https://mlsanity.com/paper/2505.17593/) (2025) - [Beyond Automation: Socratic AI, Epistemic Agency, and the Implications of the Emergence of Orchestrated Multi-Agent Learning Architectures](https://mlsanity.com/paper/2508.05116/) (2025) - [Do Students Rely on AI? Analysis of Student-ChatGPT Conversations from a Field Study](https://mlsanity.com/paper/2508.20244/) (2025) - [Evaluating LLMs for Career Guidance: Comparative Analysis of Computing Competency Recommendations Across Ten African Countries](https://mlsanity.com/paper/2510.18902/) (2025) - [Agentic AI as Undercover Teammates: Argumentative Knowledge Construction in Hybrid Human-AI Collaborative Learning](https://mlsanity.com/paper/2512.08933/) (2025) - [Understanding Critical Thinking in Generative Artificial Intelligence Use: Development, Validation, and Correlates of the Critical Thinking in AI Use Scale](https://mlsanity.com/paper/2512.12413/) (2025) - [Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative Learning](https://mlsanity.com/paper/2601.12134/) (2026) - [Framing, Judging, Steering: An Assessable Competency Model for Teach-ing Students to Reason With Generative AI](https://mlsanity.com/paper/2606.05983/) (2026) - [Metacognition in LLMs: Foundations, Progress, and Opportunities](https://mlsanity.com/paper/2607.11881/) (2026) - [Resurrecting Socrates in the Age of AI: A Study Protocol for Evaluating a Socratic Tutor to Support Research Question Development in Higher Education](https://mlsanity.com/paper/2504.06294/) (2025) - [Delving Into the Psychology of Machines: Exploring the Structure of Self-Regulated Learning via LLM-Generated Survey Responses](https://mlsanity.com/paper/2506.13384/) (2025) - [Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?](https://mlsanity.com/paper/2508.10919/) (2025) - [Socratic Mind: Impact of a Novel GenAI-Powered Assessment Tool on Student Learning and Higher-Order Thinking](https://mlsanity.com/paper/2509.16262/) (2025) - [Writing With Machines and Peers: Designing for Critical Engagement with Generative AI](https://mlsanity.com/paper/2511.15750/) (2025) - [Artificial Intelligence Competence of K-12 Students Shapes Their AI Risk Perception: A Co-occurrence Network Analysis](https://mlsanity.com/paper/2512.04115/) (2025) - [Generative AI Assisted Workflows in Architectural Conceptual Design: Performance, Creative Self-Efficacy, and Cognitive Load](https://mlsanity.com/paper/2601.10696/) (2026) - [How College Students Use AI to Navigate Course Readings: Evidence from an Eight-Week Study](https://mlsanity.com/paper/2602.09907/) (2026) - [Intellectual Stewardship: Re-adapting Human Minds for Creative Knowledge Work in the Age of AI](https://mlsanity.com/paper/2603.18117/) (2026) - [MetaCues: Enabling Critical Engagement with Generative AI for Information Seeking and Sensemaking](https://mlsanity.com/paper/2603.19634/) (2026) ## Abstract (arXiv) With the continuous development of technological and educational innovation, learners nowadays can obtain a variety of support from agents such as teachers, peers, education technologies, and recently, generative artificial intelligence such as ChatGPT. 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 (ChatGPT, 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: learners who received different learning support showed no difference in post-task intrinsic motivation; there were significant differences in the frequency and sequences of the self-regulated learning processes among groups; 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 noteworthy is that AI technologies such as ChatGPT may promote learners' dependence on technology and potentially trigger metacognitive laziness.