The Metacognitive Demands and Opportunities of Generative AI
Lev Tankelevitch, Viktor Kewenig, Auste Simkute, Ava Elizabeth Scott, Advait Sarkar, Abigail Sellen, Sean Rintel
摘要
Generative AI (GenAI) systems offer unprecedented opportunities for transforming professional and personal work, yet present challenges around prompting, evaluating and relying on outputs, and optimizing workflows. We argue that metacognition—the psychological ability to monitor and control one’s thoughts and behavior—offers a valuable lens to understand and design for these usability challenges. Drawing on research in psychology and cognitive science, and recent GenAI user studies, we illustrate how GenAI systems impose metacognitive demands on users, requiring a high degree of metacognitive monitoring and control. We propose these demands could be addressed by integrating metacognitive support strategies into GenAI systems, and by designing GenAI systems to reduce their metacognitive demand by targeting explainability and customizability. Metacognition offers a coherent framework for understanding the usability challenges posed by GenAI, and provides novel research and design directions to advance human-AI interaction.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper56
- The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge WorkersHao-Ping (Hank) Lee, Advait Sarkar, Lev Tankelevitch, Ian Drosos 等CHI 2025 · 被引用 690 次
- VeriPlan: Integrating Formal Verification and LLMs into End-User PlanningChristine P. Lee, David Porfirio, Xinyu Jessica Wang, Kevin Chenkai Zhao 等CHI 2025 · 被引用 50 次
- WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code VisualizationLiwenhan Xie, Chengbo Zheng, Haijun Xia, Huamin Qu 等UIST 2024 · 被引用 45 次
- Productive vs. Reflective: How Different Ways of Integrating AI into Design Workflows Affect Cognition and MotivationXiaotong (Tone) Xu, Arina Konnova, Bianca Gao, Cindy Peng 等CHI 2025 · 被引用 41 次
- To Rely or Not to Rely? Evaluating Interventions for Appropriate Reliance on Large Language ModelsJessica Y. Bo, Sophia Wan, Ashton AndersonCHI 2025 · 被引用 31 次
它引用的顶会 Paper24
- Why Johnny Can't Prompt: How Non-AI Experts Try (and Fail) to Design LLM PromptsJ. D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, Qian YangCHI 2023 · 被引用 892 次
- AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model PromptsTongshuang Wu, Michael Terry, Carrie Jun CaiCHI 2022 · 被引用 465 次
- Grounded Copilot: How Programmers Interact with Code-Generating ModelsShraddha Barke, Michael B. James, Nadia PolikarpovaOOPSLA 2023 · 被引用 408 次
- Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory ProgrammingMajeed Kazemitabaar, Justin Chow, Carl Ka To Ma, Barbara J. Ericson 等CHI 2023 · 被引用 348 次
- Do Users Write More Insecure Code with AI Assistants?Neil Perry, Megha Srivastava, Deepak Kumar, Dan BonehCCS 2023 · 被引用 150 次
相关 Paper
- Scaffolding Metacognition with GenAI: Exploring Design Opportunities to Support Task Management for University Students with ADHDZihao Zhu, Junnan Yu, Yuhan LuoCHI 2026 · 被引用 4 次
- Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software TeamsHari Subramonyam, Divy Thakkar, Andrew Ku, Jürgen Dieber 等CHI 2025 · 被引用 26 次
- Generative AI in the Wild: Prospects, Challenges, and StrategiesYuan Sun, Eunchae Jang, Fenglong Ma, Ting WangCHI 2024 · 被引用 63 次
- Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation WorkflowsFrederic Gmeiner, Nicolai Marquardt, Michael Bentley, Hugo Romat 等CHI 2025 · 被引用 11 次
- When Teams Embrace AI: Human Collaboration Strategies in Generative Prompting in a Creative Design TaskYuanning Han, Ziyi Qiu, Jiale Cheng, Ray LCCHI 2024 · 被引用 103 次
