The Metacognitive Demands and Opportunities of Generative AI
- Lev Tankelevitch ,
- Viktor Kewenig ,
- Auste Simkute ,
- Ava Elizabeth Scott ,
- Advait Sarkar ,
- Abigail Sellen ,
- Sean Rintel
CHI '24 |
Published by ACM
Best Paper, CHI 2024
下载 BibTexGenerative 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, enabling us to offer research and design directions to advance human-GenAI interaction.
Lev Tankelevitch, Viktor Kewenig, Auste Simkute, Ava Elizabeth Scott, Advait Sarkar, Abigail Sellen, and Sean Rintel. 2024. The Metacognitive Demands and Opportunities of Generative AI. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), May 11– 16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 24 pages.
The Metacognitive Demands and Opportunities of Generative AI
Microsoft Research Forum | Episode 2 | March 5, 2024 Lev Tankelevitch explored how metacognition—the psychological capacity to monitor and regulate one's cognitive processes—provides a valuable perspective for comprehending and addressing the usability challenges of generative AI systems around prompting, assessing and relying on outputs, and workflow optimization. See more at https://aka.ms/ResearchForum-Mar2024 (opens in new tab)