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What Impacts Endure: A Technologist's Notes on GPT's Impact on Expert Work
1Cornell Bowers College of Computing and Information Science, Cornell University, Ithaca, New York, United States.
Journal of Applied Research in Memory and Cognition
|October 6, 2025
Summary
Generative AI (genAI) like ChatGPT can boost expert cognition and training, but these advantages aren't universal. This commentary supports this, drawing parallels with older technologies and suggesting new ways to study AI's impact on expertise.
Area of Science:
- Human-Computer Interaction (HCI)
- Artificial Intelligence (AI)
Background:
- Generative AI (genAI) models, such as ChatGPT, are increasingly integrated into expert workflows.
- Existing research suggests genAI can augment expert cognition and training.
- Concerns exist regarding the equitable distribution of these benefits among users.
Purpose of the Study:
- To empirically support the arguments presented by Imundo et al. (2024) regarding genAI's impact on expertise.
- To explore the parallels between modern genAI tools and older computational technologies in mimicking expertise.
- To propose novel frameworks for understanding the evolving relationship between AI and expertise.
Main Methods:
- Review and commentary on existing arguments regarding genAI and expertise.
- Presentation of empirical evidence from Human-Computer Interaction (HCI) research.
- Analysis of how computational technologies, including genAI, influence expert cognition.
Main Results:
- The commentary validates the claims that genAI enhances expert cognition and training.
- Evidence shows that benefits from AI-driven tools are not uniformly distributed.
- Historical computational technologies also exhibited similar patterns of expertise mimicry and benefit distribution.
Conclusions:
- The findings reinforce the dual nature of genAI: its potential to enhance expertise and the unevenness of its accessibility.
- There is a need for continued research into how genAI influences the definition and practice of expertise.
- Alternative taxonomies, such as generative versus evaluative abilities, may offer more nuanced insights into AI's cognitive effects than traditional expertise categories.
Keywords:
Large Language Modelsexpertisehuman-AI interactionhuman-AI teamworkhuman-computer interactionMore Related Videos
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