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    Area of Science:

    • Artificial Intelligence
    • Human-Computer Interaction
    • Computer Vision

    Background:

    • Fine-grained visual classification (FGVC) models excel at distinguishing subtle differences in images, but their outputs (e.g., specific bird species) may not directly translate to human learning.
    • A key challenge lies in determining what constitutes transferable knowledge from AI to humans and how to effectively measure this knowledge gain.

    Purpose of the Study:

    • To investigate how AI can serve as a knowledge provider to enhance human expertise in specific domains.
    • To identify the most effective forms of transferable knowledge from AI models and develop practical methods for measuring human expertise improvement.

    Main Methods:

    • Proposed representing AI knowledge as highly discriminative, expert-exclusive visual regions.
    • Developed a novel multi-stage learning framework to instantiate this knowledge transfer.
    • Conducted a large-scale human study involving 15,000 trials to evaluate the method's effectiveness.

    Main Results:

    • The proposed method significantly improved participants' ability to recognize previously unidentifiable bird species.
    • The AI-driven knowledge transfer was effective across individuals with varying levels of prior expertise.
    • A new metric, TEMI (Transferable Expertise Measurement Instrument), was proposed for benchmarking future research.

    Conclusions:

    • AI can be a valuable tool for transferring specialized knowledge to humans, enhancing their domain expertise.
    • Focusing on discriminative visual regions as knowledge representations is a promising approach for AI-to-human learning.
    • The developed framework and TEMI metric provide a foundation for future research in human-AI collaborative learning.