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Fostering human learning in sequential decision-making: Understanding the role of evaluative feedback.

Piyush Gupta1, Subir Biswas1, Vaibhav Srivastava1

  • 1Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, United States of America.

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AI-driven feedback enhances decision-making and skill acquisition in sequential tasks. Humans perceive this evaluative feedback as key to long-term success, improving learning and strategy transfer.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Effective tutoring systems are crucial for cognitive rehabilitation and STEM skill acquisition.
  • AI-driven tutoring necessitates understanding feedback's impact on human decision-making and learning.
  • Sequential decision-making tasks, like the Tower of Hanoi, are valuable for studying learning processes.

Purpose of the Study:

  • To investigate the influence of AI-generated evaluative feedback on human decision-making in sequential tasks.
  • To analyze how feedback affects learning, skill transfer, and implicit reward structures.
  • To explore computational models of feedback incorporation in human decision-making.

Main Methods:

  • Human experiments conducted via Amazon Mechanical Turk.
  • Participants solved the Tower of Hanoi puzzle with AI-generated feedback.
  • Maximum entropy inverse reinforcement learning and computational models were employed for analysis.

Main Results:

  • Evaluative feedback was perceived by humans as indicative of long-term strategic success.
  • Feedback significantly aided skill acquisition and transfer in sequential decision-making tasks.
  • Feedback fostered a more structured learning experience compared to no feedback; intermediate goals alone did not enhance learning.

Conclusions:

  • AI-generated evaluative feedback is a powerful tool for enhancing human learning in sequential decision-making.
  • Understanding how humans process feedback is critical for developing advanced AI tutoring systems.
  • Future research should focus on optimizing feedback mechanisms for diverse learning contexts.