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This is a Quantitative Study study.

October 3, 2025Open Access

AI Knows Best? The Paradox of Expertise, AI-Reliance, and Performance in Educational Tutoring Decision-Making Tasks

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Authors

ECEason ChenCarnegie Mellon University JLJeffrey LiHumboldt-Universität zu Berlin SHSheng HuangNanjing University of Posts and Telecommunications

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Implication

Empirical analysis examines how collaboration with AI affects accuracy in tutoring tasks, indicating complexities in expert reliance and explanation types.

Key Points

Human-AI collaboration enhances decision-making accuracy compared to humans alone, yet AI performance outshines collaboration.
Non-tutors benefit from AI guidance, yielding higher accuracy; experienced tutors often overlook AI's correct suggestions.
Fine-tuned large language models achieved 88% accuracy, demonstrating the potential of AI in tutoring decision-making tasks.
Text explanations increase reliance on AI, while inline highlighting does not significantly alter performance outcomes.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68e040f3a99c246f578b37e3 https://doi.org/10.48550/arxiv.2509.16772

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Implication

Empirical analysis examines how collaboration with AI affects accuracy in tutoring tasks, indicating complexities in expert reliance and explanation types.

AI Knows Best? The Paradox of Expertise, AI-Reliance, and Performance in Educational Tutoring Decision-Making Tasks

Discussion

Member takes

Implication

Key Points

Cite This Study

AI Knows Best? The Paradox of Expertise, AI-Reliance, and Performance in Educational Tutoring Decision-Making Tasks

Discussion

Member takes

Implication

Key Points

Cite This Study

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