2025/09/02 by Gao, Eric · 1 citation
Computer Science · Medicine · #Artificial Intelligence in Healthcare and Education #FOS: Economics and business #Intelligent Tutoring Systems and Adaptive Learning #Online Learning and Analytics #Theoretical Economics (econ.TH)
paper · pdf · doi:10.48550/arxiv.2509.02879
openalex publication_date 2025/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Artificial intelligence (AI) tools such as large language models (LLMs) are already altering student learning. Unlike previous technologies, LLMs can independently solve problems regardless of student understanding, yet are not always accurate (due to hallucination) and face sharp performance cutoffs (due to emergence). Access to these tools significantly alters a student's incentives to learn, potentially decreasing the sum knowledge of humans and AI. Additionally, the marginal benefit of learning changes depending on which side of the AI frontier a human is on, creating a discontinuous gap between those that know more than or less than AI. This contrasts with downstream models of AI's impact on the labor force which assume continuous ability. Finally, increasing the portion of assignments where AI cannot be used can counteract student mis-specification about AI accuracy, preventing underinvestment. A better understanding of how AI impacts learning and student incentives is crucial for educators to adapt to this new technology.