Working with imperfect AI outputs under mentor supervision was followed by students developing skepticism and independent debugging skills¶

Claim · All claims
Evidence · 1 study · qualitative r1 · q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? Over the ten weeks, students moved from accepting LLM outputs with little scrutiny to independently identifying AI-generated errors. → Freyaa Chawla 2026

Evidence¶

Freyaa Chawla 2026¶

Freyaa Chawla, Ahan Chawla, Rishi Singh, Joe Germino, Grigorii Khvatskii. (2026). Human-AI Co-Mentorship in Project-Based Learning: A Case Study in Financial Forecasting. arXiv preprint. https://arxiv.org/abs/2605.05144 doi:10.48550/arxiv.2605.05144

q1 · i? · qualitative · r1

Authors' observational reflection from a single cohort of two high-school students and early undergraduates in a ten-week project, not a controlled comparison. The article reports this developmental arc and suggests working with imperfect AI outputs "may itself be a valuable pedagogical intervention."

"Early in the project, students accepted LLM outputs with little scrutiny; by the end, they had developed skepticism and debugging skills that allowed them to identify errors independently."

Discussion¶

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