Three recurring AI failure modes arose in this project-based learning context: plausible-but-incorrect code, missing specialized knowledge, and limited long-term context¶

Claim · All claims
Evidence · 1 study · qualitative r1 · q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q1 i? The authors identified three recurring categories of AI failure during the project, each of which became a teaching moment. → 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' qualitative reflection on failure modes encountered across the ten-week project (RQ2). The three categories are plausible but incorrect code, lack of specialized knowledge such as niche libraries like curl-impersonate, and limited long-term context windows risking incorrect advice.

"Plausible but incorrect code:AI-generated code often ran without errors but could contain errors or methodological flaws, including issues associated with assumptions around modeling, feature construction, etc."

Discussion¶

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