Catching The Correct Answer Trap: Characterising AI Tutor Blind Spots When Analysing Student Reasoning
arXiv:2605.23925v1 Announce Type: cross Abstract: Intelligent tutoring systems increasingly provide automated feedback on student work, but robust feedback requires assessing reasoning, not only final answers. We study a failure mode we call the correct answer trap (CAT): models under-detect misconceptions when students reach a correct answer via flawed reasoning. Analysing real student responses from the Eedi mathematics platform, we show that 71% of these failures concentrate in just two question types, both sharing a common structure where flawed reasoning happens to produce the correct num
The increasing deployment of AI tutors and LLM-based educational tools makes understanding their failure modes critical for effective implementation and widespread adoption.
This research highlights a significant vulnerability in current AI tutoring systems, demonstrating that AI can be 'fooled' by correct answers derived from flawed reasoning, thus impeding genuine student learning and assessment.
The focus for AI tutor development will shift towards more nuanced reasoning assessment rather than just outcome verification, requiring more sophisticated AI architectures.
- · AI ethicists
- · Educational psychology researchers
- · Developers of advanced AI reasoning models
- · AI tutor providers with simplistic feedback loops
- · Students relying solely on current generation AI tutors
AI tutors will need to incorporate more sophisticated pedagogical models to detect and correct 'correct answer trap' scenarios.
This limitation could slow the mass adoption of fully autonomous AI tutors in critical educational settings until these issues are robustly addressed.
Increased investment in AI interpretability and explainability will be required to build tutoring systems that can not only identify flaws but also explain why reasoning is incorrect.
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Read at arXiv cs.CL