Pedagogically Aligned LLM Tutors with Answer-Leakage Control for Engineering Mathematics
Pedagogically Aligned LLM Tutors with Answer-Leakage Control for Engineering Mathematics
Description
Details
Context and Problem Statement
A tutor that immediately gives complete solutions may reduce learning despite high short-term satisfaction. LLM tutors can be pressured into revealing answers through repeated reformulation or deceptive requests for verification.
Research Question
How can an LLM tutor be aligned with explicit pedagogical principles while remaining robust to answer-extraction strategies, and what effect does this constraint have on learner engagement?
Proposed Approach
Use preference-based alignment with a reward combining scaffolding quality, error diagnosis, and non-disclosure of final answers. Build an adversarial benchmark of answer-extraction strategies and evaluate the tutor with engineering-mathematics students.
Expected Contribution
A French-language benchmark for answer-leakage robustness and a quantified trade-off between pedagogical firmness and learner acceptance.
Expected Prototype
An LMS-integrated tutor providing progressive hints, error diagnosis, and teacher-facing analytics on recurring misconceptions.
Datasets
MathDial, academic tutor-evaluation datasets, and consent-based classroom dialogues.
Challenges
Human-evaluation cost, automated-judge bias, student-data protection, and learner frustration.
Research Question
Innovation
Expected Deliverable
Technologies
Required Skills
- Natural Language Processing
- Model Alignment and Preference Learning
- Human-Participant Evaluation Methodology
- Web Development and LMS Integration
Datasets
- MathDial
- Academic tutor-evaluation datasets
- Consent-based classroom dialogue corpus