Journal of Advances in Developmental Research

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Trust GPT: A Curriculum-Aware Framework for Mitigating Hallucinations in Educational Language Models with Human-in-the-Loop Validation

Author(s) Kinshuk Dutta, Sabyasachi Paul, Ankit Anand
Country United States
Abstract Large language models demonstrate impressive generative fluency, yet their deployment in educational contexts remains constrained by hallucinations and curriculum misalignment. Building upon a multi-year research arc, this paper introduces TrustGPT, a curriculum-aware framework for mitigating hallucinations in educational language models. We present a refined error taxonomy distinguishing hallucination from pedagogical misalignment, formalize curriculum-aware sampling and coverage regularization mechanisms, and integrate a practical human-in-the-loop validation cycle with teacher feedback. Unlike reinforcement learning-based alignment approaches, TrustGPT emphasizes interpretable, lightweight governance mechanisms embedded directly into training and validation pipelines. Empirical validation demonstrates a 28.5% reduction in hallucinations and a 33% improvement in pedagogical alignment scores compared to baseline fine-tuning. The framework advances trust, safety, and reliability in educational AI by operationalizing ethical principles as enforceable system-level constraints, directly addressing limitations identified in our preceding work and setting the stage for runtime alignment solutions.
Keywords Educational Language Models, Curriculum Alignment, Retrieval-Augmented Generation, Pedagogical AI, Hallucination Mitigation, Trustworthy AI, Educational AI, Human-in-the-Loop Validation, Trustworthy Language Models.
Field Engineering
Published In Volume 13, Issue 1, January-June 2022
Published On 2022-03-04
Cite This Trust GPT: A Curriculum-Aware Framework for Mitigating Hallucinations in Educational Language Models with Human-in-the-Loop Validation - Kinshuk Dutta, Sabyasachi Paul, Ankit Anand - IJAIDR Volume 13, Issue 1, January-June 2022. DOI 10.71097/IJAIDR.v13.i1.1678
DOI https://doi.org/10.71097/IJAIDR.v13.i1.1678
Short DOI https://doi.org/hbm795
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