International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AlignGPT: A Curriculum-Regularized Transformer Framework for Pedagogically Aligned Educational Language Modeling

Author(s) Kinshuk Dutta, Sabyasachi Paul, Ankit Anand
Country United States
Abstract Transformer-based language models exhibit remark- able linguistic fluency but remain poorly aligned with the pedagogical requirements of formal education. In instructional settings, correctness is defined not only by factual accuracy but by adherence to curriculum scope, sequencing, and learning objec- tives. This paper introduces AlignGPT, a curriculum-regularized transformer framework that formalizes pedagogical alignment as an explicit optimization objective during fine-tuning. Building upon prior syllabus-driven adaptations such as StudentGPT [1], we propose a curriculum alignment loss and curriculum cov- erage regularization to address both semantic relevance and topic imbalance. We provide theoretical justifications for these components, including differentiability properties, convergence guarantees under stochastic optimization, bounds on alignment deviation, and a generalization bound for the regularized ob- jective. Empirical evaluations on simulated educational datasets demonstrate superior alignment scores (mean improvement of 18.4% over baselines) and reduced coverage imbalance (from 34.7% to 12.1% relative frequency skew). All experiments use resources and tooling available in 2021–early 2022, supporting reproducibility in constrained computational environments. Index Terms—Transformer Models, Curriculum Learning, Pedagogical Alignment, Educational NLP, Fine-Tuning, Ethical AI, Language Model Regularization, Optimization Theory.
Keywords Transformer Models, Curriculum Learning, Pedagogical Alignment, Educational NLP, Fine-Tuning, Ethical AI, Language Model Regularization, Optimization Theory.
Field Engineering
Published In Volume 3, Issue 3, May-June 2021
Published On 2021-06-10
DOI https://doi.org/10.36948/ijfmr.2021.v03i03.67508
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