Fine-Tuning Language Models for Pedagogy-Aligned Lesson Plans in Cybersecurity Education

Lesson planning in cybersecurity is time-consuming and cognitively demanding, especially for less experienced instructors, and manual approaches often lack flexibility across courses and contexts. We present a framework for generating pedagogy-aligned lesson plans using a large language model, integrating measurable objectives (Revised Bloom’s Taxonomy), explicit learning theories, and evidence-based teaching strategies. We constructed a domain-specific knowledge base for cybersecurity topics and organized it with sentence-level embeddings and KMeans clustering. A pretrained large language model (GPT- 3.5) was then fine-tuned to produce lesson plans that follow this structure. On a held-out test set, the model achieved BLEU 73.5, ROUGE-1 82.2, ROUGE-L 78.2, and BERTScore F1 97.4, reflecting strong lexical and semantic fidelity to reference plans. Although the study is limited to a single academic program and relies primarily on automated metrics, the framework offers practical support for instructors by reducing preparation time, enhancing consistency, and ensuring alignment with pedagogical standards. Future work will expand the curricular scope and in-volve expert review and classroom validation to assess educational impact.

@article{Althagafi2025,
  title     = {Fine-Tuning Language Models for Pedagogy-Aligned Lesson Plans in Cybersecurity Education},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Samar Althagafi and Miada Almasre and Wafaa Alsaggaf and Lana Alshawwa},
  doi       = {10.14569/IJACSA.2025.01612124},
  url       = {https://doi.org/10.14569/IJACSA.2025.01612124}
}