A Prompt-Driven Framework for Reflective Evaluation of Course Alignment Using Large Language Models

Large language models (LLMs) such as ChatGPT are gaining attention in educational settings, yet their potential role in supporting course design and academic quality assurance remains underexplored. This study introduces a structured, prompt-driven framework that uses ChatGPT as a reflective tool to help faculty and curriculum designers evaluate the alignment between course learning outcomes (CLOs), assessment methods, and teaching strategies. Grounded in the standards of the National Center for Academic Accreditation and Evaluation (NCAAA) and Bloom’s Revised Taxonomy, the system generates targeted, context-aware feedback using structured prompts modeled after official NCAAA forms. To enhance reliability and reduce hallucinations, the framework employs template-based prompt engineering and rule-based cognitive classification. A large-scale analysis was conducted across 56 CLOs to assess internal consistency, followed by expert validation from two academic reviewers who evaluated a sample of AI-generated feedback for accuracy, usefulness, and cognitive alignment. The findings highlight the tool’s ability to surface alignment issues and offer constructive recommendations, while also demonstrating its potential as a scalable support system for curriculum review and accreditation readiness, complementing rather than replacing human expertise.

@article{Alsulami2025,
  title     = {A Prompt-Driven Framework for Reflective Evaluation of Course Alignment Using Large Language Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Mashael M. Alsulami},
  doi       = {10.14569/IJACSA.2025.0160879},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160879}
}