Montreal, Quebec, Canada
June 22, 2025
June 22, 2025
August 15, 2025
Computers in Education Division (COED)
22
https://peer.asee.org/57295
1
Eric L. Brown is an education leader with over 28 years of experience in higher education, currently serving as the Associate Director of Workforce Development for the Cybersecurity, Education, Research, and Outreach Center at Tennessee Tech University. As a senior lecturer in the Computer Science department, Eric teaches various cybersecurity courses and agile-focused software engineering.
His prior experiences include serving as a District Solutions Advocate for the Tennessee Department of Education, where he played a key role in the Chief Information Officer’s leadership team. In addition, Eric served for eight years as a school board member in Putnam County, TN, with four years in leadership positions, giving him valuable insights into K12 education.
Today, Eric's work focuses on cybersecurity education and workforce development in the K-16 sector, building pathways for students and professionals in emerging cyber fields.
Dr. Doug Talbert is a Professor of Computer Science at Tennessee Tech University, where he has worked since 2002. He work focuses on trustworthy human-AI interatction, especially in the area of clinical informatics.
I am an Assistant Professor at Tennessee Technological University in the computer science department. My research focuses on the development, evaluation, and application of empirical methods for NLP with emphasis on transformer-based LLMs. I prefer applications that contribute to digital humanities, robotics, law, cognitive science, and the preservation of endangered languages - primarily Cherokee (Tsalagi) and Irish (Gaeilge). But I'm also interested in game theory, computational physics, and any idea that can positively impact people.
Large language models (LLMs) have revolutionized content creation across various domains, yet their application in highly specialized fields such as curriculum gap analysis remains challenging. This study (full paper format), to be presented at the 2025 ASEE Annual Conference’s Computers in Education Division (COED), investigates the efficacy of LLMs in developing and evaluating cybersecurity curriculum pathways.
Traditional curriculum plans, such as those from institutions designated by the NCAE-C in cybersecurity defense education (CAE-CD) and cyber operations (CAE-CO) tracks, involve a manual process of identifying knowledge units for specific courses. Throughout the mapping process, gaps are identified in curriculum plans based on the knowledge of the subject matter expert(s) (SME). Performing this task for one framework is challenging enough; consider the increased complexity and risk of error when multiple frameworks are cross-referenced into the plan. Improvement opportunities exist in the curriculum mapping and gap analysis process. This leads to the question of whether an LLM can speed up the curriculum mapping process compared to a manual process conducted by an SME, which will be evaluated through a set of ”human in the loop” experiments.
To evaluate this question, the paper details the results of the following experiments involving a computer science/cybersecurity curriculum being mapped to the CAE-CD knowledge units (KU): 1. A single SME will create a manual KU curriculum mapping. 2. Provide an LLM with details of the full curriculum, including catalog descriptions and syllabi, and create a mapping for a single CAE-CD knowledge unit. 3. Provide an LLM with details of all CAE-CD knowledge units and information for one course (catalog description and syllabus) and create a knowledge unit mapping for that course. 4. Provide an LLM with details of all CAE-CD knowledge units, course descriptions, and syllabi, and create a knowledge unit mapping for the full curriculum.
We will evaluate the results using the following metrics: 1. Accuracy - precision, recall, F1, and error rate of LLM vs SME 2. Efficiency - time to complete and reduction of human effort 3. Qualitative - expert review and comparison of output quality
Our analysis will show that the manual curriculum mapping and gap analysis process can be accelerated and improved by suggesting possible outcomes from the LLM, allowing the SME to assume the more effective role of critical reviewer over content developer. We will also show that this assistance comes with contraints.
Brown, E. L., & Talbert, D. A., & Roberts, J. (2025, June), Toward the Use of LLMs to Support Curriculum Mapping to Established Frameworks Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . https://peer.asee.org/57295
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