Personalized Recommendation Framework Using Large Language Model and Chain-of-thought Prompting: A Case Study of a Computer Programming Course

Authors

  • Tew Hongthong Computer and Communication Engineering for Capacity Building Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand
  • Nacha Chondamrongkul Computer and Communication Engineering for Capacity Building Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand
  • Punnarumol Temdee Computer and Communication Engineering for Capacity Building Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand

DOI:

https://doi.org/10.13052/jmm1550-4646.2165

Keywords:

Recommendation system, personalized learning, chain-of-thought prompting, large language model

Abstract

Traditional learning methodologies often fall short of accommodating diverse learner needs and adapting dynamically to individual learning paces and styles. This limitation underscores the growing need for personalized learning, which has the potential to significantly improve learning outcomes, foster deeper engagement, and enhance learner motivation. This study introduces a novel personalized recommendation framework (PRF) that leverages large language models (LLMs) and chain-of-thought (CoT) prompting techniques to advance personalized learning. Specifically, it proposes a strategic personalization framework that addresses learner heterogeneity by incorporating both preference-based and performance-based features. CoT prompting is integrated to simulate human-like sequential reasoning in LLMs, thereby improving the framework’s adaptability and effectiveness. A case study was conducted in a computer programming course, a domain that requires both conceptual understanding and practical problem-solving, to evaluate the proposed framework. The assessment involved 15 expert reviewers who examined the framework’s effectiveness and overall satisfaction. Experimental results showed that the proposed PRF generated recommendations perceived as significantly more satisfactory than those produced by the non-PRF system (M = 4.50 ± 0.30 vs. 3.73 ± 0.21, p < 0.001). In addition, the experts strongly agreed that the framework effectively identified students in urgent need of support, provided timely recommendations, and delivered personalized learning experiences aligned with individual learner needs.

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Author Biographies

Tew Hongthong, Computer and Communication Engineering for Capacity Building Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand

Tew Hongthong received his B.S.Tech.Ed. in Computer Engineering from Rajamangala University of Technology Lanna, Thailand, his M.Sc in Internet and Information Technology from Naresuan University, Thailand and his M.It in Software Development from Central Queensland University, Australia. His research areas include data classification, machine learning, and educational technology.

Nacha Chondamrongkul, Computer and Communication Engineering for Capacity Building Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand

Nacha Chondamrongkul is an Associate Professor of Software Engineering with many years of experience in both industry and academia, specializing in software engineering and artificial intelligence. He earned a Ph.D. in Computer Science from the University of Auckland, New Zealand. Nacha’s research covers model-driven software engineering, linear temporal logic, model checking, semantic web, ontology reasoning, and machine learning.

Punnarumol Temdee, Computer and Communication Engineering for Capacity Building Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand

Punnarumol Temdee received her B.Eng. in Electronics and Telecommunication Engineering, M. Eng in Electrical Engineering, and Ph.D. in Electrical and Computer Engineering from the King Mongkut’s University of Technology Thonburi. Currently, she is an Associate Professor at the School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand. She is also the head of Computer and Communication Engineering for the Capacity Building Research Center (CCC). Her research interests are artificial intelligence and its applications, data classification, personalized learning, and personalized healthcare.

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Published

2025-12-19

How to Cite

Hongthong, T. ., Chondamrongkul, N. ., & Temdee, P. . (2025). Personalized Recommendation Framework Using Large Language Model and Chain-of-thought Prompting: A Case Study of a Computer Programming Course. Journal of Mobile Multimedia, 21(06), 1105–1134. https://doi.org/10.13052/jmm1550-4646.2165

Issue

2025: Vol 21 Iss 6

Section

ECTI