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A Generative AI-Based Personalized Programming Education System with Adaptive Rubric Evaluation

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2025, 30(10), pp.11~22
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 5, 2025
  • Accepted : October 9, 2025
  • Published : October 31, 2025

Euhee Kim 1

1신한대학교

Accredited

ABSTRACT

This paper presents a personalized programming education system that integrates a GPT-4-based role-play simulation and an adaptive rubric-driven automated evaluation engine. The system employs prompt engineering to guide GPT-4 in dual roles—as a conversational tutor and as an evaluator— enabling dynamic learner modeling and self-explanation through contextual dialogue. A total of 450 question–answer interactions were collected across three instructional scenarios and three learner personas. Experimental results demonstrated improvements in learning outcomes through repeated sessions, with rubric score consistency within ±0.5 points and a 91.3% agreement rate between AI-generated and human evaluator scores. These findings confirm the system's effectiveness in delivering real-time personalized feedback and formative assessment, and suggest practical applications for AI-driven education.

Citation status

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