AI-Assisted Teaching Model for Personalized Computer Education Based on Deep Learning

Jiyou  Dong; Nainapas  Injoungjirakit; Prapai  Sridama; Sombat  Teekasap

ผู้แต่ง

Jiyou Dong Digital Technology Management for Education Program, Bansomdejchaopraya Rajabhat University, Thailand.
Nainapas Injoungjirakit Digital Technology Management for Education Program, Bansomdejchaopraya Rajabhat University, Thailand.
Prapai Sridama Digital Technology Management for Education Program, Bansomdejchaopraya Rajabhat University, Thailand.
Sombat Teekasap Digital Technology Management for Education Program, Bansomdejchaopraya Rajabhat University, Thailand.

คำสำคัญ:

AI - assisted teaching, Deep learning, Personalized computer education, Teaching model, Learning effects

บทคัดย่อ

This study investigates an AI-assisted, deep learning–based personalized teaching model for computer education. With the rapid expansion of artificial intelligence in the education sector, personalized learning has become increasingly important for improving instructional effectiveness. Sixty computer science students were selected and randomly assigned to an experimental group—receiving AI-assisted personalized instruction—and a control group using traditional teaching methods. Data were collected through a learning management system, a programming practice platform, and a structured questionnaire. Descriptive statistics, independent-sample difference tests, and correlation analyses were employed to evaluate outcomes.

The findings indicate that the experimental group outperformed the control group across key indicators, including learning-path adaptability, learning-effect improvement rate, and satisfaction with learning feedback. These results demonstrate that the AI-assisted teaching model significantly enhances learning effectiveness and learner engagement.

The study contributes a practical and data-driven framework for integrating AI into personalized computer education. However, limitations related to sample size, single-discipline focus, and short intervention duration suggest the need for broader, longitudinal, and cross-disciplinary future research.

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