AI-Assisted Teaching Model for Personalized Computer Education Based on Deep Learning
This study aimed to: (1) examine AI-assisted learning readiness, utilization, and urban–rural disparities among computer science students in Guangxi Zhuang Autonomous Region; (2) develop a context-adapted AI-assisted personalized teaching model; and (3) evaluate its suitability and feasibility for routine teaching. The sample included 56 undergraduate students, divided into experimental and control groups (n = 28 each), and an 8-member expert panel. Data were collected through questionnaires, interviews, and evaluation forms, and analyzed using descriptive statistics, t-tests, chi-square, correlation, and content analysis. Results indicated high overall readiness (M = 3.68), with significant urban–rural disparities. Urban students showed higher technology acceptance and self-efficacy, while rural students demonstrated greater needs for personalized learning and faced infrastructure limitations. The developed model integrates dual-track learning paths, adaptive feedback, and lowbandwidth/offline functionalities tailored to regional constraints. Evaluation results confirmed high suitability and feasibility (M = 4.44, S-CVI = 0.953). The experimental group reported high usability (M = 4.21), and implementation strategies achieved a 91.7% integration rate, effectively supporting diverse classroom contexts and reducing instructional burden. Keywords: Artificial Intelligence, Computer Education, Teaching-Learning Model, Computational Thinking,Regional Adaptability
มหาวิทยาลัยราชภัฏบ้านสมเด็จเจ้าพระยา. สำนักวิทยบริการและเทคโนโลยีสารสนเทศ
ลิงค์ข้อมูลในระบบ Matrix
Digital Technology Management for Education