AI-Assisted Teaching Model for Personalized Computer Education Based on Deep Learning
Item
ชื่อเรือง
AI-Assisted Teaching Model for Personalized Computer Education Based on Deep Learning
ผู้แต่ง
Dong Jiyou
หัวเรื่อง
Artificial intelligence in education
Personalized learning
Computer-assisted instruction
Educational technology
รายละเอียดอื่นๆ
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
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
ผู้จัดพิมพ์/สำนักพิมพ์
มหาวิทยาลัยราชภัฏบ้านสมเด็จเจ้าพระยา. สำนักวิทยบริการและเทคโนโลยีสารสนเทศ
ผู้ร่วมสร้างรรค์ ผู้ร่วมงาน
Nainapas Injoungjirakit
Sombat Teekasap
Prapai Sridama
วันที่ ปีที่จัดพิมพ์
2025
วันที่ผลิต วันที่จัดทำ
2026-07-10
วันที่ปรับปรุงข้อมูล
2026-07-10
วันที่เผยแพร่
2026-07-10
ประเภท
thesis
รูปแบบ
application/pdf
แหล่งที่มา
TH 371.334 J619A 2025
ภาษา
eng
ลิขสิทธิ์
Bansomdejchaopraya Rajabhat University
Degree (name, level, descipline, grantor)
Doctor of Philosophy
Doctoral degree
Digital Technology Management for Education
Bansomdejchaopraya Rajabhat University
คอลเลกชั่น
Dong Jiyou, “AI-Assisted Teaching Model for Personalized Computer Education Based on Deep Learning”, มหาวิทยาลัยราชภัฏบ้านสมเด็จเจ้าพระยา. สำนักวิทยบริการและเทคโนโลยีสารสนเทศ, 2025, คลังข้อมูลวิจัย มหาวิทยาลัยราชภัฎบ้านสมเด็จเจ้าพระยา, accessed July 14, 2026, http://dlib.bsru.ac.th/s/research/item/3750