Proceedings of the 2025 International Conference on Educational Technology and Management Information Systems (ETMIS 2025)

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Exploring an AI-Enabled Digital Teaching Model Driven by Self-Directed Learning

Authors
Yingying Sun1, Yanli Fu1, *, Di Zhou1
1Engineering University of PAP, Xian, China
*Corresponding author. Email: 345765707@qq.com
Corresponding Author
Yanli Fu
Available Online 23 April 2026.
DOI
10.2991/978-94-6239-630-2_17How to use a DOI?
Keywords
Artificial intelligence; autonomous learning; competency-based; digital teaching
Abstract

This study addresses several challenges commonly encountered in programming education, including substantial differences in students’ prior knowledge, insufficient support for self-directed learning, and difficulties in implementing effective process-oriented assessment. Taking the Fundamentals of Programming course as a case study, the study explores the construction of a digital teaching model driven by artificial intelligence (AI) and self-directed learning. Relying on a SPOC-based online learning environment and a programming practice platform, learning process data and programming behavior data are collected to support the design of personalized learning pathways. During instructional implementation, a tiered, progressive competency-oriented structure and a blended learning approach are adopted to strengthen students’ practical programming skills. In addition, a learning data-driven formative assessment mechanism is introduced to enable continuous monitoring and feedback of teaching effectiveness. The results of teaching practice indicate that the proposed model contributes to improved student engagement, higher quality completion of programming tasks, and enhanced overall competency development, thereby providing a viable reference for the digital transformation of programming-related courses.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 International Conference on Educational Technology and Management Information Systems (ETMIS 2025)
Series
Advances in Computer Science Research
Publication Date
23 April 2026
ISBN
978-94-6239-630-2
ISSN
2352-538X
DOI
10.2991/978-94-6239-630-2_17How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yingying Sun
AU  - Yanli Fu
AU  - Di Zhou
PY  - 2026
DA  - 2026/04/23
TI  - Exploring an AI-Enabled Digital Teaching Model Driven by Self-Directed Learning
BT  - Proceedings of the 2025 International Conference on Educational Technology and Management Information Systems (ETMIS 2025)
PB  - Atlantis Press
SP  - 163
EP  - 175
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-630-2_17
DO  - 10.2991/978-94-6239-630-2_17
ID  - Sun2026
ER  -