Course-embedded AI-augmented teaching is associated with engagement and performance in software education

AI-augmented teaching may reduce feedback delays in software courses, but semester-long classroom evidence remains limited. This course-level quasi-experimental study evaluated a course-embedded AI-augmented teaching (AIT) model in an undergraduate Python course. Two intact classes participated: 60 students in the AIT class used a large language model teaching assistant restricted to course-specific materials and instructional rules for question answering, code diagnosis, personalized recommendations, and teacher monitoring, whereas 60 students in the conventional-instruction class received the same course instruction without access to the AIT platform.

Student-level analyses, interpreted cautiously because of the class-level design, showed higher posttest, programming assignment and final composite scores in the AIT class after baseline adjustment. Learning engagement also showed clearer gains, especially overall and behavioral engagement. AI usage intensity was moderately associated with engagement but weakly associated with academic performance.

Mediation analysis provided clearer evidence for an indirect engagement pathway for project practice performance than for final composite performance. Open-ended feedback indicated that students valued immediate feedback and debugging support but remained concerned about accuracy, dependency and ethical boundaries. This work was supported by the 2026 Project of Research and Practice on Higher Education Teaching Reform in Henan Province (No. 2026SJGLX375): Research and Practice on the Construction of Software Course Groups Based on Smart Teaching and the Paradigm of Human-Machine Collaborative Teaching.

And the 2025 Project of Research and Practice on Higher Education Teaching Reform in Henan Province (Graduate Education Category, No. 2025SJGLX413Y). School of Humanities and Arts, North China University of Water Resources and Electric Power, Longzihu Campus, Jinshui District, Zhengzhou, 450000, Henan, China The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Below is the link to the electronic supplementary material. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.