TITLE:
Intelligent Technology-Driven Teaching Innovation in University Aesthetic Education—A Case Study of “Digital Photography Technology and Art” at Southwest Petroleum University
AUTHORS:
Liang Zhao, Guili Zhang, Shijiao Qiao, Qian Wang
KEYWORDS:
Intelligent Technology, Aesthetic Education in Higher Education, Technology-Enhanced Teaching, Personalized Learning
JOURNAL NAME:
Journal of Computer and Communications,
Vol.14 No.5,
May
14,
2026
ABSTRACT: With the vigorous development of new-generation artificial intelligence large mode, AI technology has emerged as a pivotal driving force behind the new round of technological revolution and industrial transformation, propelling human society into an intelligent new era characterized by human-machine collaboration, cross-boundary integration, and co-creation and sharing [1]. In the advancement of aesthetic education in higher education institutions, photography courses currently face critical challenges, including the scarcity of teaching resources, outdated pedagogical methods, and the absence of personalized learning for students. This study takes the Digital Photography Technology and Art course at Southwest Petroleum University as a case to explore an innovative path for empowering information-based teaching of aesthetic education courses through intelligent technology. The course was delivered to three learner groups: on-campus undergraduates (experimental group), remote learners from partner institutions, and open-access public users (control group). An intelligent instructional system was constructed by integrating the “M + LIF” teaching model, the AIRVC intelligent-agent framework, a personalized knowledge graph, and VR-supported teaching resources. A mixed-methods evaluation combining online questionnaires and platform learning-record analysis was employed, yielding 763 valid responses, and learning outcomes between the experimental and control groups were compared. Empirical results show that the mean course score of the experimental group was 5.14 points higher than that of the control group; 87.63% of students reported that personalized resource recommendations significantly enhanced their learning interest; and the course pass rate rose from 41.3% to 63.9%, with the online dropout rate decreasing by 19.39%. The findings indicate that the personalized teaching model supported by intelligent technology can effectively improve the teaching quality and learning experience of aesthetic education courses, offering a scalable, practical solution for the digital transformation of aesthetic education in higher education.