Artificial intelligence-supported art education: a deep learning-based system for promoting university students’ artwork appreciation and painting outcomes

ABSTRACT

In a conventional art course, it is important for a teacher to provide feedback and guidance to individual students based on their learning status. However, it is challenging for teachers to provide immediate feedback to students without any aid. The advancement of artificial intelligence (AI) has provided a possible solution to cope with this problem. In this study, a deep learning-based art learning system (DL-ALS) was developed by employing a fine-tuned ResNet50 model for helping students identify and classify artworks. We aimed at cultivating students’ accurate appreciation knowledge and artwork creation competence, as well as providing instant feedback and personalized guidance with the help of AI technology. To explore the effects of this system, a quasi-experiment was implemented in an artwork appreciation course at a university. A total of 46 university students from two classes who took the elective art course were recruited in the study. One class was the experimental group adopting DL-ALS learning, while the other was the control group adopting conventional technology-supported art learning (CT-AL). The results showed that in comparison with CT-AL, learning through the DL-ALS could facilitate students’ learning achievement, technology acceptance, learning attitude, learning motivation, self-efficacy, satisfaction, and performance in the art course.

KEYWORDS:

• Deep learning
• artificial intelligence
• art learning
• artwork appreciation
• artwork creation

Acknowledgements

This study is supported in part by the Ministry of Science and Technology of Taiwan under contract numbers MOST-109-2511-H-011-002-MY3 and MOST 110-2511-H-167 −003-MY2.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Ministry of Science and Technology, Taiwan.

Notes on contributors

Min-Chi Chiu

Ms. Min-Chi Chiu is a PhD at the Graduate Institute of Digital Learning and Education, National Taiwan University of Applied Science and Technology, Taiwan. Her research interests include computer-assisted learning, artificial intelligence in education, system development, mobile and ubiquitous learning.

Gwo-Jen Hwang

Dr. Gwo-Jen Hwang is a chair professor at the Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology. His research interests include mobile learning, digital game-based learning, flipped classrooms and AI in education.

Lu-Ho Hsia

Dr. Lu-Ho Hsia is an Associate Professor in the Office of Physical Education, National Chin-Yi University of Technology, Taiwan. His research interests include flipped classrooms, mobile learning and physical education.

Fong-Ming Shyu

Dr. Fong-Ming Shyu is an Associate Professor in the Department of Multimedia Design, National Taichung University of Science and Technology, Taiwan. His research interests include programming, Website system design, multimedia communication, artificial intelligence and software engineering.