The Instructors’ Perceptions toward AI Teaching: Product Design Major at Shandong University

Wu Qian; Nipaporn  Sakulwongs

Authors

Wu Qian Rangsit University
Nipaporn Sakulwongs Rangsit University

Keywords:

AI teaching, product design, instructors’ perceptions, Technology Acceptance Model

Abstract

Background and Aims: This study examined instructors’ perceptions of applying artificial intelligence (AI) in teaching product design at universities in Shandong Province, China. It drew on the Technology Acceptance Model (TAM), examining perceived usefulness, perceived ease of use, self-efficacy, interest, stress, and training as constructs influencing AI adoption in higher education.

Methodology: A mixed-methods design was employed. Quantitative data were collected from 208 product design instructors through a structured questionnaire and analysed using AMOS and structural equation modelling (SEM). Qualitative data were gathered through in-depth interviews with 15 instructors and analysed thematically in NVivo. The thematic analysis surfaced instructors’ general optimism toward AI, the sources of teaching-related pressure, the perceived shortage of discipline-specific training, and concerns about over-reliance on AI. Informed consent and confidentiality were maintained throughout the study.

Results: Instructors generally held positive attitudes toward using AI in teaching. Self-efficacy, interest, and stress were positively associated with perceived usefulness and perceived ease of use, with stress the strongest predictor, followed by self-efficacy and interest. Training was positively associated with attitudes toward use. The qualitative findings corroborated these patterns: instructors described AI as improving lesson-preparation efficiency, while cautioning that over-reliance could weaken creativity and self-reflection, and noting that most available training targets other disciplines rather than product design.

Conclusion: Strengthening AI-related training and capacity building for product design instructors is recommended to lower teaching costs and improve the effective use of AI in pedagogy. Discipline-specific characteristics of product design, including tool-oriented thinking and interdisciplinary openness, further facilitate AI adoption in this field.

References

Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429.

Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). Extending the technology acceptance model (TAM) to predict university students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28(11), 15381–15413. https://doi.org/10.1007/s10639-023-11816-3

Alfadda, H. A., & Mahdi, H. S. (2021). Measuring students’ use of Zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50(4), 883–900.

Bergdahl, N., & Sjöberg, J. (2025). Attitudes, perceptions and AI self-efficacy in K-12 education. Computers and Education: Artificial Intelligence, 8, 100358.

Chounta, I.-A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 32(3), 725–755.

https://doi.org/10.1007/s40593-021-00243-5

Dekkers, R., Chang, C. M., & Kreutzfeldt, J. (2013). The interface between “product design and engineering” and manufacturing: A review of the literature and empirical evidence. International Journal of Production Economics, 144(1), 316–333.

Future unknown. (2023). Future unknown international education forum. Central Academy of Fine Arts. https://design.cafa.edu.cn

He, C., & Sun, B. (2021). Application of artificial intelligence technology in computer-aided art teaching. Computer-Aided Design and Applications, 18(S4), 118–129.

Huang, K. (2023, January 16). Alarmed by AI chatbots, universities start revamping how they teach. The New York Times. https://www.nytimes.com

Kemp, A., Palmer, E., & Strelan, P. (2019). A taxonomy of factors affecting attitudes towards educational technologies for use with technology acceptance models. British Journal of Educational Technology, 50(5), 2394–2413. https://doi.org/10.1111/bjet.12833

Kong, F. (2020). Application of artificial intelligence in modern art teaching. International Journal of Emerging Technologies in Learning, 15(13), 238–251.

Lee, Y. J., Davis, R. O., & Ryu, J. (2024). Korean in-service teachers’ perceptions of implementing artificial intelligence (AI) education for teaching in schools and their AI teacher training programs. International Journal of Information and Education Technology, 14(2), 214–219.

López-Regalado, O., Núñez-Rojas, N., López-Gil, O. R., Lloclla-Gonzáles, H., & Sánchez-Rodríguez, J. (2024). Artificial intelligence in university education: A systematic review. [Journal title, volume(issue).

Ma, S., & Lei, L. (2024). The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching. Asia Pacific Journal of Education, 44(1), 94–111.

Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2023). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28(7), 8445–8501. https://doi.org/10.1007/s10639-022-11491-w

Park, S. (2021). AI education perception of pre-service teachers according to AI learning experience, interest in AI education, and major. Journal of the Korean Association of Information Education, 25(1), 103–111.

Rafikasari, E. F., & Iriawan, N. (2021). Estimation of technology acceptance model (TAM) on the adoption of technology in the learning process using structural equation modeling (SEM) with Bayesian approach. In 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) (Vol. 1, pp. 86–91). IEEE.

Sánchez-Prieto, J. C., Cruz-Benito, J., Therón, R., & García-Peñalvo, F. J. (2019). How to measure teachers’ acceptance of AI-driven assessment in eLearning: A TAM-based proposal. In Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 181–186). ACM.

Sánchez-Prieto, J. C., Cruz-Benito, J., Therón Sánchez, R., & García-Peñalvo, F. J. (2020). Assessed by machines: Development of a TAM-based tool to measure AI-based assessment acceptance among students. International Journal of Interactive Multimedia and Artificial Intelligence, 6(4), 80–86.

https://doi.org/10.9781/ijimai.2020.11.009

UNESCO. (2019). Working towards an international community on AI in education. https://www.unesco.org/en/articles/working-towards-international-community-ai-education

Vartiainen, H., & Tedre, M. (2023). Using artificial intelligence in craft education: Crafting with text-to-image generative models. Digital Creativity, 34(1), 1–21.

Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212–227. https://doi.org/10.1111/jpim.12523

Vincent-Lancrin, S., & van der Vlies, R. (2020). Trustworthy artificial intelligence (AI) in education: Promises and challenges (OECD Education Working Papers No. 218). OECD Publishing. https://doi.org/10.1787/a6c90fa9-en

Vlasova, E. Z., Avksentieva, E. Y., Goncharova, S. V., & Aksyutin, P. A. (2019). Artificial intelligence: The space for the new possibilities to train teachers. Espacios, 40(9), 17.

Wang, T., & Wu, D. (2024). Computer-aided traditional art design based on AI and human-computer interaction. Computer-Aided Design and Applications, 21, 59–73.

Wu, S. (2022). Analysis and evaluation of the relationship between teaching pressure and self-efficacy of college teachers based on artificial neural network. Scientific Programming, 2022, 3755257.

Yang, C., Huan, S., & Yang, Y. (2020). A practical teaching mode for colleges supported by artificial intelligence. International Journal of Emerging Technologies in Learning, 15(17), 195–206.

Zheng, C., Wang, Z., Qin, X., Eynard, B., Hehenberger, P., Li, J., … Zhang, Y. (2021). Integrated design for product–service systems: A focus on multidisciplinary interface. International Journal of Production Research, 59(19), 5884–5902