Impact of AI-Generated Tools on Teaching Resources in Higher Education: The Mediating Role of Instructional Innovation Skills from the Perspective of University Teacher Preparation — A Literature Review and Future Research Directions

Impact of AI-Generated Tools on Teaching Resources in Higher Education: The Mediating Role of Instructional Innovation Skills from the Perspective of University Teacher Preparation — A Literature Review and Future Research Directions

Authors

  • Cai Jun City Graduate School, City University Malaysia, Petaling Jaya, Malaysia; Zhengzhou Health College, Zhengzhou, China
  • Qhamariah Binti Samu City Graduate School, City University Malaysia, Petaling Jaya, Malaysia

DOI:

https://doi.org/10.63808/acde.v2i3.480

Keywords:

Generative AI tools, Higher education, Teaching resources, Teaching innovation ability, Teacher preparation, Literature review

Abstract

The rapid popularization of generative artificial intelligence (AI) tools is profoundly reshaping the form and supply model of teaching resources in higher education. The instructional innovation skills and preparedness of university teachers are proposed as potentially critical mediating variables for the educational effectiveness of AI technology. From the perspective of teacher preparation, this study conducts an integrative narrative review of core literature on AI application in higher education teaching in recent years, and presents an integrated analysis of the impact paths of AI-generated tools on teaching resources, the potential mediating role of teachers’ instructional innovation skills, and practical challenges. The study shows that AI-generated tools may affect the higher education teaching resource system from three dimensions: generation and expansion of resources, personalized adaptation, and evaluation optimization. Teachers’ instructional innovation skills are proposed to play a key mediating role between AI tools and teaching resource quality through three potential paths: AI literacy improvement, teaching model reconstruction, and evaluation system innovation. Current research still has limitations such as insufficient authenticity of empirical scenarios, inadequate in-depth exploration of mediating mechanisms, and a lack of cross-cultural and interdisciplinary research. On this basis, this study proposes future research directions from five aspects: mechanism testing, context expansion, methodological optimization, teacher development, and ethical governance, to provide theoretical references for the construction of university teaching resources and the development of teacher capabilities.

References

[1] Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-023-00436-z

[2] Chu, H. C., Hwang, G. H., Tu, Y. F., & Yang, K. H. (2022). Roles and research trends of artificial intelligence in higher education: A systematic review of the top 50 most-cited articles. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.7526

[3] Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148

[4] Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8

[5] Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224

[6] Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., ... Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274[7] Liang J., Stephens, J. M., & Brown, G. T. L (2025) A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment. Frontiers in Education. 10:1522841. https://doi.org/10.3389/feduc.2025.1522841

[8] Liu, Q., Hu, A., Gladman, T., & Gallagher, S. (2025). Eight months into reality: A scoping review of the application of ChatGPT in higher education teaching and learning. Innovative Higher Education, 50, 1677–1700. https://doi.org/10.1007/s10755-025-09790-4

[9] Luo, J., Zheng, C., Yin, J., & Teo, H. H. (2025). Design and assessment of AI-based learning tools in higher education: A systematic review. International Journal of Educational Technology in Higher Education, 22(1), 42. https://doi.org/10.1186/s41239-025-00540-2

[10] Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. https://doi.org/10.1016/j.caeai.2021.100020

[11] Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0

[12] Zhang, Y., & Tang, Q. (2025). Integrating AI-generated content tools in higher education: A comparative analysis of interdisciplinary learning outcomes. Scientific Reports, 15, 25802. https://doi.org/10.1038/s41598-025-10941-y

Downloads

Published

2026-08-28
Loading...