Teaching Innovation and Competency Development in the Age of Generative AI: A Case Study of the Course “Online Communication and Public Opinion Supervision”

Zhuo Wang

doi:10.62177/jetp.v2i4.847

Authors

Zhuo Wang Guangzhou University of Software

DOI:

https://doi.org/10.62177/jetp.v2i4.847

Keywords:

Generative AI, AIGC, Teaching Innovation, Pedagogy, Public Opinion Supervision, Online Communication, AI literacy, Competency Development

Abstract

Generative Artificial Intelligence (AIGC) has emerged as a disruptive force, fundamentally altering digital communication and information ecosystems. This development poses significant challenges to public opinion supervision, as the lines between authentic and synthetic content are increasingly blurred. Traditional communication curricula are often unprepared to address the threats of deepfakes, automated manipulation, and authenticity crises. This paper presents a pedagogical case study of a redesigned undergraduate course, “Online Communication and Public Opinion Supervision,” at a teaching-focused university. To address AIGC-related challenges, a series of teaching innovations were implemented. These included updated curriculum modules on AIGC, adversarial "red team vs. blue team" simulations, AI-augmented project-based assignments, and oral crisis response drills. The aim was to cultivate four core competencies: technical understanding, critical discrimination, ethical judgment, and human-AI collaboration. Empirical outcomes from the course implementation were evaluated. Significant improvements in students' analytical performance, ethical reasoning, and engagement were observed. This case study provides insights into effective pedagogical strategies. It offers a model for adapting communication and media education to prepare students for the complexities of the generative AI era.

Downloads

Download data is not yet available.

References

Hagos, D. H., Battle, R., & Rawat, D. B. (2024). Recent advances in generative ai and large language models: Current status, challenges, and perspectives. IEEE Transactions on Artificial Intelligence.

Brennan, K. M. (2019). “Believe me”: Authenticity, federal social media use, and the problematized record in the American digital public sphere. Journal of Critical Library and Information Studies, 2(2).

Ling, J., Huang, N., Ma, Z., et al. (2025). Investigating student behaviors in multimodal AIGC-enhanced design education: An HCI-based innovative learning model. In Proceedings of the International Conference on Human-Computer Interaction (pp. 240 - 255). Cham: Springer Nature Switzerland.

Van Dyk, S. (2022). Post-truth, the future of democracy and the public sphere. Theory, Culture & Society, 39(4), 37 - 50.

Maddocks, S. (2018). From non-consensual pornography to image-based sexual abuse: Charting the course of a problem with many names. Australian Feminist Studies, 33(97), 345 - 361.

Olakoyenikan, O., & Olanipekun, S. O. Leveraging artificial intelligence to combat disinformation and deepfakes in broadcast journalism and global media communications. [Journal Name]. (Note: The original reference lacks key information such as publication year, journal volume, issue and page numbers; it is recommended to supplement it according to the official publication source.)

Carpenter, P. (2024). FAIK: A practical guide to living in a world of deepfakes, disinformation, and AI-generated deceptions. John Wiley & Sons.

Busuioc, M. (2021). Accountable artificial intelligence: Holding algorithms to account. Public Administration Review, 81(5), 825 - 836.

Huang, S. H., & Su, T. C. (2024). Learning effectiveness and reflections on AI literacy in junior high school students with game-based learning and problem-based learning. In Proceedings of the 32nd International Conference on Computers in Education. Quezon City, Philippines: Ateneo de Manila University.

Schrepel, T. (2025). Generative AI in legal education: A two-year experiment with ChatGPT. Legal Education, [Volume(Issue)], [Pages]. (Note: The original reference has a redundant title repetition and lacks specific volume, issue and page information; it needs to be supplemented according to the official published version.)

Clear, T., Cajander, Å., Clear, A., et al. (2025). AI integration in the IT professional workplace: A scoping review and interview study with implications for education and professional competencies. In 2024 Working Group Reports on Innovation and Technology in Computer Science Education (pp. 34 - 67). ACM.

Bernold, L. E., Spurlin, J. E., & Anson, C. M. (2007). Understanding our students: A longitudinal study of success and failure in engineering with implications for increased retention. Journal of Engineering Education, 96(3), 263 - 274.

Hu, Z. Y. (2024). AIGC related context: A new communication culture for human. Journal of Literature and Art Studies, 14(10), 921 - 931.