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Six Essential Pedagogical Moves for Online Education in the AI Era

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This framework is a holistic, research-based guide for responding to challenges posed by generative AI, from program-level planning to design and teaching practices in online education.

Six moves are organized by pathways corresponding to three roles — program administrator, faculty course designer, and course instructor — to demonstrate possible strategies within each role's span of control. The most urgent strategies are italicized to offer ideas about prioritization. However, a key goal of the framework is to demonstrate the interconnectedness of these pathways and how strategic decisions at the program level might shape course design and then facilitation techniques.

Proactive strategies for sustaining meaningful student learning, valid assessment, and academic integrity are woven through the framework. Users might begin with any of the six moves and then consider other strategies within their role's pathway.

Instructions

Tap or click on a colored segment of the hexagon to explore how each theme applies across Program (Re)Design, Course (Re)Design, and Instructional Paths.

Switch to Static View using the button below to see the full framework.

Take the survey to choose your role, and discover informatation relevant to your current interests.

Program (Re)Design Path

Course (Re)Design Path

Facilitation Path

References

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  2. Artemova, I. (2025). A critical analysis of GAI Learning Research: From theory to implementation risks. Online Learning, 29(4), 390–417. https://doi.org/10.24059/olj.v29i4.4895
  3. Gallant, B. T., & Rettinger, D. A. (2025). The opposite of cheating: Teaching for integrity in the age of AI. University of Oklahoma Press.
  4. Bowen, J. A., & Watson, C. E. (2024). Teaching with AI: A practical guide to a new era of human learning. Johns Hopkins University Press.
  5. Cacho, R. (2024). Integrating generative AI in university teaching and learning: A model for balanced guidelines. Online Learning, 28(3), 58. https://doi.org/10.24059/olj.v28i3.4508
  6. Damiano, A. D., Lauría, E. J. M., Sarmiento, C., & Zhao, N. (2024). Early perceptions of teaching and learning using generative AI in higher education. Journal of Educational Technology Systems, 52(3), 346–375. https://doi.org/10.1177/00472395241233290
  7. Dello Stritto, M. E., Underhill G. R., & Aguiar, N. R. (2024). Online students’ perceptions of generative AI. Oregon State University Ecampus Research Unit. https://ecampus.oregonstate.edu/research/publications
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  10. Ifenthaler, D., Majumdar, R., Gorissen, P., Judge, M., Mishra, S., Raffaghelli, J., & Shimada, A. (2024). Artificial intelligence in education: Implications for policymakers, researchers, and practitioners. Technology, Knowledge and Learning, 29(4), 1693–1710. https://doi.org/10.1007/s10758-024-09747-0
  11. Lodge, J. M., Howard, S., & Bearman, M. (2023). Assessment reform for the age of artificial intelligence. Australian Government Tertiary Education Quality and Standards Agency.
  12. Murgatroyd, S. (2024). The future of higher education in an age of artificial intelligence. Ethics International Press Ltd.
  13. Ogunleye, B., Zakariyyah, K. I., Ajao, O., Olayinka, O., & Sharma, H. (2024). Higher education assessment practice in the era of generative AI tools. arXiv. https://doi.org/10.48550/arxiv.2404.01036
  14. Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press.
  15. Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61. https://doi.org/10.1016/j.cedpsych.2020.101860
  16. Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R., Cheng, N., Durmus, E., Hatfield-Dobbs, Z., Johnston, S. R., Kravec, S., Maxwell, T., McCandlish, S., Ndousse, K., Rausch, O., Schiefer, N., Yan, D., Zhang, M., & Perez, E. (2025). Towards understanding sycophancy in language models. arXiv. https://arxiv.org/abs/2310.13548
  17. Slade, J. J., Byers, S. M., Becker-Blease, K. A., & Gurung, R. A. R. (2025). Navigating the new frontier: Recommendations to address the crisis and potential of AI in the classroom. Teaching of Psychology, 52(3), 254–261. https://doi.org/10.1177/00986283241276098
  18. Swindell, A., Greeley, L., Farag, A., & Verdone, B. (2024). Against artificial education: Towards an ethical framework for generative artificial intelligence (AI) use in education. Online Learning, 28(2). https://doi.org/10.24059/olj.v28i2.4438
  19. Tzirides, A. O., Zapata, G., Kastania, N. P., Saini, A. K., Castro, V., Ismael, S. A., You, Y., Santos, T. A., Searsmith, D., O’Brien, C., Cope, B., & Kalantzis, M. (2024). Combining human and artificial intelligence for enhanced AI literacy in higher education. Computers and Education Open, 6. https://doi.org/10.1016/j.caeo.2024.100184
  20. Vallis, C., Wilson, S., Gozman, D., & Buchanan, J. (2024). Student perceptions of AI-generated avatars in teaching business ethics: We might not be impressed. Postdigital Science and Education, 6(2), 537–555. https://doi.org/10.1007/s42438-023-00407-7
  21. Vendrell, M., & Johnston, S. (2026). Scaffolding critical thinking with generative AI: Design principles for integrating large language models in higher education. Computers and Education: Artificial Intelligence, 10. https://doi.org/10.1016/j.caeai.2026.100572
  22. Wu, R., & Yu, Z. (2023). Do AI chatbots improve students’ learning outcomes? Evidence from a meta-analysis. British Journal of Educational Technology, 55(1), 10–33. https://doi.org/10.1111/bjet.13334
  23. Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(40). https://doi.org/10.1186/s41239-024-00468-z
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