How do we respond to generative AI in education? Open educational practices give us a framework for an ongoing process

Abstract

With the release of ChatGPT in November 2022, the field of higher education rapidly became aware that generative AI can complete or assist in many of the kinds of tasks traditionally used for assessment. This has come as a shock, on the heels of the shock of the pandemic. How should assessment practices change? Should we teach about generative AI or use it pedagogically? If so, how? Here, we propose that a set of open educational practices, inspired by both the Open Educational Resources (OER) movement and digital collaboration practices popularized in the pandemic, can help educators cope and perhaps thrive in an era of rapidly evolving AI. These practices include turning toward online communities that cross institutional and disciplinary boundaries. Social media, listservs, groups, and public annotation can be spaces for educators to share early, rough ideas and practices and reflect on these as we explore emergent responses to AI. These communities can facilitate crowdsourced curation of articles and learning materials. Licensing such resources for reuse and adaptation allows us to build on what others have done and update resources. Collaborating with students allows emergent, student-centered, and student-guided approaches as we learn together about AI and contribute to societal discussions about its future. We suggest approaching all these modes of response to AI as provisional and subject to reflection and revision with respect to core values and educational philosophies. In this way, we can be quicker and more agile even as the technology continues to change.

We give examples of these practices from the Spring of 2023 and call for recognition of their value and for material support for them going forward. These open practices can help us collaborate across institutions, countries, and established power dynamics to enable a richer, more justly distributed emerging response to AI.

https://doi.org/10.37074/jalt.2023.6.1.34
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