AI pervades various facets of society, including education. In the open education domain, the notion of open AI, distinct from the entity OpenAI, is attracting attention. However, the precise connotations of "open" in conjunction with "AI" remain subject to diverse interpretations, reflecting conceptual tensions and longstanding differences around concepts of “open” in respective domains.
In a blog post last year, prominent edtech expert David Wiley wrote, “when people talk about whether or not generative AI should be “open,” they could be talking about whether the foundation models should be open, whether the modified model weights that result from fine-tuning should be open, and/or whether the prompts (which includes templates, embeddings, etc.) should be open” (Wiley, 2023). In a similar direction, focusing on licensing for different aspects of AI technology, the Open Source Initiative are currently developing an “open-source AI” definition (Open Source Initiative, 2024). On the other hand, in broader literature, definitions of open education and open technology encompass a wide range of concepts, where “open” could mean: “ethical” (Holmes et al., 2022), “inclusive” and “innovative” (Bozkurt, 2023), “co-created” and “learner-driven” (Walberg and Thomas, 1972), “non-proprietary” (Berners-Lee, 2023), “decentralised” (Crowston and Howison, 2005), “accessible without barriers” (Knox, 2013), “available to join”, “shared”, “not tightly controlled” (Weller, 2020), “available in the public domain or under an open license” (UNESCO, 2022), “interpretable” and “visible” (Conati, Porayska-Pomsta, and Mavrikis, 2018), and many others.
This indicates that “open AI” and “open-source AI” are overlapping but not identical concepts. “Open-source AI” seems to be about the tangible aspects of systems, whereas “open AI” is broader and potentially includes context of how systems can be used, who can use them, and what for. Rather than trying to resolve these debates into a single taxonomy/typology, we propose a meronomic, holistic account of openness in AI education which explores the relationship between definitions with respect to part-whole relationships. This will facilitate diverse contributions and critical discussion.
In this panel session, participants will have the opportunity to engage in the panel discussion and ask questions regarding the dimensions of openness of AI in education.
The agenda includes:
- 10 minutes: introductions, pre-recorded 1-slide lightning presentations from experts, panel reactions
- 10 minutes: panel interaction and debate
- 15 minutes: Q&A with panel members based on thoughts submitted by the audience (with backup questions prepared by the moderators).
- 5 minutes: closing discussion and synthesis.
Experts who agreed to contribute so far include David Wiley, Anne-Marie Scott, Aras Bozkurt, Chrissi Nerantzi and Leo Havemann. Some of them might join virtually. They will be provided with prompts ahead of time for their initial statements. Robert Farrow, an experienced moderator, will chair the panel discussion. During the discussion, delegates will be able to contribute reflections and questions through a back channel and these will be integrated into the discussion.
Included in
[Session 11A]: Artificial IntelligenceReferencesBerners-Lee, T. (2023). Frequently asked questions by the Press – Tim BL (w3.org). Available at https://www.w3.org/People/Berners-Lee/FAQ.html (Accessed: 2 May 2024) Bozkurt, A. (2023). Generative AI, Synthetic Contents, Open Educational Resources (OER), and Open Educational Practices (OEP): A New Front in the Openness Landscape. Open Praxis, 15(3), pp. 178–184. DOI: https://doi.org/10.55982/openpraxis.15.3.579 Conati, C., Porayska-Pomsta, K. and Mavrikis, M., 2018. AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling. arXiv preprint: https://arxiv.org/abs/1807.00154 Crowston, K. and Howison, J. (2005). The social structure of Free and Open Source software development. First Monday, Volume 10, Number 2 - 7 February 2005 Available at: https://firstmonday.org/ojs/index.php/fm/article/download/1207/1127 (Accessed: 2 May 2024) Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S.B., Santos, O.C., Rodrigo, M.T., Cukurova, M., Bittencourt, I.I. and Koedinger, K.R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, pp.1-23. DOI: https://doi.org/10.1007/s40593-021-00239-1 Knox, J. (2013). Five critiques of the open educational resources movement. Teaching in Higher Education, 18(8), pp. 821–832. DOI: https://doi.org/10.1080/13562517.2013.774354 Open Source Initiative (2024). The Open Source AI Definition – draft v. 0.0.8. Available at: https://opensource.org/deepdive/drafts/the-open-source-ai-definition-draft-v-0-0-8 (Accessed: 2 May 2024) UNESCO (2022). Understanding Open Science. UNESDOC Digital Library. DOI: https://doi.org/10.54677/UTCD9302 Walberg, H.J. and Thomas, S.C. (1972). Open education: An operational definition and validation in Great Britain and United States. American Educational Research Journal, 9(2), pp.197-208. DOI: https://doi.org/10.3102/00028312009002197 Weller, M. (2020). 25 years of ed tech. Athabasca University Press. DOI: https://doi.org/10.15215/aupress/9781771993050.01 Wiley, D. (2023). An analogy for understanding what it means for generative AI to be “Open”. Open Content. https://opencontent.org/blog/archives/7289
Author KeywordsOpen AI, Open-source AI, Artificial Intelligence, Open data, Open-source technical platforms
