This blog is a translation of a version in Dutch, slightly adapted to add information about the Dutch context.
Last week at the Dutch SURF Education Days (an annual event for innovators in secondary vocational and higher education in the Netherlands), I sat on a panel organised by Marc Dietzenbacher and Tijmen Leurs of the Dutch MBO Raad. The topic was “AI a boost for OER?”
In preparing for this panel, I built on an earlier blog on the impact of (Gen-)AI on OER (in Dutch), written by the Zone 42 collective (Jan-Bart de Vreede (Kennisnet), Ben Janssen (OpenEd Consult) and myself). At the annual OEGlobal conference in mid-October, there was a lot of focus on this topic, both in the programme and informal in between the sessions. Broadly speaking, the same lines of thought were followed there as in the blog. Much attention was paid to the ethical issues associated with EDI (Equality, Diversity, Inclusion) principles in education. With free access and rights to adaptation, OER are important tools in realising these principles. I therefore argue that, in addition to the “gain, convenience, enjoyment” angle of using (Gen-)AI with OER, one should also approach this from an EDI lens. You can do this, for instance, by critically considering all Gen-AI-delivered results through an EDI lens and, if necessary, making adjustments to them.
Closely related to this are the latest developments around ChatGPT custom. A nice development with a lot of potential, but it does require a plus licence on ChatGPT to use it (at $20 a month). Deploying this in education raises the inclusion question: may or are you willing to require students to buy a licence on this tool? In the Netherlands, it is by law that an institution must provide an alternative to learning materials used in education. But in addition, as with other digital tools, there is the requirement for a Data Processing Agreement with OpenAI, the provider of ChatGPT. In this agreement, issues like which personal data are stored from users, with whom are these personal data shared, and how long are these personal data stored after ending of the license are handled. This tool should therefore become part of the proposed agreement system, as expressed in the report Statement and report on the National Approach to digital and open educational resources.
Seven principles of Creative Commons
One of the issues surrounding OER and (Gen-)AI concerns copyright. In that context, Creative Commons has formulated seven principles for regulating generative AI models to protect the interests of creators, people building on the commons and the interests of society in the sustainability of the commons:
- It is important that people continue to have the ability to study and analyse existing works in order to create new ones. The law should continue to leave room for people to do so, including through the use of machines, while addressing societal concerns arising from the emergence of generative AI.
- All parties should work together to define ways for creators and rightsholders to express their preferences regarding AI training for their copyrighted works. In the context of an enforceable right, the ability to opt out from such uses must be considered the legislative ceiling, as opt-in and consent-based approaches would lock away large swaths of the commons due to the excessive length and scope of copyright protection, as well as the fact that most works are not actively managed in any way.
- In addition, all parties must also work together to address implications for other rights and interests (e.g. data protection, use of a person’s likeness or identity). This would likely involve interventions through frameworks other than copyright.
- Special attention must be paid to the use of traditional knowledge materials for training AI systems including ways for community stewards to provide or revoke authorisation.
- Any legal regime must ensure that the use of copyright protected works for training generative AI systems for noncommercial public interest purposes, including scientific research and education, are allowed.
- Ensure that generative AI results in broadly shared economic prosperity – the benefits derived by developers of AI models from access to the commons and copyrighted works should be broadly shared among all contributors to the commons.
- To counterbalance the current concentration of resources in the hands of a small number of companies these measures need to be flanked by public investment into public computational infrastructures that serve the needs of public interest users of this technology on a global scale. In addition there also needs to be public investment into training data sets that respect the principles outlined above and are stewarded as commons.
Especially principle 7, emphasising the importance of strong public infrastructures and a strong role for governments, I find important. This also fits well with the ongoing focus in the Netherlands on protecting public values in education. In his most recent blog the éminence grise of open distance learning, Canadian Tony Bates, puts it as follows (emphasis added by me):
Digital learning is not only a goal in itself but more importantly a necessity if higher educational institutions are to produce the kind of students needed in a fast-changing world, but that requires a hard look at the curricula we offer and our teaching methods to make sure they are fit for purpose, as well as adopting new technologies and infrastructures. We need to cut the rhetoric and the hype, and start to re-design thoughtfully and pragmatically our institutions and our teaching for this new emerging world. Digital learning is central to this.
However, we need to act with an understanding that this alone will not be enough. The biggest problem we face after climate change is the increasing divide between the very rich and the rest of us. Tinkering with access, digital learning, and re-design of teaching and institutions will not of itself address this great divide. Digital learning needs to go hand-in-hand with political and economic change if we are to avoid becoming slaves to the mega-rich.
Effect of (Gen-)AI on use of OER in education
The panel discussion at the Dutch SURF Education Days also raised the issue of how ultimately students can experience the impact of (Gen-)AI and OER. This issue can be formulated as:
What impact does AI have on Open Educational Practices and Open Pedagogy?
The terms Open Educational Practices and Open Pedagogy are used for those educational practices where OER or other forms of openness (such as open networks and open platforms) play an essential role (see here for definitions of both terms (in Dutch)).
Low-hanging fruit here is use of Gen-AI by students when creating open sources. Gen-AI could support students in this process by providing suggestions, improving the quality of writing, or generating first drafts of content.
Two great resources that can be used as inspiration to use AI in Open Educational Practices are the following:
101 Creative ideas to use AI in education, A crowdsourced collection (link).
From the preface of this open book published in June this year:
“This collection captures where we are at this moment in time with our collective thinking about potential alternative uses and applications of AI that could make a real difference and potentially create new learning, development and opportunities for our students and educators, for all of us. The collection is based on an open invitation to all educators and students to share ideas on how AI tools such as ChatGPT, DALL·E 2, and Midjourney, for example, could be used in inventive ways for learning, teaching and scholarship.
We are mindful of the importance of critical and ethical use of AI in education settings and more generally.”
The editors of this issue plan to publish two new editions next year, aimed at education professionals and students creating OER as part of their learning journey, respectively. Both editions are crowd-sourced. Ideas can be submitted until the end of this year:
- For Educational Professionals: 101 Creative Ideas: Designing AI-Powered Learning Resources
- For Students: 101 Creative Ideas: Creative Student Learning Outputs Using AI
Creative and critical engagement with AI in education (link)
This website of the metaLab (at) Harvard was recently launched. The purpose of this website is: (from the press release)
This project offers:
- A searchable collection of educator-designed assignments for integrating AI into syllabi, and doing so responsibly and critically
- Understandable AI concept descriptions designed to outline essential concepts and skills in a streamlined guide
- Recommendations for educators on how to begin their AI journey in the classroom
- An interactive tutorial on using large language models
- A resource list for further AI exploration, including related projects
Although this website has a broader scope than just collecting uses of AI in education, the assignments on this site in particular can provide inspiration for AI at Open Educational Practices and Open Pedagogy. As with the 101 Creative Ideas, crowd sourcing is used to build the collection of assignments.