Open Educational Practices for AI in Education

Dear Colleagues,

Artificial Intelligence entered higher education—and the popular consciousness—emphatically in 2023. Several years after their emergence, generative AI has developed into large language models such as ChatGPT. They can be used to produce text that is relevant to a wide range of pedagogical and administrative tasks. Images (Midjourney, DALL-E, etc.) and videos (Synthesia, Deepbrain, etc.) can now also be easily produced from natural language. This emergence of AI technology into the popular imagination has fuelled speculation on its likely impact on education and training. Many can see (while others dispute) the potential for AIED to reduce the workload of educators and learners by automating repetitive tasks; improve the personalization and contextualization of educational delivery; provide “intelligent” tutoring systems (ITS); and realize the full promise of learning analytics. Concomitantly, there is intense debate about the status of traditional forms of assessment that are dependent on writing composition in a world where an AI can be instructed to write on demand (Holmes & Tuomi, 2022).

The UNESCO 2030 Sustainable Development Agenda calls for the development of sustainable approaches to OER production and use. This, in turn, requires greater efficiency and coordination across the relevant learning technology ecosystems (Otto and Keres, 2022). A human-centered, interdisciplinary approach to AIED is essential for balance and sustainability. There is the potential that this may be achieved by adopting a sociotechnical perspective and embracing open approaches such as transparency and explicability (Tlili & Burgos, 2022; Farrow, 2022). However, current practices in AI require huge amounts of data which are often sourced indiscriminately and without due attention to existing ethical or legal requirements (Heikkilä, 2023).

There is an essential and time-sensitive need to support AI-driven innovation while preserving the integrity, effective operation, and trust of traditional educational systems. For the past decade, the AIED revolutions have been driven by technological companies. This was seen most clearly during the COVID-19 pandemic when commercial organizations moved quickly to become core providers of online learning.  The pandemic acted as a catalyst for online working, training, and education, but also established profit motive and market capture as key priorities. On the demand side, there is huge interest in using AI to facilitate study; however, generative AI also threatens to disrupt core pedagogical practices (such as assessment). Areas where AIED and OEP overlap include: using open algorithms and open data to support smarter repositories and learning platforms; developing AI tools for search, discovery, reuse and sharing of OER; use of algorithms in open learning environments; new forms of pedagogy; ameliorating injustice in education; and regulatory/policy support for “open” AIED.

Many AI services are currently open access, but this is already changing as companies seek a return on investment, threatening a new form of digital divide. Even when AIED providers call themselves ‘open’ there is typically scant commitment to the principles of open practice: OpenAI, the creators of ChatGPT, are not committed to any form of open licensing or open practice. There are also many outstanding ethical questions regarding the consequences of algorithmic bias in AIED; privacy and security of data; and its impact on pedagogical practice and professional development. How should open practitioners respond to AIED in such a way as to ensure that open education makes full use of the potential of AIED, retains its core values, and ensures the future sustainability of open education?

In this Special Issue, we welcome both original research articles and reviews. Research areas may include (but are not limited to) the following topics:

identifying, imagining and realizing open forms of AIED Open AI literacies that combine the technological dimensions of AI with human dimensions (impact on people, ecosystems, sustainability);  inequality of access to AIED: addressing the new ‘digital divide’; strategies for using AI to facilitate working with OER (search & discovery, remix, evaluation, metadata, tracking implementation); explicable AIED (XAIED) and the role of transparency in AI pedagogical and educational technology; AIED as open educational practice; ameliorating algorithmic bias through open data AI, copyright and the commons; and AIED as the new horizon for ‘open washing’.

We look forward to receiving your contributions.

References

1. Holmes, W.; Tuomi, I. State of the art and practice in AI in education. Eur. J. Educ. 2022, 57, 542–570. https://doi.org/10.1111/ejed.12533.
2. Daniel, O.; Michael, K. Increasing Sustainability in Open Learning: Prospects of a Distributed Learning Ecosystem for Open Educational Resources. Front. Educ. 2022, 7, 866917. https://doi.org/10.3389/feduc.2022.866917.
3. Wei, T.; Yang, J. Radical Solutions for Education in a Crisis Context: COVID-19 As an Opportunity for Global Learning, Edited by Daniel Burgos, Ahmed Tlili and Anita Tabacco. EaC 2022, 26, 5.

Dr. Rob Farrow
Dr. Wayne Holmes
Guest Editors

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• open educational resources (OER)
• open educational practices (OEP)
• artificial intelligence in education (AIED)