TLC series: Use of generative AI to reinforce educational processes

The work of monitoring and analysing educational trends and innovation carried out by the eLearning Innovation Center (eLinC) focuses on internal and external best practices to contribute to the development of the educational model at the Universitat Oberta de Catalunya (UOC). José López Ruiz and Desirée Gómez Cardosa, members of the Teaching and Learning Analysis team, have carried out an analysis of centres responsible for promoting educational innovation in universities. Each article in the TLC series examines an aspect linked to the work undertaken at these centres. In this post, the authors look at the measures the centres have taken to integrate generative AI into teaching and learning processes.

The rise of generative AI in universities

Having overcome the first critical stage, where institutions urgently issued all sorts of declarations and statements of principle intended to counteract the vulnerabilities created by the inappropriate use of generative AI (intellectual property, academic integrity, privacy, cognitive offloading, etc.), universities have begun a process of fostering AI literacy, while trying to further consensus on ‘where’ and ‘how’ to establish limits when exploiting its potential in educational practice. While AI’s capabilities are constantly expanding, universities are trying to move forward and have clear, up-to-date plans and recommendations for its use in assessment, teaching practice, research, course and programme design, etc. As some authors point out (Ward et al., 2024), academia simply needs to start integrating generative AI into teaching and learning (among other areas), even if we don’t have all the answers yet.

Now that this understandable initial commotion and panic in response to the shock waves that generative AI could cause in education has subsided, we need to adopt a more realistic approach, assessing the possibilities of AI as it develops and determining which tools applied to educational practice may be reaching their “plateau of productivity” on the Gartner Hype Cycle (Koh & Doroudi, 2023). This cycle describes the typical progression of an emerging technology from user and media overenthusiasm through a period of disillusionment to an eventual understanding of the technology’s relevance and role in a market or domain (Lynden & Fenn, 2003). Here, the authors analyse a series of cases in the light of the Gartner cycle, following the transition of AI from inflated expectations to more realistic scenarios. Educational institutions are entering the stage of acceptance and coexistence, where the new technology is coming to play a major role and feeding through to different aspects of educational processes. In line with their universities’ models and educational strategies, the staff of the centres, working with teaching staff (and students), are hastening to develop plans and priorities to prepare the institutions to take advantage of all the opportunities AI offers to redefine teaching and learning processes.

In this context, where AI is gradually gaining ground in students’ learning and academic experience, institutions are trying to achieve a delicate balance between ensuring that they do not miss out on the potential for AI to raise educational standards and developing specific measures to guarantee its ethical use, and manage the change and the drawbacks. For example, they must also take the necessary precautions to ensure learning remains meaningful, certification is reliable and the image of the university is not damaged by hasty or inappropriate integration of AI. Institutions are becoming aware that, in order to take advantage of all of AI’s possibilities, people need to be sure that it is being developed and used in a way we can trust (Gillespie et al., 2023).

The role of TLCs: incorporating AI in the technology and the pedagogy

Since ChatGPT appeared in November 2022, centres for innovation in teaching and learning have, understandably, been key agents in transforming the academic leadership’s missions into specific programmes and measures for implementing AI adoption strategies in educational models. TLCs, together with other stakeholders, have created resources, spaces for collaboration, research and all kinds of activities and services, reflecting one or more of the categories of centre in the HITS classification. They have helped to make teachers and students aware of how AI is beginning to change the course of learning processes. The work of raising awareness and developing literacy to maximize the educational benefit of these tools involves not only scenarios where they can be used on a one-off or generalized basis, as our eLinC colleagues Xavier Mas and Guillem Garcia (2023) explain, it also requires identifying possible or even desirable scenarios for the development of AI and the educational ecosystem.

Measures to integrate generative AI in HEIs

In their analysis of centres (López & Gómez, 2024), eLinC researchers monitored and examined a number of cases, grouped under the following headings to make them easier to understand and study.

●     Integration in the classroom

In order to further the effective use of AI in teaching and learning processes, centres have made repositories or knowledge spaces available to institutions, with content, strategies, best practices, tools, and other resources.

Monash University has created a set of resources with example scenarios, AI-assisted creative approaches, study strategies and tools for teaching or helping students learn using AI. Teaching staff have a separate section to learn about the role of AI technologies in assessment. The ITaLI at the University of Queensland has created a guide and resources for educational practice with AI (teaching, learning and assessment).

The CILT at the University of Cape Town has also published a guide on the use of AI for teaching and learning, aimed at both teaching staff and students. The UOC’s eLearning Innovation Center (eLinC), has provided the teaching community with a wide range infographics and guides on the use of generative AI and strategies for assessment, teaching methodologies and activities to assess and learn with this technology, alongside other support material to illustrate the possibilities.

●     AI literacy

The more structured measures for digital literacy include all kinds of training, workshops, seminars, talks, etc., to ensure university communities (teaching staff, students and administration) understand how AI functions for teaching and learning.

Some of them take the form of online and face-to-face workshops. The AI Literacy for All. A Toolkits Series, produced by the TALIC at the University of Hong Kong, offers teachers essential AI skills for their work. The University of Toronto has organized sessions and workshops at its CTSI, and institutions such as the University of the Andes have created series of workshops on the educational uses of GenAI. The University of Sydney has produced a website with students, where they compile ways to use generative AI productively and responsibly as part of their learning journey at the university.

●     Ethics, accessibility, privacy and security

Many institutions and centres show great interest in developing specific measures to support the ethical and responsible development of AI (academic integration, equity, access and individual privacy, etc.).

The CILT at the University of Cape Town, for example, has produced a guide offering practical strategies, approaches and recommended tools for safeguarding academic integrity.

• Expert panels

Numerous centres have created meeting points for experts who share their opinions, findings and expertise on generative AI in different kinds of sessions with heterogeneous audiences from their institutions.

A case in point is Les mardis de l’IA (AI Tuesdays) at the CIPEN at the Université Gustave Eiffel in Paris, where specialists analyse and evaluate the educational impact of AI, in a range of different settings.

Conclusions

As we have seen, the activities organized by the staff of teaching and learning centres help to gradually integrate generative artificial intelligence in those aspects of the educational process where it can add value. These initiatives take the form of specific measures such as helping to establish guidelines based on critical thinking about the use of generative AI tools, reinforcing AI literacy to ensure that users fully understand the uses and risks of these tools, thus generating a framework of trust, and offering spaces for experimentation and for implementation in the classroom. New technologies can bring many challenges but, as Aithal and Aithal (2023) say, the future of higher education also offers numerous opportunities such as global cooperation, adaptive assessment techniques and individualized learning experiences.

Achieving this objective successfully will need time, since faculty, administrative staff and students, as well as other parties at the university who are responsible for educational quality, will have to learn about generative AI and how to use it effectively. It presents great opportunities in education, but at the same time it raises important practical and ethical concerns (Sharples, 2023). Generative AI is a tool that not only directly affects students’ learning, it also indirectly influences how and what they learn (Koh & Doroudi, 2023). As with other educational technologies, it requires centres to try to get the most out of it to reinforce the educational model.

References

Aithal, P. S., & Aithal, S. (2023). Application of ChatGPT in Higher Education and Research–A Futuristic Analysis. International Journal of Applied Engineering and Management Letters (IJAEML), 7(3), 168-194. Available at http://dx.doi.org/10.2139/ssrn.4674364

Gillespie, N., Lockey, S., Curtis, C., Pool, J., Akbari, A. (2023). Trust in Artificial Intelligence: A Global Study. The University of Queensland and KPMG Australia. https://doi.org/10.14264/00d3c94. Available at https://policy-futures.centre.uq.edu.au/files/16650/Trust%20in%20AI%20Global%20Report_2023_UQ.pdf

Koh, E., Doroudi, S. (2023). Learning, teaching, and assessment with generative artificial intelligence: towards a plateau of productivity. Learning: Research and Practice, 9(2), 109–116. https://doi.org/10.1080/23735082.2023.2264086

Linden, A., Fenn, J. (2003). Understanding Gartner’s hype cycles. Strategic Analysis Report. R-20-1971. Gartner, Inc., 88, 1423. Available at http://ask-force.org/web/Discourse/Linden-HypeCycle-2003.pdf

López Ruiz, J., Gómez Cardosa, D. (2024). Centres d’innovació en docència i aprenentatge. Available at http://hdl.handle.net/10609/150476  

Sharples, M. (2023). Towards social generative AI for education: theory, practices and ethics. Learning: Research and Practice, 9 (2), 159–167. https://doi.org/10.1080/23735082.2023.2261131

Ward, D., Loshbaugh, H.G., Gibbs, A.L., Henkel, T., Siering, G., Williamson, J. and Kayser, M. (2024). How Universities Can Move Forward With Generative AI in Teaching and Learning. Change: The Magazine of Higher Learning, 56 (1), 47–54. https://doi.org/10.1080/00091383.2024.2297635