AI communities of practice can help us face its challenges head on

When evaluating the impact of AI in university classrooms, we must first mention ChatGPT, the pioneering model that disrupted higher education, among countless other industries. As users, we noticed a shift away from the familiar: ChatGPT generates text in natural language as opposed to early chatbot technologies or traditional search engines, which offers a new experience. As does the co-creation process between human users and AI.

Since ChatGPT’s arrival at the end of 2022, similar tools have been launched. It is worth noting that these innovations did not come to overthrow traditional search engines but to complement and enhance them, evolving as new species in the media ecosystem.

GenAI’s challenges and opportunities for universities

The popularisation of ChatGPT and other GenAI models presents a significant challenge to universities. Rapid AI-driven change means we can’t wait for top-down guidelines that will inevitably take time to arrive. Even small changes to our daily practices can have a significant impact on the educational experience. These include adjustments to teaching strategies, the way we organise our classrooms and how we engage our students. 

Integrating AI into HE through communities of practice

Creating AI-focused communities of practice can facilitate knowledge exchange, paving the way for an enhanced understanding of how to better integrate AI into the higher education experience. Members would be required to participate in activities that lead to informal learning based on the exchange of experiences, problem-solving activities and joint reflection.

Since the widespread use of AI in higher education affects the entire community and impacts every aspect of social life, AI communities of practice need to be diverse, including not only teachers but educational technology specialists, researchers, professionals and students. Including collaborators from a range of industries and backgrounds is essential for addressing and understanding it more comprehensively.

How can we facilitate these communities of practice?

Here are concrete proposals for promoting the use and integration of AI through communities of practice:

Organise meetings for ideas exchange: communities of practice should meet regularly, allowing time for members to reflect and develop ideas between sessions. These meetings should take place in multifunctional rooms that encourage both formal discussions and informal networking.

At Universidad Austral, we run workshops on AI literacy in which participants explore specific tools and strategies. We then open the floor for dialogue. A mix of structured presentation and open exchange is effective in this context.

Organise working sessions on applications and programmes: offer time for participants to experiment with tools either individually or in teams. They can use it to test features in real or simulated university settings, with an emphasis on creating pilot projects to demonstrate practical applications. For instance, at Austral, we conducted a pilot project in 2023 and 2024 with students as part of a course during which they wrote a critical analysis assisted by AI. This project provided a valuable learning experience in iterative processes, because it required them to progressively refine their prompts to achieve the desired results, deepening their understanding of how to interact with AI effectively.

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Develop online environments for hands-on experimentation with AI tools: widely used learning management system, Moodle, can facilitate collaboration and experimentation. It enables group work and collaboration through features such as forums, wikis and peer review systems, allowing participants to exchange insights, share resources, and collaborate on AI-related projects or experiments. We can use it to work together to explore AI concepts, develop skills and apply our knowledge.

Create collaborative resource repositories: tutorials and guides can provide step-by-step instructions on implementing AI tools and techniques, case studies can offer real-world examples of AI implementations across various fields and research papers can include key academic articles on the latest AI developments, offering a theoretical foundation for understanding new concepts. Additionally, a comprehensive database of tools and platforms would give participants access to resources where they can practise and apply their AI skills. Class recordings would also be valuable, allowing participants to revisit and review important topics covered during live sessions.

Establish cross-mentoring opportunities between members with different skill levels: this can be achieved by creating a mentoring program where mentors and mentees are paired based on complementary skills and experience levels. To ensure an effective match, a skills assessment could be conducted to identify areas of expertise and learning needs. Regular check-ins and feedback mechanisms should be implemented to monitor progress and adjust pairings if necessary.

Promote open debates on the ethical implications of AI to raise awareness and guide responsible practices: regular discussion forums or workshops facilitated by experts can provide a platform for open debates on key ethical concerns such as data privacy, bias and the impact of AI on employment. Summaries of these discussions could be distributed through internal newsletters.

A university that aspires to produce employable graduates must create support networks to reduce the sense of isolation and confusion that rapid socio-technical change brings. In the case of AI, facilitating communities of practice that serve as spaces for experimentation and inclusion is a valuable strategy and its members can become agents of change.

Mariángeles Castro-Sánchez is a professor and the director of studies and degree programmes at the Institute of Family Sciences at Universidad Austral, Argentina.

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