University

Can universities keep up with AI?

19/11/2025

Oliver Vettori and Johanna Warm explore the challenges and potential pathways for integrating AI skills into higher education.

Demand for artificial intelligence (AI) skills is skyrocketing across the labor market, putting universities under pressure to adapt their curricula. AI literacy refers to the ability to understand and categorize AI systems and to use them competently and responsibly. Yet, when it comes to integrating AI into curricula, universities are moving much more slowly than the technology itself. Why is that?

Oliver Vettori

Oliver Vettori, Department Program Management & Teaching and Learning Affairs © Harald Krischanz

AI challenges universities

According to Oliver Vettori and Johanna Warm from the Department of Program Management & Teaching and Learning Affairs, one major hurdle is the diverse starting point and attitudes of both faculty and students. Students arrive with very different levels of prior knowledge, shaped by factors such as age, disciplinary background, gender, and cultural or geographic context. At the same time, institutional constraints and limited resources often make it difficult for teachers to build up AI related skills.

Another obstacle is the slow “codification” of AI skills in curricula. “AI technologies evolve at an extraordinary pace, while universities need time to formalize AI skills and embed it within existing programs,” Vettori explains.

Johanna Warm

Johanna Warm, Department Program Management & Teaching and Learning Affairs © Harald Krischanz

Quick and easy approaches to strengthen AI skills

Because structural chsnges take time, Vettori and Warm argue that quick and easy approaches are essential for developing AI skills. Peer learning is one option. “Collaborative learning formats allow students to bring in their existing experience with AI and learn from one another,” says Warm. Another approach is Co-creation: students and instructors jointly work on learning materials and tasks, drawing on their respective strengths – students’ digital familiarity and instructors’ disciplinary expertise and critical evaluation. This can help flatten hierarchies, boost motivation, improve personal responsibility, and give instructors clearer insight into where additional support is needed.

Micro-designs offer another way to promote AI competencies. Vettori and Warm describe these as small, flexible learning units such as brief workshops, reflective tasks on AI use, self-learning modules, or practical mini-formats like hackathons. “These formats are easy to embed into existing courses, encourage active engagement with AI, and support skill development through hands-on experiential learning.”

However, such initiatives depend heavily on the commitment of individual instructors and should therefore be seen as building blocks for more comprehensive and systematic long-term approach to AI literacy.

Reference

Oliver Vettori, Johanna Warm (2025). The race for AI skills as an obstacle course: Institutional challenges and low threshold suggestions. In: Project Leadership and Society, Volume 6, 2025, 100183. https://doi.org/10.1016/j.plas.2025.100183

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