In a remarkably short time, generative AI has surged into the university world — but what does this really mean for higher education? New research from the Department of Communication and Learning in Science at Chalmers University of Technology explores how this development might unfold through scenario-based analysis.
The study “Navigating generative AI in higher education – six near future scenarios” explores how generative AI may affect university teaching over the next two years, using six scenarios based on educators’ own predictions. The study employs a method called informed educational fiction — using storytelling to grasp complex future issues. Researchers conducted both individual and group interviews with students and then invited university teachers, postdocs and educational developers to workshops where they used these insights to create possible future scenarios.
The result: fictional stories grounded in empirical data and theory.
Help to reflect
These narratives illustrate how AI could influence everything from student learning to teachers’ work situations and the institutional support needed to handle generative AI.
“We heard stories of both opportunities and risks in how teaching, the teacher’s role, and the entire campus environment may change in the next few years,” says Tiina Leino Lindell, postdoctoral researcher, who co-authored the study together with Christian Stöhr, Professor.
The researchers emphasize that the scenarios are not forecasts, but tools to help universities and decision-makers reflect on the kind of future they want to move toward — and what they might want to avoid. The study shows that AI in higher education is not just about new technology, but about deeper questions of purpose, roles, and responsibility.
“Our findings suggest that if universities manage this transition well, AI could become a driver of renewal. But without coordination and support, the development risks leading to confusion, conflict — and paralysis,” says Christian Stöhr.
We heard stories of both opportunities and risks in how teaching, the teacher’s role, and the entire campus environment may change in the next few years.
The six near-future scenarios in brief
"Conflicting learning goals" explores how students use AI, the conflicts that may arise, and how these could be managed.
"Excessive Self-Direction among Students" considers the growing independence of students using AI - but how much freedom is actually beneficial?
"Unpredictable Development of GenAI" questions how education can be planned when technological progress is rapid and its direction uncertain.
"Contradictory and Counterproductive Regulations" discusses how differing attitudes toward AI among students, teachers, and institutions may create inconsistent or conflicting rules.
"Changing educator roles and interactions with students" explores how AI might reduce teacher-student interaction and how new forms of collaboration could emerge without increasing workload.
"Forging and AI-ready campus" focuses on institutional support needed to prevent AI management from collapsing at the individual level.
About the Study “Navigating generative AI in higher education – six near future scenarios”
The study was conducted in two phases. The first included individual and group interviews with engineering students from 13 different programs, focusing on whether, how, and why they use generative AI — and their thoughts on its benefits, drawbacks, and possible need for guidelines. Their responses were grouped into five themes.
In the second phase, university teachers, postdocs and educational developers used these themes in a workshop to explore multiple possible futures rather than predict a single outcome. This scenario planning method is an effective way to identify challenges and imagine future directions. The two-year timeframe was chosen to be both realistic and forward-looking.
Questions about our research?
- Postdoc, Engineering Education Research, Communication and Learning in Science
- Director of Graduate Studies, Communication and Learning in Science