Geoscience Education in the Age of Generative AI: What Do Students Actually Learn?

Generative AI is now woven into the daily study practices of geoscience students, often more deeply than educators acknowledge. This study examines how bachelor students in Earth Sciences (GEOL1008, NTNU) and master students in Engineering Geology (TGB4200, NTNU) use AI tools to understand literature, analyse data, synthesise research findings, and prepare written and oral assignments. The analysis draws on two structured surveys designed to map the extent and character of AI use in both cohorts.

Preliminary results indicate that AI has become the default support tool. Students turn to it to decode complex concepts, troubleshoot coding tasks, analyze data, structure reports, and polish presentations. Many see little distinction between traditional digital tools and generative AI, and the boundary between personal work and AI-augmented work is increasingly blurred. At the same time, students express uncertainty and worry about ethical expectations, disclosure practices, and the legitimacy of relying heavily on AI in academic work.

These trends have immediate consequences for assessment. Home exams, reports, and pre-prepared presentations no longer reliably reveal individual understanding, since nearly all students now use AI during preparation. Emerging evidence from portfolio-based courses suggests grade inflation and reduced differentiation between students, not because learning outcomes have improved, but because AI elevates the baseline quality of submitted work. In practice, written in-person exams and oral examinations remain among the few ways to assess unassisted reasoning.

The findings underscore a need to rethink teaching and assessment in geoscience education. AI is not a future challenge but a present reality, and universities must adapt if they aim to evaluate what students actually know rather than what their tools can produce.