Navigating uncertainty: university teachers' experiences and perceptions of generative AI in teaching and learning

Navigating uncertainty — a qualitative study of 24 Swedish university teachers (higher education, disciplines centered on long-form writing: philosophy, law, sociology, education) who took part in assessment workshops with GAI outputs followed by focus-group interviews. Drawing on postphenomenology and technological mediation theory, the authors find that the emergence of GAI was experienced as alarming and overwhelming, inducing a state of vulnerability and ethical challenges (fairness, unequal access, bias), while prompting teachers to rethink assessment, re-evaluate teaching priorities (especially critical thinking), and worry that student learning is at risk (weakening of skills, desocialisation of learning). The study contributes a technology-mediation account of how teachers' roles and confidence are unsettled when GAI enters established educational practice.

Key Findings

Experiencing the emergence of GAI as alarming and overwhelming. Teachers described a hype-driven public narrative ("all students are going to cheat") that demanded urgent institutional and personal response, alongside an opportunity framing: GAI forced reconsideration of what it means to know and to test knowledge in a system whose assessment mechanisms "don't really match" a new view of knowledge.
A state of vulnerability. Teachers felt discomfort, insecurity, and low confidence driven by limited knowledge of GAI's capabilities/limitations, limited exposure, fear of "not being ahead of students," and worry about feeling incompetent when assessing student work potentially (co-)produced with AI. Junior teachers were seen as especially exposed.
Emergence of ethical challenges. Concerns centred on (1) treating students fairly and avoiding falsely suspecting cheating (which could harm institution–teacher–student trust), (2) unequal access to GAI artefacts across paid/free tiers, and (3) GAI outputs perpetuating bias and inaccuracies that could misinform students.
Rethinking assessment. GAI was seen as requiring re-evaluation of assessment purpose and design. Strategies discussed included "AI-proof" formats (banning home examinations, sit-in and oral exams, raising complexity), alternative/multiple and incremental submission types, transparency measures (honour statements, declarations of AI use), and more Feedback to help students see alignments/misalignments in GAI output.
Re-evaluating teaching priorities. Teachers identified essential skills for learning with GAI — critical thinking, evaluating data sources, fact-checking output, "thinking on your feet," ownership of knowledge — and the responsibility to foster ethical use and academic integrity (authorship/ownership, avoiding plagiarism).
Student learning at risk. Concerns about weakening skills through reliance on GAI (writing, reasoning, argumentation, research, on-the-spot responsiveness), the erosion of deep intuitive understanding, and the desocialisation of learning (students turning to chatbots instead of peers; GAI output missing the "social aspects" of disciplines like law).

Postphenomenological framing

The study is theoretically distinctive in grounding the analysis in postphenomenology and technological mediation theory (Ihde 1990; Verbeek 2006, 2011; Rosenberger & Verbeek 2015), treating GAI not as a neutral "tool" but as multistable technological artefacts that mediate teachers' perceptions and reconfigure their practices. On this account, GAI's "scripts" — rapid responsiveness, natural-sounding text that can achieve passing grades — shape teachers' experience of what competence means for learners and themselves, unsettle confidence, and push them beyond instrumental questions ("what use is acceptable?") toward re-evaluating their role, the meaning of teaching, and the future of the university in a landscape that feels "out of control." Theoretically, the paper applies an established philosophical framework (technological mediation) to the AI-in-education context rather than proposing a new one — an example of advancing an established theory into AIEd.

Implications for AI in Education

For teacher anxiety and well-being: teachers' "state of vulnerability" and feeling "stuck" is a genuine emotional and professional response to GAI, not mere resistance — it signals a need to support teacher confidence and Well Being, not just train tool use.
For Educational Development: institutions should provide designated spaces and time for teachers to experiment with GAI, exchange experiences, and collaboratively develop practices and guidelines at institutional, departmental, and course levels — treating AI literacy and readiness as a supported, collective endeavour.
For Assessment and Academic Integrity: the assessment rethinking themes (AI-proof formats, multiple/incremental submissions, transparency declarations, feedback) echo the knowledge base's shift from detection toward redesign; but the paper also surfaces teachers' concern that returning to traditional/sit-in formats risks losing the pedagogical value of at-home, self-paced long-form writing.
For Teacher Role and Curriculum Design: teachers identified cultivating critical thinking, evaluative judgement, and ethical GAI use as newly central responsibilities — pointing to a reconfiguration of the teacher role around guiding critical engagement with GAI rather than transmitting content.
For Equity In AI Education: unequal access to paid vs. free GAI tiers was a live teacher concern, framing equity as an access-and-infrastructure problem within classrooms, not only across countries or institutions.

Connected Concepts

Connected Articles

Stenalt Good Education Teacher AI Conceptions 2026 — phenomenographic study of university teachers' conceptions of AI
Enright Staff Perspectives GenAI 2026 — staff perspectives on GenAI in higher education
Beyond Hype Stakeholder Perceptions GenAI 2026 — higher-education stakeholder SWOT of GenAI
Laidlaw GenAI Identity Crisis Faculty 2026 — GenAI as identity crisis for faculty
Teachers Reflective Regulators Cognition Offloading — teachers as reflective regulators

Citation

Farazouli, A., Cerratto Pargman, T., Bolander Laksov, K., & McGrath, C. (2026). Navigating uncertainty: university teachers' experiences and perceptions of generative artificial intelligence in teaching and learning. Studies in Higher Education, 51(9), 1898–1913.

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