This week I would like to share a recent SoTL study by Tierney, Peasey, and Gould (2025) entitled, “Student perceptions on the impact of AI on their teaching and learning experiences in higher education”. This research provides much-needed qualitative insights into how students perceive the integration ofI in higher ed. This investigation shares student voices through focus groups and follow-up surveys, enabling a richer understanding of the affective, cognitive, and ethical dimensions of AI in learning contexts.
Students identified pragmatic benefits and pressing concerns about AI:
Benefits: Academic support, efficiency gains, improved writing, enhanced reading comprehension, and scaffolding of independent study. Students also valued AI for reducing cognitive overload—using tools to organize, summarize, and provide initial drafts that make complex tasks less overwhelming.
Concerns: Academic integrity, misinformation, equity, and the potential erosion of critical thinking, creativity, and intrinsic motivation. Several participants voiced existential worries about the purpose of higher ed in an AI-rich world.
Policy Expectations: Students called for institutional clarity and leadership. They requested guidance, consistent communication, and integration of AI-literacy into curricula. Many advocated for authentic assessments that emphasize creativity, criticality, and applied skills.
Future Orientation: Students overwhelmingly acknowledged AI’s inevitability in professional contexts, signaling the need for higher ed to prepare them with not only technical competence but also ethical discernment and adaptive expertise.
Implications for SoTL
This study affirms that SoTL must treat AI not merely as a tool to be managed, but as a pedagogical partner that reshapes learning design. Three imperatives emerge:
AI-Literacy as Foundational: Universities must embed AI ethics, functionality, and limitations into curricula, equipping students to critically evaluate outputs and apply them responsibly.
Assessment Innovation: The shift to authentic, process-oriented tasks is critical for sustaining academic integrity while fostering higher-order skills.
Student Partnership: As this research demonstrates, involving students in policy co-design is essential. Their perspectives reveal not only concerns but also opportunities for reimagining higher ed in an AI age.
Impact Points for Faculty
1. Prioritize Academic Support and Cognitive Scaffolding
Students view AI as a valuable support tool for reading, writing, feedback, and organization.
Faculty can design scaffolded tasks where AI provides first-draft summaries or feedback, while students critically evaluate and refine outputs. This strengthens metacognition and prevents shallow learning.
2. Embed AI-Literacy Across the Curriculum
Students want clear institutional guidance and AI-pedagogy. They express anxiety about unclear policies and detection tools.
Instructors should integrate mini-modules on AI literacy: how to use tools ethically, check outputs for accuracy, and cite AI use appropriately.
3. Reimagine Assessment through Authenticity
Concerns about academic integrity and creativity call for assessment redesign.
Faculty should employ authentic assessments that emphasize critical thinking, creativity, and applied skills. For example, projects requiring students to use AI tools while justifying their choices mirror real-world applications and reduce plagiarism risks.
4. Design for Equity and Access
Students raised concerns about unequal access to premium AI tools.
Faculty should ensure course design leverages freely accessible AI tools and advocate for institutional support (e.g., site licenses, bursaries) to mitigate inequities.
5. Normalize AI as a Professional Skill
Students expect AI to be central to their future careers.
Embedding discipline-specific AI applications (e.g., data analysis in STEM, writing assistance in Humanities, tutoring in Social Sciences) prepares students for workplace realities and positions them competitively.
6. Continuous Communication and Transparency
Confusion arises when students receive contradictory messages about AI from different instructors.
Faculty should coordinate course-level and program-level policies, ensuring consistency in expectations, permissible uses, and assessment design.
Tierney et al. (2025) remind us that AI’s impact on higher ed is not determined solely by institutions but by the lived experiences of students navigating learning with and through these technologies. Anchoring AI adoption in foundational learning theories—information processing, constructivism, self-regulated learning, and authentic assessment—ensures that integration enhances rather than erodes human learning. For SoTL, this moment presents both a challenge and an opportunity: to critically examine how AI reshapes the processes by which students think, create, and grow.
References
Bruner, J. (1996). The culture of education. Harvard University Press.
Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42. https://doi.org/10.3102/0013189X018001032
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic Press.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
Tierney, A., Peasey, P., & Gould, J. (2025). Student perceptions on the impact of AI on their teaching and learning experiences in higher education. Research and Practice in Technology Enhanced Learning, 20(5). https://doi.org/10.1186/s41039-025-00005-x
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wiggins, G. (1998). Educative assessment: Designing assessments to inform and improve student performance. Jossey-Bass.