Rethinking Pedagogy in the Age of AI
Rethinking Pedagogy in the Age of AI
November 24th, 2025
By Jean Larson, Associate Director of Instructional Effectiveness and Innovation
Attending the 2025 Frontiers in Education (FIE) Conference in Nashville this month was energizing, especially because I was presenting my own work on bringing classical and quantum machine learning to community college classrooms. But one presentation in particular has stayed with me: Conceptual Analysis and Conceptual Engineering: Methodological Issues in the Philosophy of Computing Education by McDermott, Daniels, Brown, and Cajander. As I listened, I found myself thinking ahead to the Principled Innovation Fall Convening at ASU just a few days later, specifically its focus on how our values shape our daily choices as educators.
The FIE paper argues that as AI transforms education, we need to interrogate and sometimes re-engineer the very concepts that ground our teaching. Their example of rethinking “explanation” in the age of opaque AI models really struck me. If AI systems cannot offer transparent reasoning, what does it mean for us, as educators, to commit to practices that are fair, understandable, and aligned with human flourishing? The authors show that traditional concepts of explanation may no longer serve learners well and that we may need more practical, contrasting, or well-established definitions to maintain trust and legitimacy in educational decisions.
Only a few days later, at the Principled Innovation Fall Convening, the Character in Action Panel invited us to examine precisely this tension: How do we act with empathy and integrity when our tools, institutions, and expectations are rapidly changing? As I listened and engaged with their Commonplace Book Resources, I realized that conceptual engineering isn’t only a philosophical exercise – it’s a moral one. When we refine concepts like explanation, assessment, or even learning itself, we are also revising the relationships and experiences our students have with us and with knowledge.
Both events left me with a renewed sense of responsibility. If I want to integrate AI, and even quantum tools, into the learning environment, I must do so with a focus on purpose and desired outcome. That means questioning long-accepted assumptions, being transparent about what AI can and cannot do, and ensuring that my educational design decisions reflect empathy, involvement, and actual enrichment of every learner.
In that sense, conceptual engineering and Principled Innovation are deeply connected: both ask us to pause, reflect, and reshape our practices so that it is our character, and not convenience, that guides our work.
References
Arizona State University. (n.d.). Principled innovation. https://pi.education.asu.edu/
Arizona State University. (n.d.). Principled innovation fall convening. https://web.cvent.com/event/d92be8aa-704e-46e3-b779-a6747d017c07/summary
Larson, J. S., Haywood, A., Marfai, F., Johnson, M., Babar, N., Uehara, G., Klein-Seetharaman, J., Gulick, D., Blain Christen, J., & Spanias, A. (2025, November 2–5). Bringing classical and quantum machine learning in biomedical and environmental applications to the community college setting. [Paper presentation]. IEEE Frontiers in Education Conference, Nashville, TN, United States.
McDermott, R., Daniels, M., Brown, J. N. A., & Cajander, A. (2025, November 2–5). Conceptual analysis and conceptual engineering: Methodological issues in the philosophy of computing education. [Paper presentation]. IEEE Frontiers in Education Conference, Nashville, TN, United States.Parham, A. A. (n.d.). Commonplace book resources: An intellectual journal that used to be well-known until the nineteenth century and contributes to the cultivation of intellectual and moral virtues. Google Drive. https://drive.google.com/drive/u/0/folders/1g4YM1Hhk3axIB0aDAM-1hiipo-cv_wPK
