We Could Get There From Here
R.E.M. was wrong. We can get there. Philomath is where to go.
How might we think of teaching practice, and the evidence of student learning, as problems to be investigated, analyzed, represented, and debated? (Bass, 1999)
If I am bullish on AI in education it is precisely because we have a tremendous opportunity to learn things about our teaching and about our students’ learning in ways I would have never thought possible throughout my career.1 Indeed, this era requires us to do so. Having championed the Scholarship of Teaching and Learning (SoTL) throughout my journey leading centers for teaching and learning and various levels of academic programs, I'm convinced its principles are perfectly poised for our evolving AI-driven landscape in education. While SoTL has traditionally been the domain of higher education, isn't it time we infused its essence into our secondary schools too? By doing so, not only can we offer our students richer experiences, but we can also foster a deeper sense of community and camaraderie among educators, especially in these uncertain times. First advanced by folks such as Ernest Boyer, Lee Shulman, and Daniel Bernstein in the early 90’s the initial introduction of SoTL examined, “not merely the existence of a scholarly component in teaching, but a particular kind of activity, in which faculty engage, separate from the act of teaching, that can be considered scholarship itself.”2 More recently folks such as Randy Bass at Georgetown, Peter Felten at Elon, and Allison Cook-Sather at Bryn Mawr & Haverford have taken up the mantle of SoTL, advancing it forward and advocating for what Felten describes as a community of 'teacher-scholars.’ But this isn’t meant to be an exploration of SoTL. Instead, I’d like to argue why I think a SoTL-esque approach works so well for our specific time in education.
Why SoTL and Why Now?
In his 2013 piece, Principles of Good Practices in SoTL, Peter Felten lays a solid foundation that could guide our journey of integrating AI into classrooms, and weighing its impact. By juxtaposing the five cornerstone principles with the transformative potential of generative AI, we can identify promising directions for all stakeholders. True, the crux of SoTL’s methodology has been to cement its status as a credible scholarly pursuit within academia. And while that motive resonates with higher ed enthusiasts, it might hold less weight in secondary school circles. However, the beauty of this framework is in its versatility, offering us numerous avenues to explore. Let’s explore each of the five pillars below:
Inquiry focused on student learning: that this falls first feels important. I’ve written previously about my perceived impact on teacher expertise and how I think AI makes teaching harder in many ways, rather than easier. Hopefully I’m more wrong than right in the long run, but I do think it will take some trial and error for us to get wherever “there” might be. Felten writes, “learning should be understood broadly to include not only disciplinary knowledge or skill development, but also the cultivation of attitudes or habits that connect to learning.” These feels doubly important in our AI era. The cultivation of attitudes and habits related to learning directly addresses the how and why of AI usage by students amidst their learning journey. This compels us to consider aspects of our own teaching, and also of the assessment of their learning and the quality of our teaching. Dylan William, John Hattie, and Arran Hamilton’s The Future of AI in Education: 13 Things We Can Do to Minimize the Damage explores this head on. We must center student learning as we explore this new frontier.
Grounded in context: Many teachers are notoriously reticent to embed research practices in their classroom. If that’s the case, then why not embed a scholarly mindset in our classrooms alongside of the AI disruptions we’re currently grappling with? Dylan William famously wrote, “Everything works somewhere; nothing works everywhere.” Start by really knowing your somewhere.
Methodologically sound: In an age where the allure of shiny tech tools can sometimes overshadow the core tenets of education, it's paramount that any AI introduced into the classroom is grounded in robust methodologies. It's not enough for an AI tool to be 'cool' or 'innovative'; it must also align with tried and tested pedagogical practices that prioritize student learning outcomes. We should be wary of tools that claim to revolutionize education without the research to back it up. Every AI tool we consider must be evaluated not just for its technical prowess but also for its pedagogical validity. This ensures we're not sacrificing substance for style and that our students are genuinely benefiting from the best of both worlds. So too ought we engage in robust methodologies to capture our understanding related to the implementation of these tools.
Conducted in partnership with students: If these tools are going to play a significant role in students' learning journeys, it's only right that they have a say in their implementation and usage. By fostering a collaborative approach, we're acknowledging our students as active participants in their education, not just passive recipients. This partnership can provide invaluable feedback for teachers and tech developers alike. We know that students are using these tools, and often doing so in ways we never would have imagined. Loop them in. These are emergent features of education and we’d do well to promote student agency by actively including them along the way.
Appropriately public: Transparency is the cornerstone of trust. As we navigate this new frontier of AI in education, it's essential that we maintain an open dialogue with all stakeholders: teachers, students, parents, and even the wider community. Everyone should be aware of which AI tools are being used, why they're being used, and the impact they're having on learning outcomes. This openness not only keeps everyone informed but also encourages feedback and critique, ensuring a system of checks and balances. Moreover, when successes are achieved, sharing them widely can serve as a beacon for other institutions, highlighting best practices and the tangible benefits of AI in education.
It’s impossible to predict with any certainty what comes next. Yet we can be confident that the blending of SoTL principles with the emerging influence of AI in education is not just a theoretical musing, but a practical guidepost for our educational journey ahead. It empowers teachers and gives us a way of knowing and of being amidst uncertainty. We're on the brink of a significant shift where technology and traditional teaching are melding in unique ways. Our challenge, then, is to navigate this evolution with intentionality, always keeping the genuine needs and interests of our students at the forefront. Whether we're looking at a new AI tool or pedagogical strategy, the litmus test should always be its relevance and efficacy for student learning. As we delve deeper into this intertwined realm of AI and education, let's ensure that our compass remains calibrated towards authentic, meaningful, and transformative learning experiences.3
Last Word? Last Word!
I’m sure I’ll do something on feedback at some point this year. I always think about this clip of Baltimore Orioles’ coach Bobby Dickerson coaching the 1st baseman, Trey Mancini. The way he conveys the feedback, his emphasis on details, his knowledge of the feedback recipient, the way he brings in various sensory elements…I love it.
“A baseball is not an egg.”—“Try to catch it with your face.”—“I can hear a good infield.”
As of writing this piece, this week I am bullish again. Give it a few minutes, a few new illuminations about AI, or the advent of some new, unrealized platform and I might shift back again. As always, I am thinking very publicly here as a means of working out my own understanding of our current moment.
Randall Bass. (1999). The scholarship of teaching: What’s the problem? Inventio: Creative thinking about learning and teaching, 1(1), 1-10. [The referenced names do not reflect the entirety of scholarship on SoTL at the outset. This is not a blog about the historiography of SoTL.]
Bit of a deep cut for today’s title, drawn from R.E.M’s Can’t Get There From Here. There’s a lyric, “If you're needing inspiration, Philomath is where I go.” A ‘philomath’ is a lover learning; a scholar. This is ‘The Academic DJ’ after all.