Navigating Uncertainty in a Rapidly Evolving Landscape

This post (and the following one) have sat in my drafts for a few weeks. Over the Christmas holidays, I had the opportunity to meet with educators from three different Jesuit schools within our educational network. While I’d originally planned these as casual conversations to just touch base on where they were at, and what they were thinking regarding AI in education, what I found was quite a bit of uncertainty and a sense of being overwhelmed in the schools. I felt there was a sense of disorientation when it comes to AI.

I observed a particular kind of institutional vertigo. Conversations about AI would begin with enthusiasm but quickly spiral into an overwhelming array of questions, each branching into more questions. What struck me was the struggle to even formulate the [right?] questions. Where does one begin when the starting point itself is obscured?

This disorientation manifests as a peculiar vulnerability. In the absence of a grounding framework, these educational institutions find themselves susceptible to a form of techno-solutionism with the seductive promise of comprehensive answers from those who project certainty in uncertain terrain.

Perhaps the most concerning pattern I observed was what might be called an authority vacuum around AI in education. Traditional sources of educational guidance – pedagogical research, institutional experience, professional networks – are struggling to keep pace with AI’s rapid evolution. Into this vacuum step various voices: technology evangelists, commercial interests, and self-proclaimed experts, each offering clarity amid confusion.

What makes this particularly precarious is that the complexity of AI creates an asymmetry of perceived expertise. Those who speak the language of algorithms, neural networks, and machine learning fluently often command disproportionate influence in educational decision-making spaces, regardless of their understanding of educational principles or contexts.

I felt that its not merely a lack of technical knowledge or implementation strategies – there’s also a lack conceptual frameworks through which to approach AI in education the first place. It’s as if they find themselves speaking a new language without understanding its grammar. They can identify individual words (specific AI tools or applications) but struggle to construct meaningful sentences (coherent approaches to integration) because the underlying structure remains opaque.

I’m concerned that this disorientation opens the door to technological determinism masquerading as innovation. Without grounding principles or critical frameworks, these educational institutions risk adopting AI not on their own terms, but on terms dictated by those who present technology as a force to which education must simply adapt. The risk is that they might embrace AI without the necessary critical engagement, accepting narratives about its role in education that serve interests other than their students’ holistic development.

I’m trying to reflect on how we might begin to address this disorientation. I don’t think I’m up to creating a whole systematic framework for AI in Education. I have some possible prototypes which I would like to use to help stimulate critical thinking on AI in Education, and empower educators’ agency in how AI can be implemented in Education. But I’m not sure if I should focus on helping schools establish where they currently are, or begin to navigate a path forward for them to travel.

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