Rethinking Academic Excellence in the Age of AI (Peter Nilsson)

But even more important than assessing quality or driving imagination is making meaning.

AI can help students analyze text, identify detail in an image, and structure a work of writing — many people have done this before, so there are plenty of examples for AI models to draw from — but only the student can apply this understanding to her world. Only the student can integrate new understanding into his school community and personal relationships. Only the student can practice new habits based on new ideas and understanding enabled by AI.

Local levels of specificity are beyond the reach of AI. AI can produce a skilled work of writing about a common text, and it can produce statistically common insights, but it cannot access the details of our students’ specific world. So while AI can help students compose an analysis of the imagery in Shakespeare’s Othello, it is the student who must absorb the Bard’s investigation of race in society and then see relationships with his peers in a new light. 

Assignments designed to investigate personal experiences in a local context require active reflection and social engagement by students. AI can write an analysis — and even what looks like a reflection — but its analysis and reflection are not the same as students transferring understanding from books to their own lives and context. The more we ask students to make connections between texts and personal or local contexts, then the more likely the work will belong to students — and the more likely it will have meaning to them, too.

I am reminded that a school leader once shared his regret that while students were once asked to explore authorial intention — a fundamental act of stretching one’s mind to understand and think like someone else — now students are increasingly asked to share personal responses. He framed it as a less sophisticated learning experience. I tend to agree, but I also believe that this isn’t an either/or scenario.  We must ask students to articulate other people’s perspectives and insights, and then make sense of these new perspectives and insights in the context of their own lives and communities.  AI can help more with the former, less with the latter.

In my teaching years, I would often ask students to write two-part essays: part one would focus on an explication of some element of a text or piece of data, and part two would focus on an element of the student’s life as seen through the lens of part one.  This works with primary sources in history, labs in physics, literature in English, and many other academic settings. Today, AI might help more with part one — and that’s ok — but part two entails a level of specificity that requires engagement and reflection and meaning making by the student.  And AI may yet help with developing and refining these ideas and the language they are couched in.

In an AI age, we might redefine excellence. Excellence across disciplines has often been typified by fluency of expression combined with domain-specific insights around content.  Now both of these are commodities. Anyone, with the help of AI, can assemble fluently composed, well-written essays and reports. With fluid writing and flexible thinking about core content at our students’ fingertips, we might redefine excellence to describe how well students make connections between what they’re learning and their lives and schools. AI can’t do this. One of technology’s greatest limitations is that it does not understand context even as well as a child. In today’s age, excellence might focus on how well students connect content to context in thoughtful, precise and specific ways. Excellence might focus on how students make meaning not in the abstract, but concretely in their world. Fruitfully, this definition of excellence insists on teaching for transfer, which has always been a central goal of well developed programs.

AI will do some of the heavy lifting in this work going forward, and that’s ok. Calculators do some of what once may have been thought of as the heavy lifting in math. As a foundation, competency begins with being able to assess the work done by calculators and AI. But more important is that calculators and AI can’t imagine the way humans do, and this — thinking with insight and originality — remains a grounding for ongoing academic excellence, as it has in a pre-AI time. Most significant, however, is that AI won’t be able to make meaning for us. This, which may be the heart of how we redefine excellence, we have to do in our own context, by ourselves, and with each other.