The traditional academic model rests on a simple assumption: the time and effort required to produce quality work forces students to learn. Writing a comprehensive essay demands time ( planning, research, writing, proof reading ) and effort ( critical thinking, synthesis, reflection, …) and these cognitive processes facilitate deep learning.
AI has disrupted this paradigm. Students can now generate polished essays and solve complex problems with minimal cognitive investment. While the outputs remain impressive, the neural pathways traditionally strengthened through struggle and iteration remain underdeveloped.
This decoupling of time and effort from output/product creates a assessment crisis in education. Measuring outputs, like essays, was always just a proxy for measuring the thing we cared about, learning. The proxy used to work “ok” but now it is broken. If we do not change how we assess, our assessments will become meaningless.
The implications extend beyond academia. While AI-driven efficiency gains benefit many professions where learning isn’t the primary goal, it can leave staff stagnant in there professional development, unsatisfied and going nowhere. We need to focus on finding ways to automate the mundane, while focusing on the creative and curatorial.
For education the solution lies in helping students find intrinsic motivation to learn. This is partly why I teach game development ( I am currently hosting a Global Game Jam hashtag#GGJ site, surrounded by 40 developers, half of them students on a Saturday afternoon working on our shared passion of creating video games ). Gamers and Game Developers recognize that the struggle is part of the fun. Gamers play levels on hard because the challenge is the point. “Grinding” in games is negative because it is time and effort that does not result in improvement or learning.
In my classes we use AI to help us learn, because we want to learn, not merely conform to an arbitrary rubric satisfying someone-else’s interests. That is the power of engagement, agency and enjoyment.
Educators are now struggling with reimagining assessment methods that measure genuine learning rather than just output quality. We have to do this, and more. We need to re-evaluate if what we are teaching is still what the students need. What skills are needed in a future of abundant cognitive power. How do we communicate about the quality of the work done by our students and colleagues. Do we use summative grading at all?
In classrooms AI can help by allowing students to convert learning tasks from the interests of the teacher into similar tasks associated with their personal passion. Students need to embrace their passions and ride the motivation into learning new skills and understanding. Games are my students’ passion and we use that passion to support great learning.
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