Dan Drake 🦆CS education folks!
I just read a very interesting paper:
Margaret-Anne Storey. 2026. From Technical Debt to Cognitive and Intent Debt: Rethinking Software Health in the Age of AI. https://doi.org/10.48550/arXiv.2603.22106
It gives us a couple good ways to describe to students what is lost when they have an AI write all their code -- namely, that you're incurring cognitive and intent debt.
But I have a colleague (Dave Musicant at Carleton College) who had an experience that seems to suggest we need another category of this debt/capital.
The assignment was a relatively large project (implement a Scheme interpreter in C). My colleague had a student who he was quite certain had AI write all the code. His assessment includes an oral exam in which he asks the student questions about the code. This student was able to answer his questions pretty well. So the student had, in terms of that paper, very little technical, cognitive, or intent debt.
But it seems like the student is missing...something. It seems like a student who completed that assignment without any AI use would get something out of the experience that the AI-using student didn't. Some knowledge? Some skill? Some attitude or habit of mind? What kind of capital is developed by that student?
Thoughts?
From Technical Debt to Cognitive and Intent Debt: Rethinking Software Health in the Age of AI
Generative AI is accelerating software development, but may quietly shift where the most significant risks lie. As AI generates code faster than teams can understand it, two under appreciated forms of debt accumulate: cognitive debt, the erosion of shared understanding across a team, and intent debt, the absence of externalized rationale that developers and AI agents need to work safely with code. This article proposes a Triple Debt Model for reasoning about software health, built around three interacting debt types: technical debt in code, cognitive debt in people, and intent debt in externalized knowledge. Cognitive debt is a team-level, project-level property reflecting the erosion of shared understanding across a software system over time, leading to increasingly inadequate shared mental models for reasoning about and safely changing the system. Intent debt refers to the absence or erosion of explicit rationale, goals, and constraints that guide how humans and agents evolve the system. We discuss how generative AI changes the relative importance of these debt types, how each can be diagnosed and mitigated, and surface points of debate for practitioners.
Elizabeth Patitsas 🏳️🌈♿😷
IDEATE
gallaugher
