Heartland Adjunct Faculty Association

Our working conditions are our student's learning conditions. Together we will organize for the public schools we all deserve.

I’m exercising a little presidential prerogative to share some end-of-the-semester thoughts about academic belonging, AI, and the burden shift onto faculty. It's long -- but based on deep concern for how we move forward.

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Over the past several years, many colleges have invested significant time and energy into helping students feel that they belong. That work matters. Some students are more likely to persist, engage, ask questions, and take academic risks when they believe they are part of a community that sees them and supports their success.

But belonging to an academic community involves more than feeling welcome. It also involves understanding and accepting the responsibilities that come with membership in that community.

The rise of generative AI has made that conversation more urgent — and, for faculty, much more exhausting.

Too often, the institutional response to student misuse of AI focuses almost entirely on what faculty must do differently. Faculty are encouraged to redesign assignments, create AI-resistant assessments, revise syllabi, build AI disclosure statements, teach responsible AI use, identify suspicious work, document concerns, meet with students, manage denials, respond to conflict, and defend their professional judgment. And because generative AI is now pervasive, this is not an occasional problem faculty encounter in isolated cases. It is a standing condition of teaching, touching nearly every course, every assignment, and every semester.

Some of that work is necessary. Faculty do have an important role in setting expectations for their courses and helping students understand how learning happens within a discipline.

But when every solution to protecting academic integrity begins with asking faculty to do more, we have missed the larger problem.

We have shifted the burden.

Student misuse of AI is, at its core, a student responsibility issue. When students submit work that does not reflect their own thinking, effort, judgment, or learning, that is not simply a failure of assignment design. It is not simply evidence that the prompt was insufficiently “AI-resistant.” It is not simply a sign that faculty need another workshop.
It is a breach of academic integrity.

Yet much of the current conversation places the practical, emotional, and ethical burden of that breach on faculty. We are expected to prevent it before it happens, recognize it when it occurs, prove it carefully enough to withstand challenge, meet with the student, absorb the student’s reaction, and then decide whether pursuing the concern is worth the time and energy it will require.

That work is not abstract. It is showing up in faculty workload, faculty morale, and faculty exhaustion.

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