GoKawiilCarnegie Mellon professor redesigns ML course after AI could complete all homework
Christian Kästner, who teaches Machine Learning in Production at Carnegie Mellon, says he has overhauled most assessments in his course because AI agents can now complete take-home assignments convincingly. Instead of grading homework, he now focuses evaluation on TA interactions, exams, and video demonstrations, while keeping the course's core learning goals on engineering tradeoffs and teamwork largely unchanged. Students are permitted to use AI tools freely except during written and oral exams.
GoKawiil's interpretation of the reporting above, not reported fact.
The case illustrates how educators are being forced to redesign evaluation methods rather than course content as AI tools erode the reliability of take-home work as a measure of learning. Kästner's approach—permitting AI use everywhere except exams—suggests some instructors see teaching responsible AI use as itself a legitimate learning goal, rather than something to police. This could signal a broader shift in higher education toward live, supervised assessment formats as trust in unsupervised assignments declines.
- Kästner redesigned most assessments in his upper-level ML course after AI could complete assignments convincingly.
- Evaluation now centers on TA interactions, exams, and video demos rather than take-home work.
- Students are allowed to use AI tools in all course settings except written and oral exams.
Source: thelastsoftwareengineer.substack.com — Christian Kästner, 2026-09-24
Published there as: “How I changed teaching after AI managed to do all my homework assignments”
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