How Grad CS Students Want to Work With AI — Not Be Replaced
Generative AI is reshaping coursework—but where do students want help, and where do they want control?
This case study of online graduate CS students audits student–AI collaboration across 12 academic tasks. Using two back-to-back surveys, it maps current AI capabilities against the level of automation students actually prefer.
- Surveys capture perceived benefits (speed, support) and risks (over-automation, loss of agency, hallucinated outputs).
- Students report how they currently use AI and set boundaries for where it should assist vs. defer.
- Open-ended responses surface design ideas for AI that is more transparent, reliable, and respectful of student goals.
The goal: reveal gaps between today’s AI tools and students’ normative expectations, so universities and builders can create collaboration that enhances learning—without sidelining the learner.
Read the paper: https://arxiv.org/abs/2601.08697v1
Paper: https://arxiv.org/abs/2601.08697v1
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