Tran, Harper & Price (2026) — cs.CY 📄 Full text (arXiv)
Tran, Harper & Price (2026) examine a pressing motivational paradox in contemporary computing education: the ready availability of AI tools that can complete programming assignments undermines students' willingness to invest effort in developing their own skills. Drawing on self-determination theory, the study identifies how the perception of AI as a 'shortcut' reduces autonomous motivation and fosters a transactional orientation toward learning. The findings resonate with existing work on over-reliance and ai-assistance-reduces-persistence, suggesting that easy access to AI-generated code may erode the very persistence that produces deep learning. The paper has direct implications for course design: instructors must either redesign assessments to be AI-resistant or restructure motivation around goals that AI cannot fulfill. This connects to broader debates about academic-integrity in the age of generative AI and how student-experience of learning shifts when effort becomes optional. The study also highlights equity concerns — students who resist AI assistance may fall behind peers who use it, complicating assessment fairness in higher-ed.
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