"Why Put in This Much Effort?": How AI Availability Shapes Students' Motivation in Introductory Programming

• URL: http://arxiv.org/abs/2606.30480v1
• Date: Mon, 29 Jun 2026 15:41:43 GMT
• Title: "Why Put in This Much Effort?": How AI Availability Shapes Students' Motivation in Introductory Programming
• Abstract summary: We study how engineering students experience and respond to AI availability.<n>Our findings complicate the assumption that students need external constraints to protect their learning.<n>Students who managed the tension found motivation in the learning process itself.
• Score: 0.15293427903448018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When AI tools can easily complete programming assignments, students face a motivational question: why invest effort in completing them independently? While prior work has examined instructor policies and usage patterns, we focus on how students themselves experience and respond to AI availability, a perspective important for designing courses that sustain engagement with programming practice. We investigate two research questions: (1) How do engineering students describe how AI availability shapes their motivation to put effort into programming assignments? (2) How do students navigate the tension between their expressed value for learning through effort and the constant availability of AI as an alternative to effort? We conducted semi-structured interviews with 13 engineering majors in an introductory MATLAB course where students could use a course-specific AI chatbot. Using Situated Expectancy-Value Theory (SEVT) as an analytical framework, we examined how students described their expectancy, values, and costs in the context of AI availability. When AI could complete assignments quickly, students questioned whether their time on programming was well spent (cost), questioned the long-term usefulness of programming skill (utility value), reported less satisfaction when AI bypassed productive struggle (intrinsic value), and described confidence that depended on AI being available (expectancy). Nearly all students expressed a preference for learning through effort and a simultaneous temptation to take shortcuts with AI (sanctioned or otherwise). Our findings complicate the assumption that students need external constraints to protect their learning. Students who managed the tension found motivation in the learning process itself, suggesting that course design may need to shift from valuing what students produce to supporting how they learn.

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