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

● high · 2026-06-30academic-integrity, higher-ed, llm, over-reliance, student-experience, ai-assistance-reduces-persistence

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 Cognitive Offloading, suggesting that easy access to AI-generated code may erode the very persistence that produces deep learning.

The motivational paradox

Introductory programming is effort-intensive: learning to code requires sustained practice, debugging, and tolerance for failure. When an AI tool can complete an assignment in seconds, the perceived value of that effort collapses. Drawing on Self Determination Theory, the authors argue this shifts students from autonomous motivation (learning for its own sake, driven by interest and mastery) toward a transactional orientation in which work is completed for the grade, not the skill. The availability of an effortless shortcut doesn't just make cheating easier — it makes the honest path feel pointless.

How AI availability shapes motivation

  • Reduced autonomous motivation: the perception of AI as a shortcut lowers the intrinsic value of developing skills, because the goal (producing working code) can be reached without the effort that builds competence.
  • Transactional learning orientation: students come to treat assignments as tasks to dispatch rather than opportunities to learn, optimizing for output over understanding.
  • Persistence erosion: because Over Reliance and easy access reduce the need for struggle, students miss the productive-failure cycles that drive deep learning in programming.
  • Equity concern: students who consciously resist AI assistance may fall behind peers who use it to complete work faster, complicating assessment fairness and the fairness of grading in Higher Ed — a dynamic with direct implications for Academic Integrity.

    • Implications for course design

    The study argues that instructors cannot assume motivation will survive easy AI access on its own. Two levers are central:

    1. AI-resistant assessment: redesign assessments so they measure what AI cannot trivially supply — reasoning, debugging, explaining one's own code — aligning with Authentic Assessment and Assessment Validity principles.

    2. Motivation restructuring: frame learning goals that AI cannot fulfill (deep understanding, the ability to debug and defend one's code under pressure) rather than treating output quality as the sole objective. This connects to the broader AI Misuse Learning Harm finding that the cost of bypassing effort is reduced durable learning.

    Connection to the broader wiki

    This paper sits at the intersection of Over Reliance, Motivation, and Self Regulated Learning. Its emphasis on why effort feels optional links to AI Availability Student Motivation-adjacent work on the cognitive costs of AI, to Agentic Literacy Debt (the cumulative skill deficit from habitual reliance), and to the socio-emotional side documented in Shame Guilt AI Regulation Computing Education. For instructors, the motivational lens complements the tool-design and assessment-design interventions in Reducing AI Misuse: students are more likely to use AI productively when they have reasons — not just rules — to do the work themselves.

    Connected Concepts

  • Over Reliance
  • Cognitive Offloading
  • Self Determination Theory
  • Motivation
  • Self Regulated Learning
  • Academic Integrity
  • Student Experience
  • Higher Ed
  • AI Misuse Learning Harm
  • Reducing AI Misuse

    • Connected Articles

  • AIED Unfinished Mission Bypass — AIED's Unfinished Mission: Agency and Motivation
  • AI Making Us Stupid — Is AI Making Us Stupid?
  • Cognitive Offloading Speedup Illusion — Cognitive Offloading and the Speedup Illusion
  • Efficiency Gain Illusion AI Overreliance — The Efficiency-Gain Illusion
  • GenAI Reliance Types Scale — GenAI Reliance Types and Scale
  • Shame Guilt AI Regulation Computing Education — Shame and Guilt as Social Regulators of AI Use
  • Agentic Literacy Debt — Agentic Literacy Debt
  • Rethinking Scaffolding LLM Tutors — Rethinking Scaffolding in LLM Tutors
  • AI Engineering Education Balancing Act — AI in Engineering Education: A Balancing Act
  • Post Instrumental Learning Capacity Dissolution — Post-Instrumental Learning and Capacity Dissolution

    • Citation

    Keith Tran, Colton Harper, Thomas Price (2026). "Why Put in This Much Effort?": How AI Availability Shapes Students' Motivation in Introductory Programming. arXiv:2606.30480. cs.CY.