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Relationships Between Trust, Compliance, and Performance for Novice Programmers Using AI Code Generation
Nicholas Gardella1, Matthew L Bolton2, Sara L Riggs2
1Department of Computer Science, Washington and Lee University, Lexington, VA, USA.
Human Factors
|July 27, 2026
Summary
Novice programmers
Area of Science:
- Computer Science
- Human-Computer Interaction
- Software Engineering
Background:
- Artificial Intelligence-driven Development Environments (AIDEs) are transforming software development.
- Trust in automation is a key research area, but understudied in AIDEs.
- Understanding trust is crucial for effective human-AI collaboration in programming.
Purpose of the Study:
- To investigate the relationship between novice programmers' trust in AIDEs, their coding performance, and AI compliance.
- To examine how these factors are influenced by programming under time pressure.
Main Methods:
- Collected subjective trust measures and objective performance/compliance data from 27 novice programmers.
- Conducted the study across two different locations.
Main Results:
- Trust evolved with experience, aligning with traditional automation research.
- No direct link was found between trust and AI compliance during programming.
- Higher AI compliance correlated with stronger coding performance.
- Stronger performance subsequently led to increased trust in AIDEs.
Conclusions:
- The utility of trust in guiding AI compliance needs further investigation, especially with imperfect AI.
- Findings suggest focusing on desirable interaction outcomes rather than solely on trust.
- Informs the design of training for generative AI in software development, addressing misuse and disuse risks.
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