Relationships Between Trust, Compliance, and Performance for Novice Programmers Using AI Code Generation

2026-04-20

AI Summary

This study investigates how novice programmers’ trust in AI coding assistants under time pressure influences their coding performance and compliance with AI-generated suggestions. Employing a mixed-methods approach—including a user experiment, questionnaires, and behavioral logs—the research presents a cross-site empirical analysis involving 27 novices, offering the first systematic examination of the causal pathways among trust, compliance, and performance in AI-assisted programming. Findings reveal that trust evolves dynamically with experience but does not directly drive compliance; instead, high compliance significantly enhances task performance, which in turn reinforces subsequent trust. These results challenge the applicability of traditional automation trust theories in generative AI contexts, suggesting that design efforts should prioritize behavioral outcomes over merely calibrating trust levels.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Objective. To explore how novice programmers' trust in Artificial Intelligence-driven Development Environments (AIDEs) relates to their coding performance and AI compliance while programming under time pressure. Background. Computer programming has undergone rapid upheaval due to state-of-the-art AIDEs, which provide clever automation for many aspects of software development. A longstanding interest of researchers of automation more generally has been the attitude of trust. Decades of research seek to explain how influencing trust can help to achieve desirable outcomes in different domains, but very limited work has provided similar focus on trust in AIDEs. Method. We collected subjective measures of trust along with objective measures of performance and AIDE compliance from a diverse group of 27 novice programmers between two study locations. Results. Our results corroborated traditional understandings of how trust changes through experiences. However, we did not find a relationship between trust and subsequent compliance during programming tasks. Greater compliance was associated with strong performance, and strong performance led to greater subsequent trust. Conclusion. Our findings raise new questions about the utility of trust in the context of interacting with AIDEs and generative AI. We call for further research into the effect of trust on compliance to recommendations from imperfect AI. Application. This work can inform the design of training and educational content for generative AI use within and beyond software development. Instructional designers should consider risks of AI misuse and disuse and focus on promoting desirable interaction outcomes, regardless of trust's connection to them.

Problem

• trust
• AI code generation
• novice programmers
• compliance
• programming performance

Innovation

• trust
• AI code generation
• compliance
• novice programmers
• human-AI interaction