Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.HC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Randomized trial finds diverse LLM explanations improve open-ended accuracy by 7.7% over generic ones in introductory programming without raising cognitive load.
citing papers explorer
-
Relationships Between Trust, Compliance, and Performance for Novice Programmers Using AI Code Generation
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
-
Exploring the Value of Diverse LLM Explanations in Introductory Programming
Randomized trial finds diverse LLM explanations improve open-ended accuracy by 7.7% over generic ones in introductory programming without raising cognitive load.