Verification-Conditioned Use: A Qualitative Study on How Generative AI Reshapes Learning, Autonomy, and Market Entry for Junior Software Developers
TL;DR
What makes AI use sustainable, from participants'own point of view, is not the tool itself but the individual practice of reviewing before accepting, refusing to use AI without understanding it, asking the tool for explanations, and keeping deliberate practice outside of AI-assisted work.
Abstract
Objective: to investigate how the use of generative Artificial Intelligence (AI) tools affects the early stages of a career in software development, from the perspective of the newcomers themselves. Method: thirteen interns and junior developers were interviewed individually, by videoconference. Interviews were analyzed using the six phases of Braun and Clarke's thematic analysis, with inductive coding and a semantic approach. Results: sixteen themes emerged, organized around a central concept: verification-conditioned use. Across the study's four research questions (usage patterns, learning, autonomy, and market entry), the criterion that most often decides between AI and manual work is not deadline or task complexity, but the ability to check the result. Two themes expose tensions in newcomers'self-perception: the autonomy paradox (feeling more capable yet less in ownership of the result) and the first-person denial of dependence. Together, these findings point to a theoretical contribution, the formative paradox: the shallow learning that AI induces makes it harder to build the very critical-judgment competence that, according to participants, the market has begun to demand. Conclusion: what makes AI use sustainable, from participants'own point of view, is not the tool itself but the individual practice of reviewing before accepting, refusing to use AI without understanding it, asking the tool for explanations, and keeping deliberate practice outside of AI-assisted work.