Verification-Conditioned Use: A Qualitative Study on How Generative AI Reshapes Learning, Autonomy, and Market Entry for Junior Software Developers
2026-07-27 • Software Engineering
Software Engineering
AI summaryⓘ
The authors studied how new software developers use AI tools early in their careers by interviewing interns and junior devs. They found that people choose to use AI mostly based on whether they can easily check the AI's work, rather than on deadlines or task difficulty. The study revealed a "formative paradox," where relying on AI can limit deep learning and critical judgment skills needed in the job market. To use AI well, participants said it’s important to always review AI output, understand how it works, and keep practicing coding without AI help.
generative AIsoftware developmentBraun and Clarke's thematic analysisverification-conditioned useautonomy paradoxformative paradoxcritical judgmentinternshipnewcomer learningAI-assisted coding
Authors
Pedro Henrique Andriotte, Danilo Monteiro Ribeiro
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.