Jul 2026· Journal of Management Information Systems· Vol 43, pp. 754 - 785· 0 citations· 98 references
TL;DR
This study explains the paradoxical findings through cognitive load theory, showing that GenAI reduces extraneous load while preserving germane processing during ideation and debugging and links observed performance effects to underlying cognitive mechanisms and usage strategies.
Abstract
ABSTRACT Generative AI (GenAI) has advanced rapidly and made significant impacts. However, its effect on developers remains a topic of industry debate. Companies want to know whether GenAI can enhance developers’ coding performance, as an unclear understanding may put companies at a disadvantage. While the literature has begun addressing this issue, a formal understanding of GenAI’s impact remains incomplete. Moreover, existing findings are often short-term, fragmented, or lack explanatory mechanisms. To fill these gaps, we designed a multimethod research program comprising a longitudinal field study and a randomized controlled experiment. In Study 1, we collaborated with a global information technology organization and applied a difference-in-differences approach to over 27 weeks of proprietary data. In Study 2, we designed a randomized experiment involving 253 software developers. From these studies, we find that GenAI usage affects both developers’ coding quantity and quality. These effects, however, depend critically on how the tool is used. While reduced cognitive effort can be associated with diminished quality, interestingly, GenAI usage enables developers to produce higher-quality code with less cognitive effort. In this current study, we explain the paradoxical findings through cognitive load theory, showing that GenAI reduces extraneous load while preserving germane processing during ideation and debugging. Using a multimethod research design that integrates longitudinal field data with a randomized controlled experiment, we link observed performance effects to underlying cognitive mechanisms and usage strategies. We also offer guidance on effective usage styles and propose boundary conditions for realizing GenAI’s benefits in practice.
It is concluded that AI meaningfully augments developer productivity but does not yet demonstrably improve satisfaction or earnings, and that a hybrid human-AI model, supported by governance and training, remains the most defensible direction for application development.
Perseus Bhavnagri· International Journal for Re...· 0 citations
GenAI can function as a research tool, but not as a substitute for methodological expertise, and has potential to increase efficiency of tasks which take advantage of its search and summarization abilities, as well as basic code debugging and algorithm formation.
Natalie Morosin, A. A. Nadi, Michael P Wallace· 0 citations
Generative AI is increasingly permeating software engineering, enabling developers to generate functions, files, and even entire applications from natural language specifications. AI systems are also becoming more personalized, adapting outputs based on inferred user characteristics and interaction history. While personalization may improve the development experience, it raises concerns that generated software could be shaped by attributes of the developer rather than by task requirements alone. Prior work has shown that generative AI can produce biased software artifacts, but little is known about how developer identity can bias generated code. We characterize three dimensions through which inferred developer attributes can influence generated artifacts: interface design, template content, and code structure. First, through controlled experiments on 800 AI-generated websites, we find that age- and gender-related signals produce significant differences across all three dimensions. Second, we conduct an observational study and follow-up interviews with 20 participants who used AI to create a personal website to both examine how personalization impacts software artifacts in practice, and also to understand how programmers perceive the boundary between personalization and bias. Together, our results show that developer attributes can meaningfully influence generated software beyond stated requirements, highlighting a previously underexplored tension between personalization and fairness in AI-assisted programming.
In this systematic literature review, we aimed to identify and thoroughly analyze the existing scientific knowledge related to a notably emerging field of generative AI. In strict adherence to the SPAR-4-SLR protocol, we focus on 1,104 peer-reviewed articles from the Scopus database, published between 2015 and 2025. Using bibliometric and thematic mapping methods, we address two main research questions: the first concerns the major publication trends in GenAI research, and the second deals with the intellectual structures and thematic domains shaping the field. Our results indicate an abrupt increase in scientific production since 2022, a consequence of the launch of models such as GPT, DALL·E, and Stable Diffusion, which are becoming increasingly powerful. We further identify five main research clusters: the technological foundations of generative AI, where researchers focus on building and utilizing LLMs and deep learning; professional and educational applications; ethical and governance issues; AI-assisted creativity; and user perceptions. Additionally, we find that higher education plays a significant role in the area, both in the application of ideas and the exploration of relevant questions. This review highlights the field's strong interdisciplinary character and, at the same time, reveals the current challenges that the sector faces. We outline a systematic research program to guide further studies of the implementation, impact, and problems of GenAI in enterprises and communities.
Majdouline Attaoui, Wissal Attaoui, Anas Moukrim et al.· International journal of mul...· 0 citations
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.
Pedro Henrique Andriotte, Danilo Monteiro Ribeiro· 0 citations
: We are in a time of change in regards to the emergence of software development as we know it due to the growing number of developers using large language models (LLMs), which eventually will enable major shifts toward the "post-code" era in which software development will become less reliant on coding through using AI-driven development systems that accept natural language and high-level specifications as inputs. This research will analyze the impact of these AI assistants (e.g., GitHub Copilot, Gemini and GPT) through quantitative data collected from Stack Overflow Developer Surveys, GitHub Octoverse Reports, and JetBrains Developer Ecosystem Survey regarding how developers are currently embedding AI into their current practices and what it will look like moving forward. The research found out three things about how developers use Artificial Intelligence. These things are adoption of Artificial Intelligence satisfaction, with Artificial Intelligence the different ways developers are using Artificial Intelligence is changing. The results indicate that there is a distinct directional trend toward AI-native development environments, and that developers are in the midst of rapid change to adopt these tools.
P. Vijayakumar, Jegatheeswari Perumalsamy, Priya Ranjan Parida et al.· Proceedings of the 1st Inter...· 0 citations