ReviewFrontiers in artificial intelligence2026
Digital nudging in software development: a review and research agenda.
Review in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 authors.
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Abstract
Digital nudging, a behavioral approach that subtly guides decision-making, is gaining increasing attention in software development as a means to support developers in complex and cognitively demanding tasks. This article analyzes the evolution of digital nudging and persuasive technology in software engineering from 2010 to April 2026. Based on a structured literature review, a temporal heatmap and qualitative trend analysis are used to examine how established nudging strategies, such as feedback, framing, and personalization, have been applied to support software development activities. The results demonstrate a shift from rule-based and socially oriented interventions toward more adaptive, context-aware, and AI/LLM-mediated approaches. In particular, recent work shows an increasing integration of personalized and real-time nudging mechanisms directly into development environments. Building on these findings, the article introduces a harmonized perspective on digital nudging, in which multiple strategies are systematically integrated to provide consistent and context-aware decision support. An illustrative AI-driven plug-in prototype demonstrates how personalized nudging mechanisms can be integrated into a development environment to support software quality and adherence to design principles such as SOLID. Furthermore, key challenges are identified, including the selection and combination of nudging strategies, their evaluation, and their integration into real-world development workflows. Overall, the article contributes a structured synthesis of existing research and outlines directions for future work on harmonized, AI-driven nudging in software engineering.
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