DEVELOPER PRODUCTIVITY IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MICROSERVICES: A CASE STUDY OF GITHUB COPILOT INTEGRATION AT DUOLINGO

Solomiya Ohinok, Андрій Лесюк · Economics and Management · 23 Oct 2026

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Abstract

This article examines the real-world experience and results of the strategic implementation of the generative artificial intelligence (GenAI) tool "GitHub Copilot" into the development ecosystem of Duolingo – the world's leading educational platform with a global audience of over 500 million users [1]. Amid rapid business scaling, the company faced a critical need to optimize code writing processes. The paper details a comprehensive approach to overcoming cognitive friction and workflow fragmentation that inevitably arose from the evolutionary technological transition of the architecture from 3 large monolithic repositories to a complex distributed network of over 400 microservices [1; 2]. Such decentralization significantly complicated codebase navigation and increased engineer onboarding time. To evaluate the effectiveness of the AI assistant's integration, a multidimensional methodology was applied in the study. Based on an in-depth combined analysis of objective quantitative GitHub ecosystem metrics (specifically pull request processing velocity) and subjective qualitative indicators measured using the "SPACE" framework (satisfaction, performance, activity, communication, efficiency) [6], the positive impact of the innovation is empirically proven. The results demonstrate that the systematic use of AI assistants in close combination with modern cloud development environments (such as GitHub Codespaces) provides a substantial increase in engineering velocity: 25% among developers working in new or unfamiliar repositories, and a consistent 10% in a familiar environment [1]. Furthermore, the article conceptually shows that GenAI tools today go beyond simple autocompletion. They act as a powerful systemic multiplier of team expertise, reduce cognitive load, and serve as an effective means of maintaining mental flow state while solving complex algorithmic tasks [3; 7; 8]. The findings create a scientific and practical foundation for rethinking the role of artificial

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Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.

Published: 2026 (recently)
Citations: 0 (OpenAlex)

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DEVELOPER PRODUCTIVITY IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MICROSERVICES: A CASE STUDY OF GITHUB COPILOT INTEGRATION AT DUOLINGO

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