HAX Research
HAX Research
Invent and investigate Human-AI interaction scenarios that enhance the work of technology creators.
About Us
JetBrains develops tools for technology creators, and in the Human-AI Experience (HAX) research team, we’re all about making those tools even better.
Our research spans three core areas of interaction with AI: design, impact, and quality. We investigate critical questions, such as the most effective HAX implementation in the development lifecycle, the influence of HAX on users' experiences, and the key attributes of AI output that best help to support our users. By understanding how developers think, learn, and work with AI, we can suggest new seamless and intuitive AI feature ideas and prototypes tailored to JetBrains users' needs.
Our approach goes beyond traditional UX research and A/B testing. We leverage our expertise in behavioral sciences and data analysis to conduct experimental mixed-method studies, adhering to rigorous academic standards. Our expertise also helps to approach technical tasks with attention to unique human characteristics, optimizing the user interface and functionality of AI-driven JetBrains tools.
A lot of our work happens in partnership with academic researchers. We learn from their experience and help them to improve the efficiency and robustness of their studies, pushing the boundaries of knowledge together.
Overall, we’re here to keep growing and applying our expertise to be part of the transformative changes happening in the world.
The HAX Team
Born in Minsk, Agnia graduated with distinction from St. Petersburg State University with a Specialist degree in Clinical Psychology. She then went on to pursue a PhD in Computer Science at TU Delft. In her spare time, Agnia enjoys playing Dungeons and Dragons and traveling.
Ilya graduated with distinction from Lomonosov Moscow State University. He then went on to earn a PhD in Psychology, with the background in the intersection between psychology, neuroscience, and behavioral genetics. In another life (and with better music abilities), he would have liked to become an avant-garde musician.
Born in Novokuznetsk, Ekaterina received a Master’s degree in Applied Mathematics and Informatics from the Higher School of Economics in St. Petersburg and now lives in Amsterdam. In her professional capacity, she researches how AI influences IT specialists and their interactions. Outside of work, she spends her time dancing, bouldering, surfing, and doing nerdy things.
Originally from Russia, Dariia has a Master’s degree in Applied Mathematics and Informatics from the Moscow Institute of Physics and Technology. She loves anything that involves data, from downloading it to processing and analyzing it (and everything in between). Now living in sunny Cyprus, she enjoys cooking and hiking in her free time.
Ongoing Research Collaborations
AI in Developer Teams
The integration of GenAI in software development teams has the potential to transform traditional workflows, communication, and collaboration methods. As AI tools become more accessible and robust, many teams are beginning to rely on them to streamline problem-solving, knowledge-sharing, and task management. However, the impact of AI on team dynamics, productivity, and knowledge transfer remains underexplored. By examining when, why, and how software developers choose AI over human interactions, this study aims to uncover the patterns and behaviors that emerge, providing insight into the evolving role of AI within collaborative team environments.
The primary goal of this study is to explore how GenAI changes the way software development teams work while also investigating how GenAI improves or deteriorates team communication and problem-solving. Beyond this, it aims to examine what aspects of traditional workflows or human roles are being replaced or becoming obsolete following the arrival of AI assistants. As a result, we hope to identify usage patterns and factors that correlate with team success or challenges in using AI, aiding in the formulation of best practices for AI integration.
Longitudinal Analysis of AI Impact
with UC Irvine
At JetBrains, we have a lot of data that could be analyzed to derive trends and changes in developers' behavior when using our IDEs following the integration of AI into JetBrains tools. We want to use this data to predict the future development of the AI field.
The goal of the project is to derive trends in AI-affected metrics of in-IDE activity. We also want to understand and define changes of metrics of in-IDE activity that might be attributed to AI integration. This knowledge of trends can be later used for developing new in-IDE AI features for improved user satisfaction.
User-Centered In-IDE HAX
Essential to the success of tools like GitHub Copilot and ChatGPT is their interface. While some of these models' capabilities were available before, the tools brought them to larger audiences without dramatically disrupting their workflow or requiring lots of additional steps.
IDE users have their own workflows and the injection of new complex features, such as LLM-based ones, is a non-trivial task. We believe that there is significant space for innovation in the field of human-AI interaction. While chat is a popular and user-friendly way of using LLMs, we believe there are better ways to implement them in IDEs.
The goal of this project is to embed emerging LLMs practices like code generation or code explanation into the developer workflow without disturbing the user and, at the same time, improving her productivity.
Select publications
Want to collaborate?
We’re looking for collaborators – researchers, developers, and other HCI professionals – interested in advancing human-AI experience in real-world software development tools.
By collaborating with us, you can:
- Build and evaluate HAX features directly in production-grade developers' tools.
- Test your hypotheses using real-world IDE data or with a sample of professional tech creators.
- Bring your ideas about the future of human-AI collaboration in tech creation, and get support in planning, implementing, and evaluating them.
Our current areas of interest include how mental models of AI tools influence their acceptance and effective use, how AI is shaping collaboration in developer teams, and how multimodal AI can support coding tasks. We are also open to exploring other promising topics related to human-AI interaction in tech creation.
We provide access to infrastructure, developer workflows, and research-grade support to enable rigorous, impactful experimentation.
Interested? Email us at collaborations@research.jetbrains.org.
You are also welcome to take part in our prediction tournament dedicated to the future of AI in Software Engineering.