Current in-IDE AI coding tools typically rely on time-consuming manual prompting and context management, whereas proactive alternatives that anticipate developer needs without explicit invocation remain underexplored. Understanding when humans are receptive to such proactive AI assistance during their daily work remains an open question in human-AI interaction research. We address this gap through a field study of proactive AI assistance in professional developer workflows. We present a five-day in-the-wild study with 15 developers who interacted with a proactive feature of an AI assistant integrated into a production-grade IDE that offers code quality suggestions based on in-IDE developer activity. We examined 229 AI interventions across 5,732 interaction points to understand how proactive suggestions are received across workflow stages, how developers experience them, and their perceived impact. Our findings reveal systematic patterns in human receptivity to proactive suggestions: interventions at workflow boundaries (e.g., post-commit) achieved 52% engagement rates, while mid-task interventions (e.g., on declined edit) were dismissed 62% of the time. Notably, well-timed proactive suggestions required significantly less interpretation time than reactive suggestions (45.4s versus 101.4s, W = 109.00, r = 0.533, p = 0.0016), indicating enhanced cognitive alignment. This study provides actionable implications for designing proactive coding assistants, including how to time interventions, align them with developer context, and strike a balance between AI agency and user control in production IDEs.

New technologies often change how an individual performs work, such as how generative AI (GenAI) can help a developer write code. New technologies can also impact how people interact with one another, such as how GenAI’s ability to summarize API documentation can reduce the need for developers to ask each other technical questions. In this paper, we report on a two-phase mixed-method study exploring how GenAI influences how humans interact in software development. During phase one, 30 industrial software developers provided data over a period of 5 to 12 days as they worked, contributing 627 experience sampling responses and 207 end-of-workday survey responses. To gain further insight into their work, we interviewed 22 of these developers. During phase two, 131 additional professional developers responded to a survey to explore whether and how the results from phase one are seen across a larger population. Our analysis of the data found that (1) the ability of GenAI to help answer low-level technical questions in a timely way enables developers to see GenAI as a technical mentor, providing opportunities for developers to turn to tools rather than teammates; (2) developers perceive that GenAI can help them experience more focus and experience fewer flow disruptions; (3) GenAI can help developers pursue more meaningful conversations with their colleagues by shifting human interaction towards clarification, joint reasoning, and exploring alternative perspectives; and (4) in the presence of GenAI, developers report still seeking human-to-human interaction for contextual expertise, mentorship, and social connection. Together, these findings showwhat changes,when it changes, andwhat teams can do nextin response to this shift in team interaction dynamics, where GenAI increasingly handles routine technical queries and human conversations center on context, reasoning, and connection. Teams can adopt norms for delegating questions, sustain human judgment in complex decisions, and create space for expertise, mentorship, and connection, alongside increasing technical self-sufficiency.

The rise of AI tools is reshaping computer science education, shifting the focus from coding skills to teaching students how to effectively use these technologies. Understanding students' mental models and fostering computational and metacognitive skills are now essential, as over-reliance on AI can weaken critical thinking. This panel explores how a human-centered approach can balance these challenges, sharing strategies to optimize learning while addressing the risks of cognitive offloading in an AI-driven world.

Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy that encompasses intent, author role, software development lifecycle stage, and prompt type. To improve prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer’s prompt library.Our taxonomy study of 1,108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.

In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions and codes by systematically reviewing 115 papers, while leveraging the expertise of researchers in various disciplines. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the design of new writing assistants.

To ensure that Large Language Models (LLMs) effectively support user productivity, they need to be adjusted. Existing Code Readability (CR) models can guide this alignment. However, there are concerns about their relevance in modern software engineering since they often miss the developers' notion of readability and rely on outdated code. This research assesses existing Java CR models for LLM adjustments, measuring the correlation between their and developers' evaluations of AI-generated Java code. Using the Repertory Grid Technique with 15 developers, we identified 12 key code aspects influencing CR that were consequently assessed by 390 programmers when labeling 120 AI-generated snippets. Our findings indicate that when AI generates concise and executable code, it's often considered readable by CR models and developers. However, a limited correlation between these evaluations underscores the importance of future research on learning objectives for adjusting LLMs and on the aspects influencing CR evaluations included in predictive models.