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Newcomers onboarding to Open Source Software (OSS) projects face many challenges. Large Language Models (LLMs), like ChatGPT, have emerged as potential resources for answering questions and providing guidance, with many developers now turning to ChatGPT over traditional Q&A sites like Stack Overflow. Nonetheless, LLMs may carry biases in presenting information, which can be especially impactful for newcomers whose problem-solving styles may not be broadly represented. This raises important questions about the accessibility of AI-driven support for newcomers to OSS projects. This vision paper outlines the potential of adapting AI responses to various problem-solving styles to avoid privileging a particular subgroup. We discuss the potential of AI persona-based prompt engineering as a strategy for interacting with AI. This study invites further research to refine AI-based tools to better support contributions to OSS projects. 

Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students. We validated our findings through member checking and triangulation with instructors. Our findings provide practical considerations of where and why genAI should (not) be used in the context of supporting SE students. 

Generative AI (genAI) tools, such as ChatGPT or Copilot, are advertised to improve developer productivity and are being integrated into software development. However, misaligned trust, skepticism, and usability concerns can impede the adoption of such tools. Research also indicates that AI can be exclusionary, failing to support diverse users adequately. One such aspect of diversity is cognitive diversity -- variations in users' cognitive styles -- that leads to divergence in perspectives and interaction styles. When an individual's cognitive style is unsupported, it creates barriers to technology adoption. Therefore, to understand how to effectively integrate genAI tools into software development, it is first important to model what factors affect developers' trust and intentions to adopt genAI tools in practice? We developed a theoretically grounded statistical model to (1) identify factors that influence developers' trust in genAI tools and (2) examine the relationship between developers' trust, cognitive styles, and their intentions to use these tools in their work. We surveyed software developers (N=238) at two major global tech organizations: GitHub Inc. and Microsoft; and employed Partial Least Squares-Structural Equation Modeling (PLS-SEM) to evaluate our model. Our findings reveal that genAI's system/output quality, functional value, and goal maintenance significantly influence developers' trust in these tools. Furthermore, developers' trust and cognitive styles influence their intentions to use these tools in their work. We offer practical suggestions for designing genAI tools for effective use and inclusive user experience. 

Context: Newcomers joining an unfamiliar software project face numerous barriers; therefore, effective onboarding is essential to help them engage with the team and develop the behaviors, attitudes, and skills needed to excel in their roles. However, onboarding can be a lengthy, costly, and error-prone process. Software solutions can help mitigate these barriers and streamline the process without overloading senior members. Objective: This study aims to identify the state-of-the-art software solutions for onboarding newcomers. Methods: We conducted a systematic literature review (SLR) to answer six research questions. Results: We analyzed 32 studies about software solutions for onboarding newcomers and yielded several key findings: (1) a range of strategies exists, with recommendation systems being the most prevalent; (2) most solutions are web-based; (3) solutions target a variety of onboarding aspects, with a focus on process; (4) many onboarding barriers remain unaddressed by existing solutions; (5) laboratory experiments are the most commonly used method for evaluating these solutions; and (6) diversity and inclusion aspects primarily address experience level. Conclusion: We shed light on current technological support and identify research opportunities to develop more inclusive software solutions for onboarding. These insights may also guide practitioners in refining existing platforms and onboarding programs to promote smoother integration of newcomers into software projects. 

Research within sociotechnical domains, such as Software Engineering, fundamentally requires the human perspective. Nevertheless, traditional qualitative data collection methods suffer from difficulties in participant recruitment, scaling, and labor intensity. This vision paper proposes a novel approach to qualitative data collection in software engineering research by harnessing the capabilities of artificial intelligence (AI), especially large language models (LLMs) like ChatGPT and multimodal foundation models. We explore the potential of AI-generated synthetic text as an alternative source of qualitative data, discussing how LLMs can replicate human responses and behaviors in research settings. We discuss AI applications in emulating humans in interviews, focus groups, surveys, observational studies, and user evaluations. We discuss open problems and research opportunities to implement this vision. In the future, an integrated approach where both AI and human-generated data coexist will likely yield the most effective outcomes.