Ally Limke

Bahare Riahi, Ally Limke, Xiaoyi Tian et al.

AI-based chatbots have the potential to accelerate learning and teaching, but may also have counterproductive consequences without thoughtful design and scaffolding. To better understand teachers' perspectives on large language model (LLM)-based chatbots, we conducted a study with 11 teams of middle school teachers using chatbots for a science and computational thinking activity within a block-based programming environment. Based on a qualitative analysis of audio transcripts and chatbot interactions, we propose three profiles: explorer, frustrated, and mixed, that reflect diverse scaffolding needs. In their discussions, we found that teachers perceived chatbot benefits such as building prompting skills and self-confidence alongside risks including potential declines in learning and critical thinking. Key design recommendations include scaffolding the introduction to chatbots, facilitating teacher control of chatbot features, and suggesting when and how chatbots should be used. Our contribution informs the design of chatbots to support teachers and learners in middle school coding activities.

Benyamin Tabarsi, Heidi Reichert, Ally Limke et al.

Large language models (LLMs) like OpenAI ChatGPT, Google Gemini, and GitHub Copilot are rapidly gaining traction in the software industry, but their full impact on software engineering remains insufficiently explored. Despite their growing adoption, there is a notable lack of formal, qualitative assessments of how LLMs are applied in real-world software development contexts. To fill this gap, we conducted semi-structured interviews with sixteen early-adopter professional developers to explore their use of LLMs throughout various stages of the software development life cycle. Our investigation examines four dimensions: people - how LLMs affect individual developers and teams; process - how LLMs alter software engineering workflows; product - LLM impact on software quality and innovation; and society - the broader socioeconomic and ethical implications of LLM adoption. Thematic analysis of our data reveals that while LLMs have not fundamentally revolutionized the development process, they have substantially enhanced routine coding tasks, including code generation, refactoring, and debugging. Developers reported the most effective outcomes when providing LLMs with clear, well-defined problem statements, indicating that LLMs excel with decomposed problems and specific requirements. Furthermore, these early-adopters identified that LLMs offer significant value for personal and professional development, aiding in learning new languages and concepts. Early-adopters, highly skilled in software engineering and how LLMs work, identified early and persisting challenges for software engineering, such as inaccuracies in generated content and the need for careful manual review before integrating LLM outputs into production environments. Our study provides a nuanced understanding of how LLMs are shaping the landscape of software development, with their benefits, limitations, and ongoing implications.