LLMs Integration in Software Engineering Team Projects: Roles, Impact, and a Pedagogical Design Space for AI Tools in Computing Education
This work takes a pedagogical lens to explore the implications of generative AI (GenAI) models and tools, such as ChatGPT and GitHub Copilot, in a semester-long 2nd-year undergraduate Software Engineering Team Project. Qualitative findings from survey (39 students) and interviews (eight students) provide insights into the students' views on the impact of GenAI use on their coding experience, learning, and self-efficacy. Our results address a particular gap in understanding the role and implications of GenAI on teamwork, team-efficacy, and team dynamics. The analysis of the learning aspects is distinguished by the application of learning and pedagogy informed lenses to discuss the data. We propose a preliminary design space for GenAI-based programming learning tools highlighting the importance of considering the roles that GenAI can play during the learning process, the varying support-ability patterns that can be applied to each role, and the importance of supporting transparency in GenAI for team members and students in addition to educators.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
On the Application of Agile Project Management Techniques, V-Model and Recent Software Tools in Postgraduate Theses Supervision
Due to the nature of most postgraduate theses in control engineering and their similarities to industrial and software engineering projects, invoking novel project control techniques could be effective. In recent decades…
ManagementCollaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process
The introduction of machine learning (ML) components in software projects has created the need for software engineers to collaborate with data scientists and other specialists. While collaboration can always be challengi…
FairnessImpact of LLMs on Team Collaboration in Software Development
Large Language Models (LLMs) are increasingly being integrated into software development processes, with the potential to transform team workflows and productivity. This paper investigates how LLMs affect team collaborat…
Code GenerationGenerative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 2023
The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework based on human and large language model generated labels.…
Binary ClassificationInformation RetrievalLanguage ModelingLanguage Modelling+2Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge
Deep learning (DL) systems present unique challenges in software engineering, especially concerning quality attributes like correctness and resource efficiency. While DL models excel in specific tasks, engineering DL sys…