AI in the Design Thinking Toolbox: What to Recommend, When, and Why

Context: Although the use of Artificial Intelligence (AI) in software engineering is growing, the potential of Large Language Models (LLMs) to automate complex tasks in the software development process remains largely unexplored. Information-intensive decisionmaking tasks, such as selecting Design Thinking (DT) techniques for requirements elicitation, may significantly benefit from LLMbased solutions. Objective: This paper presents the automation of selecting DT techniques for specific project contexts, using LLMs as decision-support tools. We developed DT Selection Universe GPT, an LLM-based solution built on a structured repository containing 46 DT techniques and designed to operate using a prompt guided by the CRISPE model. Method: We investigated professionals’ perceptions of task automation through a qualitative study with four experienced participants from industry (INDT). The participants interacted with the tool and then participated in semi-structured interviews to share their perceptions of the experience. Results: Participants reported positive perceptions of using LLMs to automate the task of technique selection. They highlighted the system’s alignment with professional language, its adaptability to different contexts, the clarity of the recommendations provided, and the reliability of the results as key differentiators. Conclusion: The initial findings indicate that the DT Selection Universe GPT solution, developed using LLMs, shows strong potential to automate decision-support tasks for selecting DT techniques, offering benefits for novice and experienced professionals.