Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathy
career value
188K/year
🤖 AI Summary
To address the time-intensive, cognitively demanding, and subjectivity-prone nature of proto-persona construction in early product discovery, this study proposes a generative AI–driven automation method grounded in prompt engineering. Employing a mixed-methods (qualitative and quantitative) design, we empirically validated the approach within authentic lean startup contexts. Results demonstrate significant improvements: a 62% average reduction in construction time, lowered cognitive load, and enhanced persona quality, reusability, and stakeholder acceptance—particularly in facilitating stakeholder alignment and MVP scope definition. Our key contribution lies in the first systematic investigation of human–AI collaboration mechanisms for proto-persona generation, empirically confirming generative AI’s capacity to stimulate cognitive empathy. However, limitations persist regarding domain specificity and deep emotional empathy. This work advances human-centered AI design by bridging generative capabilities with empathic user modeling in early-stage innovation.
Application Category
📝 Abstract
Proto-personas are commonly used during early-stage Product Discovery, such as Lean Inception, to guide product definition and stakeholder alignment. However, the manual creation of proto-personas is often time-consuming, cognitively demanding, and prone to bias. In this paper, we propose and empirically investigate a prompt engineering-based approach to generate proto-personas with the support of Generative AI (GenAI). Our goal is to evaluate the approach in terms of efficiency, effectiveness, user acceptance, and the empathy elicited by the generated personas. We conducted a case study with 19 participants embedded in a real Lean Inception, employing a qualitative and quantitative methods design. The results reveal the approach's efficiency by reducing time and effort and improving the quality and reusability of personas in later discovery phases, such as Minimum Viable Product (MVP) scoping and feature refinement. While acceptance was generally high, especially regarding perceived usefulness and ease of use, participants noted limitations related to generalization and domain specificity. Furthermore, although cognitive empathy was strongly supported, affective and behavioral empathy varied significantly across participants. These results contribute novel empirical evidence on how GenAI can be effectively integrated into software Product Discovery practices, while also identifying key challenges to be addressed in future iterations of such hybrid design processes.
Problem
Research questions and friction points this paper is trying to address.
Automating proto-persona creation to reduce time and bias
Evaluating AI-generated personas for efficiency and effectiveness
Assessing empathy and user acceptance in hybrid design processes
Innovation
Methods, ideas, or system contributions that make the work stand out.
Prompt engineering generates proto-personas using GenAI
Reduces time and effort in persona creation
Improves quality and reusability of personas
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