Defining AI Roles in Requirements Elicitation and Analysis

Abstract

Artificial intelligence is increasingly used to support Requirements Engineering (RE), especially in text-intensive activities such as summarizing stakeholder input, extracting candidate requirements, and organizing requirements-related information. Yet in early RE, where requirements elicitation and requirements analysis depend on contextual interpretation, incomplete information, and stakeholder negotiation, it remains unclear how responsibilities should be allocated between human practitioners and AI systems. This thesis investigates how human–AI roles are configured in requirements elicitation and requirements analysis, and how practitioners evaluate the benefits, risks, and trust conditions associated with these configurations. A sequential exploratory mixed-methods design was used, combining six semi-structured interviews with soft ware engineering practitioners and a follow-up survey with 67 valid responses. Interview data were analyzed thematically and used to inform the survey design, while survey data were analyzed descriptively. The findings show that AI involvement in early RE is task-contingent rather than general. Practitioners were most willing to use AI as an assistant or preliminary processor in bounded, information-heavy, and verifiable tasks, including summarizing, extracting, organizing, and structuring requirements-related material. By contrast, tasks involving stakeholder interaction, interpretation of implicit needs, prioritization, trade-off decisions, and accountability remained strongly human-led. AI-assisted configurations were perceived as the most useful, whereas AI-only configurations were rare and associated with the highest perceived risk. Trust in AI was therefore conditional rather than general: it increased when outputs were easy to verify and tasks were low in decision criticality, and decreased when work depended on contextual judgment, sensitive information, or consequential decisions. Based on exploratory mixed-methods evidence, this thesis proposes a practitioner informed role-boundary framework at the task level for AI involvement in early RE. The framework clarifies where AI can productively support elicitation and analysis work and where human judgment should remain dominant. Overall, the study argues that AI should be integrated into early RE as a calibrated collaborative support tool rather than as a substitute for human practitioners.