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This chapter offers a critical and Generative AIpractical exploration of how Generative AIGenerative AI (GenAI) can be meaningfully integrated into research design through methodological co-creation. It introduces a conceptual framework that highlights three essential roles the researcher plays when working with GenAI: contextual interpretation, normative judgement, and theoretical alignment. These dimensions reflect the ongoing importance of human agency in AI-supported research. Rather than treating GenAI as just a technical tool, the chapter positions it as a potential co-creative partner, one that can help stimulate methodological thinking. However, the value of this collaboration depends on the researcher’s ability to guide, critique, and place AI outputs within normative human judgement. Two practical case studiesCase studies illustrate this process: (1) a thematic analysis of African AI policy documents using Ubuntu ethicsethics as a guiding lens, and (2) a fieldwork study on how women micro-entrepreneurs in Lagos adopt digital payment technologies. Both examples show how GenAI can support methodological innovationInnovation when shaped by the researcher’s experiential and theoretical insight.

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Towards Methodological Innovation: Co-Creating Research Design with Generative AI

  • 章节

  • 发表日期：

    2025年

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  • Oluwaseun Kolade,
  • Abiodun Egbetokun,
  • Adebowale Owoseni

摘要

This chapter offers a critical and Generative AIpractical exploration of how Generative AIGenerative AI (GenAI) can be meaningfully integrated into research design through methodological co-creation. It introduces a conceptual framework that highlights three essential roles the researcher plays when working with GenAI: contextual interpretation, normative judgement, and theoretical alignment. These dimensions reflect the ongoing importance of human agency in AI-supported research. Rather than treating GenAI as just a technical tool, the chapter positions it as a potential co-creative partner, one that can help stimulate methodological thinking. However, the value of this collaboration depends on the researcher’s ability to guide, critique, and place AI outputs within normative human judgement. Two practical case studiesCase studies illustrate this process: (1) a thematic analysis of African AI policy documents using Ubuntu ethicsethics as a guiding lens, and (2) a fieldwork study on how women micro-entrepreneurs in Lagos adopt digital payment technologies. Both examples show how GenAI can support methodological innovationInnovation when shaped by the researcher’s experiential and theoretical insight.