QUALITATIVE FRAMEWORK FOR AI AND IOT IN INDUSTRY 5.0

Abstract

With particular emphasis on human-centered automation and sustainability, this study qualitatively examines the role of artificial intelligence and the Internet of Things within the Industry 5.0 framework. Unlike Industry 4.0, which primarily emphasized automation and operational efficiency, Industry 5.0 promotes collaborative intelligence by integrating human capabilities with AI-supported decision-making and real-time monitoring. The study aims to identify the distinctive characteristics of Industry 5.0 and examine how emerging technologies are reshaping the relationship between human labor and automation while contributing to efficiency, resilience, safety, and sustainability in industrial environments. Methodologically, the research combines a systematic literature review with bibliometric analysis to identify central thematic axes, conceptual relationships, and emerging trends associated with the integration of AI and IoT in industrial process optimization. This combined approach supports a qualitative interpretation of technological, organizational, ethical, and human-centered dimensions that might remain fragmented if examined exclusively through technical or quantitative perspectives. The analysis identified eight conceptual categories, sixteen theoretical links, and twenty-eight emerging themes related to human-machine collaboration, digital competencies, ethical AI governance, workforce development, real-time monitoring, and sustainable industrial practices. The methodological contribution lies in the integration of qualitative thematic interpretation and bibliometric mapping within a coherent analytical framework for examining complex technological transformations. This approach provides a structured means of connecting conceptual patterns in the literature with broader questions concerning human agency, responsible automation, and sustainability. The findings indicate that Industry 5.0 requires not only advanced technologies but also methodological approaches capable of interpreting the social, ethical, and organizational implications of their implementation. The proposed framework therefore contributes to qualitative research by offering a transferable analytical structure for studying human-technology relationships in industrial contexts while preserving attention to contextual complexity, interpretive depth, and human-centered values.