The future of work and education in AI-driven innovative systems: A systematic literature review and lexicometric analysis

Purpose: The present study aims to analyze the impact of AI-driven innovative systems on work and education by exploring available literature settings covering variables such as artificial intelligence, innovation, work, and education (AIWE), as well as examining the collective domain of AIWE through applied theories, contextual settings, and methodology. Additionally, it considers potential directions for future AIWE research. Design/methodology/approach: This study conducts a lexicometric analysis of 146 literature along with a systematic literature review of 64 studies published from 1997 to 2024 to comprehensively explore the collective domain of AIWE. It examines prevalent topics of study, publication status, citation patterns, as well as elements of the theory-context-method and antecedents-decisions-outcomes framework used in the field of AIWE research. Findings: The current review highlights crucial insights for educational authorities, managers, and policymakers to address challenges posed by technological advancements. Numerous theoretical frameworks, contextual elements, applied methodologies as well as factors such as antecedents, decisions, and consequences have been identified. The review contributes to the field of AIWE domain by offering a comprehensive analysis of more than two decades and provides valuable insights to guide future research toward future preparedness within regional innovation systems. Originality/value: Our article emphasizes the need for further study by presenting a structured review of the collective domain AIWE research. It helps stakeholders of territorial innovation systems to develop strategies to prepare academic institutions and the workforce for an AI-driven future, guide future research, and provide practical information on the factors and fundamentals of the domain of AIWE.