As knowledge work becomes more digital and distributed, organizations increasingly rely on two levers to sustain knowledge management (KM) performance: Artificial Intelligence (AI) to expand what KM systems can do and gamification to strengthen why people contribute and reuse knowledge. Yet prior research rarely specifies how AI and gamification are jointly organized across KM subfields, thereby limiting both theory building and design guidance. This study analyzes the AI–gamification–KM triad using a structured evidence synthesis that combines systematic searching, qualitative content analysis, and matrix-based modeling. Drawing on 44 peer-reviewed studies retrieved from four major scholarly databases with coverage through December 1, 2025, two independent coders classified each study by KM subfield, sector, outcome type, and AI and gamification role assignments. Descriptive statistics were produced in SPSS, and role matrices were constructed and visualized in R to generate a configurational evidence map linking role combinations to effect categories. The results show a clear division of labor: human involvement concentrates on learning and knowledge acquisition, while AI involvement concentrates on system-supported functions, most frequently knowledge representation, collection, and analysis. Evidence also clusters by context, with higher education dominating and outcomes primarily reported as systems and platforms. Across effect categories, the evidence base is dominated by a narrow configuration that combines engagement-oriented gamification with tailoring AI functions, while many other role combinations remain sparse or absent. These gaps are most pronounced in KM processes that require transfer, externalization, fusion, and refinement. The study contributes a framework that clarifies how AI and gamification co-configure KM outcomes and provides an evidence-based agenda for testing missing configurations across KM subfields and sectors.

AI–Gamification Role Matrices in Knowledge Management: Evidence and Research Gaps

Behrooz Moradi
;
Ettore Bolisani
Supervision
;
Enrico Scarso
Methodology
;
Furong Cai
Investigation
2026

Abstract

As knowledge work becomes more digital and distributed, organizations increasingly rely on two levers to sustain knowledge management (KM) performance: Artificial Intelligence (AI) to expand what KM systems can do and gamification to strengthen why people contribute and reuse knowledge. Yet prior research rarely specifies how AI and gamification are jointly organized across KM subfields, thereby limiting both theory building and design guidance. This study analyzes the AI–gamification–KM triad using a structured evidence synthesis that combines systematic searching, qualitative content analysis, and matrix-based modeling. Drawing on 44 peer-reviewed studies retrieved from four major scholarly databases with coverage through December 1, 2025, two independent coders classified each study by KM subfield, sector, outcome type, and AI and gamification role assignments. Descriptive statistics were produced in SPSS, and role matrices were constructed and visualized in R to generate a configurational evidence map linking role combinations to effect categories. The results show a clear division of labor: human involvement concentrates on learning and knowledge acquisition, while AI involvement concentrates on system-supported functions, most frequently knowledge representation, collection, and analysis. Evidence also clusters by context, with higher education dominating and outcomes primarily reported as systems and platforms. Across effect categories, the evidence base is dominated by a narrow configuration that combines engagement-oriented gamification with tailoring AI functions, while many other role combinations remain sparse or absent. These gaps are most pronounced in KM processes that require transfer, externalization, fusion, and refinement. The study contributes a framework that clarifies how AI and gamification co-configure KM outcomes and provides an evidence-based agenda for testing missing configurations across KM subfields and sectors.
2026
IFKAD 2026 Proceeding: Intelligent Knowledge for Sustainable Organizations
21st International Forum on Knowledge Asset Dynamics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3604778
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