Digital governance scholarship has expanded rapidly over the past decade,yet the field lacks a systematic mapping of its conceptual architecture.This article presents a comprehensive systematic review of institutional...Digital governance scholarship has expanded rapidly over the past decade,yet the field lacks a systematic mapping of its conceptual architecture.This article presents a comprehensive systematic review of institutional,accountability,and algorithmic power scholarship from 2015 to 2025,analysing how concepts have emerged,consolidated,and fragmented across four distinct phases:the Transparency Turn(2015–2018),when algorithmic opacity dominated scholarly attention;the Accountability Turn(2018–2021),which shifted focus toward institutional mechanisms;the Regulatory Turn(2021–2023),driven by binding legislative initiatives;and the Agentic Horizon(2024–2025),where autonomous AI systems challenge existing frameworks.Drawing on 94 core sources identified through Web of Science and Scopus using PRISMA screening procedures,the review constructs a thematic taxonomy organised into four clusters and diagnoses three structural research gaps:the conceptual vacuum surrounding agentic accountability,governance fragmentation across jurisdictions,and temporal misalignment between institutional design and technological evolution.The article introduces the Governance Coupling Framework(GCF),an original three-layer analytical model connecting institutional configurations,accountability tools,and algorithmic power control points.The framework’s central mechanism—transparency–accountability decoupling(TAD)—identifies the systematic failure through which information disclosure fails to produce institutional accountability.Three research propositions operationalise these findings with corpus-derived evidence.The review provides a periodised knowledge map,a diagnostic analytical framework,and a structured research agenda for digital governance research.展开更多
Generative AI severs the link between polished products and genuine learning,exposing the limits of outcome-only assessment.This paper advances a socio-technical framework for Al-enabled process-based assessment(PBA)t...Generative AI severs the link between polished products and genuine learning,exposing the limits of outcome-only assessment.This paper advances a socio-technical framework for Al-enabled process-based assessment(PBA)that reframes evaluation as continuous diagnosis embedded in learning.A five-stage pipeline-task→trace→model→feedback→validation-aligns pedagogical intent with instrumentation of interaction,dis-course,and multimodal evidence,and treats the human Al pair as the unit of analysis within a distributed cognition perspective.Methodologically,the framework maps trace types to appropriate model families(e.g.,sequential pattern mining,HMMs,NLP)while requiring explainability so insights are actionable.For practice,it specifies teacher AI orchestration roles that preserve human judgment and defines governance protocols for privacy,fairness,transparency,and cultural responsiveness.The result is a principled route to assess complex problem solving with integrity in the age of generative AI.展开更多
文摘Digital governance scholarship has expanded rapidly over the past decade,yet the field lacks a systematic mapping of its conceptual architecture.This article presents a comprehensive systematic review of institutional,accountability,and algorithmic power scholarship from 2015 to 2025,analysing how concepts have emerged,consolidated,and fragmented across four distinct phases:the Transparency Turn(2015–2018),when algorithmic opacity dominated scholarly attention;the Accountability Turn(2018–2021),which shifted focus toward institutional mechanisms;the Regulatory Turn(2021–2023),driven by binding legislative initiatives;and the Agentic Horizon(2024–2025),where autonomous AI systems challenge existing frameworks.Drawing on 94 core sources identified through Web of Science and Scopus using PRISMA screening procedures,the review constructs a thematic taxonomy organised into four clusters and diagnoses three structural research gaps:the conceptual vacuum surrounding agentic accountability,governance fragmentation across jurisdictions,and temporal misalignment between institutional design and technological evolution.The article introduces the Governance Coupling Framework(GCF),an original three-layer analytical model connecting institutional configurations,accountability tools,and algorithmic power control points.The framework’s central mechanism—transparency–accountability decoupling(TAD)—identifies the systematic failure through which information disclosure fails to produce institutional accountability.Three research propositions operationalise these findings with corpus-derived evidence.The review provides a periodised knowledge map,a diagnostic analytical framework,and a structured research agenda for digital governance research.
文摘Generative AI severs the link between polished products and genuine learning,exposing the limits of outcome-only assessment.This paper advances a socio-technical framework for Al-enabled process-based assessment(PBA)that reframes evaluation as continuous diagnosis embedded in learning.A five-stage pipeline-task→trace→model→feedback→validation-aligns pedagogical intent with instrumentation of interaction,dis-course,and multimodal evidence,and treats the human Al pair as the unit of analysis within a distributed cognition perspective.Methodologically,the framework maps trace types to appropriate model families(e.g.,sequential pattern mining,HMMs,NLP)while requiring explainability so insights are actionable.For practice,it specifies teacher AI orchestration roles that preserve human judgment and defines governance protocols for privacy,fairness,transparency,and cultural responsiveness.The result is a principled route to assess complex problem solving with integrity in the age of generative AI.
