In the past decade,the great change has taken place in the field of cataloging from theoretical models and standards to applications since the invention of Machine Readable Cataloging(MARC).This change is directly rel...In the past decade,the great change has taken place in the field of cataloging from theoretical models and standards to applications since the invention of Machine Readable Cataloging(MARC).This change is directly related to linked data technology and can be summarized as cataloging from digitization to datafication,i.e.,bibliographic data from machine-readable to machine-actionable for integrating into the web.展开更多
The rapid diffusion of AI in education is commonly framed as a pedagogical,ethical,or technological challenge.This paper argues that AI constitutes a fundamentally governance-related issue,as it reshapes how authority...The rapid diffusion of AI in education is commonly framed as a pedagogical,ethical,or technological challenge.This paper argues that AI constitutes a fundamentally governance-related issue,as it reshapes how authority,responsibility,and accountability are distributed within education systems.Building on governance theory and critical scholarship on digitalisation,platformisation,and datafication,the paper conceptualises AI as a systemic and transversal actor that operates across boundaries between centralised regulation and decentralised educational practice.The paper develops an analytical framework grounded in reconfigured hybrid governance models and introduces a conceptual distinction between foundational AI infrastructures(AI models),AI content,and AI tutors.Through a structured literature review and conceptual analysis,it demonstrates how existing governance arrangements—designed for earlier phases of digitalisation—are increasingly misaligned with AI-mediated education systems.The analysis highlights four key governance risks,including the homogenisation of learning processes,intensified surveillance,blurred accountability,and the erosion of student and teacher agency.In response,the paper proposes a reconfigured hybrid governance approach that differentiates governance responsibilities across system levels and AI functions.It further advances concrete policy recommendations aimed at operationalising this approach through regulatory oversight,accountability mechanisms,and the protection of educational purpose and professional autonomy.By foregrounding governance as a central analytical and policy concern,the paper contributes to current debates on how education systems can harness the benefits of AI while safeguarding democratic values and human-centred education.展开更多
文摘In the past decade,the great change has taken place in the field of cataloging from theoretical models and standards to applications since the invention of Machine Readable Cataloging(MARC).This change is directly related to linked data technology and can be summarized as cataloging from digitization to datafication,i.e.,bibliographic data from machine-readable to machine-actionable for integrating into the web.
基金supported by the Slovenian Research Agency(ARIS)research programme P5-0433.
文摘The rapid diffusion of AI in education is commonly framed as a pedagogical,ethical,or technological challenge.This paper argues that AI constitutes a fundamentally governance-related issue,as it reshapes how authority,responsibility,and accountability are distributed within education systems.Building on governance theory and critical scholarship on digitalisation,platformisation,and datafication,the paper conceptualises AI as a systemic and transversal actor that operates across boundaries between centralised regulation and decentralised educational practice.The paper develops an analytical framework grounded in reconfigured hybrid governance models and introduces a conceptual distinction between foundational AI infrastructures(AI models),AI content,and AI tutors.Through a structured literature review and conceptual analysis,it demonstrates how existing governance arrangements—designed for earlier phases of digitalisation—are increasingly misaligned with AI-mediated education systems.The analysis highlights four key governance risks,including the homogenisation of learning processes,intensified surveillance,blurred accountability,and the erosion of student and teacher agency.In response,the paper proposes a reconfigured hybrid governance approach that differentiates governance responsibilities across system levels and AI functions.It further advances concrete policy recommendations aimed at operationalising this approach through regulatory oversight,accountability mechanisms,and the protection of educational purpose and professional autonomy.By foregrounding governance as a central analytical and policy concern,the paper contributes to current debates on how education systems can harness the benefits of AI while safeguarding democratic values and human-centred education.
