Artificial Intelligence Use by University Educators: A Critical Integrative Review Towards a Professionally Situated Conceptualisation

Artificial intelligence (AI) is increasingly becoming embedded in higher education teaching, assessment, research and academic support. However, the educational value of AI cannot be adequately explained by the availability of AI tools, frequency of use or technical competence alone. University educators operate within professional, disciplinary, institutional and ethical contexts in which AI generated outputs must be interpreted, evaluated, adapted and sometimes rejected. This article critically integrates recent research on AI use by university educators to develop a professionally situated conceptualization of AI mediated educational practice. The review focuses particularly on educators' patterns of AI use, AI literacy, professional and disciplinary knowledge, human AI interaction, professional judgement, ethical and regulatory accountability, institutional context and reflective professional learning. The synthesis indicates that educators increasingly use AI for preparation of learning materials, generation of activities, feedback, formative assessment, research support and administrative work, but adoption remains selective and context dependent. AI literacy is important but does not by itself ensure educationally appropriate practice. Similarly, institutional policies and professional development influence adoption without determining how educators ultimately use AI. The review therefore proposes Professionally Situated AI Mediated Practice (PS AIMP), a conceptual framework in which professional judgement is positioned as the central proposed mechanism through which AI capability is translated into educationally legitimate and professionally accountable practice. Professional judgement is informed by professional purpose, disciplinary knowledge, AI literacy and human AI interaction, while being bounded by ethical and regulatory accountability and shaped by institutional context. Quality of AI mediated professional practice constitutes the principal outcome, while reflective professional learning provides a recursive process through which practice is continually evaluated and improved. The framework offers a basis for empirical investigation of why similar AI capabilities may produce different educational outcomes across educators, disciplines and institutions.

Artificial intelligence; generative AI; higher education; university educators; AI literacy; professional judgement; professional knowledge; AI mediated practice; professional learning; PS AIMP

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