Creating and Sustaining the Judgement Space: Professional Judgement and Dialogic Inquiry in AI-Supported Higher Education

Abstract Professional judgement, the situated, normative practice of weighing what is educationally desirable rather than merely technically effective, is central to educational professionalism. Yet international frameworks for faculty development converge on what individual faculty should know and do, without conceptualising the conditions under which professional judgement is exercised, or how AI restructures them. This paper introduces the judgement space: the pedagogical condition within which professional judgement is not only exercised but articulated, examined, and sustained through pedagogical interaction. In AI-supported higher education, where students engage with AI independently and beyond the pedagogical relationship, the judgement space becomes a three-party condition: AI-generated evaluative outputs enter the interaction alongside the judgement of teacher and student. We draw on Biesta’s account of educational purpose across qualification, socialisation, and subjectification. From this account, we advance three claims. First, professional judgement is foundational to faculty development, not reducible to one competency among many. Second, AI restructures the evidence base on which professional judgement operates. It captures qualification-domain evidence more readily than evidence of socialisation and subjectification, privileging responsive practice unless the pedagogical framework positions AI to enrich rather than impoverish the judgement space. Third, the dialogic inquiry triangle, developed within the dialogic inquiry tradition and examined in school-based practitioner research, provides such a framework. Extended to AI-supported higher education, it structures the three-party interaction through which the judgement space can be created and sustained. We derive six structural principles for the teacher-student-AI interaction and a professional judgement audit for faculty development.

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Publication Details

Journal
Frontiers of digital education.
Published
2026-10-26
DOI
https://doi.org/10.1007/s44366-026-0107-x
Primary Topic
Artificial Intelligence in Education
Type
article
Field-Weighted Citation Impact
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article

Creating and Sustaining the Judgement Space: Professional Judgement and Dialogic Inquiry in AI-Supported Higher Education

Neil Mercer, Ying Ji
2 links
Frontiers of digital education.
Artificial Intelligence in Education
article

Creating and Sustaining the Judgement Space: Professional Judgement and Dialogic Inquiry in AI-Supported Higher Education

Neil Mercer, Ying Ji
article en

Abstract

Abstract Professional judgement, the situated, normative practice of weighing what is educationally desirable rather than merely technically effective, is central to educational professionalism. Yet international frameworks for faculty development converge on what individual faculty should know and do, without conceptualising the conditions under which professional judgement is exercised, or how AI restructures them. This paper introduces the judgement space: the pedagogical condition within which professional judgement is not only exercised but articulated, examined, and sustained through pedagogical interaction. In AI-supported higher education, where students engage with AI independently and beyond the pedagogical relationship, the judgement space becomes a three-party condition: AI-generated evaluative outputs enter the interaction alongside the judgement of teacher and student. We draw on Biesta’s account of educational purpose across qualification, socialisation, and subjectification. From this account, we advance three claims. First, professional judgement is foundational to faculty development, not reducible to one competency among many. Second, AI restructures the evidence base on which professional judgement operates. It captures qualification-domain evidence more readily than evidence of socialisation and subjectification, privileging responsive practice unless the pedagogical framework positions AI to enrich rather than impoverish the judgement space. Third, the dialogic inquiry triangle, developed within the dialogic inquiry tradition and examined in school-based practitioner research, provides such a framework. Extended to AI-supported higher education, it structures the three-party interaction through which the judgement space can be created and sustained. We derive six structural principles for the teacher-student-AI interaction and a professional judgement audit for faculty development.

Frontiers of digital education.Vol. 4(1)
University of Cambridge (GB)
Quality education
Openalex Percentile: Top 32%
Artificial Intelligence in Education
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