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What Can a Representation Support? Generative Artificial Intelligence and Professional Knowing in Practice

Abstract Generative Artificial Intelligence (GenAI) is increasingly embedded in professional university work, yet its significance cannot be inferred from individual prompts, outputs or nominal categories of use. This study examines how differently organised professional practices shape what practitioners do with GenAI and what changes, persists, or is reopened as GenAI-mediated work becomes part of continuing professional projects. Drawing on practitioner-led collaborative action research and the theory of prac…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1831· cite & verify
What Can a Representation Support? Generative Artificial Intelligence and Professional Knowing in Practice
REVIEW OF THE UNIVERSITY OF CALIFORNIA'S MANAGEMENT CONTRACT FOR LOS ALAMOS NATIONAL LABORATORY by Committee on Energy and Commerce. Public domain

In brief

Researchers used practitioner-led collaborative action research and the theory of practice architectures to analyse a longitudinal corpus of documented generative AI interactions in university professional work. The practices covered assessment and calibration, course development, validation and credit transfer, practicum supervision, academic supervision and scholarly work.

The findings matter because universities are building disclosure rules, assessment policies and accountability expectations around AI use. The study indicates that checking final outputs or keeping chat histories may not be enough to show how AI shaped a professional project. The abstract does not quantify these effects, so how widely the patterns hold beyond the studied practices remains unclear.

Main points

  1. Professional knowing was practice-specific: what practitioners had to distinguish and judge varied with their professional project and practice architectures.
  2. Cross-practice comparison found recurring but overlapping configurations linking professional grounds, material conditions and situated responsibility.
  3. Analytical work could be handed to GenAI without professional mandate becoming symmetrical.
  4. Earlier GenAI-mediated formulations and documents could persist, be reopened, recontextualised or affect later practice.
  5. Professional judgement was enacted throughout collaboration via negotiation, restriction, attribution, reopening and project-level monitoring.

The problem

Oversight that focuses on verifying final AI outputs misses how GenAI-mediated formulations persist, get reopened, or drift from a wider professional project, so disclosure labels and chat logs do not make AI participation inspectable.

The rundown

The study analysed a longitudinal corpus of documented GenAI-mediated interaction embedded in continuing professional projects, rather than isolated prompts or outputs. It argues that the significance of GenAI cannot be inferred from individual prompts, outputs or nominal categories of use.

A central finding is that generative work can remain locally coherent yet stop serving the wider professional project without being factually false. This challenges the idea that verifying accuracy at the end of a task is sufficient oversight.

The authors point to implications for scholarly disclosure, student assessment and the design of accountable AI-mediated knowledge work, noting that disclosure categories and preserved chat histories do not by themselves make AI participation inspectable.

Sources

  1. Peer-reviewedPostdigital Science and Education2026-10-17

The debate