Generative AI Practices in European Higher EducationDigital ReportADMIT ReportMapping and Comparative Analysis of Best Practices Across Educational LevelsErasmus+ EthicAI Project · Work Package 2 · Task 2.2 — Prepared by Anadolu University
Practice Matrix · Macro

Quality Assurance, Monitoring and Evaluation

National policy · Evidence collection, pilot evaluation, indicators, accreditation expectations and accountability mechanisms.

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practices
2
Who
2
Where
1
Source report
1

The reading of this intersection

The count in the cell shows density; what the intersection says is carried by the reading below.

The quality assurance route carries two instruments of markedly different force. The first is a curated set of resources and supplementary advice intended to assist the sector in using generative AI while maintaining academic standards, which constitutes guidance. The second explicitly prohibits assessors, reviewers and panellists from using generative tools in assessment activities while advising applicants to exercise caution, which constitutes a rule.

That asymmetry merits attention. It is one of the few binding national measures recorded anywhere in this material, and it binds the evaluators rather than the evaluated.

Who, and where

The actors carrying out the practice and where they are. Where a source does not name the institution, no attribution is made.

Institutions and actors
  • Quality Assurance Agency for Higher Education
  • UK Research and Innovation
Countries
  • United Kingdom
Context
  • Higher education

How it was done

Each record stands with its imprint, its short reading and the sentence found in the source. Quotations are not shortened, so that a reader can check the inference.

Sector-wide resources and supplementary advice on generative AI

The quality assurance agency curates resources aimed at helping the education sector use generative AI while maintaining academic standards, and has published supplementary advice on the tools. The instrument works through the quality assurance route rather than through legislation.

Institution
Quality Assurance Agency for Higher Education
Country
United Kingdom
Delivery model
not stated in the source
Context
Higher education
Contributed by
not stated in the source
Year
2024
Maturity
Established
Scalability
Embedded
Transferability
Embedded
Source report
D3.1
Evidence

The partner names the agency, the purpose of the resources and their public availability.

These resources aim to help the education sector use Generative AI as a positive tool while maintaining academic standards.
D3.1, p. 36

Prohibition on generative AI in research funding assessment

Assessors, reviewers and panellists are explicitly prohibited from using generative AI in their assessment activities, while applicants are advised to exercise caution over bias and data protection. The asymmetry between a prohibition for evaluators and advice for applicants is itself a policy design choice.

Institution
UK Research and Innovation
Country
United Kingdom
Delivery model
not stated in the source
Context
Higher education
Contributed by
not stated in the source
Year
2024
Maturity
Embedded
Scalability
Embedded
Transferability
Embedded
Source report
D3.1
Evidence

The prohibition is reported as explicit, which separates this record from the advisory instruments that dominate the national level.

Assessors, reviewers, and panellists are explicitly prohibited from using Generative AI tools in their assessment activities.
D3.1, p. 36