The role of artificial intelligence in higher education quality assessment
Opportunities and limitations in predicting the quality risk in degree programmes
Bibliographic Data
| ID | 22421858 |
|---|---|
| Authors | Dani Torrents (0000-0001-9061-445X, AQU Catalunya, corresponding author), Núria Mancho (0000-0002-8860-9544, AQU Catalunya), José Luis Mateos-González (0000-0002-8348-3057, AQU Catalunya), Anna Prades (AQU Catalunya), Anna Prades Nebot (0000-0002-0103-4435, AQU Catalunya) |
| Year | 2026 |
| Volume | 32 |
| Issue | 1 |
| Pages | 41-57 |
| Publication date | 2026-01-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Quality in Higher Education (JOURNAL) |
| Journal identifiers | ISSN: 1353-8322 • E-ISSN: 1470-1081 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/13538322.2026.2642507 |
| OpenAlex | W7154386969 |
| Language | EN |
| References cited | 14 |
This article explores the use of artificial intelligence in higher education external quality assessment. Specifically, the study demonstrates the feasibility and accuracy of predicting potential non-full compliance (defined as quality risk) in the quality assessment of higher education degree programmes. Using data from the Catalan University Quality Assurance Agency (Catalonia, Spain) covering the period 2015–2023, the study applies machine learning techniques to identify hidden patterns between 74 quality indicators and the accreditation outcomes of 1.416 degree programme evaluations. This research provides an innovative perspective to optimise and complement institutional assessment processes that are widely established at the international level.
Higher education · Quality assurance · Risk assessment · Risk management · Artificial Intelligence in Healthcare and Education · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics
| Highly cited | No |
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