ArticleFrontiers in artificial intelligence2025
Testing the applicability of a governance checklist for high-risk AI-based learning outcome assessment in Italian universities under the EU AI act annex III.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The EU AI Act classifies AI-based learning outcome assessment as high-risk (Annex III, point 3b), yet sector-specific frameworks for institutional self-assessment remain underdeveloped. This creates accountability gaps affecting student rights and educational equity, as institutions lack systematic tools to demonstrate that algorithmic assessment systems produce valid and fair outcomes. Methods: This exploratory study tests whether ALTAI's trustworthy AI requirements can be operationalized for educational assessment governance through the XAI-ED Consequential Assessment Framework, which integrates three educational evaluation theories (Messick's consequential validity, Kirkpatrick's four-level model, Stufflebeam's CIPP). Following pilot testing with three institutions, four independent coders applied a 27-item checklist to policy documents from 14 Italian universities (13% with formal AI policies plus one baseline case) using four-point ordinal scoring and structured consensus procedures. Results: Intercoder reliability analysis revealed substantial agreement (Fleiss's Discussion: The framework demonstrates feasibility for self-assessment but reveals critical misalignment: universities document aspirational commitments more readily than technical safeguards, with particularly weak capacity for validity testing and fairness monitoring. Findings suggest three interventions: (1) ministerial operational guidance translating EU AI Act requirements into educational contexts, (2) inter-institutional capacity-building addressing technical-pedagogical gaps, and (3) integration of AI governance indicators into national quality assurance systems to enable systematic accountability. The study contributes to understanding how educational evaluation theory can inform the translation of abstract trustworthy AI principles into outcome-focused institutional practices under high-risk classifications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.