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.

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 instituti

SUBMITTER: Manganello F 

PROVIDER: S-EPMC12738910 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature