Beyond Scores: Explainable Intelligent Assessment Strengthens Pre-service Teachers' Assessment Literacy
This work addresses the challenge of cultivating assessment literacy in pre-service teachers, which is crucial for personalized education, though it appears incremental in applying explainable AI to an existing educational tool.
The paper tackled the problem of underdeveloped assessment literacy in pre-service teachers by proposing XIA, an explainable intelligent assessment platform, and found in a controlled study with 21 participants that it supported reflection, reduced errors, and shifted reasoning from score-based to evidence-based.
Assessment literacy (AL) is essential for personalized education, yet difficult to cultivate in pre-service teachers. Conventional teacher preparation programs focus on theoretical knowledge, while digital assessment tools commonly provide opaque scores or parameters. These limitations hinder reflection and transfer, leaving AL underdeveloped. We propose XIA, an eXplainable Intelligent Assessment platform that extends statistics-informed support with visualized cognitive diagnostic reasoning, including contrastive and counterfactual explanations. In a pre-post controlled study with 21 pre-service teachers, we combined quantitative tasks and questionnaires with qualitative interviews. The findings offer preliminary evidence that XIA supported reflection, self-regulation, and assessment awareness, and helped reduce assessment errors. Interviews further showed a shift from score-based judgments toward evidence-based reasoning. This work contributes insights into the design of intelligent assessment tools, showing how explanatory scaffolding can bridge assessment theory and classroom practice and support the cultivation of AL in teacher education.