Automated Assessment of Young Children’s Handwritten Essays: Empirical Analysis from AIED Unplugged’s Perspective

Scaling writing interventions in resource-constrained educational systems is challenging due to the heavy workload of manual essay assessment. This study investigates the feasibility of using AI-based text analysis to monitor primary education argumentative writing under the AIED Unplugged paradigm. Analyzing 261 handwritten essays from Brazilian public school elementary students, we compared AI-powered and manual transcription methods. The AI transcription achieved a high mean F1-score of 0.918, performing slightly better in higher grades. Critically, while structural metrics like sentence length showed differences due to AI segmentation errors, lexical and discourse-level features remained remarkably robust across both methods. These findings suggest AI transcription is a reliable, scalable solution for supporting intensive writing pedagogical programs in low-resource environments.