<![CDATA[Content validity assessment is essential for determining whether educational materials adequately represent intended learning outcomes. However, conventional assessment procedures require substantial expert time and may produce inconsistent decisions across large item collections. This study develops a transformer-based framework to support content-validity pre-screening through two complementary tasks: predicting expert-derived Aiken’s V coefficients and classifying instructional-item essentiality. The final dataset comprised 652 Indonesian-language educational text items independently evaluated by four subject-matter experts. To reduce information leakage, identical and normalized-equivalent texts were grouped before applying a group-aware 70:15:15 training–validation–test split. Classical TF-IDF-based baselines were compared with IndoBERT, multilingual BERT, XLM-RoBERTa, and multilingual DeBERTa-v3. For Aiken’s V regression, multilingual BERT achieved the lowest MAE of 0.0501, the lowest RMSE of 0.0625, and the highest R² of 0.5239, whereas multilingual DeBERTa-v3 achieved the highest Spearman correlation of 0.7532. For essentiality classification, XLM-RoBERTa achieved the highest accuracy of 0.8557 and Macro-F1 of 0.8161, whereas multilingual BERT achieved the highest balanced accuracy of 0.8135 and ROC-AUC of 0.9111. Error analysis showed that the models captured textual patterns associated with expert-derived outcomes but remained limited when judgments depended on broader curricular context, competency hierarchies, prerequisite relationships, or relationships among instructional items. The findings support the use of transformer models as human-in-the-loop decision-support tools for prioritizing uncertain or potentially problematic educational items. However, the framework should be interpreted as a pre-screening mechanism rather than a replacement for expert judgment, and external validation across institutions and disciplines remains necessary.]]>
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