What differentiates educational literature? A multimodal fusion approach of transformers and computational linguistics

Bird, JJ ORCID: https://orcid.org/0000-0002-9858-1231, 2026. What differentiates educational literature? A multimodal fusion approach of transformers and computational linguistics. International Journal of Artificial Intelligence in Education: 100007. ISSN 1560-4292

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Abstract

The integration of new literature into the English curriculum remains a challenge since educators often lack scalable tools to rapidly evaluate readability and adapt texts for diverse classroom needs. This study proposes to address this gap through a multimodal approach that combines transformer-based text classification with linguistic feature analysis to align texts with UK Key Stages. Eight state-of-the-art Transformers were fine-tuned on segmented text data, with BERT achieving the highest unimodal F1 score of 0.75. In parallel, 500 deep neural network topologies were searched for the classification of linguistic characteristics, achieving an F1 score of 0.392. The fusion of these modalities shows a significant improvement, with every multimodal approach outperforming all unimodal models. In particular, the ELECTRA Transformer fused with the neural network achieved an F1 score of 0.996. Unimodal and multimodal approaches are shown to have statistically significant differences in all validation metrics (accuracy, precision, recall, F1 score) except for inference time. The proposed approach is finally encapsulated in a stakeholder-facing web application, providing non-technical stakeholder access to real-time insights on text complexity, reading difficulty, curriculum alignment, and recommendations for learning age range. The application empowers data-driven decision making and reduces manual workload by integrating AI-based recommendations into lesson planning for English literature.

Item Type:	Journal article
Publication Title:	International Journal of Artificial Intelligence in Education
Creators:	Bird, J.J.
Publisher:	Elsevier
Date:	16 March 2026
ISSN:	1560-4292
Identifiers:	Number Type 10.1016/j.ijaied.2026.100007 DOI 2593328 Other
Rights:	Copyright: © 2026 Published by Elsevier Ltd on behalf of International AIED Society. This article licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	18 Mar 2026 16:14
Last Modified:	18 Mar 2026 16:14
URI:	https://irep.ntu.ac.uk/id/eprint/55442

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