Applications of the Transformer Architecture in AI-Assisted English Reading Comprehension

• URL: http://arxiv.org/abs/2604.23615v1
• Date: Sun, 26 Apr 2026 09:11:38 GMT
• Title: Applications of the Transformer Architecture in AI-Assisted English Reading Comprehension
• Abstract summary: This paper studies interpretable and fair artificial intelligence architectures for understanding English reading.<n>The model's lack of interpretability, reduction of algorithmic bias, and unreliable performance in learning environments are the current issues faced in natural language teaching.<n>The proposed method aims to ensure high prediction accuracy and fairness for different learners.
• Score: 7.644563519492337
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper studies interpretable and fair artificial intelligence architectures for understanding English reading. Introduced transformer-based models, integrating advanced attention mechanisms and gradient-based feature attribution. The model's lack of interpretability, reduction of algorithmic bias, and unreliable performance in learning environments are the current issues faced in natural language teaching. A unified technical pipeline has been constructed, including adversarial bias correction methods, token-level attribution analysis, and multi-head attention heatmap visualization. Experimental validation was conducted using a large-scale labeled English reading comprehension dataset, and the data partitioning scheme and parameter optimization procedures have been determined. The method significantly outperforms the state-of-the-art models for this task in terms of accuracy and macro-average F1 score; in some aspects, it even surpasses or closely matches the results of human evaluations. In multi-week user experiments, the explainable transformer improved teachers' trust and operability in feedback-based assessments within the scoring system. The proposed method aims to ensure high prediction accuracy and fairness for different learners. This indicates that it is a real-world educational application based on artificial intelligence with a focus on interpretation. Improve the user experience in AI-assisted reading comprehension systems, counteract biases, and enhance the details explained by transformers.

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