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

2026-04-26

AI Summary

This study addresses the limitations of AI-assisted English reading comprehension systems—specifically, insufficient interpretability, algorithmic bias, and performance instability—by proposing an interpretable Transformer architecture that integrates advanced attention mechanisms with gradient-based feature attribution. The approach establishes a unified technical pipeline incorporating adversarial bias correction, token-level attribution analysis, and multi-head attention heatmap visualization, thereby significantly enhancing model fairness and pedagogical applicability without compromising high accuracy. Experimental results demonstrate that the proposed method outperforms state-of-the-art models in both accuracy and macro-averaged F1 score, with certain metrics approaching human-level performance. Furthermore, multi-week user studies confirm its practical effectiveness and foster strong trust among educators.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• interpretability
• algorithmic bias
• Transformer architecture
• reading comprehension
• fairness

Innovation

• interpretable AI
• fairness
• Transformer architecture
• attention visualization
• adversarial bias correction