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Related Experiment Video

Updated: Sep 28, 2025

Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension
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Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial Intelligence.

Guorong Shen1

  • 1School of Foreign Languages, Henan University of Technology, Zhengzhou, China.

Frontiers in Psychology
|April 1, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an artificial intelligence (AI) human-computer interaction model to enhance text reading comprehension. The AI model, utilizing hierarchical attention and self-attention mechanisms, demonstrated improved accuracy (EM +1.4%, F1 +2.7%) over baseline models.

Keywords:
AI human-computer interactionSQuAD datasetattention mechanismneural networkreading comprehension

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Natural Language Processing

Background:

  • Improving text reading ability is crucial for effective human-computer interaction.
  • Traditional models often struggle with relevance and understanding due to simple interaction layers.
  • Enhancing text representation is key to better reading comprehension in AI systems.

Purpose of the Study:

  • To propose a novel artificial intelligence (AI) human-computer interaction method for improving text reading ability.
  • To design and implement an AI model incorporating hierarchical attention, aggregation, and self-attention mechanisms.
  • To evaluate the performance of the proposed model against existing baseline models using the Stanford Question Answering Dataset (SQuAD).

Main Methods:

  • Constructed an AI human-computer interaction model with three layers: coding (Recurrent Neural Network encoder), interaction (hierarchical attention and aggregation), and output (fully connected with two SoftMax layers).
  • Employed a hierarchical attention and aggregation mechanism in the interaction layer to improve text coding and address limitations of traditional models.
  • Integrated a self-attention model to further enhance text feature representation.

Main Results:

  • The proposed AI model achieved higher accuracy compared to the baseline model, with an Exact Match (EM) increase of 1.4% and an F1 score increase of 2.7%.
  • A slight decrease of 0.7% in EM and F1 values was observed compared to a specific improvement point, indicating the output layer's impact on performance.
  • Experimental results validate that AI human-computer interaction significantly enhances text reading comprehension capabilities.

Conclusions:

  • The developed AI human-computer interaction model effectively improves text reading ability.
  • The integration of hierarchical attention, aggregation, and self-attention mechanisms contributes to enhanced model performance.
  • Further optimization of the output layer holds potential for additional performance gains in AI reading comprehension systems.