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Updated: Jul 17, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

An AI-driven tools assessment framework for english teachers using the Fuzzy Delphi algorithm and deep learning.

¹Department of Basic Courses, Xinyang Vocational College of Art, Xinyang, 464000, Henan, China. wdeyouxiang0421@163.com.

Scientific Reports

|November 25, 2025

Summary

This study introduces an Artificial Intelligence (AI)-driven framework to enhance English teaching. The AI tools improved student engagement and learning clarity, achieving high accuracy in predicting pedagogical effectiveness.

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Area of Science:

Educational Technology
Artificial Intelligence in Education
Humanities Pedagogy

Background:

Traditional English literature and linguistics teaching methods face challenges in meeting diverse learner needs and providing personalized feedback.
There is a need for innovative approaches to improve student engagement and learning outcomes in humanities education.

Purpose of the Study:

To propose and evaluate a comprehensive, multi-technique Artificial Intelligence (AI)-driven tools assessment framework for enhancing English pedagogy.
To investigate the impact of AI tools on student engagement, learning clarity, and overall pedagogical effectiveness.

Main Methods:

A mixed-methods research design combining classroom case studies, in-depth interviews, and student document analysis.
Statistical techniques were used to validate relationships between engagement, tool usage, and learning clarity.

Keywords:

Artificial intelligence Digital humanities Education technology English linguistics Literary pedagogy

Related Experiment Videos

Last Updated: Jul 17, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Fuzzy Delphi Technique identified key evaluation criteria, while eXplainable AI (XAI) techniques (LIME, SHAP) ensured model transparency.

Main Results:

A deep learning Bi-LSTM model achieved 90% accuracy, 92% precision, 93% recall, and 92% F1-score in predicting pedagogical effectiveness.
The framework demonstrated significant relationships between AI tool usage, student engagement, and learning clarity.
High-importance attributes like AI usage, usability, and analytical quality were identified.

Conclusions:

The proposed AI-driven framework effectively enhances English pedagogy by improving learning clarity and student engagement.
eXplainable AI (XAI) techniques provide crucial transparency in understanding AI tool performance and pedagogical impact.
The study validates the potential of integrated AI tools to revolutionize humanities education through data-driven insights and accurate prediction models.