Article: Transformer-based AI framework for optimising English teaching evaluation strategies: a data-driven and explainable approach Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.9 pp.107 - 127 Abstract: Teacher effectiveness needs to be examined to improve the quality of education. However, traditional evaluation methods are found to have subjectivity and difficulty in the scalability and integration of data. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) offer potential solutions. Building on the discussion of traditional quantitative and qualitative methods of English teacher evaluation, this study proposes a transformer-based framework for integrating qualitative feedback and quantitative metrics to optimise English teacher evaluations. An objectivity tool model that combines BERT for NLP processing and Shapley additive explanations (SHAP) for transparency, making objectivity easier. The approach was validated as a pilot study involving 100 English teachers at ten schools. Qualitative feedback contributed 30%, and RMSE (0.50) and R<SUP align="right"><SMALL>2</SMALL></SUP> (0.95) were the lowest values for the transformer-based model. Stakeholders highly reported accuracy and interpretability as being good. The proposed framework offers a scalable and explainable solution to the classical approach's limitations. It shows how AI-driven evaluation systems can enhance teaching quality and assist in data-driven educational decisions. Inderscience Publishers - linking academia, business and industry through research

Title: Transformer-based AI framework for optimising English teaching evaluation strategies: a data-driven and explainable approach

Authors: Guangyong Zhang

Addresses: College Affairs Office, Dazhou Vocational and Technical College, Dazhou, 635001, China

Abstract: Teacher effectiveness needs to be examined to improve the quality of education. However, traditional evaluation methods are found to have subjectivity and difficulty in the scalability and integration of data. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) offer potential solutions. Building on the discussion of traditional quantitative and qualitative methods of English teacher evaluation, this study proposes a transformer-based framework for integrating qualitative feedback and quantitative metrics to optimise English teacher evaluations. An objectivity tool model that combines BERT for NLP processing and Shapley additive explanations (SHAP) for transparency, making objectivity easier. The approach was validated as a pilot study involving 100 English teachers at ten schools. Qualitative feedback contributed 30%, and RMSE (0.50) and R² (0.95) were the lowest values for the transformer-based model. Stakeholders highly reported accuracy and interpretability as being good. The proposed framework offers a scalable and explainable solution to the classical approach's limitations. It shows how AI-driven evaluation systems can enhance teaching quality and assist in data-driven educational decisions.

Keywords: teacher evaluation; transformer-based framework; natural language processing; NLP; explainable AI; XAI; qualitative feedback analysis; teaching effectiveness; scalable evaluation systems.

DOI: 10.1504/IJICT.2025.145828

International Journal of Information and Communication Technology, 2025 Vol.26 No.9, pp.107 - 127

Received: 19 Feb 2025
Accepted: 05 Mar 2025
Published online: 28 Apr 2025 *

Title: Transformer-based AI framework for optimising English teaching evaluation strategies: a data-driven and explainable approach

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