Optimizing Cognitive Load in ESP Teacher Development: Designing Research and Teaching Activities in an AI-Driven Context

Chunyan Peng

doi:10.47297/wspiedWSP2516-250033.20250903

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Optimizing Cognitive Load in ESP Teacher Development: Designing Research and Teaching Activities in an AI-Driven Context

Volume 9, Issue 3

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Optimizing Cognitive Load in ESP Teacher Development: Designing Research and Teaching Activities in an AI-Driven Context
Optimizing Cognitive Load in ESP Teacher Development: Designing Research and Teaching Activities in an AI-Driven Context
Zhejiang Yuexiu University, Shaoxing, 312000, China
Vol. 9, Issue 3, Pages: 165-171(2025)
Published：24 March 2025
DOI：10.47297/wspiedWSP2516-250033.20250903
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Peng Chunyan.Optimizing Cognitive Load in ESP Teacher Development: Designing Research and Teaching Activities in an AI-Driven Context[J].Journal of International Education and Development,2025,09(03):165-171.
DOI：

Peng Chunyan.Optimizing Cognitive Load in ESP Teacher Development: Designing Research and Teaching Activities in an AI-Driven Context[J].Journal of International Education and Development,2025,09(03):165-171. DOI： 10.47297/wspiedWSP2516-250033.20250903.

摘要

Abstract

The rapid integration of artificial intelligence (AI) into education has reshaped pedagogical practices

yet its implications for specialized domains such as English for Specific Purposes (ESP) teacher development remain underexplored. This study addresses this gap by investigating how AI-driven teaching and research (T&R) activities can optimize cognitive load for ESP educators

who face unique challenges in balancing domain-specific content

language pedagogy

and technological adaptation. Grounded in Cognitive Load Theory (CLT)

the research employs a phenomenological approach to analyze the experiences of 12 Chinese ESP teachers through semi-structured interviews

focus groups

and document analysis. Results reveal a three-phase adaptation trajectory: initial cognitive overload from technical unfamiliarity

iterative experimentation with AI tools

and eventual empowerment through structured training and redesigned T&R activities. Findings demonstrate AI's dual role in reducing extraneous load while fostering germane efforts

though outcomes varied across disciplines and institutional contexts. The study advances CLT by applying it to teacher cognition in technology-rich environments and proposes actionable strategies

including discipline-specific AI tools and collaborative professional development frameworks

to transform cognitive challenges into sustainable growth opportunities. These insights hold critical implications for ESP teacher training

AI design

and policy-making in the era of educational digitalization.

关键词

Keywords

references

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