9卷 第3期
Zhejiang Yuexiu University, Shaoxing, 312000, China
纸质出版日期：2025-03-24
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(2025). Optimizing Cognitive Load in ESP Teacher Development: Designing Research and Teaching Activities in an AI-Driven Context. 国际教育与发展（英文版）, 9(3), 165-171.
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.
(2025). Optimizing Cognitive Load in ESP Teacher Development: Designing Research and Teaching Activities in an AI-Driven Context. 国际教育与发展（英文版）, 9(3), 165-171. DOI： 10.47297/wspiedWSP2516-250033.20250903.
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.
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.
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