Fostering collaborative learning in ESP: AI-driven approaches integrating learning styles and multiple intelligences

Andi Asrifan, Luís Miguel Oliveira de Barros Cardoso, K.J. Vargheese

Abstract


The growing demand for English for Specific Purposes (ESP) calls for innovative learning methodologies that address diverse cognitive profiles. However, traditional ESP education often overlooks individual learning styles and multiple intelligences. This study explores how AI can enhance collaborative ESP learning by accommodating varied learning preferences, asking: How can AI improve collaborative ESP training by adapting to diverse learning styles and intelligences? A mixed-methods experimental design involved 100 university students from Engineering, Medicine, and Business, divided into experimental and control groups. The experimental group received AI-supported collaborative ESP training tailored to their learning styles and intelligences, while the control group followed conventional methods. Results showed the experimental group demonstrated significantly higher motivation, engagement, learning outcomes, and improved communication and collaboration skills. These findings suggest that integrating AI with cognitive-based learning models enhances collaborative ESP environments through adaptive content delivery, dynamic grouping, and personalized feedback, fostering more inclusive and effective professional language learning.

Keywords


AI-driven learning; Collaborative learning; English for Specific Purposes (ESP); Learning styles and multiple intelligences; Adaptive education technology

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References


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DOI: http://dx.doi.org/10.22373/ej.v12i2.29330

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