STUDENTS’ PERSPECTIVES ON THE IMPACT OF ARTIFICIAL INTELLIGENCE IN LEARNING BUSINESS ENGLISH
The intrinsic link between the development of technology and the future of higher education is no longer a doubt. New possibilities along with new challenges both for teaching and for learning change the landscape of education and the possibilities of it.
Cristina Laura ABRUDAN
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Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm.
Deng L, Hu X, Liu F.
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Developing Speaking Skills through Debates. Case Study Proposal in Business English [PDF]
This paper tackles the speaking skill, in general, and its role in language teaching and learning, focusing also on various ways of developing it, in particular by the employment of the debate technique EFL/ ESP classes.
Alina Buzarna-Tihenea (Galbeaza)
doaj
Business English Translation Model Based on BP Neural Network Optimized by Genetic Algorithm.
Chen Y.
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An intelligent single valued neutrosophic MCDM framework for Business English language analysis curriculum planning and pedagogical support under uncertainty. [PDF]
Ding C, Tang R, Ji W.
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Assessing core competencies of business English students in Chinese higher vocational colleges. [PDF]
Jiang L, Qu Y.
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Exploring the transformative power of blended learning for Business English majors in China (2012-2022) - A bibliometric voyage. [PDF]
Asmawi A, Dong H, Zhang X, Sun L.
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Democratizing cloud data lake analytics: natural language access to Apache Iceberg via LLM agents. [PDF]
Kataria V, Kumar N.
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