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Empowerment of College English Task —— Based Teaching by Means of Generative Artificial Intelligence (AIGC ): A Case Study

Bo Han

Abstract


Generative Artificial Intelligence (AIGC) is a recent technological development useful for reforming college English teaching. With
task-based language teaching (TBLT) as its foundation, the present paper considers both theoretical possibilities and real applications of AIGC
in connection with college English task-based classes—— four such cases are analyzed. The four tasks dealt with are: information matching,
data analysis, strategic interaction, and experiential inquiry. AIGC is applied—— respectively—— as an aid to lesson planning, to visualization, to information search, and to strategic thinking in all of these cases. From the findings it is evident that AIGC offers several types of
intervention and achieves variable effectiveness with regard to particular tasks. What is clearly established here is that teaching based on tasks
(with help from AIGC) may be highly beneficial for English application competence and is worth considering in connection with reforms of
college English under professional accreditation.

Keywords


AIGC; Task-based language teaching; College English; Case study; Application competence

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References


[1] Zhao Dan. Blended college English teaching under the background of engineering education accreditation[J]. English Square, 2024(10):

108-111.

[2] Willis J. A Framework for Task-based Learning[M]. London: Longman, 1996.

[3] Ellis, R. Task-based Language Learning and Teaching[M]. Oxford: Oxford University Press, 2003.

[4] He Hengheng. Innovative application of AIGC technology in college English teaching[J]. English Square, 2024(29): 97-100.




DOI: http://dx.doi.org/10.70711/neet.v4i9.10053

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