Human-Machine Collaboration in Contextualized Vocabulary Learning for Junior Secondary English: A Case Study of Generative AI-Assisted Thematic Discourse Creation

Journal: Journal of Higher Education Research DOI: 10.32629/jher.v7i3.5339

Haoxuan Fang, Hongkun Li

Foreign Studies College, Hunan Normal University, Changsha, Hunan, China

Abstract

In response to the persistent challenge in junior secondary English vocabulary teaching, characterized by "high dictation scores but poor vocabulary application," this paper proposes a human-machine collaborative teaching model based on generative AI. Centered on the integrated framework of pronunciation, spelling, meaning, and use, the model follows a five-step procedure — word selection, constraint setting, dialogue, review, and input and output — and employs generative AI to support thematic discourse creation and multimodal input, with the aim of facilitating contextualized vocabulary learning. Scenario simulation and pilot application indicate that the model effectively enhances students' ability to actively use vocabulary, fosters AI literacy characterized by precise prompting and critical evaluation, and provides a feasible approach to contextualized vocabulary instruction.

Keywords

generative artificial intelligence; junior secondary English; vocabulary teaching; human-machine collaboration; contextualized learning

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References

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