Exploration and Research on the Path of AIGC Intelligent Evaluation Empowering "Same Course, Different Approaches" in College English in the Context of Tiered Instruction

DOI: https://doi.org/10.62517/jhet.202615411

Author(s)

Yu Gong

Affiliation(s)

School of Foreign Languages, Yangtze Normal University, Chongqing, China

Abstract

With rapid development, AI has had a significant impact on higher education and generative AI has been continuously promoting iterative innovation in tiered college English teaching. The "same course, different approaches" model has problems under traditional tiered instruction: the objectives lack clarity, the design is homogeneous, and the model lacks innovation. Generative AI serves as a technical aid and guarantee to solve these problems. This paper first explores how AIGC intelligent evaluation can empower the 'same course, different approaches' model. In the scenario of tiered instruction, it emphasizes some teaching and learning methods such as "human-intelligence collaboration" and "human-machine interaction" to build differentiated teaching methods for students of different levels. This "refined reclassification" of the same course will provide a foundation for the personalized English teaching model, thereby promoting the "same course, different approaches" practice and innovation in tiered college English teaching.

Keywords

Tiered Instruction; College English; AIGC Intelligent Evaluation; Same Course; Different Approaches; Human-Machine Collaboration.

References

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