Empowering Inquiry-Based Higher Vocational Education with Large Language Models: A Case Study of Python Language Course

Ying-Chuan Tang （ Neusoft Institute Sichuan, Chengdu, Sichuan 611844, China ）

Abstract

With the rapid iteration of Large Language Models (LLMs), their applications have permeated various fields, demonstrating significant value in higher vocational education. Research indicates that the traditional Inquiry-Based Learning (IBL) model faces challenges such as feedback latency, disconnection between industry and education, and homogenized evaluation dimensions. Taking the Python language course as an example, this paper analyzes the current limitations of the IBL model. It explores a path to refine traditional IBL in terms of course content and evaluation systems by integrating LLM-based agents. The study aims to provide a novel approach for higher vocational education to enhance the quality of instructional content.

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