OBE理念下AI赋能的《Python语言》课程改革框架设计与应用

The Framework Design and Application of the AI-Enabled “Python Language” Course Reform under the OBE Concept

摘要: 针对工科《Python语言》课程存在专业适配性弱、能力评价难以量化、个性化教学不足等问题，本文基于OBE成果导向教育理念，融合AI技术开展课程教学改革研究。确立分层培养、场景适配、成果量化的设计原则，构建包含分层教学目标、AI赋能教学路径、多维度成果评价的一体化改革框架，搭建三大AI教学功能模块，覆盖个性化学习、代码智能评测、工程项目辅助指导全场景。以126名工科专业学生为对象开展对照教学实践，结果表明：相较于传统教学模式，AI赋能OBE教学模式可显著提升学生学业成绩与工程实践能力，优化学习体验。本研究有效解决了工科Python课程分层育人、精准评价的教学痛点，契合工程教育认证要求，可为工科编程类课程教学改革提供参考。

Abstract: Aiming at the problems of weak professional adaptability, non-quantifiable ability evaluation and insufficient personalized teaching in the engineering “Python Language” course, this paper carries out curriculum teaching reform research by integrating AI technology based on Outcome-Based Education (OBE). Following the design principles of hierarchical training, scenario adaptation and quantitative achievement, an integrated reform framework consisting of hierarchical teaching objectives, AI-empowered teaching paths and multi-dimensional outcome evaluation is constructed, with three AI teaching modules covering personalized learning, intelligent code evaluation and engineering project guidance. A controlled teaching practice with 126 engineering students shows that the AI-empowered OBE teaching mode can significantly improve students’ academic performance and engineering practical ability, and optimize learning experience compared with the traditional mode. This study effectively solves the teaching pain points of hierarchical education and accurate evaluation of engineering Python courses, conforms to engineering education accreditation requirements, and provides a reference for the teaching reform of engineering programming courses.