基于AI的创新教学模式在《概率论与数理统计》课程中的研究与实践

Research and Practice of an AI-Based Innovative Teaching Model in the Course of “Probability Theory and Mathematical Statistics”

摘要: 随着人工智能技术的广泛应用，利用人工智能赋能教育教学，是推进高校课程混合式教学改革的必经之路。为更好地推进我校“1 + 3 + 2”教育教学改革，文章以我校《概率论与数理统计》教学课程为依托，针对该课程理论知识抽象难懂、线上线下学习环境脱节，以及公共基础课与各专业知识内容融合不足，导致的学生缺乏学习热情、实践能力不达标等问题，围绕“立德树人”的根本任务，立足于AI赋能教学改革的现状，通过重构课程内容体系、强化课程思政建设、创新教学方法和手段、完善考核评价机制等一系列改革举措，提出基于AI的“一核三段五化”创新教学模式，即践行“以学生为中心”这一核心育人理念，以AI为纽带，将“五化”(学情分析数据化、学生学习个性化、教学内容情境化、教学过程可视化、教学评价多元化)融入“三段”(课前、课中、课后)。课前数据借助AI精准指导课中和课后教学，课后数据经AI分析反哺课前，形成教学闭环。这种创新的教学模式将为应用型本科院校的公共基础课建设和人才培养的双重推进提供参考。

Abstract: With the widespread application of artificial intelligence technology, empowering education and teaching with AI has become an inevitable path for advancing the reform of blended teaching in university courses. To further promote the “1 + 3 + 2” education and teaching reform in our university, this paper takes the course of “Probability Theory and Mathematical Statistics” in our university as the research carrier. Aiming at the problems existing in the course, such as the abstract and obscure theoretical knowledge, the disconnection between online and offline learning environments, and the insufficient integration of public basic course content with professional knowledge of various disciplines, which result in students’ lack of learning enthusiasm and inadequate practical abilities. This paper centers on the fundamental task of “fostering virtue through education”, based on the current situation of AI-empowered teaching reform, through a series of reform measures including reconstructing the curriculum content system, strengthening the ideological and political construction of the course, innovating teaching methods and means, and improving the assessment and evaluation mechanism, we propose an AI-based innovative teaching model of “One Core, Three Stages, and Five Modernizations”. Specifically, this model practices the core educational philosophy of “student-centeredness”, takes AI as the link, and integrates the “Five Modernizations” (data-driven learning analysis, personalized student learning, situational teaching content, visualized teaching process, and diversified teaching evaluation) into the “Three Stages” (pre-class, in-class, and post-class). Pre-class data provides precise guidance for in-class and post-class teaching with the support of AI, and post-class data, after AI analysis, feeds back to pre-class teaching, thus forming a closed teaching loop. This innovative teaching model will provide a reference for the dual promotion of the construction of public basic courses and talent cultivation in application-oriented undergraduate universities.