AI赋能数字信号处理本科教学中的应用初探——以“连续时间信号的抽样”为例

Exploration of AI-Powered Undergraduate Teaching of Digital Signal Processing—Taking “Sampling Theorem” as an Example

摘要: 随着生成式人工智能技术的繁荣发展，以强大内容生成、逻辑推理与知识迁移能力为核心的大模型层出不穷，正在深刻重塑各行各业的发展模式。数字信号处理(DSP)是本科电子信息类专业的核心课程，但因其理论抽象繁杂、缺乏个性化实时指导、理论与实践存在割裂感等问题，长期制约教学质量与学生工程能力培养。本文以讲述“连续时间信号的抽样”为例，探究了AI大模型赋能教学中的应用路径，从课前摸底、课上实时答疑与编程辅助、课后巩固提升，到专用教学智能体的搭建及新型考评方式的实施，全方位展示了AI为DSP教学带来的变革。本文构建了AI赋能课程教学的新范式，为推进教学改革、培养适应时代需求的数字化人才提供重要参考。

Abstract: With the rapid advancement of generative artificial intelligence technology, large language models, with powerful capabilities of content generation, logical reasoning, and knowledge transfer, are reshaping various industries. Digital Signal Processing (DSP), a core course for undergraduate students majoring in electronic information, has long been plagued by highly abstract and complex theories, a lack of personalized real-time guidance, and a disconnect between theory and practice. These issues have hindered teaching quality and the cultivation of students’ engineering competencies. Taking the “Sampling Theorem” as an example, this paper explores AI-empowered teaching strategies, covering pre-class prior knowledge assessment, in-class instant Q&A and coding assistance, post-class consolidation, the construction of a DSP learning agent, and the implementation of a new evaluation system. Through these strategies, the paper demonstrates the ongoing revolution in DSP education. The study proposes a replicable paradigm for AI-empowered teaching and provides a valuable reference for cultivating digitally competent talents.