《机器人技术》课程人工智能体系化赋能教学改革研究

Research on the Systematic Empowerment of Teaching Reform in the Field of Artificial Intelligence through Robotics Technology Course

摘要: 针对当前机器人技术课程体系中普遍存在的知识模块割裂、智能技术标签化以及学生系统思维缺失等核心问题，本文提出一种以人工智能体系化赋能为核心导向的课程重构框架，创新性地构建了基础认知强化-AI使能工具化–系统综合创新三级递进模型。该模型以结构化教学案例库为核心中枢，将机器学习、计算机视觉等前沿AI技术，以需求牵引、问题驱动的工程逻辑，深度融入从机械基础到控制集成的传统教学脉络，在此基础上进一步规划了关联渗透–专题探究–项目整合三阶段融合教学路径，旨在系统性地培养学生核心素养，为新工科背景下的课程改革提供一套兼具理论前瞻性与实践可操作性的系统解决方案。

Abstract: Addressing the core issues prevalent in current robotics technology curricula, such as the fragmen-tation of knowledge modules, the “labeling” of intelligent technologies, and the lack of systematic thinking among students, this paper proposes a curriculum reconstruction framework centered on systematized AI capability empowerment. It innovatively constructs a three-tier progressive model: “Foundation Cognition Enhancement - AI Enabling Toolization - System Synthesis and Innovation”. With the “Structured Teaching Case Library” as its central pillar, this model deeply integrates cutting-edge AI technologies like machine learning and computer vision into the traditional teaching trajectory—from mechanical fundamentals to control integration—following an engineering logic of “demand traction and problem-driven”. Furthermore, it outlines a three-phase integrated teaching path: “Correlation and Infiltration - Thematic Inquiry - Project Integration”. The aim is to systematically cultivate students’ core competency in using AI technology to analyze, design, and optimize intelligent robotic systems. This provides a systematic solution for New Engineering curriculum reform, combining theoretical foresight with practical operability.