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In this work, we introduce an intelligent digital platform for electronic circuit experiment teaching, designed to address specific technical challenges in optimise the student-centered educational process. The platform integrates advanced technologies, including real-time data acquisition, AI-driven assistance, and hybrid experiment modes, to streamline and enhance laboratory operations and learning outcomes. A key technical feature of the platform is its implementation of micro-experiments and experiment knowledge graphs, which provide modular, scalable pathways for knowledge acquisition and reinforcement. Remote experiment capabilities are embedded to address resource constraints, enabling students to conduct experiments outside traditional laboratory settings while maintaining high fidelity in results. Additionally, the platform incorporates an AI-driven assistant for automated trouble-shooting, reducing reliance on direct instructor intervention. The platform’s infrastructure includes a dynamic laboratory scheduling with real-time monitoring, and automated data acquisition from laboratory instruments. This enables seamless digitization of experimental processes, including direct waveform capture and automated data upload for analysis. Post-experiment, the system supports automated marking of objective questions and analysis of experimental data, significantly reducing the manual workload for educators while providing consistent and transparent assessments. The platform supports hybrid learning environments, seamlessly integrating online-offline and local-remote experiment modes. By providing students with enhanced access to resources and personalised learning pathways, the platform ensures precise alignment with learning objectives. Its data-driven design facilitates deeper analysis of student performance trends, enabling instructors to make informed pedagogical adjustments. This initiative sets a new benchmark in experimental learning, addressing challenges of scalability, accessibility, and resource efficiency in technical education.

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Advancing Experimental Learning: Building an Intelligent Digital Platform for Electronic Circuit Laboratories

  • 会议论文

  • 发表日期：

    2025年

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  • Dandan Sun,
  • Yue Chen,
  • Ling Ma,
  • Hongda Li

摘要

In this work, we introduce an intelligent digital platform for electronic circuit experiment teaching, designed to address specific technical challenges in optimise the student-centered educational process. The platform integrates advanced technologies, including real-time data acquisition, AI-driven assistance, and hybrid experiment modes, to streamline and enhance laboratory operations and learning outcomes. A key technical feature of the platform is its implementation of micro-experiments and experiment knowledge graphs, which provide modular, scalable pathways for knowledge acquisition and reinforcement. Remote experiment capabilities are embedded to address resource constraints, enabling students to conduct experiments outside traditional laboratory settings while maintaining high fidelity in results. Additionally, the platform incorporates an AI-driven assistant for automated trouble-shooting, reducing reliance on direct instructor intervention. The platform’s infrastructure includes a dynamic laboratory scheduling with real-time monitoring, and automated data acquisition from laboratory instruments. This enables seamless digitization of experimental processes, including direct waveform capture and automated data upload for analysis. Post-experiment, the system supports automated marking of objective questions and analysis of experimental data, significantly reducing the manual workload for educators while providing consistent and transparent assessments. The platform supports hybrid learning environments, seamlessly integrating online-offline and local-remote experiment modes. By providing students with enhanced access to resources and personalised learning pathways, the platform ensures precise alignment with learning objectives. Its data-driven design facilitates deeper analysis of student performance trends, enabling instructors to make informed pedagogical adjustments. This initiative sets a new benchmark in experimental learning, addressing challenges of scalability, accessibility, and resource efficiency in technical education.