A Low-Code Workflow Automation (n8n) and Vibe Coding for Learning and Teaching: A Classroom Attendance Prototype

Abstract—This paper presents the use of n8n, a low-code workflow-automation platform, as a lightweight Internet of Things (IoT) tool to support operational efficiency and hands-on learning in higher education. It further explores vibe coding, a rapid, visual, and iterative approach to automation that enhances engagement and creativity in workflow design. Unlike complex IoT stacks or high-code environments, n8n’s node-based interface allows students to visualise data flow, modify nodes, and experience immediate system feedback. A QR-code and token-based attendance workflow provides real-time verification and response logging, transforming abstract concepts such as event triggers (webhooks), latency handling, cloud integration, agentic AI, and lightweight blockchain mechanisms into observable actions on the workflow canvas. The prototype illustrates how learners can modify and extend the workflow to design their own automations, bridging theoretical understanding with practical implementation through short, iterative vibe cycles. It also suggests that vibe-coded n8n activities can lower technical barriers, enhance digital literacy, and foster active participation while keeping implementation simple. The paper concludes with implications for embedding low-code workflow tools and vibe coding into Learning and Teaching (L&T) practice to promote applied learning in IoT and digital transformation. Keywords—Learning and Teaching (L&T), Internet of Things (IoT), low-code workflow automation, n8n, vibe coding, agentic AI, blockchain.