Teaching Machines to Teach: How AI Is Transforming Vocational Learning

Training for advanced manufacturing has long struggled with a fundamental challenge: helping learners understand the invisible dynamics of machining. Variables such as vibration, tool wear, heat, and material behavior interact in complex, non-linear ways, particularly when working with legacy equipment that offers little real-time feedback. As a result, learners often rely on trial and error, while industry depends heavily on tacit expertise that is difficult to scale. 

In this session, the team from Singapore’s Institute of Technical Education shares the story behind AMCAM, an AI-assisted approach to Computer-Aided Machining that integrates real-time sensor data, digital twins, and multiple AI agents into the learning experience. Rather than replacing human judgment, the system was designed to make machine behavior visible and understandable, allowing learners to simulate outcomes, monitor live conditions, and reflect on performance using predictive feedback.

The session unpacks how specialized AI agents were embedded across the machining workflow to support calibration, prediction, monitoring, optimization, and reflection. Attendees will see how this approach transformed both learning and operational outcomes by enabling safer experimentation, improving consistency, and building AI literacy alongside technical skill. Beyond the specific manufacturing context, the session offers transferable insights into how AI can be designed as a learning partner that supports sense-making, decision quality, and continuous improvement.

Key Topics Include:

• Using AI and real-time data to make complex work visible to learners
• Designing learning experiences around digital twins and predictive simulation
• Embedding multiple AI agents to support different stages of performance
• Teaching AI concepts through hands-on, vocational practice
• Bridging legacy systems with modern AI-enabled learning environments