QCI Experience-Based & Operation-Based Learning by NUTN & UniMAP  

• Abstract: The Human and Machine Co-Learning model educates students of various ages in Computational Intelligence (CI), covering Neural Networks, Fuzzy Logic, and Evolutionary Computation. The learning process consists of four stages, leading to co-learning with machines and real-life application of CI concepts. Tools align with IEEE standard 1855 and include STEM subjects, offering functionalities like sensing and coding. The QCI&AI-FML Learning Platform enables interaction between machine intelligence and human knowledge. The concept involves creating a workshop for young students, coupled with a competition, during IEEE-sponsored conferences. This approach has been successfully implemented at the IEEE CEC 2023 in the USA, the FUZZ-IEEE 2023 in Korea, the IEEE WCCI 2024 in Japan, the IEEE SSCI 2025 in Norway, the FUZZ-IEEE 2025 in France, and the IEEE Region 10 SPNIC Activity on Nov. 18, 2023, Feb. 20, 2024, and Feb. 13 and Apr. 12, 2025. At the beginning of the event, or during its initial hours, students will receive educational materials and guidance from tutors and invited speakers on CI-related topics. Subsequently, they will have the opportunity to apply their newfound knowledge in a practical, real-world setting using the QCI&AI-FML Learning Tool (Hardware/Software) provided by IEEE CIS and IEEE R10, which involves programming QCI&AI-FML robots to accomplish various tasks. 

• Experience-based and Operation-based Learning: Click Here

• Whisper-Taiwanese Model for QCI&GAI Experience: Click Here