Quantum CI for Pre-University and Undergraduate Students

Naoyuki Kubota, Tokyo Metropolitan University, Japan

Chang-Shing Lee, National University of Tainan, Taiwan

Eri Sato-Shimokawara, Tokyo Metropolitan University, Japan

Yusuke Nojima, Osaka Metropolitan University, Japan

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 competition, during IEEE-sponsored conferences.

This approach has been successfully implemented at IEEE CEC 2023 in the USA, FUZZ-IEEE 2023 in Korea, and the IEEE Region 10 SPNIC Activity in Tainan/Taiwan, Tokyo/Japan, Penang/Malaysia, and Hong Kong on Nov. 18, 2023, Feb. 20, 2024, March 30, 2024, and April 13, 2024, respectively.

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.

On the competition day, we encourage students to form teams, fostering cooperation as they apply their real-world applications.

June 28: Workshop on QCI for Pre-University and Undergraduate Students

June 30: A Sandbox for Teaching and Learning in QCI for Pre-University and Undergraduate Students @ IEEE WCCI 2024