Overview
Overview
Embedded Files
Aims
The 2025 IEEE CIS Summer School on Computational Intelligence for High School Students with Human-Machine Interaction aims to promote STEM education by introducing high school students to the theories and practical applications of Computational Intelligence (CI) and Quantum Computational Intelligence (QCI), with a particular emphasis on Human-Machine Interaction (HMI).
The program also welcomes early undergraduates interested in AI, machine learning, quantum computing, robotics, and interdisciplinary technologies.
Students will engage with these topics through a co-learning model that emphasizes collaborative problem-solving with intelligent machines. Building on these experiences, the 2025 program will further integrate CI, QCI, and HMI in real-world learning scenarios, enhancing students’ hands-on abilities, collaborative thinking, and creative application skills in interdisciplinary intelligent systems while expanding IEEE’s educational outreach.
It continues the legacy of earlier IEEE CIS schools and IEEE events including IEEE CEC 2023 (USA), FUZZ-IEEE 2023 (Korea), IEEE WCCI 2024 (Japan), NTNU & IEEE SSCI 2025 (Norway), FUZZ-IEEE 2025 (France), and IEEE R10 SPNIC Activities (2023–2025) in Taiwan, Malaysia, Japan, and Hong Kong, consistently drawing global student participation and strengthening the IEEE CIS educational network.
Venue and Dates
Venue Website (Engineer Building E, National Yang Ming Chiao Tung University No.1001, Daxue Rd., East Dist., Hsinchu City 300093, Taiwan)
Dates: Nov 5−Nov 8
Duration: 4 days
Technical Co-sponsors
IEEE CIS High-School Outreach Subcommittee
IEEE CIS ETTC Task Force on Quantum Computational Intelligence
KWS Center, National University of Tainan, Taiwan (http://kws.nutn.edu.tw)
Computational Intelligence Lab., Osaka Metropolitan University, Japan
Supporters
IEEE CIS Taipei Chapter
IEEE Taipei Section
National University of Tainan, Taiwan
Registration Deadline
Registration must be received before June 3, 2025 via the competition website (Register from).
Reference
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C. S. Lee, M. H. Wang, Y. L. Tsai, L. W. Ko, B. Y. Tsai, P. H. Hung, L. A. Lin, and N. Kubota, "Intelligent agent for real-world applications on robotic edutainment and humanized co-learning," Journal of Ambient Intelligence and Humanized Computing, 2019.
C. S. Lee, M. H. Wang, L. W. Ko, Y. Hsiu Lee, H. Ohashi, N. Kubota, Y. Nojima, and S. F. Su, "Human intelligence meets smart machine: a special event at the IEEE International Conference on Systems, Man, and Cybernetics 2018," IEEE Systems, Man, and Cybernetics Magazine, vol. 6, no. 1, pp. 23-31, Jan. 2020.
C. S. Lee, M. H. Wang, L. W. Ko, N. Kubota, L. A. Lin, S. Kitaoka, Y. T Wang, and S. F. Su, "Human and smart machine co-learning: brain-computer interaction at the 2017 IEEE International Conference on Systems, Man, and Cybernetics," IEEE Systems, Man, and Cybernetics Magazine, vol. 4, no. 2, pp. 6-13, Apr. 2018.
C. S. Lee, M. H. Wang, S. C. Yang, P. H. Hung, S. W. Lin, N. Shuo, N. Kubota, C. H. Chou, P. C. Chou, and C. H. Kao, "FML-based dynamic assessment agent for human-machine cooperative system on game of Go," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 25, no. 5, pp. 677-705, 2017.
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C. S. Lee, M. H. Wang, H. Hagas, Z. W. Chen, S. T. Lan, S. E. Kuo, H. C. Kuo, and H. H. Cheng, "A novel genetic fuzzy markup language and its application to healthy diet assessment," International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, vol. 20, no. 2, pp. 247-278, Oct. 2012.
C. S. Lee, M. H. Wang, L. C. Chen, Y. Nojima, T. X. Huang, J. Woo, N. Kubota, E. Sato-Shimokawara, T. Yamaguchi, "A GFML-based robot agent for human and machine cooperative learning on game of Go," in Proceeding of 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019), Wellington, New Zealand, Jun. 10-13, 2019, pp. 793-799.
C. S. Lee, Y. L. Tsai, M. H. Wang, W. K. Kuan, Z. H. Ciou, and N. Kubota, "AI-FML agent for robotic game of Go and AIoT real-world co-learning applications," 2020 World Congress on Computational Intelligence (IEEE WCCI 2020), Glasgow, Scotland, UK, Jul. 19-24, 2020.
C. S. Lee, M. H. Wang, Y. Nojima, M. Reformat, and L. Guo, "AI-Fuzzy Markup Language with Computational Intelligence for High-School Student Learning," arXiv, Cornell University, Nov. 2021.
C. S. Lee, M. H. Wang, W. K. Kuan, S. H. Huang, Y. L. Tsai, Z. H. Ciou, C. K. Yang, and N. Kubota, "BCI-based hit-loop agent for human and AI robot co-learning with AIoT application," Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 3583–3607, Oct. 2021
C. S. Lee, Y. L. Tsai, M. H. Wang, S. H. Huang, M. Reformat, and N. Kubota, "Adaptive fuzzy neural agent for human and machine co-learning," International Journal of Fuzzy Systems, vol. 24, pp. 778–798, Nov. 2021.
C. S. Lee, M. H. Wang, Z. H. Ciou, R. P. Chang, C. H. Tsai, S. C. Chen, T. X. Huang, E. Sato-Shimokawara, and T. Yamaguchi, "Robotic assistant agent for student and machine co-learning on AI-FML practice with AIoT application," 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021), Luxembourg, Luxembourg, Jul. 11-14, 2021.
C. S. Lee, M. H. Wang, M. Reformat, S. H. Huang, "Human intelligence-based Metaverse for co-learning of students and smart machines," Journal of Ambient Intelligence and Humanized Computing, 2023.
C. S. Lee, M. H. Wang, S. H. Huang, F. J. Yang, C. H. Tsai, and L. Q. Wang, "Fuzzy ontology-based intelligent agent for high-school student learning in AI-FML Metaverse," 2022 IEEE World Congress on Computational Intelligence (IEEE WCCI 2022), Padua, Italy, Jul. 18-23, 2022.
C. S. Lee, M. H. Wang, R. P. Chang, H. C. Liu, S. C. Chiu, Y. C. Chang, L. A. Lin, and S. C. Chen, "Computational intelligence and AI-FML experience model for pre-university student learning and practice," The 18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2022), Kitakyushu, Japan, Dec. 16-18, 2022.
C. S. Lee, M. H. Wang, C. Y. Chen, M. Reformat, Y. Nojima, and N. Kubota, "Knowledge graph-based genetic fuzzy agent for human intelligence and machine co-learning," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023. (Accepted).
G. Acampora, R. Schiattarella, and A. Vitiello, "On the implementation of fuzzy inference engines on quantum computers," IEEE Transactions on Fuzzy Systems, vol. 31, no. 5, pp. 1419-1433, 2023.
C. S. Lee, M. H. Wang, M. H. Wang, P. Y. Wu, R. Schiattarella, G. Acampora, and A. Vitiello, "Fuzzy markup language-based quantum FIE for student and robot co-learning model assessment," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023. (Late Breaking paper)
A. Pourabdollah, C. Wilmott, R. Schiattarella, and G. Acampora, "Fuzzy inference on quantum annealers," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023.
G. Acampora, M. Grossi, and R. Schiattarella, "A comparison of quantum computer architectures in running fuzzy inference engines," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023.
G. Acampora, A. Massa, R. Schiattarella, and A. Vitiello, "Distributing fuzzy inference engines on quantum computers," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023.
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