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Introducing Artificial Intelligence (AI), Swarm Intelligence (SI) and Bio-Inspired Algorithms Concepts to Elementary and Secondary (K-12) Education Using Block-Based Programming Environments: A Simplified Simulation Inspired by Artificial Fish Swarm Optimization Algorithm (AFSO)

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  • Konstantinos Salpasaranis

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have the potential to revolutionize education, with applications ranging from personalized learning systems to teaching students about AI concepts. Beyond utilizing and integrating these technologies, it is crucial to comprehend the fundamental principles governing the field. Choosing an “attractive” area of AI suitable for students and engaging them is essential to introducing difficult Computer Science concepts. In particular, introducing these concepts in elementary and secondary (K-12) Education is not a simple task, as it involves complex algorithms and theories that could overwhelm young learners. To overcome this challenge, we can rely on nature-inspired or bio-inspired algorithms such as Swarm Intelligence (SI) family, and leverage block-based programming environments (like MIT Scratch or other Logo-like environments) to make AI concepts more accessible and intuitive for students. This article proposes the creation and implementation of simplified simulations inspired by the Artificial Fish Swarm Optimization Algorithm (AFSO)-namely how fish behave collectively in the ocean–as an educational tool for both elementary and secondary school students. The proposed educational methodology combines the integration of Constructionist Learning principles, as the “Creative Thinking Spiral” learning model, with the inquiry-based approach of the 5Es Instructional Model.

Suggested Citation

  • Konstantinos Salpasaranis, 2024. "Introducing Artificial Intelligence (AI), Swarm Intelligence (SI) and Bio-Inspired Algorithms Concepts to Elementary and Secondary (K-12) Education Using Block-Based Programming Environments: A Simplified Simulation Inspired by Artificial Fish Swarm ," European Journal of Artificial Intelligence and Machine Learning, European Open Science, vol. 3(3), pages 9-17, June.
  • Handle: RePEc:epw:ejai00:v:3:y:2024:i:3:id:1042
    DOI: 10.24018/ejai.2024.3.3.42
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