AI from concrete to abstract: demystifying artificial intelligence to the general public
Project Overview
The document explores the use of generative AI in education through the AIcon2abs methodology, which integrates visual programming, educational robotics, and WiSARD weightless neural networks. This innovative approach is designed to make AI accessible to the general public, particularly children, by enabling them to engage with machine learning concepts in a tangible, hands-on way. Through practical activities, learners can create their own learning machines and visualize the learning process, which not only enhances their understanding of AI but also encourages reflection on its societal implications. The findings suggest that this methodology effectively demystifies AI, fostering insight and promoting a deeper comprehension of technology among young learners, thereby preparing them for a future increasingly influenced by artificial intelligence. Overall, the document highlights the potential of generative AI to transform educational practices and cultivate critical thinking about technology's role in society.
Key Applications
AIcon2abs methodology using WiSARD and block-based programming
Context: Educational context for children and the general public to understand AI concepts and machine learning
Implementation: Incorporated into educational settings with hands-on activities using block-based programming tools and educational robotics to create learning machines.
Outcomes: Improved understanding of AI concepts, engagement with machine learning, and development of computational thinking skills.
Challenges: Complexity of AI concepts for beginners, need for appropriate instructional materials, and potential accessibility issues for low-income communities.
Implementation Barriers
Accessibility Barrier
Limited access to AI education resources for low-income or geographically isolated communities.
Proposed Solutions: Development of low-cost educational tools and methods that do not require internet access.
Conceptual Barrier
Complexity of AI and machine learning concepts makes them difficult for the general public to grasp.
Proposed Solutions: Use of visual programming and concrete examples to simplify learning and understanding of AI.
Project Team
Rubens Lacerda Queiroz
Researcher
Fábio Ferrentini Sampaio
Researcher
Cabral Lima
Researcher
Priscila Machado Vieira Lima
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Rubens Lacerda Queiroz, Fábio Ferrentini Sampaio, Cabral Lima, Priscila Machado Vieira Lima
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai