Little Green Hero

Little Green Hero (LGH), an inventive mobile and web application that harnesses the power of AI to promote environmental awareness among children aged toddler to 8 years. As much as the on-time vaccination throughout early childhood is essential in providing immunity before children are exposed to potentially life-threatening diseases, environmental education (EE) should be provided at early stage of human life, immunizing therefore a whole generation’s behavior against human harming impact on the environment. The proposed application highlights the significance of EE and introduces how LGH tactfully navigates the complex terrain of environmental topics, fostering understanding without inducing fear or anxiety. The goals of the LGH program align seamlessly with the theme's emphasis on innovative educational approaches to environmental awareness more particularly to ESD for SDGs. The use of AI technology amplifies the program's aim of providing personalized, engaging, and age-appropriate Environmental Education (EE) to young children, fostering understanding without inducing fear or eco-anxiety. Educating young minds about environmental complexities necessitates an approach that balances imparting knowledge with ensuring that children feel empowered, curious, and unburdened by ecological concerns. The central challenge lies in explaining intricate concepts like climate change, energy efficiency, sufficiency, renewable energy, waste management, and others in ways that are both informative and relatable. LGH addresses this challenge by employing AI to create a dynamic and interactive learning experience. Through games and educational videos, the application takes children on an engaging journey where they learn about these topics with the assistance of a friendly AI-driven character who facilitates learning by interacting, giving feedback, and providing context-sensitive information. LGH tailors the learning experience to the child's age, comprehension level, and pace of learning. As the child interacts with the AI character and progresses through different levels, the AI adapts its responses and content delivery to ensure a personalized and enriching experience so that the child remains engaged, fostering a sustained interest in environmental topics. Furthermore, the program encourages outdoor exploration and engagement with the environment, complementing the AI-facilitated lessons. By instilling a love for nature, LGH aims to inspire a generation of environmentally conscious individuals who are motivated to safeguard their surroundings. The AI features used in the LGH are: (1) Answer Accuracy (Tracks how correctly children answer game questions); (2) Response Time (Measures the time taken to respond, indicating engagement and difficulty level); (3) Child Feedback (Integrates children's input for personalized game experiences). LGH builds personal profiles: (1) Data-Driven History (Each session records the child's gaming profile); (2) Learning & Preferences (AI analyzes historical data to understand learning patterns). AI Learning & Adaptation will be done by: (1) Continuous Learning (AI evolves with each child's interactions, better understanding their abilities); (2) Game Selection (Chooses games based on skill level, interests, and educational value).

LGH program align seamlessly with the theme's emphasis on innovative educational approaches to environmental awareness more particularly to ESD for SDGs. The use of AI technology amplifies the program's aim of providing personalized, engaging, and age-appropriate EE to young children. LGH is considered as Tailored Learning Paths: Customizes gaming experiences to individual learning styles. It enhances Engagement: Keeps games challenging and fun, maximizing educational benefit. LGH presents a promising avenue for delivering impactful EE to young children. It is foreseen to include all the other SDGs in next steps. By employing AI-driven interactions, LGH makes learning about complex environmental topics accessible, engaging, and enjoyable. This approach not only imparts knowledge but also empowers children to understand their role in environmental preservation. As AI continues to redefine educational paradigms, LGH stands as an exemplar of how technology can foster a generation of environmentally aware individuals.

The team leader Dr. Jinane Mounsef (co-founder of the project) received the Ph.D. degree in Electrical Engineering from Arizona State University, US. She is currently an Assistant Professor with the Department of Electrical Engineering, Rochester Institute of Technology (RIT) in Dubai, UAE. She has been in the research field for over more than 15 years in machine learning, computer vision and image processing, during which she worked with multidisciplinary teams on a long track of research papers. Her methodological and theoretical research as well as a considerable portion of her applied and collaborative work address novel techniques in computer vision, in addition to designing and implementing smart systems. She is leading the AI/Robotics lab at RIT Dubai, an advisory board member at New York Institute and Laboratory for Artificial Intelligence (NYILAI), and Head of Research and Education for Women in AI (WAI), UAE section.  

Dr. Sabine Saad (co-founder of the project) is the director of Ecological Transition at the Lebanese Association for Energy Saving and for Environment. Specialized with a Ph.D in Renewable Energy and Sustainability, she has a large experience and knowledge in developing projects, build capacities, educating and raising awareness on topics such as climate change, Energy (conservation, efficiency, sufficiency, renewables), waste management, green and sustainable buildings, Sustainable mobility, environment and ecological transition, in Lebanon and the Mediterranean region. 

The current prototype of our app is designed to engage children in an interactive learning environment focused on environmental topics. Upon starting the app, it prompts the child to input necessary information, allowing for a personalized learning experience. The child can then select from various environmental themes to explore through games. If a child answers a question incorrectly, the app not only shows the correct answer but also presents a similar question to reinforce learning. Upon achieving full accuracy in responses, the app unlocks the next level, progressively challenging the child.

Through games and educational videos, the application takes children on an engaging journey where they learn about topics, such as

climate change, energy efficiency, sufficiency, renewable energy, waste management, and others, with the assistance of a friendly AI-driven character who facilitates learning by interacting, giving feedback, and providing context-sensitive information. 

LGH tailors the learning experience to the child's age, comprehension level, and pace of learning.

As the child interacts with the AI character and progresses through different levels, the AI adapts its responses and content delivery to ensure a personalized and enriching experience so that the child remains engaged, fostering a sustained interest in environmental topics.

Furthermore, the program encourages outdoor exploration and engagement with the environment, complementing the AI-facilitated lessons.

By instilling a love for nature, LGH aims to inspire a generation of environmentally conscious individuals who are motivated to safeguard their surroundings.

There will be some published computer vision algorithms that we might use such as face recognition. In addition to LLM that might be based on OpenAI.

The Environmental Education provided by LGH is dedicated to Early childhood (less than 8 years). It is a basic-learning system about the environmental topics. Since there is NO curriculum for early childhood around the world, the content of LGH can be tested later with potential to be considered as a basic curriculum for environmental education. Moreover, it tracks how correctly children answer game questions, and measures the time taken to respond, indicating engagement and difficulty level. Therefore, LGH tailors the learning experience to the child's age, comprehension level, and pace of learning. 

The experience offered by LGH is grounded in AI's adaptability. LGH tailors the learning experience to the child's age, comprehension level, and pace of learning. As the child interacts with the AI character and progresses through different levels, the AI adapts its responses and content delivery to ensure a personalized and enriching experience so that the child remains engaged, fostering a sustained interest in environmental topics. LGH targets different level of intelligence among kids, as well as children with special needs and difficulties.

Only 2 people worked on LGH. Dr. Jinane who developed the software part with the AI technology, and me who developed the environmental content.

We are planning to finalize the development of the mobile app in less than one year once financed and we could enlarge the team. LGH will be piloted after in a public school and in a kindergarten in UAE, in collaboration with the Ministry of Education. We are open to implement any other pilot in U.S. 

Lack of Environmental Education for Early Childhood period will open an opportunity for LGH to be tested as a material for the curriculum of early childhood. If integrated into the national educational curriculum, LGH will be accessible for all children especially learners in public schools experiencing poverty. Moreover, tracking how correctly children answer game questions, and measuring the time taken to respond, indicating engagement and difficulty level, LGH can be inclusive, easily used by children with special needs or with some learning difficulties.

- Technical Advancements: Funding can support development efforts.

- Market Expansion and Accessibility: Funding can be utilized to make our solution more accessible and inclusive, ensuring that individuals and schools can benefit from them. This includes developing user-friendly interfaces and providing multilingual support.