Ethical Dimensions of Generative and Edge AI for Participatory Citizen Science and STEAM Education, integrating Human-Centered Frameworks

The adoption of Artificial Intelligence (AI) technologies at the edge and in participatory research settings is rapidly accelerating. Today Tiny Machine Learning (TinyML) allows ML and even Large Language Model (LLM) inference on low-power microcontrollers, enabling local environmental monitoring, health tracking, and citizen-led research, empowering communities worldwide to leverage the "edge of Agentic AI" through advanced AI/ML approaches in addressing local problems that they know best with locally sourced data combined with open data. 
Although the ambitious advantage of decentralizing the compute power to run AI/ML, other concerns come along including data bias, trustworthiness of the algorithms as well as the ethics and explainability of the AI used.
This paper critically investigates the ethical dimensions of integrating LLM-enabled TinyML into citizen science and education, guided by the UNESCO Recommendations on the Ethics of AI, complemented by the UNESCO Guidance on Generative AI in Education and Research. These can help us understand how citizen-led AI initiatives leveraging TinyML/LLMs can be ethically designed, governed, and implemented to foster inclusivity and human rights while aligning with global AI ethics frameworks. Employing a qualitative, interdisciplinary methodology, the research synthesizes critical AI ethics and participatory design approaches within a theoretical framework grounded in UNESCO’s principles of transparency, inclusivity, fairness, environmental responsibility, and cultural diversity. The study examines citizen science projects utilizing TinyML for environmental and public health monitoring across varied socio-economic and geographic contexts. 
Findings suggest that ethically integrating TinyML into citizen science demands a layered strategy combining participatory governance, inclusive pedagogy, and localized policy frameworks. The paper proposes preliminary guidelines including embedding AI ethics into citizen science curricula, establishing community-led data governance practices, fostering interdisciplinary collaborations with indigenous and local knowledge systems, promoting open-source tools to mitigate access inequities, and creating sustainability protocols for edge device management. This research advances AI ethics discourse by highlighting the distinctive ethical risks and opportunities arising from community-driven, small-scale AI systems. It demonstrates how global AI ethics principles can be operationalized in grassroots citizen science and STEAM education to promote more inclusive, rights-based, and ecologically responsible AI practices.