Eco-Feminism and Artificial Intelligence in Education: Examining Gender, Sustainability, and Ethical AI Practices in Learning Environments (Poster 2)

• In Event: Artificial Intelligence, Gender, Race, Culture, and Environmental Justice in Education: An Intersectional Analysis of Bias, Equity, and Sustainability Globally

Abstract

As artificial intelligence (AI) becomes increasingly integrated into educational systems, its environmental and social implications require critical examination. While AI-driven tools offer innovative solutions for personalized learning, assessment, and administration, they also reinforce systemic inequalities and contribute to environmental degradation. This poster explores AI in education through an eco-feminist lens, analyzing how AI technologies disproportionately impact marginalized communities—particularly women, Indigenous populations, and students in the Global South—through exploitative labor practices, data extraction, and energy-intensive infrastructures.
Drawing on eco-feminist theory and critical pedagogy, this study examines the intersection of AI, gender, and environmental justice in education. It highlights how AI-powered educational tools often exclude feminist and ecological perspectives, favoring technocratic, efficiency-driven approaches over holistic, justice-oriented solutions. Furthermore, it critiques the sustainability challenges posed by AI in education, including the carbon footprint of AI-driven learning platforms, the ethical dilemmas of data-intensive surveillance, and the environmental impact of hardware production.
By integrating eco-feminist ethics into AI development and educational policy, this research advocates for sustainable, community-driven AI solutions that prioritize equity, ecological responsibility, and inclusive knowledge systems. The study calls for an alternative AI paradigm in education, one that moves beyond extractive and exploitative practices to foster ethically conscious, environmentally sustainable, and socially just learning environments.

Author

• Miaofu Tian, University of Southern California