Generative Artificial Intelligence in Education: Ethical Challenges, Regulatory Frameworks and Educational Quality in a Systematic Review of the Literature

Iván Miguel García-López^*Laura Trujillo-Liñán

• Panamerican University, Benito Juárez, Mexico

Generative Artificial Intelligence (GenAI) is changing how education works, making learning more personalized and teaching more efficient. But it also brings serious questions around ethics, regulation, and educational equity. This study reviews 53 research papers published since 2020 to identify key concerns like data privacy, algorithmic bias, and unequal access, while also exploring how GenAI is being implemented responsibly in education. To ground the analysis, the paper draws on three main perspectives: (1) Self-Determination Theory (Ryan & Deci), which explains how GenAI can support student autonomy and engagement; (2) Constructivist learning theories (Piaget, Vygotsky), which focus on active learning through interaction; and (3) Ethical and regulatory frameworks, including IEEE’s Ethically Aligned Design and Bostrom’s alignment theory, both of which stress the need for transparency and human oversight. Alongside the literature review, two case studies, Stanford and the University of Toronto show how GenAI is actually being used in universities today, including the real challenges that come with it. The study finds that while GenAI offers real benefits, like tailored feedback and better learning experiences, it also demands clear policies, strong digital literacy efforts, and oversight to avoid ethical risks. What makes this study stand out is its mix of data, theory, and real-world cases, which together lead to practical ideas for using GenAI more ethically and effectively in education.