Description
Book SynopsisThe Ethics of Artificial Intelligence in Education identifies and confronts key ethical issues generated over years of AI research, development, and deployment in learning contexts. Adaptive, automated, and data-driven education systems are increasingly being implemented in universities, schools, and corporate training worldwide, but the ethical consequences of engaging with these technologies remain unexplored. Featuring expert perspectives from inside and outside the AIED scholarly community, this book provides AI researchers, learning scientists, educational technologists, and others with questions, frameworks, guidelines, policies, and regulations to ensure the positive impact of artificial intelligence in learning.
Trade Review"Pursuing educational AI along more ethical lines requires considerable time and effort, and a considerable amount of deliberation, debate, dialogue, and consensus building. All of this implies replacing ambitions of ‘scaling-up’ with a commitment to slowing-down. This book takes a great initial step in the right direction."
—Neil Selwyn, Distinguished Professor in the Faculty of Education, Monash University, Australia, from his foreword
"This book contributes importantly to inform and sensibilize readers towards encoding ethics in the AI used in education, at times challenging the status quo, as well as current pedagogical and technological practices."
—Gabriela Ramos, Assistant Director-General for Social and Human Sciences, UNESCO, from her foreword
Table of ContentsPart I: Ethics of AI In Education: An Outside Perspective 1. Learning to learn differently 2. Educational research and Artificial Intelligence in education: Identifying ethical challenges 3. AI in education: An opportunity riddled with challenges 4. Student-centered requirements for the ethics of AI in education 5. Pitfalls and pathways for trustworthy Artificial Intelligence in education Part II: Ethics of AI In Education: An Inside Perspective 6. Equity and Artificial Intelligence in education: Will “AIED” amplify or alleviate inequities in education? 7. Algorithmic fairness in education 8. Beyond “Fairness:” Structural (in)justice lenses on AI for education 9. The overlapping ethical imperatives of human teachers and their Artificially Intelligent assistants. 10. Integrating AI ethics across the computing curriculum
