Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
AI-Driven Learning
0
Zitationen
3
Autoren
2025
Jahr
Abstract
This chapter examines how student privacy can be protected as educational institutions adopt Artificial Intelligence (AI) tools. The chapter analysis real-world cases including InBloom, AltSchool, and Pearson to explore the benefits and risks of collecting student data for educational purposes. The chapter investigates the types of student information being gathered and its uses in modern classrooms. Based on these findings, the chapter developed a framework that combis privacy protection technology, ethical guidelines, and legal consideration to help schools implement AI responsibly. The chapter also provides essential guidance for school administrators, teachers, and policymakers who must balance innovation with student privacy rights. The findings emphasize that successful AI integration requires careful attention to privacy concerns and sustained collaboration between educators and technology experts. This work offers a path forward for schools to use AI tools effectively while protecting student information.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.291 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.143 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.535 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.452 Zit.