The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Safeguarding Student Data: Privacy and Security Challenges in AI-Powered Education Tools
Abstract
This chapter explores the critical privacy and security challenges posed by AI-powered education tools. It examines how AI enhances student support and personalized learning while highlighting risks associated with chatbots, data collection, and processing. Ethical concerns, including consent and fairness, are discussed alongside the complexities of global regulations such as GDPR and FERPA. The chapter also addresses cybersecurity threats targeting educational AI systems and presents strategies to mitigate these risks. Emerging privacy-preserving techniques like federated learning and differential privacy are evaluated for their potential to safeguard student data. Drawing on case studies, it identifies best practices for ethical AI implementation and offers actionable recommendations for educators and policymakers. Finally, this chapter underscores the need for a balanced approach that protects student privacy without stifling innovation in AI-driven education.
Related Content
|
Wan Zuhainis Saad, Nor Aziah Alias, Chou Min Chong, Suriana Sabri.
© 2026.
26 pages.
|
|
V. Krishnamoorthy, Nishant Bhuvanesh Trivedi, Ratan Sarkar, Ranjeeta Saini, Archudha Arjunasamy.
© 2026.
30 pages.
|
|
Prasanna Ramakrisnan, Mohd Farhan Shah Ahmad Rusli, Mike Soon Tai Gan Hou.
© 2026.
18 pages.
|
|
Rippandeep Kaur, Ratan Sarkar, M. Lalitha, Saurabh Chandra, Taruna Anand.
© 2026.
30 pages.
|
|
M. Dhanasekar, Rijuta Prashant Joshi, R. Somasundaram, Kavya D. N., Uma Patil, Subhi Boopa.
© 2026.
28 pages.
|
|
Billur Köfter, Canan Koçak Altundağ, Ayşem Seda Yücel.
© 2026.
38 pages.
|
|
Nazurah Nik-Eezammuddeen, Najwa Baharudin.
© 2026.
34 pages.
|
|
|