TY - GEN
T1 - Navigating Ethical Dilemmas in the Implementation of AI-Driven Educational Technologies
AU - Ismail, Muhusina
AU - Madathil, Nisha Thorakkattu
AU - Alalawi, Meera
AU - Alalawi, Shamma
AU - Alrabaee, Saed
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Artificial Intelligence (AI) is transforming education by offering innovative tools that enhance teaching, learning, and administrative processes. However, its integration introduces significant ethical challenges that demand critical attention. This systematic literature review (SLR) explores key ethical concerns associated with AI-driven educational technologies, including data privacy, algorithmic bias, student autonomy, and inclusivity. It systematically analyzing existing literature to provide actionable guidelines for promoting ethical AI use, emphasizing transparency, fairness, and accountability. The review also examines the impact of AI on the dynamics of instructor-student relationships, highlighting both opportunities for personalized learning and risks of reduced human interaction. By addressing these challenges and proposing strategies for responsible AI implementation, this study aims to guide educational institutions in navigating the complexities of AI adoption while fostering equitable and meaningful learning experiences.
AB - Artificial Intelligence (AI) is transforming education by offering innovative tools that enhance teaching, learning, and administrative processes. However, its integration introduces significant ethical challenges that demand critical attention. This systematic literature review (SLR) explores key ethical concerns associated with AI-driven educational technologies, including data privacy, algorithmic bias, student autonomy, and inclusivity. It systematically analyzing existing literature to provide actionable guidelines for promoting ethical AI use, emphasizing transparency, fairness, and accountability. The review also examines the impact of AI on the dynamics of instructor-student relationships, highlighting both opportunities for personalized learning and risks of reduced human interaction. By addressing these challenges and proposing strategies for responsible AI implementation, this study aims to guide educational institutions in navigating the complexities of AI adoption while fostering equitable and meaningful learning experiences.
KW - AI ethics guidelines
KW - AI in education
KW - algorithmic bias
KW - data privacy
KW - education technologies
KW - ethical challenges
KW - student autonomy
UR - http://www.scopus.com/inward/record.url?scp=105008202196&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=105008202196&partnerID=8YFLogxK
U2 - 10.1109/EDUCON62633.2025.11016612
DO - 10.1109/EDUCON62633.2025.11016612
M3 - Conference contribution
AN - SCOPUS:105008202196
T3 - IEEE Global Engineering Education Conference, EDUCON
BT - EDUCON 2025 - IEEE Global Engineering Education Conference, Proceedings
PB - IEEE Computer Society
T2 - 16th IEEE Global Engineering Education Conference, EDUCON 2025
Y2 - 22 April 2025 through 25 April 2025
ER -