Generative artificial intelligence in fifth-generation education systems: A systematic review

Academic integrity is a major concern: The paper identifies academic integrity risks as a key challenge in generative AI adoption in education, suggesting institutions need clear policies on appropriate use of these tools by students and educators.

Algorithmic bias threatens educational equity: The review highlights algorithmic bias as a significant ethical concern, indicating that AI systems may perpetuate or amplify existing inequalities in educational outcomes if not carefully designed and monitored.

Data privacy requires urgent attention: Data privacy issues are flagged as a critical challenge, emphasizing the need for robust protections when educational institutions collect and process student information through AI systems.

Unequal access could deepen educational divides: The paper identifies unequal access to advanced educational technologies as a key barrier, warning that without deliberate efforts to ensure broad availability, generative AI could widen gaps between well-resourced and under-resourced schools and students.

Human-machine collaboration is the goal, not replacement: The findings emphasize that responsible AI in education should support "human-machine collaboration" rather than replacing teachers and human interaction, suggesting the most beneficial approach involves AI as a tool to enhance—not substitute for—human educators.