Recent research identifies AI tutoring as a transformative force in modern education, capable of accelerating concept mastery and providing highly personalized instruction at scale. These systems benefit both higher education and special education by offering real-time feedback, reducing teacher administrative burdens, and creating non-judgmental environments for student inquiry. However, significant challenges persist, including algorithmic bias that results in unequal feedback across different demographic groups and critical risks regarding student data privacy. Furthermore, experts warn that over-reliance on these tools may impair student self-regulation and decrease vital human interaction. The sources ultimately conclude that a hybrid model—integrating AI efficiency with human mentorship—represents the most effective and ethical approach for future learning.