AI in Education

AI in Education develops and evaluates tools such as intelligent tutoring systems, knowledge tracing, adaptive feedback, and large language model tutors grounded in course materials. These systems aim to personalize instruction, assist teachers with tasks like feedback and grading, and expand access to learning support. Researchers assess them through classroom studies, subject-specific benchmarks, learning-outcome measures, and audits of accuracy, bias, privacy, and accessibility. A central tension is that AI can improve performance during assisted tasks without necessarily building durable, independent skills, so effective use depends on pedagogy, human oversight, and evidence from real learning settings.