SENSAI: Large Language Models as Applied Cybersecurity Tutors

The modern educational landscape faces the challenge of maintaining effective, personalized mentorship amid expanding class sizes. This challenge is particularly pronounced in fields requiring hands-on practice, such as cybersecurity education. Teaching assistants and peer interactions provide some relief, but the student-to-educator ratio often remains high, limiting individualized attention. The advent of Large Language Models (LLMs) offers a promising solution by potentially providing scalable and personalized guidance. In this paper, we introduce SENSAI, an AI-powered tutoring system that leverages LLMs to offer tailored feedback and assistance by transparently extracting and utilizing the learner's working context, including their active terminals and edited files. Over the past year, SENSAI has been deployed in an applied cybersecurity curriculum at a large public R1 university and made available to a broader online community of global learners, assisting 2,742 users with hundreds of educational challenges. In total 178,074 messages were exchanged across 15,413 sessions, incurring a total cost of 1,979--comparable to that of a single undergraduate teaching assistant but with a significantly wider reach. SENSAI demonstrates significant improvements in student problem-solving efficiency and satisfaction, offering insights into the future role of AI in education.