An AI tutor is a conversational agent embedded in a course or learning platform that answers learner questions, explains concepts, and gives feedback — acting as an always-available study partner. Most production implementations are built on a large language model (LLM) combined with a retrieval-augmented generation (RAG) layer that fetches relevant course passages before the model responds, reducing the risk of the model fabricating information. The tutor can be scoped to a single course, a full curriculum, or an organizational knowledge base depending on what the retrieval index covers. Unlike a static FAQ, an AI tutor handles novel phrasings of questions and can ask clarifying questions back, which keeps learners from abandoning a confusing section. A key trade-off is accuracy: even with RAG, an LLM can produce plausible but incorrect answers, so high-stakes programs require human-reviewed response policies and fallback escalation to a human instructor. AI tutors tie closely to adaptive learning — the questions learners ask reveal gaps that can feed into a personalized path. Latency, data-privacy handling of learner queries, and the cost of inference at scale are the main operational concerns when deploying one.
