Complex tasks can be difficult to train and difficult to learn. Though training one-on-one with a human instructor is considered the gold standard for learning, social agency theory posits that social cues in virtual instructors can elicit the same mental processes in learners that occur with human instructors. We contend that artificial intelligence (AI)-driven pedagogical agents (PAs) could be superior for analyzing learner data, adapting instructional content, and providing scalable, consistent, on-demand training, particularly when instructors are expensive or unavailable. Ideally, an AI-driven PA would surpass the capability of a human instructor if these features could be fully realized. Because there are key differences in the way that humans and AI present learning content, we must ascertain whether a PA can deliver a quality of learning similar to a human instructor. We compared the knowledge levels of two groups after learning a complex task with either a human instructor or PA. We present nonexperimental preliminary findings that suggest learning from a human instructor and PA are comparable and discuss instructional considerations for PA design approach.