During the last decade, interdisciplinary researchers have developed technologies with animated pedagogical agents that interact with the student in language and other communication channels (such as facial expressions and gestures). These pedagogical agents model good learning strategies and coach the students in actively constructing knowledge during learning. This article describes computer technologies that have been developed during the last decade with tutors that attempt to facilitate deep comprehension (e.g., causal explanations, plans, logical justifications), reasoning in natural language, and inquiry (i.e., question asking, question answering, hypothesis testing). These tutors target high school and college students who learn about topics in science and technology. The primary example is AutoTutor, a system on the Internet that helps students compose answers to deep-reasoning questions and solutions to problems by holding a conversation. AutoTutor's dialogue moves include "feedback" (positive, neutral, and negative), "pumps" for more information ("Tell me more."), "hints", "prompts" to fill in missing words, "summaries", "corrections" of student misconceptions, and "answers" to student questions. Other learning technologies with agents include the Human Use Regulatory Affairs Advisor (HURAA); Source, Evidence, Explanation, and Knowledge (SEEK) Web Tutor; Interactive Strategy Trainer for Active Reading and Thinking (iSTART); Instruction with Deep-level Reasoning questions In Vicarious Environments (iDRIVE); and Acquiring Research Investigative and Evaluative Skills (ARIES). These systems have been tested on their effectiveness in facilitating knowledge construction. They also have uncovered insights on the prospects of designing agents to effectively communicate in language and discourse. (Contains 1 table and 1 figure.)