This project combines insight from recent work in computer science, psychology and education to create and study "teachable agent" (TA) environments in mathematics and science that are motivating to students, intuitive to teachers and parents, and lead to hight degrees of student learning. The hallmark of these environments is that students learn by instructing "teachable agents" who then venture forth in simulation-based exploratory environments and attempt to solve problems that require knowledge relevant to the disciplines of mathematics or science. If the agents have been taught properly they solve the problems; otherwise they need to be educated further. The simulation-based enviroments are carefully designed to focus attention on important concepts in science and mathematics, and to make explicit the errors that occur during problem solving. Students "scout" the problem solving requirements of various environments before attempting to teach their agents. Additional help and coaching agents are available to point students in the right direction when they make errors or produce sub-optimal solutions. The focus of the TA environments is on learning standards-based content in science and mathematics, not on learning to program.
One key issue to be studied is how students learning is affected by opportunities to teach agents to prepare for particular sets of challenges. Also to be studied is how learning is influenced by the design of systems that vary in the degree to which they let students:
(a) "scout" to find problems that arise in agents' environments
(b) teach the agents with different representations and techniques
(c) measure the successfulness of their teaching by placing their agents in mini-assessment environments prior to engaging them in full-blown "challenge environments"
(d) receive different degrees and forms of feedback when their agents encounter difficulties
(e) educate the personality, as well as the knowledge variables, relevant to learning, problem solving and collaboration.
The project requires contributions from, and has important implications for, at least three disciplines: computer science, psychology, and education. The project has the potiential to create new forms of assessment, and to transform popular video technologies into environments that help students learn important content. The quality and impact of the project will be enhanced throught its association with the NSF-funded Center for Innovative Learning Technologies (CILT), whose mission is to foster collaboration among members of the education and technology community.
See also TeachableBot