Teachable Agents (TA) is a learning technology that draws on the social metaphor of teaching a computer agent to help students learn. Students teach their agent by creating a concept map that serves as the agent’s “brain.” An artificial intelligence engine enables the agent to interactively answer questions posed to it by traversing the links & nodes in its map. As the agent reasons, it also animates the path it is following, thereby providing feedback, as well as a visible model of thinking for the students. Students can then use the feedback to revise their agent’s knowledge (and consequently, their own).
The teaching metaphor enlists fruitful social attitudes during the interaction, including a sense of responsibility for one’s agent that appears to motivate students to work harder to organize their understanding. TA has been found to improve children’s scientific reasoning in both causal and taxonomic (hierarchical) domains.
- Chin, D.B., Dohmen, I.M., & Schwartz, D.L. (in revision). Teachable Agents make scientific thinking visible and improve learning for younger children. IEEE Transaction on Learning Technologies.
- Chin, D.B., Dohmen, I.M., Cheng, B.H., Oppezzo, M.A., Chase, C.C., & Schwartz, D.L. (2010). Preparing students for future learning with Teachable Agents. Educational Technology Research and Development, 58(6): 649-669. doi: 10.1007/s11423-010-9154-5
- Chase, C., Chin, D.B., Oppezzo, M, & Schwartz, D.L. (2009). Teachable agents and the protégé effect: Increasing the effort towards learning. Journal of Science Education and Technology, 18(4), 334-352. doi: 10.1007/s10956-009-9180-4