Young Children Can Learn Scientific Reasoning with Teachable Agents

A teachable agent (TA) is an instructional technology that capitalizes on the organizing metaphor of teaching another, in this case, a computer agent. As one instance, students teach their agents by creating concept maps that connect nodes with relational links. TAs use simple artificial intelligence to draw conclusions based on what they have been taught and to trace the path of their reasoning visually. TAs literally make thinking visible, with the goal of helping children learn to reason. TAs also provide interactive feedback and engender in students a sense of responsibility toward improving their agents' knowledge. We describe, in detail, a TA designed to teach hierarchical reasoning in science, and then present a 2-year research study using this TA with 153 fourth-grade children learning about organisms, taxonomies, and ecosystems. We show that the TA improved learning from the standard curriculum as measured by the curriculum's accompanying tests. The TA also helped children learn hierarchical reasoning, as measured by researcher-designed tests. The research indicates that, contrary to some theoretical positions, it is possible to help younger children learn scientific modes of reasoning, specifically by means of TA software.