The role of affect and sociality in the agent-based collaborative learning system

Abstract

As computer systems are evolving and coming to be regarded as social actors, the importance of social intelligence that enables natural and socially appropriate interactions is gaining a growing interest among the human-computer interaction researchers. This article discusses the definition, importance, and benefits of social intelligence as agent technology. It then describes a collaborative learning system that incorporates agents that are equipped with a social intelligence model. We argue that socially appropriate affective behaviors provide a new dimension for collaborative learning systems. The system provides an environment in which learning takes place through interactions with a coaching computer agent and a co-learner, an autonomous agent that makes affective responses. The social intelligence model that handles affective responses is based on psychological theories of personality, emotion, and human-media interaction, such as appraisal theory and the Media Equation. Experiments conducted with this collaborative learning system to examine the effect of the social intelligence model suggested that users had more positive impressions about the usefulness, the application, and their learning experience when the co-learner agent displayed social responses with personality and emotions than when it did not express them. It should be noted here that the colearner agent did not provide any explicit assistance for the learner, such as giving clues and showing answers, yet it influenced the user's evaluation on the usefulness of the learning system. Experimental data also suggest that the colearner agent contributed to the effectiveness of the learning system.