A pedagogical agent is a concept borrowed from computer science and artificial intelligence and applied to education, usually as part of an intelligent tutoring system (ITS). It is a simulated human-like interface between the learner and the content, in an educational environment. A pedagogical agent is designed to model the type of interactions between a student and another person. Mabanza and de Wet define it as "as a character enacted by a computer that interacts with the user in a socially engaging manner". A pedagogical agent can be assigned different roles in the learning environment, such as tutor or co-learner, depending on the desired purpose of the agent. "A tutor agent plays the role of a teacher, while a co-learner agent plays the role of a learning companion".
The history of Pedagogical Agents is closely aligned with the history of computer animation. As computer animation progressed, it was adopted by educators to enhance computerized learning by including a lifelike interface between the program and the learner. The first versions of a pedagogical agent were more cartoon than person, like Microsoft's Clippy which helped users of Microsoft Office load and use the program's features in 1997. However, with developments in computer animation, pedagogical agents can now look lifelike. By 2006 there was a call to develop modular, reusable agents to decrease the time and expertise required to create a pedagogical agent. There was also a call in 2009 to enact agent standards. The standardization and re-usability of pedagogical agents is less of an issue since the decrease in cost and widespread availability of animation tools. Individualized pedagogical agents can be found across disciplines including medicine, math, law, language learning, automotive, and armed forces. They are used in applications directed to every age, from preschool to adult.
Distributed cognition theory
Distributed cognition theory is the method in which cognition progresses in the context of collaboration with others. Pedagogical agents can be designed to assist the cognitive transfer to the learner, operating as artifacts or partners with collaborative role in learning. To support the performance of an action by the user, the pedagogical agent can act as a cognitive tool as long as the agent is equipped with the knowledge that the user lacks. The interactions between the user and the pedagogical agent can facilitate a social relationship. The pedagogical agent may fulfill the role of a working partner.
Socio-cultural learning theory
Socio-cultural learning theory is how the user develops when they are involved in learning activities in which there is interaction with other agents. A pedagogical agent can: intervene when the user requests, provide support for tasks that the user cannot address, and potentially extend the learners cognitive reach. Interaction with the pedagogical agent may elicit a variety of emotions from the learner. The learner may become excited, confused, frustrated, and/or discouraged. These emotions affect the learners' motivation.
It has been suggested by researchers that pedagogical agents may take on different roles in the learning environment. Examples of these roles are: supplanting, scaffolding, coaching, testing, or demonstrating or modelling a procedure. A pedagogical agent as a tutor has not been demonstrated to add any benefit to an educational strategy in equivalent lessons with and without a pedagogical agent. According to Richard Mayer, there is some support in research for pedagogical agent increasing learning, but only as a presenter of social cues. A co-learner pedagogical agent is believed to increase the student's self-efficacy. By pointing out important features of instructional content, a pedagogical agent can fulfill the signaling function, which research on multimedia learning has shown to enhance learning. Research has demonstrated that human-human interaction may not be completely replaced by pedagogical agents, but learners may prefer the agents to non-agent multimedia systems. This finding is supported by social agency theory.
The appearance of a pedagogical agent can be manipulated to meet the learning requirements. The attractiveness of a pedagogical agent can enhance student's learning when the users were the opposite gender of the pedagogical agent. Male students prefer a sexy appearance of a female pedagogical agents and dislike the sexy appearance of male agents. Female students were not attracted by the sexy appearance of either male or female pedagogical agents.
- AI: Artificial Intelligence Research at USC Information Science Institute
- Stanford University: Interactive Animated Pedagogical Agents
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- Govindasamy, Malliga K. (2014). "Animated pedagogical agents: A review of agent technology software in electronic learning environments". Journal of Educational Multimedia and Hypermedia. 23 (2): 163–188.
- Schroeder, Noah L.; Adesope, Olusola O. (2012). "A case for the use of pedagogical agents in online learning environments". Journal of Teaching and Learning with Technology. 1 (2): 43–47.
- Apostol, Ailviu; Şoica, Oana; Manasia, Lorendana; Ştefan, Cătălin (2013). "Virtual Pedagogical Agents in the Context of Virtual Learning Environments: Framework and Theoretical Models". ELearning & Software for Education (2): 531–536.
- Schroeder, Noah L.; Adesope, Olusola O. (Spring 2014). "A Systematic Review of Pedagogical Agents' Persona, Motivation, and Cognitive Load Implications for Learners". Journal of Research on Technology in Education (Routledge). 46 (3): 229.
- Clark, Richard E.; Fledon, David F. (2014). "Ten Common but Questionable Principles of Multimedia Learning". In Mayer, Richard. The Cambridge Handbook of Multimedia Learning. New York, NY: Cambridge University Press. ISBN 978-1-107-61031-6.
- Mayer, Richard (2014). "Principles Based on Social Cues". In Mayer, Richard. The Cambridge Handbook of Multimedia Learning. New York, NY: Cambridge University Press. ISBN 978-1-107-61031-6.
- Wang, Chih-Chien; Yeh, Wei-Jyh (2013). "Avatars with Sex Appeal as Pedagogical Agents: Atttractiveness, Trustworthiness, Expertise, and Gender Differences". Journal of Educational Computing Research. 48 (4): 403–429.