A model for negotiation in teaching-learning dialogues
Journal of Artificial Intelligence in Education
Real-world applications of Bayesian networks
Communications of the ACM
The abc of rational agent modelling
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Elements of the Theory of Computation
Elements of the Theory of Computation
Measuring (and enhancing?) student confidence with confidence scores
FIE '00 Proceedings of the 30th Annual Frontiers in Education - Volume 01
AMPLIA Learning Environment Architecture
Proceedings of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation
A model of pedagogical negotiation
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
A multi-agent intelligent environment for medical knowledge
Artificial Intelligence in Medicine
A collaborative Bayesian net editor to medical learning environments
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
AMPLIA: A Probabilistic Learning Environment
International Journal of Artificial Intelligence in Education
A model of pedagogical negotiation
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
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This paper presents a model of pedagogical negotiation developed for the AMPLIA, an Intelligent Probabilistic Multi-agent Learning Environment. Three intelligent software agents: Domain Agent, Learner Agent and Mediator Agent were developed using Bayesian Networks and Influence Diagrams. The goal of the negotiation model is to increase, as much as possible: (a) the performance of the model the students build; (b) the confidence that teachers and tutors have in the students’ ability to diagnose cases; and the students’ confidence on their own ability to diagnose cases; and (c) the students’ confidence on their own ability to diagnose diseases.