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This paper describes a system which incorporates natural language technologies, database manipulation and educational theories in order to offer learners a Negotiated Learner Model, for integration into an Intelligent Tutoring System. The system presents the learner with their learner model, offering them the opportunity to compare their own beliefs regarding their capabilities with those inferred by the system. A conversational agent, or "chatbot" has been developed to allow the learner to negotiate over the representations held about them using natural language. The system aims to support the metacognitive goals of self-assessment and reflection, which are increasingly seen as key to learning and are being incorporated into UK educational policy. The paper describes the design of the system, and reports a user trial, in which the chatbot was found to support users in increasing the accuracy of their self-assessments, and in reducing the number of discrepancies between system and user beliefs in the learner model. Some lessons learned in the development have been highlighted and future research and experimentation directions are outlined. [ABSTRACT FROM AUTHOR]

This paper explores the feasibility of using conversational agents, or chatbots, in negotiated learner modelling. This approach aims to combine the motivational, intuitive and domain-independent benefits of natural language dialogue using a chatbot, with the opportunities for learner reflection and increased model accuracy that can be achieved through negotiation of the learner model contents. A Wizard-of-Oz paradigm allowed investigation into the interactions between learners and their learner model in order to highlight key issues for the design of a chatbot for this purpose. Users appreciated interacting with a chatbot, and found it useable and an aid to negotiation. The study suggested many avenues for future investigation of the role of conversational agents in facilitating user-system dialogue about learner understanding. [ABSTRACT FROM AUTHOR]

Open learner models (OLM) are learner models which are accessible to the learner, allowing them to view, and sometimes modify, their model. This openness may raise questions of learner trust in their learner model: if users do not agree with, or trust the information they can see about themselves, their trust in the interaction will likely be reduced. Using a Wizard-of-Oz approach, we consider learner trust and possibilities for developing trust in OLMs. [ABSTRACT FROM AUTHOR]