A multimodal conversational agent for personalized language learning

Proceedings of: 5th International Symposium on Ambient Intelligence (ISAmI 2014), Salamanca (Spain), June 4th-6th, 2014.Conversational agents have became a strong alternative to enhance educational systems with intelligent communicative capabilities. In this paper, we describe a multimodal conversational agent that facilitates an independent and user-adapted second language learning. The different modules of the system cooperate to interact with students using spoken natural language and visual modalities, and adapt their functionalities taking into account their evolution and specific preferences. The results of a preliminary evaluation show that users' satisfaction with the system was high, as well as the perceived didactic potential and adaptive functionalities.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485)

Interacting with educational chatbots: A systematic review

Chatbots hold the promise of revolutionizing education by engaging learners, personalizing learning activities, supporting educators, and developing deep insight into learners’ behavior. However, there is a lack of studies that analyze the recent evidence-based chatbot-learner interaction design techniques applied in education. This study presents a systematic review of 36 papers to understand, compare, and reflect on recent attempts to utilize chatbots in education using seven dimensions: educational field, platform, design principles, the role of chatbots, interaction styles, evidence, and limitations. The results show that the chatbots were mainly designed on a web platform to teach computer science, language, general education, and a few other fields such as engineering and mathematics. Further, more than half of the chatbots were used as teaching agents, while more than a third were peer agents. Most of the chatbots used a predetermined conversational path, and more than a quarter utilized a personalized learning approach that catered to students’ learning needs, while other chatbots used experiential and collaborative learning besides other design principles. Moreover, more than a third of the chatbots were evaluated with experiments, and the results primarily point to improved learning and subjective satisfaction. Challenges and limitations include inadequate or insufficient dataset training and a lack of reliance on usability heuristics. Future studies should explore the effect of chatbot personality and localization on subjective satisfaction and learning effectiveness