Building Pedagogical Conversational Agents, Affectively Correct

摘要

Despite the visionary tenders that emerging technologies bring to education, modern learning environments such as MOOCs or Webinars still suffer from adequate affective awareness and effective feedback mechanisms, often leading to low engagement or abandonment. Artificial Conversational Agents hold the premises to ease the modern learner's isolation, due to the recent achievements of Machine Learning. Yet, a pedagogical approach that reflects both cognitive and affective skills still remains undelivered. The current paper moves towards this direction, suggesting a framework to build pedagogical driven conversational agents based on Reinforcement Learning combined with Sentiment Analysis, also inspired by the pedagogical learning theory of Core Cognitive Skills.