Bridging the Gap to Natural Language: A Review on Intelligent Tutoring Systems based on Latent Semantic Analysis

Abstract. One of the major drawbacks in the implementation of intelligent tutoring systems is the limited capacity to process natural language and to automatically deal with unexpected or unknown vocabulary. Latent Semantic Analysis (LSA) is a statistical technique of automatic language processing, which can attenuate the “language barrier ” between humans and tutoring systems. LSA-based intelligent tutoring systems address the goals of modelling human tutoring dialogues (AutoTutor), enhancing text comprehension and summarisation skills (State-The-Essence, Summary Street®, conText, Apex), training of comprehension strategies (iStart, a French system in development) and improving story and essay writing (Write To Learn, Select-a-Kibitzer, StoryStation). The systems are reviewed concerning their efficacy in modelling skilled human tutors and regarding their effects on the learner