Enhance Conversation-Based Tutoring System with Blended Human Tutor


Conversation-based learning technology is playing important role in adaptive instructional systems (AIS). As a part of the adaptivity of an instructional system it would be ideal to incorporate a human tutor to deal with conversation that is beyond the capability of a chat-bot or virtual tutor. Moreover, it is possible to answer many research questions if experiments are performed with a blended human tutor. In this research we have implemented a prototype that blends a human tutor with a virtual tutor in a typical conversation-based tutoring system (i.e., AutoTutor). We performed R&D with server-based and serverless implementations. Additionally, we have implemented audio-visual blending through WebRTC so that the conversation between students and teachers can take place through spoken language with video. We found that the serverless chat blending with AutoTutor is fast, easy to implement, and reliable. We made the so-called serverless implementation possible by using some very powerful features of a learning record store (LRS).

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)