A framework for adapting conversational intelligent tutoring systems to enable collaborative learning
Pablo Arnau‐González, Sergi Solera-Monforte, Yuyan Wu, Miguel Arevalillo‐Herráez · Expert Systems with Applications · 2025
Despite the general consensus on the advantages of collaborative learning, most of the research in Intelligent Tutoring Systems (ITSs) only considers the case of individual learners. This is mainly due to the technical challenges in adapting individual learning systems to support collaborative learning. In this paper, we present a framework aimed at adapting individual-learner tutoring systems to enable collaboration among students, leveraging the advantages of both intelligent tutoring systems and peer collaboration, without significant changes to the original Human–Computer Interaction model. The proposed framework is designed under the assumption that the adapted ITS is built as a web application, ensures horizontal scalability of the ITS, and relies exclusively on Open Source software and tools. Evaluation results of the framework demonstrate that while the system’s complexity increases, there are no perceivable rises in response times or cpu usage. • We present a framework for adapting Intelligent Tutoring Systems to collaborative. • Reduced technical challenge for implementing Collaboration. • We provide a case study of the adaptation of the system. • The modified system is not perceivably slower.