A novel framework for AI-based dynamic teaming up of students in the context of online collaborative learning activities

In the era of online learning, automated dynamic suggestions for fruitful collaborations among learners have become crucial. We propose an algorithm that takes into account ethical considerations and suggests interactions among students for collaborative involvement in group activities in an inherently fair manner: all students participate in fairly weighted activities and have a balanced number of weighted interactions. Students' preferences are taken into consideration and can be expressed either directly via their statements or indirectly via their conversations in the context of learning gamification. A synthetic preference graph that simulates students' views and thoughts is constructed. Moreover, we proceed to build a model for personalized interactions that prevents user isolation. The quality of the evolution of interactions is measured based on the diversity of interactions, the interaction graph edit similarity that affects storage cost, granularity of graph construction and fairness criteria. We use deep machine learning for sentiment detection in dialogues in order to conduct sentiment analysis of real data in the context of an online game chat among students. We categorize text as positive, negative or neutral in terms of emotion and produce student profiles. We produce embeddings with an accuracy of 92%. This analysis is extended to all texts in the discussion, resulting in a preference profile for each learner, from which ultimately a quantitative estimate of potential interaction derives for use in the interaction graph. Our approach can be utilised in the teaching practice as an efficient novel framework for online learning activities and gamification, and serves as an automated dynamic method for fair collaboration choices in teaching practice.