Enhancing Personalized Education through an Adaptive Framework: Assessing the Impact of an Optimized Planning Generator

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The COVID-19 crisis has exposed the inefficiencies of many e-learning platforms, highlighting the importance of face-to-face interactions between students and professors. To address these challenges, this study proposes an adaptive blended learning framework that considers the unique profiles of students, professors, and university policies. The proposed framework consists of three main components: (i) a program generator that generates a list of sessions with an optimal blend of face-to-face and e-learning modes, (ii) an evaluation model that proposes scheduling planning that is optimal for both students and professors, and (iii) an AI model that predicts student engagement in courses using the output of the evaluation model.

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