Despite advancements in personalized learning technology, their adoption in schools have been slow. Unfortunately, such environments do not systematically foster self-regulated learning for more efficient learning, and rather take over regulation from learners. This project proposes a novel approach and a prototype for hybrid regulation of learning where artificial intelligence is employed to assist learners to self-regulate themselves.

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This research introduces a novel and interpretable approach for Image-based Learner Modelling using CNNs and transfer learning to model learners’ performance and accordingly classify their computational thinking solutions. The approach integrates Grad-CAM, enabling it to provide insights into its decision-making process.

This research proposes a novel neural-symbolic AI approach for temporal learner modelling, called TemporaLM, that leverages unsupervised deep neural networks (i.e., autoencoders enriched with symbolic educational knowledge) and dynamic Bayesian networks for learners’ knowledge tracing over time. The approach employs a dynamic Bayesian network for temporal knowledge tracking and employs a knowledge-based autoencoder to enhance predictive performance through synthetic data augmentation

The LEADER AI project enhances the capacity of higher education institutions to personalize digital learning through AI-based tools and data-driven decision-making. Specifically, higher education faculty will be equipped with innovative resources, such as hands-on instruction, scenario-based training, and an interactive platform, leveraging the aforementioned emerging technologies to meet the individual needs of learners based on their strengths, skills, and interests.

This research argues that the neural-symbolic family of AI has the potential to address the named challenges. To this end, it adapts a neural-symbolic AI framework and accordingly develops an approach called NSAI, that injects and extracts educational knowledge into and from deep neural networks, for modelling learners’ computational thinking.