AI Prediction and Teaching Strategies for a Two-Phase Engine in a Smart Learning Platform

Abstract

The impact and progress of Information Technologies has led to a process of change in most environments of our society, specially education. Even more with the current rise of Artificial Intelligence, what has led to the creation of different new tools aiming to improve the learning experience. This fact has contributed to the creation of systems that aim to adapt the learning process to each individual learner and offer them a personalised experience. The problem of letting automated systems manage the whole learning process is the lack of human factor, but learning objectives and teacher criteria are crucial. That is why this research proposes a solution that combines the potential of AI without neglecting the teacher decision. Concretely, the proposal is an AI model that selects the most suitable activity to each learner. To do so, this proposed model is structured in two phases. The first is the prediction phase, in which the model predicts the score a learner will obtain and the time they will spend to complete an activity. Then, in the second phase, the selection of a single activity is done by means of instructional strategies. These strategies are based on the previously obtained metrics and establish the criteria to follow for selecting activities. The selected strategy is always set by the teacher, who will guide the learners through the process. With this model, this research proposes a combination of AI techniques with human decision-making. Instead of relying the learning process to an automated engine, it includes the teacher as the one to guide the AI by making the last decision.