Artificial intelligence (AI) is increasingly being integrated into digital learning. Some people are wary of this combination for fear, among other things, of seeing teachers dispossessed of their mission. However, AI can enable teachers to focus on the most complex and rewarding aspects of their work while improving learning effectiveness. This is because AI makes it possible to integrate the principles necessary for optimal learning, which neuroscience has confirmed in recent years. Here are four of AI’s strengths that are a win-win situation for learners and teachers alike!

Artificial intelligence can…

Save teachers’ time. AI can relieve teachers of certain repetitive tasks that are less complex and less rewarding than others, but which still need to be done. This is the case, for example, with the correction of objective answers in exams, or the creation of formative tests and quizzes. Teachers then have more time and energy to devote to the more complex and fundamentally human aspects of their role with their students.

Provide immediate feedback. AI makes it possible to provide immediate and detailed feedback, which is essential for effective learning, as confirmed by neuroscience. This feature also enables teachers to obtain a report on their students’ performance and adjust their teaching. Whether positive or negative, receiving feedback activates the reward system and triggers a dopamine release in the brain (Wilkinson et al., 2014). While so-called “negative” feedback – which aims to correct – is essential, positive feedback – highlighting successes – should not be neglected. Indeed, it has been found that the latter leads to the greatest activation of the striatum, the small nerve structure below the cortex that forms an integral part of the brain’s reward system, releasing dopamine in particular (DePasque and Tricomi, 2014). The more successful a learner is, the more its reward system is activated and releases dopamine. The resulting sense of pleasure and satisfaction reinforces the behaviour in question.

Feedback, therefore, has not only an informative but also a motivational role. The same study also observed that the reward system becomes even more active when the challenge is perceived as more difficult (without being too arduous) rather than too easy. Numerous empirical studies have demonstrated the link between better performance and a more challenging task – within the competence limits of the individual performing it – (Locke and Latham, 2002; Latham, 2007; Latham and Locke, 2007). AI can assess each learner’s level and gradually increase the difficulty of the proposed challenges so that each individual can remain as motivated as possible and, therefore, engaged throughout their learning journey.

Optimize memory consolidation. AI can identify recurrent errors in learners and propose targeted personalized exercises to remedy them. What’s more, it can organize the repetition of these exercises at an optimal frequency so that the information becomes anchored in the brain – as demonstrated by neuroscience – either in a distributed way, by splitting up the learning sessions, rather than in a massed way over a too long interval. More important than the time devoted to study, it is the fact of being tested several times, alternating with study sessions, that optimizes memorization and respects our cognitive functions’ need for rest (Karpicke and Roediger, 2008). Researchers have found that four study sessions alternated with four short tests are more beneficial before an exam than six study sessions alone.

Stimulate memory retrieval. Again, with a view to integrating the neuroscientifically-confirmed principles that are essential to successful learning, AI can be used to exercise memory retrieval. This term refers to searching in one’s head for information that has been learned but not yet consolidated, to transfer it from long-term memory to working memory. So, instead of repeating to the learner the notions studied, AI can design tests whose questions, pace and user options (a pause button, for example) are designed to stimulate memory retrieval. In the brain, all learning results from the repeated activation of neurons related to the targeted learning. One of the most effective ways of stimulating neuronal activation is to train for memory retrieval rather than simply repeating lessons, which, in the long run, risks diminishing neuronal activation.

Catherine Meilleur

Catherine Meilleur

Communication Strategist and Senior Editor @KnowledgeOne. Questioner of questions. Hyperflexible stubborn. Contemplative yogi

Catherine Meilleur has over 15 years of experience in research and writing. Having worked as a journalist and educational designer, she is interested in everything related to learning: from educational psychology to neuroscience, and the latest innovations that can serve learners, such as virtual and augmented reality. She is also passionate about issues related to the future of education at a time when a real revolution is taking place, propelled by digital technology and artificial intelligence.