The Impact of Artificial Intelligence in Reshaping Education: An Analysis Based on Learning Theories

A change in behavior that results from experience and is relatively permanent in nature can be defined as Learning. Behaviorism, a dominant theory used to explain learning, sought to measure only observable behaviors. In AI training, supervised and reinforcement learning methods can help machines learn from feedback and improve their performance and this process influences or biases the learner when he adopts AI as a tool for learning and when it is in their zone of understanding. Exposure to AI technologies in higher education prepares students for future careers, as they gain experience with tools and skills that are increasingly relevant in the workforce. To identify the implications of AI interaction in learning we have taken the experiences of 38 students and 15 educators from different fields. We have identified different levels of AI interactions in scaffolding, individualization, challenges, and stress. The design and implementation of different learning platforms aligned with the Zone of Proximal Development (ZPD) in higher education can also be associated with several learning theories. therefore, the features of such a platform also correspond to different learning theories.