Using AI to Implement Distributed Practice on Learning: A Discussion on the Promises and Perils of This Approach

Abstract

This paper provides guidance to adopt AI to easily implement distributed practice of learning as a teaching strategy that academicians can integrate into their teaching. The outcome of the process of studying extends beyond learning to being educated. To achieve this, students need to retrieve information throughout their learning process. They should practice such retrieval of information not just once in a while, but multiple times during a particular period of study or during a course of study. Such distributed practice on learning of study material several times for a prolonged period, is highly essential to develop a reasonable level of robust and flexible knowledge. Distributed practice across a course will help the students to make connections between concepts by easily retrieving information. This practice of learning, supports students to recall factual information even when it is partially forgotten. Distributed practice on learning can motivate students to actively connect varied information from separate topics and ideas to have a better understanding and implement the same during novel situations. Including distributed practice in a classroom can be difficult due to factors such as resistance as it is time consuming. This paper discusses on the promises and perils of this approach and also provides guidelines for how AI can support distributed practice on learning.