Leveraging Artificial Intelligence with Zone of Proximal Development: An ARCS Motivational E-Learning Model

Matilda Isaac, Muhammad Ateeq, Hadyan Hafizh, Bintao Hu, Dolapo Shodipo

Abstract: The study aims to investigate the complex effects of Artificial Intelligence (AI) in Education (AIED) on Zone of Proximal Development (ZPD). ZPD a concept introduced by Lev Vygotsky during the late 1920's, stands on the premise that the distance between actual development level (independence) and the level of potential development (dependence) lies in mentorship by adult guidance and peer collaboration. Several social-cultural theories have utilized this to analyze the relationship between instructor and learner achievement. The idea is to influence the learner's knowledge zone by active and productive interaction. It has been noted that when learners are given the opportunity to collaborate it not only motivates the learner but also influences their sociological development and this helps to bolster their already established knowledge. However, the role of AI on the ZPD of the learner creates a dichotomy that needs further exploration. A ubiquitous AI educational setting may encourage learners to remain passive rather than foster participatory behaviour and positive outcome. These inclinations toward passive compliance are evident in the concept of AI systems enabling" personalized learning," which departs from the theory of learning as a social action and civic engagement. We further explore the role of motivation through the ARCS model in minimizing an adverse effect of AI on a learner's ZPD.

External IDs:dblp:conf/tale/IsaacAHHS23