Abstract:
A smart learning environment is equipped with personal digital devices, wireless communication,
learning platforms, and sensors that associate to provide input into Artificial intelligence systems. Artificial
intelligence makes decisions about regulating the physical aspects of the environment or learning systems. These
requirements may be identified by analyzing learning performance, behaviors, and the real-world and online
settings in which students are situated. There are several challenges in implementing smart learning environments
that are highly cost-effective, connectivity issues (Internet), impairing students' problem-solving capacity,
technical challenges, e.g., malfunctioning of electronic gadgets. Hence, in this paper, Artificial Intelligence based
Efficient Smart Learning Framework (AI-ESLF) has been proposed to overcome the challenges faced by a smart
learning environment. This study aims to designate the smart learning environment's current concept based on AI
application and examine its fundamental criteria and demonstrate how tests can be performed in this smart
learning environment by case studies. The experimental results show that the suggested system enhances the
prediction ratio in terms of students' learning behavior compared to other existing approaches.
