Early Detection of Attention Deficiency Using ML

Abstract

Working parents find it difficult to check upon their
children’s academic performance and monitor them frequently.AI-based study companion assistants can help reduce the workload of parents by providing an additional support system for their children’s learning. This can take some of the burden off of parents, who may otherwise have to spend time helping their children with their studies or coordinating with teachers and tutors. One promising application of AI is the development of study companion assistants that use machine learning techniques to help students improve their academic performance. These
assistants can provide personalized recommendations, feedback, and support based on a student’s learning style, strengths, and weaknesses. In this paper, we present a study companion assistant that uses ML techniques to help students stay organized, manage their time, and develop effective study strategies. The assistant is designed to adapt to a student’s needs and learning progress over
time, providing support and guidance as needed. We demonstrate the effectiveness of our approach through a series of experiments and user studies, showing that our AI-based study companion assistant can significantly improve student performance and satisfaction.