Smart Education Ecosystems for Enhancing Learning Experiences through Deep Attention Learning and Artificial Intelligence

Anitha Kumari, D and Gowtham, M and Sudha, S and Divya, R and Bala Thirupura Sundari, K S and Vardhini, V (2024) Smart Education Ecosystems for Enhancing Learning Experiences through Deep Attention Learning and Artificial Intelligence. In: 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), Faridabad, India.

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Abstract

Expanding knowledge at a time of fast change in the field of education depends absolutely on the creation of intelligent learning ecosystems. The aim of this work is to investigate the possibilities of using artificial intelligence and deep learning respectively to improve the learning surroundings. Emphasizing the need of developing new approaches, the first part of the presentation addressed the challenges the conventional educational system is today facing. The problem statement is Reality Statement of Mind The current problem is that the surroundings lack personal training and cooperation; hence, course of action must be altered. This research suggests that deep learning be combined with technology to customize instructional content for every and every student. The approach under use is creating an intelligent learning ecosystem with cutting-edge algorithms to investigate student involvement, interests, and learning patterns by means of intricate analysis. The system helps one to focus the learning process as necessary, so improving knowledge and memory retention. Studies reveal that student involvement, learner general, and comprehension have much improved. Applied in the field of education, deep learning and artificial intelligence have the potential to help close the gap and generate a learning environment that is both better and more efficient.

Item Type: Conference or Workshop Item (Paper)
Subjects: Computer Science Engineering > Artificial Intelligence
Domains: Management Studies
Depositing User: Mr IR Admin
Date Deposited: 23 Aug 2025 09:00
Last Modified: 23 Aug 2025 09:00
URI: https://ir.vistas.ac.in/id/eprint/10395

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