“Intelligent Tutoring System in Education for Disabled Learners Using Human–Computer Interaction and Augmented Reality”

Abstract

Learning through an Intelligent Tutoring System (ITS) lies in performing well in academics and improving the learner’s learning outcomes. The recent developments within an Intelligent Tutoring System are not focused on improving human–computer interactions. The best way to overcome this constraint is to develop ITS interfaces to provide learners with better learning experiences. In this study, Augmented Reality (AR) potential along with AI methodologies is utilized within the developed ITS interface to improve the learning experience of the learning-disabled learners. Augmented Reality is where virtual images overlay the physical world, and Mixed Reality is an emerging technology that presents an altered reality, which engages users to interact with an environment developed using virtual objects. With several tools and applications available, creating and providing immersive learning experiences for learners is rising. AR in educating the specially-abled is widespread, and its benefits are sufficiently explored. However, limited AR applications are designed specifically for supporting the education of individuals with learning disabilities. The available application, and their impact on learning-disabled learners, needs detailed investigation. This work presents an Intelligent Tutoring System (ITS) to educate learners with Augmented Reality (AR) based content. The ITS learner module implemented in this study was developed for learning disabilities identification and we have assessed total 105 participants (with or without Learning Disabilities) for the experiment. The ITS performance is compared (with or without) AR content-based learning and based on the findings, AR-based learning through ITS is effective. The benefits are manifold, increase in motivation, ease of interaction, development in cognitive skills, enhancement in short-term memory, and making lessons more enjoyable, turning the overall experience stimulating and engaging.

Acknowledgement

The authors gratefully acknowledge the funding support received from the Technology Interventions for Disabled and Elderly (TIDE) scheme under the Department of Science and Technology (DST). The authors express their gratitude toward the management of UPES for their support in research work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research work has been carried out at the University of Petroleum and Energy Studies (UPES) with Project No SEED/TIDE/133/2016.

Notes on contributors

Neelu Jyothi Ahuja

Neelu Jyothi Ahuja is a Professor and Head at Systemics-Cluster, at School of Computer Science at UPES, Dehradun. She earned PhD in 2010. She has successfully delivered government sponsored R&D projects worth 1.5+ crores since 2013. She has published widely in areas of AI-ML applications for learning disabled individuals.

Sarthika Dutt

Sarthika Dutt completed her Bachelor’s and Master’s of Technology in Computer Science Engineering from Uttarakhand Technical University (UTU), Uttarakhand. She is currently pursuing PhD in School of Computer Science, UPES Dehradun, India. Her research interest includes machine learning and their applications in areas of expert systems and higher education.

Shailee lohmor Choudhary

Shailee Lohmor is an alumnus of Delhi University and has completed her PhD in domain Evolutionary Algorithms and Artificial Intelligence/Machine Learning techniques from Bharathiar University. Dr Shailee is currently Head of Artificial Intelligence and Machine Learning department at NDIM, New Delhi.

Manoj Kumar

Manoj Kumar completed his Ph.D. from The Northcap University. He has more than 11.6years of research, teaching, and corporate experience. He is currently working on the post of Associate Professor-Cyber Security in University of Wollongong in Dubai, UAE. He published over 110 articles.