Asee peer logo

Adaptive Virtual Assistant for Virtual Reality-based Remote Learning

Download Paper |


2022 ASEE Annual Conference & Exposition


Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

Computers in Education 8 - Video Technology

Page Count


Permanent URL

Download Count


Request a correction

Paper Authors

author page

Hannah Sloan University of Calgary


Richard Zhao University of Calgary

visit author page

Dr. Richard Zhao is an Assistant Professor in the Department of Computer Science at the University of Calgary and the principal investigator on this research. He leads the serious games research group, focusing on games for training and education where he utilizes artificial intelligence, virtual reality, and eye-tracking technologies. He received his M.Sc. and Ph.D. in Computing Science from the University of Alberta. Dr. Zhao has served as a program committee member on academic conferences such as the International Conference on the Foundations of Digital Games (FDG), the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), and the ACM Special Interest Group on Computer Science Education (SIGCSE) Technical Symposium.

visit author page


Faisal Aqlan Pennsylvania State University, Behrend College

visit author page

Dr. Faisal Aqlan is an Associate Professor of Industrial Engineering and Director of the Master of Engineering in Engineering Management Programs at the University of Louisville. He received his Ph.D. in Industrial and Systems Engineering from Binghamton University in 2013. He is a Senior Member of the Institute of Industrial and Systems Engineers (IISE), and currently serves as the IISE Vice President of Student Development, and holds a seat on the IISE Board of Trustees. Aqlan’s research interests are in system simulation and automation, process improvement, engineering education, and sensor-based virtual reality for manufacturing and healthcare applications. He is currently a PI on multiple NSF grants.

visit author page

author page

Hui Yang Pennsylvania State University

author page

Rui Zhu

Download Paper |


This research describes the development of an adaptive virtual assistant in an immersive virtual reality (VR) serious game aimed at teaching engineering students manufacturing concepts. For undergraduate manufacturing education, students need to learn product design and manufacturing systems that require well-coordinated analysis of requirements and hands-on practices in complex manufacturing assembly lines. While it is often not feasible and practical for students to participate in real factory environments, simulations are created to offer a flexible alternative of digital learning. With the advancements in immersive technologies, VR opens new opportunities for teaching and learning manufacturing, and enables remote learning from any physical location. In this research, we describe the elements of a serious game built using the Unity game engine with VR technology that allows students to practice the concept of craft production.

Prior research has shown that adapting learning material to suit individual student needs increases motivation and student successes. While learning remotely using an immersive virtual environment, a student is often working in an independent manner. Seeking help often requires the student to leave the virtual environment and break immersion. In this research, we propose an adaptive virtual assistant in the game environment to support the student learning process. By tracking student actions in the game environment and building a model of the student using reinforcement learning, the virtual assistant can learn and adapt to the student’s preference in the types of assistance to provide. We show the adaptation of the virtual assistant through simulated experiments of typical student preferences.

Sloan, H., & Zhao, R., & Aqlan, F., & Yang, H., & Zhu, R. (2022, August), Adaptive Virtual Assistant for Virtual Reality-based Remote Learning Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN.

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2022 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015