Theses and Dissertations

ORCID

0009-0005-9111-949X

Advisor

Ma, Junfeng

Committee Member

Marufuzzaman, Mohammad

Committee Member

Wang, Haifeng

Committee Member

Wang, Jun

Committee Member

Jones, Adam

Date of Degree

8-12-2026

Original embargo terms

Immediate Worldwide Access

Document Type

Dissertation - Open Access

Major

Industrial & Systems Engineering

Degree Name

Doctor of Philosophy (Ph.D.)

College

James Worth Bagley College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

The integration of Artificial Intelligence (AI) and Extended Reality (XR) represents a transformative frontier in human–computer interaction, offering the potential for adaptive, intelligent, and immersive systems. Despite recent advances, the mechanisms of effective AI-XR integration, methods for practical implementation, approaches for systematic assessment, and pathways for adoption remain underexplored. This dissertation addresses these gaps by developing a conceptual framework for AI-XR integration and validating it through applied studies across safety, healthcare, and behavioral domains. The work establishes a taxonomy of AI algorithms, XR modalities, and sensor ecosystems. Empirical evaluations demonstrate the feasibility and effectiveness of AI-XR systems, including real-time monitoring of human behavior, immersive training environments, and affective computing applications. Multimodal physiological signals such as EEG, eye tracking, and heart rate are leveraged to assess cognitive workload and user engagement, while usability and adoption considerations are examined to inform practical deployment. The findings provide both theoretical clarity and practical insights into the design, implementation, evaluation, and adoption of AI-XR systems. By advancing understanding of intelligent immersive technologies, this work contributes to safer, more effective, and user-centered applications in real-world contexts.

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