Presented here are 3 selected research papers from my graduate studies at the University of North Texas, where my major was Learning Technologies (LTEC) and my concentration was in Artificial Intelligence (AI) in Education (AIEd). These papers each come from different courses but share a thread of seeking insights into the use of AIEd in conjunction with extended reality (XR) technology to provide an immersive, simulated, portable, and personalized learning and training experience for the 21st century.
Cognitive Systems
The paper chosen from LTEC 5300 deals with cognitive systems and how both human and computer systems process information. In this paper, the goal is to describe how AI can be used to enhance and guide the learning of the student in a way similar to how a mentor or tutor would, while immersing the student in a realistic and convincing environment that can provide a praxis for even highly technical or dangerous tasks, but safely and securely.
GenAI
LTEC 5704 deals with Generative AI (GenAI) technologies and is part of the AIEd concentration. This paper examines the use and potential bias of the DALL-E AI image generation tool and whether a subliminal bias could be introduced into the classroom examples if graphics generated by the tool were used in the classroom. What seems to be interesting from the research and exercises is that much like humans, AI learns from what it is exposed to and can unintentionally produce outputs that could be seen as biased if examined from a new point of view.
Data Analytics
The third paper of the selections comes from LTEC 5610, which deals with analytical methods of documentation in the use of learning techniques and technologies. Here, statistical analysis using Structural Equation Modeling (SEM) is discussed after introducing Virtual Reality (VR) training for high-stress and dangerous mine rescue to first-time VR-trained professionals as a part of ongoing professional development.
Summary
Although each paper seems to deal with a completely unrelated topic (cognitive systems, graphics generation, and statistics), I feel this is a good representation of not only the sort of varied skills required by a learning technology manager, but also of teaching/training content development, and even representative of the field of AI in general. After all, a key element of AI is machine learning (ML), where the goal is to train the computer to work and “think” in the way that a human does. The mirror parallel is that in the field of learning technology and education, we’re using computers to train humans. And the statistical analysis from the research supports the fact that this approach is not only practical, but a sound choice with a high potential success rate when done well.
Mission
While not expressly stated in the papers sampled here, but uncovered in various discussion threads throughout the program, one of the most ironic issues with both AI- and XR-based learning platforms is that despite research showing great promise, the field seems to be in a perpetual state of prototyping and research with little longevity exercised. The literature is full of pilot tests and proof-of-concepts, but woefully lacking in long-term studies in any environment (academic or professional) (Létourneau et al., 2025). That is something I plan to change.
References
Létourneau, A., Martineau, M. D., Charland, P., Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. NPJ Sci Learn, 10(1), 29. https://doi.org/10.1038/s41539-025-00320-7