Ibrahim Tahmid successfully defended his dissertation on September 10th, 2025. The abstract for his dissertation follows:
We live in the peak of the information age, surrounded by more data than current tools can easily handle.
To manage this complexity, two disruptive technologies, extended reality (XR) and artificial intelligence (AI), offer complementary strengths. XR provides an expansive workspace where digital information can be spread out, navigated, and manipulated as naturally as physical documents, freeing analysts from the constraints of two-dimensional screens. AI, on the other hand, excels at processing vast information spaces quickly, surfacing insights that would take humans much longer to uncover. Yet, true value emerges only when AI adapts to human intent, aligning its exploration with the human’s evolving needs.
This dissertation argues that the synergy of XR and AI can fundamentally improve workflows in domains where professionals must sift through large, interconnected datasets, such as intelligence analysis, investigative journalism, or academic research. The key lies in leveraging the built-in eye-tracking sensors of XR headsets. By observing where users look, what they read, and how long they dwell on different topics, we can construct an interest model that maps their perceived interest in different topics.
With empirical evidence, we showed that this model can, indeed, predict evolving user interests with high precision. In parallel, we investigated how analysts perceive automation in immersive environments.
Our studies revealed that users welcome intelligent assistance as long as they retain control, the automation is transparent, and they can accept, dismiss, or even undo system actions.
Guided by these insights, we developed a gaze-aware immersive analytic tool that uses the interest model to generate interpretable visual cues: global cues, which reflect overall interest of the user through color-encoded documents and ranking, and local cues, which reveal inter-document relationships grounded in the user’s attention. Evaluations with novice and professional intelligence analysts showed that these gaze-derived cues help users navigate complex datasets more efficiently, identify relevant information while avoiding distractions, and synthesize their findings with reduced need for manually typing out their intents. Together, the cues enabled a form of semantic interaction that lowered physical workload and enriched human–AI collaboration.
While this work centers on predicting user perception from gaze, our approach paves the way for a broader future. Other implicit signals, such as indicators of stress, could extend the model to capture not just interest but also cognitive and emotional states. This opens the door to richer semantic interactions and more adaptive human–AI partnerships in immersive analytics.
The findings presented here lay the foundation for such systems, demonstrating how gaze-aware interest models can leverage the strengths of XR and AI together to meaningfully support AI-mediated immersive sensemaking.
Congratulations, Dr. Tahmid!