But a new AI tool from researchers at the University of Maryland and Tilburg University wants to change that—by predicting, with unsettling accuracy, whether you'll actually look at an ad before anyone bothers placing it there.
The team trained the system on eye-tracking data from 3,531 digital display ads. Real people wore eye-tracking equipment, browsed pages, and their gaze patterns were recorded. AdGazer learned from all of it.
When tested on ads it had never seen before, it predicted attention with a correlation of 0.83—meaning its forecasts lined up with actual human gaze patterns about 83% of the time.
Unlike other tools that focus on the ad itself, AdGazer reads the whole page around it. A financial news article next to a luxury watch ad performs differently than that same watch ad next to a sports score ticker.
The system uses a multimodal large language model to extract high-level topics from both the ad and the surrounding page content, then figures out how well they match semantically—basically the ad per se vs the context it is placed on. These topic embeddings feed into an XGBoost model, which combines them with lower-level visual features to produce a final attention score.
For now it's an academic tool. But the architecture is already there. The gap between a research demo and a production ad-tech product is measured in months—not years.
