Building a Multimodal AI/ML Insights Engine for Data-Driven VLT Game Optimization

This project aims to help International Game Technology (IGT), a global leader in gaming, better understand what makes their Video Lottery Terminal (VLT) games successful. Currently, IGT finds it hard to link specific game features (like bonus rounds or themes) to how well a game performs or how long players stay engaged. This project will solve this by using advanced Artificial Intelligence (AI) and Machine Learning (ML) to analyze both player feedback (from focus groups) and game performance data. By combining these different types of information, IGT will get a powerful “AI/ML Insights Engine”. This new tool will allow them to make more informed decisions about designing games, allocating resources, and improving player engagement, moving beyond guesswork to a data-driven approach.

Faculty Supervisor:

Shadi Aljendi

Student:

Partner:

IGT

Discipline:

Computer science

Sector:

Arts, entertainment and recreation; Information and cultural industries

University:

University of New Brunswick

Program: