Using Generative AI to Uncover What Drives Player Enjoyment in PC and VR Games

TL;DR

This paper presents a scalable AI-driven framework that analyzes large-scale game reviews to identify key factors influencing player enjoyment across PC and VR games, providing actionable insights for developers.

Contribution

It introduces a novel application of generative AI and machine learning to convert unstructured reviews into structured data for comprehensive game analysis.

Findings

Distinct player preference patterns in PC and VR games

Identification of key design and monetization factors affecting enjoyment

Scalable framework for large-scale review analysis

Abstract

As video games continue to evolve, understanding what drives player enjoyment remains a key challenge. Player reviews provide valuable insights, but their unstructured nature makes large-scale analysis difficult. This study applies generative AI and machine learning, leveraging Microsoft Phi-4 small language model (SLM) and Google Cloud, to quantify and analyze game reviews from Steam and Meta Quest stores. The approach converts qualitative feedback into structured data, enabling comprehensive evaluation of key game design elements, monetization models, and platform-specific trends. The findings reveal distinct patterns in player preferences across PC and VR games, highlighting factors that contribute to higher player enjoyment. By using Google Cloud for large-scale data storage and processing, this study establishes a scalable framework for game review analysis. The study's insights offer actionable guidance for game developers, helping optimize game mechanics, pricing strategies, and player engagement.