Dynamic Difficulty Adjustment in a VR Game: Comparing Fixed Progression with Two Adaptive Progression Strategies

Dynamic difficulty adjustment (DDA) can help maintain a suitable level of challenge by modifying gameplay in response to player performance. However, comparisons of complete progression strategies within the same VR game remain limited. To examine these approaches, we developed Pulse Run VR, an arcade game combining target shooting with physical obstacle avoidance. Thirty-four eligible participants aged 16–26 completed one 210 s run using Fixed Time-Based Progression, Performance-Based DDA, or Skill-Specific DDA. The primary analysis included data from 28 participants who used Meta Quest 3; an all-headset sensitivity analysis included all 34. Gameplay telemetry and post-run ratings were used to compare content delivery, gameplay performance, perceived difficulty, and enjoyment. Fixed Time-Based Progression exposed players to more advanced patterns. Performance-Based DDA showed the strongest descriptive target performance results, and Skill-Specific DDA had the largest descriptive percentage of “just right” difficulty ratings. Advanced-pattern exposure was the only significant primary outcome, and the target performance advantage of Performance-Based DDA appeared only in the sensitivity analysis. The adaptive strategies shared the core game and evaluation settings but represented player performance differently and applied it to progression in different ways. The findings indicate that content delivery, gameplay performance, and perceived challenge captured different aspects of the evaluated progression strategies.