aceAdaptive level modification via player skill classification and large language models
Maintaining player engagement in video games requires a careful balance between challenge and player competence. Static difficulty settings fail to account for individual skill variation, while existing dynamic difficulty adjustment systems are limited to tuning low-level game parameters rather than restructuring level content. This paper presents an adaptive level modification framework that personalizes gameplay by continuously inferring player skill and applying targeted structural modifications to level cont…
LLM-driven adaptive level modification framework classified players by skill with 97.82% accuracy and generated modified levels that remained traversable at 74.1% full-level and 83.5% chunk-level rates.
Evaluation was limited to Super Mario Bros. levels and post-modification playability did not fully reach original baseline, indicating domain-specific testing and imperfect traversability.
Evidence
- Peer-reviewedScientific Reports2026-07-28
How should this claim be treated?
Truvace Impact Record TRV-2026-0594, v1: “Adaptive level modification via player skill classification and large language models.” Truvace, 2026-07-30. /record/TRV-2026-0594 (accessed at citation time). sha256 e5efafb400e2c8c9…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0594 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.