Adaptive XR Difficulty-Adjustment Framework — Pilot Data and Code (Engineering Feasibility Study)

Complete data and code archive for the manuscript "An Adaptive XR Framework for Real-Time Difficulty Personalization: A Pilot Engineering Feasibility Study" (submitted to IEEE Access, manuscript Access-2026-28737). The archive contains: (1) all 16 session logs (CSV) from the pilot campaign — 12 anonymized participant sessions and 4 empty logs from aborted technical test runs (282 interaction events; 86 release trials; no records excluded); (2) the C# scripts of the deployed Unity prototype (Unity 2022.3.62f3 LTS, XR Interaction Toolkit 2.6.5, Meta Quest 2), including the proportional difficulty controller; (3) a Python analysis script that regenerates the quantitative user-summary results, session accounting, and data-derived figures of the paper from the raw logs; (4) a README documenting all column definitions, logging conventions, controller parameters, and release-position handling. The study is an engineering feasibility evaluation with non-clinical adult volunteers. It makes no claims about learning, engagement, or clinical effectiveness, and involved no autistic participants. No personal, demographic, or health information was collected; sessions are identified only by random numeric codes. Version 1.1 corrects the README: manuscript title updated; release-position handling documented as varying across pilot builds (earlier sessions log exact-zero errors); reproducibility scope clarified; replay verification added (replaying the recovered controller rule over the logged outcomes reproduces all 86 logged trial difficulties exactly); note added that reaction time is logged but not used by the controller. Code: MIT License. Data (data/*.csv): CC BY 4.0.