"The biggest surprise was how dramatically cognitive abilities varied within our target population. During our user testing sessions, I watched one participant solve complex spatial puzzles in under ten seconds while expressing frustration that the game wasn't challenging them enough. Twenty minutes later, another participant struggled with what I considered the simplest tutorial level. Both users had the same diagnosis. Both were part of our target demographic. But their cognitive strengths and challenges were completely different."
"This taught me that traditional difficulty curves don't work for cognitive remediation games. You can't design three difficulty settings and call it accessible. Instead, you need systems that automatically adapt in real time based on user performance. If someone breezes through the first five levels, the algorithm should immediately jump them ahead. If someone struggles with basic interactions, the system needs to provide more scaffolding without making them feel patronized."
Designing cognitive remediation games requires systems that adapt difficulty in real time because cognitive abilities can vary dramatically among players with similar diagnoses. User testing showed some individuals solved complex spatial puzzles quickly while others struggled with basic tutorials. Static difficulty presets are insufficient; adaptive algorithms should accelerate capable players and provide unobtrusive scaffolding for those who struggle. Implementing auto-balance presents technical challenges, but the user experience risks of getting it wrong are greater, including churn at both performance extremes. Many players in neurodiverse groups also possess extensive gaming experience, which affects design decisions.
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