BETA-MIND

Most AI evaluations measure whether a model produces a capable answer. They rarely measure what repeated use leaves behind in the person: whether they can still reason independently, tolerate disagreement, generate diverse ideas, and own consequential decisions without AI.

Existing single-session studies are also poorly suited to an AI-native cohort for whom exposure is continuous across work, learning, relationships, and judgment.

Designed BETA-MIND as a longitudinal research program organized around four dimensions of human agency: independent reasoning, socialization, creativity, and decision accountability. Specified two open artifacts—a multi-agent simulator for long-horizon dependency dynamics and a Human Agency Benchmark for scored behavioral evaluation.

Defined comparative study arms, observable success gates, a non-compensatory agency index, and a bounded twelve-month work package separating synthetic simulation from later human-subject validation.

Produced a proposal-ready research architecture with defined hypotheses, behavioral measures, evaluation thresholds, open deliverables, and a phased execution plan.

The framework makes human agency a first-class AI evaluation target and prevents gains in task capability from concealing losses in reasoning, dissent, creativity, or accountability.

The same commitment to rigorous, evidence-led educational design extends to Model-Training Micro-Courses ↗, a structured course on LoRA, QLoRA, distillation, and RLHF with a free sample module. A separate demo, the BETA-MIND Research Instrument ↗, illustrates the reflection-prompt design described here directly — without scoring or diagnosing anyone.