Expected Confidence Benefits and Verification of AI Migration Information in Bangladesh

Artificial intelligence can make migration information easier to access, yet perceived confidence benefits must be distinguished from accurate advice, informed decisions and effective rights protection. This study examines that distinction using 385 adult questionnaire records from Bangladesh. It analyses five migration-information statements separately rather than treating a heterogeneous item block as a validated confidence scale. The analysis combines complete response distributions, four paired endorsement contrasts, joint response configurations and an adjusted proportional-odds model of agreement that accurate AI information could improve migration-related decision confidence. Verification against official or authoritative sources received 87.5% endorsement, compared with 64.2% for the conditional confidence proposition. Their paired difference was 23.4 percentage points, with a whole-record bootstrap 95% interval of 17.9-28.8 points. Verification exceeded endorsement of comparison benefits, the distinction between general information and legal advice, and supplementary use; all four exact paired comparisons remained below p = .001 after Holm correction. Adjusted associations with the conditional confidence outcome were imprecise: comparison benefits, migration-specific verification, the legal-advice boundary and rights-information search all had intervals spanning an odds ratio of one. The results identify a stronger stated verification norm than endorsement of several practical conditions for reliance. They do not establish actual checking, improved decision quality or a causal effect of AI services. The article develops an information-governance account in which confidence is evaluated alongside source traceability, professional referral and usable verification routes. Its contribution is a measurement-specific account of anticipated benefit and verification, with implications for migration-information design and the evaluation of AI-supported legal information.