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Responsible professional AI use requires both institutional safeguards and individual decisions about data handling, but support for governance principles is not equivalent to demonstrated knowledge or compliance. This study examines that distinction using 385 adult, consenting, AI-aware questionnaire records from Bangladesh. The research team confirms mixed online and in-person collection; 242 respondents are law students. Four governance-preference statements are separated from the single statement of personal compliance intention. Analysis combines item distributions, paired endorsement contrasts, a descriptive count of endorsed governance preferences, rank correlations and adjusted binary and ordinal models. Clear organisational rules receive 76.4% endorsement and high-risk data-protection assessments 75.6%, compared with 54.8% for minimisation, 54.0% for accessible data rights and 51.9% for personal compliance intention. Rules and assessments exceed intention by 24.4 and 23.6 percentage points, respectively; the other two paired differences are not established after correction. Eighty respondents endorse all four governance preferences, of whom 47 endorse personal intention. In the primary binary model, stronger agreement with the rule-preference statement is positively associated with intention, with adjusted odds ratio 1.43 and Holm-adjusted p = 0.042; the association is not retained in ordinal or student-restricted sensitivity analyses. Low block consistency supports item-level interpretation rather than latent knowledge or risk pathways. The contribution is an account of uneven alignment between institutional governance preferences and intended personal conduct, without inferring a measured intention-behaviour gap. Implications prioritise practical data-minimisation tasks, accessible rights procedures and clear allocation of professional responsibility. The study does not measure actual regulatory knowledge, workplace implementation or observed compliance.
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Akter et al. (2026) studied this question.
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