Along with the rapid expansion of smart sound boxes in family places, present products have on the whole converged toward unified forms and voice-guided interaction patterns, while have not sufficiently taken into account different user behavior customs. For the purpose of filling this empty position, this research has proposed a user-behavior-driven artificial intelligence auxiliary design framework which connects behavior expression, model selection, and multi-object design search for intelligent loudspeaker interaction and form optimization. Based on literature, one composite dataset which has 48 typical user character documents is used for constructing a controlled action space, and it does not replace real experience observation. By means of K-means and Gaussian Mixture Models, the analysis of user profiles is conducted by us for the extraction of interpretable archetypes. After that step, one mapping that behavior turns into design is given for four design variables: interaction strategy, feedback richness, form factor, and transparency/control. NSGA-II hence is utilized by us to carry out exploration of Pareto-optimal trade-off relations among these objectives. The optimization outcome displays directional gathering across the artificial design space: interaction comfort increases from 0.571 to 0.680, feedback-richness matching from 0.595 to 0.750, transparency matching from 0.586 to 0.645, meanwhile the form-punishment goal reduces from 0.208 to 0.000. These findings show that the variables at interaction level control design effect, whereas compact and visually not noticeable shapes still keep structural advantages. Therefore, this study should be regarded as a framework-validation work which makes the translation from behavior to design be clear and repeatable inside a controlled artificial space. Its contribution is in the providing of a systematic working flow for behavior-informed AI-aided design that can be expanded to wider smart-home and human-AI interaction contexts.
