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Interactions based in Motivational Interviewing (MI) [1], driven by machine learning, may provide an efficient way to evaluate readiness to quit, elicit behavior change preferences, and scalable flexibility to extend reach to more diversified target populations. This study used patient and public sources of conversational data to develop a Technology Assisted Motivational Interviewing chatbot (TAMI), a digital agent employing machine learning models to deliver MI for tobacco cessation. Consistent with the four tasks of MI (engagement, focusing, evocation, and planning), the creation of TAMI involved 1) utilization of existing de-identified datasets to identify specific smoking topics of interest; 2) incorporation of MI-Consistent utterances to enhance relational connection to TAMI and elicit language expressing interest in quitting (change talk); 3) instilling change talk recognition and topic classification to guide discussion with TAMI and 4) providing tailored treatment options if indicated. Informed by patient, provider, and public discussions about smoking, TAMI can explore motivation associated with tobacco use, and privately employ interventions to elicit change talk, or accurately evaluate readiness for tobacco cessation. TAMI is also scalable technology, which opens the possibility of having more tools for clinical assessment, delivering MI, and tailoring cessation referrals to underserved populations or those with other target behavior goals.