Publication Date


Document Type


Committee Members

Ashutosh Shivakumar, Ph.D. (Committee Chair); Yong Pei, Ph.D. (Committee Co-Chair); Thomas Wischgoll, Ph.D. (Committee Member); Paul J. Hershberger, Ph.D. (Committee Member)

Degree Name

Master of Science (MS)


Motivational Interviewing (MI) is an evidence-based brief interventional technique that has been demonstrated to be effective in triggering behavior change in patients. To facilitate behavior change, healthcare practitioners adopt a nonconfrontational, empathetic dialogic style, a core component of MI. Despite its advantages, MI has been severely underutilized mainly due to the cognitive overload on the part of the MI dialogue evaluator, who has to assess MI dialogue in real-time and calculate MI characteristic metrics (number of open-ended questions, close-ended questions, reflection, and scale-based sentences) for immediate post-session evaluation both in MI training and clinical settings. To automate dialogue assessment and produce instantaneous feedback several technology-assisted MI (TAMI) tools like ReadMI based on Natural Language Processing (NLP) have been developed on mobile computing platforms like Android. These tools, however, are ill-equipped to support remote work and education settings, a consequence of the COVID-19 pandemic. Furthermore, these tools lack data visualization features to intuitively understand and track MI progress. In this thesis, to address the aforementioned shortcomings in the current landscape of TAMI, a web-based MI data visualization dashboard tool has been designed and developed. The proposed dashboard leverages the highperformance computing capacity of cloud-based Amazon Web Service (AWS) to implement the NLP-based dialogue assessment functionality of ReadMI and a vibrant data visualization capability to intuitively understand and track MI progress. Additionally, through a simple Uniform Resource Locator (URL) address, allows MI practitioners and trainers to access the proposed dashboard anywhere and anytime. Therefore, by leveraging the high-performance computing and distribution capability of cloud computing services, has the potential to reach the growing population of MI practitioners and thereby provide a pathway for largescale MI adoption.

Page Count


Department or Program

Department of Computer Science and Engineering

Year Degree Awarded