This project promotes fair and ethical treatment of veterans in the future job landscape by providing the empirical knowledge needed to remove implicit bias and misconceptions against veterans and prepare veterans for obtaining and maintaining competitive positions in the future workforce. Despite their strong work ethic and dedication, many veterans in the U.S still face major barriers to participating in the civilian workforce. After separation from duty, service members often participate in a week-long transition assistance program that, at best, can be described as a convenient "one-size-fits-all" solution. Research studying the limitations of the veteran population in entering this dynamically changing job market is scarce and does not provide a full understanding of the challenges faced by the veteran population as well as their train of thought during the time of the job interview. This project gathers empirical evidence to understand veterans' common feelings, thoughts, and potential weaknesses in social effectiveness skills during the civilian job interviews. The project further provides a preliminary assistive technology enabled by artificial intelligence for promoting veterans' interview skills in a tailored and inclusive manner, ultimately preparing them for the future workforce and broadening their participation in fields where they are traditionally underrepresented, such as computing. In addition to interview training, through effective partnerships with industry, this work creates educational materials for promoting unexplored strengths of the veteran population, such as commitment, reliability, and sense of duty, to the potential employers, thereby changing the job hiring culture and providing veterans with more opportunities in the future job landscape.

This project explores the above goals through a collaboration between computational and behavioral sciences for acquiring new insights into veterans' experiences during civilian interviews and designing novel technologies for supporting veterans in this task. The research work will be carried out with three technical aims. The first aim is on data collection through focus group discussions and real-life interviews of veterans with industry representatives to identify challenging encounters during the interview. Data include behavioral reactions, physiological reactivity, and subjective assessments of both the interviewer and interviewee, which are examined in association with the interview setting and are further triangulated. The second aim will explore quantifiable measures of interviewees' moment-to-moment stress based on their vocalizations, visual expressions, and physiological reactivity. These quantifiable measures are employed for the preliminary design of training interventions that can assist veterans on coping with stress during the job interview training. The third aim will examine the interviewee's ability to engage with the interviewer. In particular, the researchers will develop new methods in natural language processing and affective computing for detecting overly formal conversational language specific to the military, as well as degradation in social aspects of the interaction from acoustic and visual cues.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Balakrishnan Prabhakaran
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Texas A&M Engineering Experiment Station
College Station
United States
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