SBIR Phase I: Game-Based Psychometric Assessments for College and Career Match

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The broader social impact and commercial potential of this Small Business Innovation Research (SBIR) Phase I project reflects the increasing importance and proliferation of artificial intelligence and algorithmic systems in the educational arena and in other domains of human resources. This places the project strategically at the vanguard of scientific discovery, in building a technology tool that will serve society?s goals. In the post-secondary education and training domains, market inefficiencies require excess spending on both sides of the transaction, adversely affecting both supply and demand. As the nation continues to deliberate the value of higher education in the face of $1.3 billion in student loan debt, streamlining the college application process by matching students and institutions based on an applicant?s career goals can reduce costs for students, their families, and institutions alike. Outside the field of education, this project will impact workplaces where employer-employee matching algorithms will be used in recruiting, hiring, training, and team building, across the domains of health, innovation, national defense, and beyond. Ultimately, people analytics platforms have unlimited potential for national and international commercialization, thus generating tax revenue, creating jobs, and otherwise bolstering the economy. The proposed project focuses on the deployment of people analytics in the domain of college and career readiness and success. While talent evaluation through such innovative software is in its infancy, the small company's approach to college match will revolutionize how students identify their professional goals and then optimize their college experience for successful life launch. The small company's team of behavioral and data scientists, game designers, and software engineers, are creating video games that provide insights into a college applicant's career potential. Their platform captures massive amounts of micro-behavioral data -- how long players hesitate before making a move, persistence in the face of challenge or frustration, ability to parallel process -- to create a psychographic profile of the player's career-critical characteristics, including traits like competitiveness, extroversion, risk tolerance, and other enduring, personal traits difficult to discern through resumes and traditional, college application materials. Once a student's career objectives become clear, the company's proprietary recommendation engine suggests educational institutions and curricula best suited to attain the player?s career goals. In addition to expanding the game platform and collecting player data to develop and fine tune predictive algorithms, this project also will examine factors that influence a player's response to the insights and recommendations the platform provides. This work will investigate the impact of transparency, or "scrutability", of game-based assessments and implement visualization tools that promote the player's understanding of why specific recommendations are made.