DISC, Birkman, and MBTI: Merging Personality Assessments with Artificial Intelligence for Customized, Human-Centric Tools

Understanding human behavior, motivation, and cognition is essential for tailoring solutions in workplaces and personal development. Personality assessments like the DISC test, Birkman Method, and Myers-Briggs Type Indicator (MBTI) offer frameworks to evaluate job-related competencies and behaviors, supporting decisions in hiring, promotion, and retention. DISC categorizes observable traits into Dominance, Influence, Steadiness, and Compliance, revealing how people act. The Birkman Method explores underlying needs, stress responses, and interests, showing what drives individuals and how they react under pressure. MBTI maps cognitive preferences through dichotomies like Extraversion/Introversion and Thinking/Feeling, producing 16 types that explain decision-making styles. Together, these tools provide insights into actions, motivations, and thought processes, proving valuable in career planning and team dynamics. This thesis explores merging these assessments with AI technologies, such as xAI’s Grok and OpenAI’s ChatGPT, to design customized, human-centric tools. While AI excels at processing data, its potential to incorporate personality nuances is underexplored. The study proposes integrating DISC’s behavioral clarity, Birkman’s motivational depth, and MBTI’s cognitive richness with AI to enhance personnel assessments and personal growth. Using a self-conducted Birkman test, it examines practical applications—like aligning roles with behaviors or addressing unmet needs—and considers broader implications for workplaces and society.