HMN 2026: How AI is set to transform personality testing, new

Personality tests are widely used in workplaces to shape recruitment, leadership training and team building. But what if artificial intelligence could make them faster, smarter and more accurate? New research from the University of East London (UEL) suggests that machine learning could significantly improve the way organizational psychologists and managers use one of the most widely used personality tools, the DISC assessment.

How the DISC model is used

DISC assessment classifies individuals into four behavioral styles—Dominance, Influence, Steadiness and Conscientiousness—and is commonly used by organizations to understand how people communicate, lead and work in teams. The model’s appeal lies in its simplicity, allowing organizational psychologists and managers to gain quick insights into behavioral tendencies.

However, traditional DISC assessment relies on straightforward scoring rules that assign people to a single category based on their highest score. While efficient, this approach can sometimes oversimplify personality by overlooking individuals whose traits span more than one behavioral style.

Applying machine learning to DISC

The new study explores whether machine learning can provide a more flexible and data-driven way of analyzing DISC responses, offering potentially more accurate and nuanced personality insights. Rather than assigning people to a single category, the approach can also identify blended behavioral patterns when individuals show traits from more than one DISC style.

Using responses from over 1,000 participants, researchers tested several machine learning models to predict DISC personality types based on a standard 40-question assessment. The most successful models achieved accuracy rates of more than 93%, demonstrating that artificial intelligence can reliably replicate traditional DISC classifications.

The research also examines whether the questionnaire itself can be streamlined. By identifying the most informative questions within the assessment, the team shows that a much shorter version can still produce highly reliable results.

A model using just 10 carefully selected questions retained accuracy of more than 91%—suggesting that DISC assessments could be delivered far more quickly without losing much of their predictive strength.

Beyond prediction, the researchers also applied clustering techniques to explore how people naturally group together based on behavioral traits. The analysis reveals four clear personality clusters that closely align with the established DISC categories, while also highlighting subtle overlaps between behavioral styles.

What the findings mean for workplaces

Research lead Dr. Mohammad Hossein Amirhosseini, Associate Professor in Computer Science and Digital Technologies at UEL, said the findings show how modern data science can strengthen established psychological tools without losing their practical value.

“DISC has long been valued in workplaces because it is simple and easy to apply,” he said. “What our research shows is that machine learning can retain that simplicity while adding a deeper layer of insight, helping organizations understand behavioral patterns with greater accuracy and flexibility.”

Shorter assessments could also make personality profiling easier to use in fast-moving professional environments where time is limited.

“A 10-question assessment tool that still captures the underlying personality structure would make these assessments far more practical in contexts such as recruitment, leadership development and team building,” Dr. Amirhosseini said.

Toward more flexible personality profiling

The study also suggests that machine learning could help move personality assessment beyond rigid categories by identifying hybrid or blended behavioral profiles that traditional scoring methods may miss.

As organizations increasingly turn to data and artificial intelligence to support decision-making, such approaches could help bring personality assessment into a more flexible and evidence-based era.

“Human personality rarely fits neatly into a single box,” Dr. Amirhosseini added. “By using machine learning, we can better reflect the complexity of behavior while still keeping the clear, practical insights that have made DISC so widely used.”

The study, “Reinventing DISC personality assessment: machine learning approaches for deeper insights and greater efficiency,” was published in the Journal of Artificial Intelligence & Robotics.

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