Summary: The traditional DISC assessment—a staple of workplace recruitment and team building—just got a high-tech makeover. New research demonstrates that machine learning can replicate DISC results with 93% accuracy while significantly reducing the time needed for testing.
The study shows that AI can trim a standard 40-question personality test down to just 10 “high-information” questions without losing predictive power. Beyond speed, the AI approach identifies “blended” personality profiles, moving away from rigid boxes to better reflect the complexity of human behavior.
Key Facts
- The 10-Question Breakthrough: Researchers identified the most informative questions, creating a “short-form” DISC test that maintains over 91% accuracy.
- Beyond Single Categories: Unlike traditional scoring that forces a person into one of four buckets, machine learning can identify hybrid patterns (e.g., someone who is high in both Dominance and Conscientiousness).
- Data-Driven Clustering: AI analysis of over 1,000 participants confirmed four natural “personality clusters” that align with the classic DISC model but highlight subtle, previously overlooked overlaps.
- Practical Utility: Shorter, smarter assessments make personality profiling more viable for fast-paced environments like high-volume recruitment or leadership workshops where time is a luxury.
Source: University of East London
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 organisational psychologists and managers use one of the most widely used personality tools, the DISC assessment.
DISC assessment classifies individuals into four behavioural styles – Dominance, Influence, Steadiness and Conscientiousness – and is commonly used by organisations to understand how people communicate, lead and work in teams. The model’s appeal lies in its simplicity, allowing organisational psychologists and managers to gain quick insights into behavioural 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 behavioural style.
The new study explores whether machine learning can provide a more flexible and data-driven way of analysing DISC responses, offering potentially more accurate and nuanced personality insights. Rather than assigning people to a single category, the approach can also identify blended behavioural 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 per cent, 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 per cent – 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 behavioural traits. The analysis reveals four clear personality clusters that closely align with the established DISC categories, while also highlighting subtle overlaps between behavioural styles.
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 organisations understand behavioural 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.
The study also suggests that machine learning could help move personality assessment beyond rigid categories by identifying hybrid or blended behavioural profiles that traditional scoring methods may miss.
As organisations 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 behaviour while still keeping the clear, practical insights that have made DISC so widely used.”
Key Questions Answered:
A: According to the UEL data, yes! By using machine learning to find the “heaviest hitters”—the questions that reveal the most about your behavior—the AI achieved 91% accuracy. For most workplace settings, that 9% trade-off is well worth the 75% time saving.
A: That is exactly what this study highlights. Traditional DISC scoring usually picks your highest score and ignores the rest. Dr. Amirhosseini’s AI model recognizes blended profiles, acknowledging that human personality is rarely a “single box” and often reflects a mix of different styles depending on the situation.
A: The goal isn’t to take the decision away from humans, but to give managers better data. By providing a more nuanced, flexible view of how a candidate communicates or leads, AI helps ensure people are placed in roles and teams where they are naturally most likely to thrive.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this AI and psychology research news
Author: Kiera Hay
Source: University of East London
Contact: Kiera Hay – University of East London
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Reinventing DISC personality assessment: machine learning approaches for deeper insights and greater efficiency” by Fatima Kalabi; Mohammad Hossein Amirhosseini. Journal of Artificial Intelligence and Robotics
DOI:10.52768/3067-7947/1037
Abstract
Reinventing DISC personality assessment: machine learning approaches for deeper insights and greater efficiency
The DISC personality framework, while widely adopted in applied settings, relies on a fixed rule-based classification method that may oversimplify individual behavioural profiles. This study explores whether machine learning can offer a more flexible, efficient, and accurate approach to DISC classification.
Using a dataset of over 1,000 participants, we evaluated multiple supervised models—including Logistic Regression, XGBoost, SVM, MLP, Random Forest, and K-Nearest Neighbours—alongside unsupervised clustering techniques. Logistic Regression emerged as the top-performing model, achieving 93.53% accuracy and demonstrating superior cross-validation stability.
Recursive Feature Elimination identified a reduced set of ten key questionnaire items, maintaining over 91% accuracy and enabling the development of a concise assessment tool. Such a shortened questionnaire offers substantial practical benefits for real-world applications, particularly in fast-paced organisational contexts like recruitment, leadership coaching, and team composition, where rapid yet reliable personality insights are invaluable.
Clustering analysis further revealed alignment with traditional DISC categories, while uncovering potential hybrid profiles. A comparative clustering analysis between the full 40-item and reduced 10-item questionnaires confirmed that the same behavioural trait structures could be recovered using fewer items.
Despite minor differences in cluster alignment, DISC trait patterns remained consistent across both models. These findings confirm that machine learning can replicate and enhance conventional DISC assessments, not only in terms of classification accuracy but also by preserving the conceptual integrity of the DISC framework.
The study validates that the reduced DISC assessment captures the latent personality structure of the original model, offering a scalable and empirically grounded solution for modern psychological evaluation. The complete modelling pipeline, including feature selection and clustering insights, contributes to the growing field of data-driven psychometrics.
