S-DAT: A Multilingual, GenAI-Driven Framework for Automated Divergent Thinking Assessment

TL;DR

S-DAT is a multilingual, AI-driven framework that automates divergent thinking assessment using semantic distance, enabling scalable, cross-cultural creativity evaluation with consistent results across eleven languages.

Contribution

This paper presents S-DAT, a novel multilingual framework utilizing large language models for automated, objective divergent thinking assessment across diverse languages and cultures.

Findings

S-DAT demonstrates robust scoring across eleven languages.

The framework shows convergent validity with existing DT measures.

S-DAT enables scalable, cross-cultural creativity assessment.

Abstract

This paper introduces S-DAT (Synthetic-Divergent Association Task), a scalable, multilingual framework for automated assessment of divergent thinking (DT) -a core component of human creativity. Traditional creativity assessments are often labor-intensive, language-specific, and reliant on subjective human ratings, limiting their scalability and cross-cultural applicability. In contrast, S-DAT leverages large language models and advanced multilingual embeddings to compute semantic distance -- a language-agnostic proxy for DT. We evaluate S-DAT across eleven diverse languages, including English, Spanish, German, Russian, Hindi, and Japanese (Kanji, Hiragana, Katakana), demonstrating robust and consistent scoring across linguistic contexts. Unlike prior DAT approaches, the S-DAT shows convergent validity with other DT measures and correct discriminant validity with convergent thinking. This cross-linguistic flexibility allows for more inclusive, global-scale creativity research, addressing key limitations of earlier approaches. S-DAT provides a powerful tool for fairer, more comprehensive evaluation of cognitive flexibility in diverse populations and can be freely assessed online: https://sdat.iol.zib.de/.