用AI自动评估跨语言发散思维，打破传统测评的地域与人力限制。
S-DAT: A Multilingual, GenAI-Driven Framework for Automated Divergent Thinking Assessment
- 基于大模型和多语言嵌入计算语义距离，实现无语言依赖的创造力评分
- 在11种语言中表现稳定，与传统测评具高度一致性，且能区分发散与聚合思维
- 支持全球范围公平测评，适合跨文化认知研究与教育评估
本文提出S-DAT（合成发散联想任务）——一种可扩展的多语言框架，用于自动化评估发散思维（DT），即人类创造力的核心成分。传统创造力测评常耗时费力、局限于特定语言，依赖主观评分，限制了其可扩展性与跨文化适用性。S-DAT利用大语言模型与先进的多语言嵌入技术，计算语义距离——一种语言无关的发散思维代理指标。我们在包括英语、西班牙语、德语、俄语、印地语及日语（汉字、平假名、片假名）在内的11种语言中评估S-DAT，证明其在不同语言环境下均具稳健且一致的评分能力。与以往的DAT方法不同，S-DAT与其它发散思维测量工具表现出收敛效度，并正确区分了聚合思维，展现出良好的判别效度。该跨语言灵活性使更包容、全球化的创造力研究成为可能，解决了早期方法的关键局限。S-DAT为多样人群的认知灵活性提供了更公平、全面的评估工具，可免费在线使用：https://sdat.iol.zib.de/。
原文摘要 · Abstract (English)
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/.
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