Diana Kozachek
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Item type:Publication, Entering the age of hybrid futures: A comparative study of human and GPT-generated scenariosThe increased use of Large Language Models (LLMs) in scenario generation presents new opportunities and challenges for futures research. While scenario development has traditionally emphasized human creativity, judgment, and deliberation, generative AI models such as GPT-3, GPT-3.5, and GPT-4 are now capable of producing seemingly plausible future narratives at scale. This study investigates how scenarios generated by three generations of GPT models compare to human-authored scenarios on Europe’s futures. Using a curated dataset of 1080 human-authored scenarios pre-ChatGPT, the models were fine-tuned and prompt-engineered to generate 3240 scenarios. A comparative analysis was conducted using quantitative measures (n-gram overlap, lexical diversity, topic modeling, sentiment analysis) and qualitative expert evaluation through a Delphi survey (N = 42). Results reveal substantive differences in thematic emphasis, linguistic features, and emotional tone between human and machine-generated outputs. Experts frequently misclassified the origin of scenarios, highlighting the increasingly blurred boundary between human and AI authorship. However, plausibility assessments showed a subtle bias against scenarios labeled as AI-generated, while AI-generated scenarios were thematically biased towards technology-centered topics. These findings raise important questions for the methodological integration of LLMs into scenario practices and suggest future directions for hybrid human–AI collaboration in futures research. This study contributes to the notion that scenario work increasingly relies on hybrid human–AI collaboration, requiring futures researchers to engage with technology-augmented methods in meaningful ways.Type:journal articleJournal:FuturesVolume:176Scopus© Citations 3