AIが人間の創造性を高める可能性を示す新研究(Can AI make us more creative? New study reveals surprising benefits of human-AI collaboration)

2025-11-26 スウォンジー大学

Swansea University の研究チームは、AI と人間の協働(Human–AI コラボレーション)が創造性を高める可能性があることを示した新しい実験結果を発表した。実験では、参加者が AI を使ってデザイン案を生成し、複数のアイデアを提示されたギャラリー形式のタスクを実施。結果、参加者は単独よりも AI と協働したときに「多様なアイデア」に触れやすくなり、初期の思考にとらわれにくくなり、創造的な試み(クリエイティブなリスクテイク)をしやすくなることが分かった。同時に、AIが幅広い案を示すことで人間のアイデアの多様性が促され、新たな発想や非定型な解決策を探索しやすくなるという。研究者は、このような「構造化された多様性」が、AIと人の協働による創造性の増幅につながると述べている。

AIが人間の創造性を高める可能性を示す新研究(Can AI make us more creative? New study reveals surprising benefits of human-AI collaboration)Study participants were tasked with designing a virtual car on the Genetic Car Designer Game.

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From Metrics to Meaning: Time to Rethink Evaluation in Human–AI Collaborative Design 指標から意味へ:人間とAIの協働設計における評価を再考する時

Sean P. Walton, Ben J. Evans, Alma A. M. Rahat, James Stovold, Jakub Vincalek
ACM Transactions on Interactive Intelligent Systems  Published:27 October 2025
DOI:https://doi.org/10.1145/3773292

Abstract

As AI systems increasingly shape decision making in creative design contexts, understanding how humans engage with these tools has become a critical challenge for interactive intelligent systems research. This paper contributes a challenge to rethink how to evaluate human–AI collaborative systems, advocating for a more nuanced and multidimensional approach. Findings from one of the largest field studies to date (n = 808) of a human–AI co-creative system, The Genetic Car Designer, complemented by a controlled lab study (n = 12) are presented. The system is based on an interactive evolutionary algorithm where participants were tasked with designing a simple two dimensional representation of a car. Participants were exposed to galleries of design suggestions generated by an intelligent system, MAP–Elites, and a random control. Results indicate that exposure to galleries generated by MAP–Elites significantly enhanced both cognitive and behavioural engagement, leading to higher-quality design outcomes. Crucially for the wider community, the analysis reveals that conventional evaluation methods, which often focus on solely behavioural and design quality metrics, fail to capture the full spectrum of user engagement. By considering the human–AI design process as a changing emotional, behavioural and cognitive state of the designer, we propose evaluating human–AI systems holistically and considering intelligent systems as a core part of the user experience—not simply a back end tool.

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