Modeling The Future Of Creativity Human Ai Teaming In Problem Space

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15769)) Included in the following conference series: This paper explores key research topics in Human-Computer Interaction (HCI) regarding future human-AI collaboration in creative activities. By extending Matthew Syed’s model of human diversity to include AI, we identify future research challenges in HCI for a world where AI actively supports and enhances human creativity through collaboration. This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout

As artificial intelligence (AI) capabilities rapidly advance, organizations are exploring new ways to leverage these technologies for creative problem-solving and innovation. A recent HBS working paper, “The Crowdless Future? Generative AI and Creative Problem Solving”, – by Léonard Boussioux, Assistant Professor at the University of Washington; Jacqueline N. Lane, Assistant Professor at Harvard Business School and co-Principal Investigator at the Digital Data Design Institute’s (D^3’s) Laboratory for Innovation Science at Harvard (LISH); Miaomiao Zhang, doctoral candidate and researcher at LISH; Vladimir Jacimovic,... Lakhani, HBS Professor and co-founder of D^3 – investigates how human-AI collaboration compares to traditional crowdsourcing approaches in generating novel and valuable solutions to complex challenges. In the study, the researchers launched a crowdsourcing challenge to develop sustainable business ideas centered on the circular economy.

They engaged 125 global participants from diverse industries and used prompt engineering to facilitate human-AI collaborative solutions. Solutions were generated through two main approaches: human crowd (HC) and human-AI (HAI), in which human solvers partnered with LLMs to co-create solutions. Three hundred external evaluators assessed a random subset of 13 solutions from a total of 234, resulting in 3,900 evaluator-solution pairs. Each solution was rated across five criteria: Novelty, Strategic Viability, Environmental Value, Financial Value, and Overall Quality. “When considering all factors collectively, the HAI solutions are deemed superior in quality compared to the HC solutions.” [1] The researchers found that while HC solutions were rated as more novel, HAI-generated solutions scored higher on measures of strategic viability, environmental value, and financial value.

Importantly, when all factors were considered together, the HAI solutions were judged to be of higher overall quality. This suggests that AI-augmented approaches may be particularly effective at producing implementable ideas with tangible business value. “Our results demonstrate that for current LLM capabilities, the single instance configuration with iterative human prompts can effectively increase the novelty of outputs while preserving their perceived value.” [2] arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

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