IntElicit: Eliciting and Assessing Contextualized Creativity via Dialogue Policy Optimization
arXiv:2606.12086v1 Announce Type: cross Abstract: Contextualized assessment offers high ecological validity for evaluating creativity but introduces a critical challenge: observed performance may be confounded with cognitive proficiency (domain knowledge) and agency (willingness to engage). Meanwhile, in the age of generative AI, creative problem solving increasingly occurs in tool-mediated and human--AI interactive environments, making fully static assessment less aligned with contemporary creative practice. To address these issues, this paper proposes IntElicit, a framework for eliciting and
The proliferation of generative AI necessitates new methods for evaluating human creativity, especially in human-AI collaborative environments, leading to frameworks like IntElicit.
This framework is crucial for understanding and fostering creativity in an AI-dominated landscape, directly impacting R&D, education, and the future of work.
Traditional, static creativity assessments become less relevant, replaced by dynamic, context-aware evaluations that account for human-AI interaction.
- · AI developers focused on human-computer interaction
- · Educational institutions adapting assessment methods
- · Research & development sectors
- · Generative AI platforms
- · Traditional creativity assessment methodologies
- · Static evaluation systems
- · Organizations slow to integrate AI in creative processes
Improved methods for evaluating and enhancing human creativity in AI-assisted contexts will emerge.
This could lead to more robust and ethically sound human-AI co-creation paradigms across various industries.
The definition of 'creativity' itself might evolve to include the ability to effectively leverage AI tools.
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Read at arXiv cs.LG