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AI framework IntElicit assesses creativity through adaptive dialogue

Researchers have developed IntElicit, a novel framework designed to assess creativity in interactive, AI-mediated learning environments. This system uses dialogue policy optimization to adaptively provide knowledge and agency scaffolds, thereby reducing confounding factors like domain expertise and willingness to engage. Through a decomposed process reward mechanism, IntElicit encourages participants to generate creative content and reasoning, rather than simply providing answers. Experiments, including a study with 64 human participants, demonstrated that IntElicit elicits better creative outcomes compared to existing methods. AI

IMPACT Introduces a new method for evaluating creativity in AI-assisted learning, potentially improving educational assessments.

RANK_REASON The cluster contains a research paper detailing a new framework for assessing creativity. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiajun Guo ·

    IntElicit: Eliciting and Assessing Contextualized Creativity via Dialogue Policy Optimization

    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…