IntElicit: 通过对话策略优化引出和评估情境化创造力
IntElicit: Eliciting and Assessing Contextualized Creativity via Dialogue Policy Optimization
AI总结：
提出IntElicit框架，通过分解过程奖励机制优化对话策略，在交互中减少非创造性混淆因素，从而更有效地引出和评估情境化创造力。
摘要：
情境化评估为评估创造力提供了高生态效度，但也引入了一个关键挑战：观察到的表现可能与认知熟练度（领域知识）和能动性（参与意愿）相混淆。同时，在生成式AI时代，创造性问题解决越来越多地发生在工具中介和人机交互环境中，使得完全静态的评估与当代创造性实践不太一致。为了解决这些问题，本文提出了IntElicit，一个通过对话策略优化来引出和评估情境化创造力的框架。IntElicit作为一个受约束的自适应AI面试官：它在多轮交互中提供非指导性的知识和能动性支架，以减少非创造性混淆因素，同时保留参与者生成被评估的创造性内容的责任。具体来说，为了解决开放教育对话中的稀疏奖励和潜在奖励破解（例如，答案听写），IntElicit引入了一种分解过程奖励机制。该机制将策略与教学引出对齐，奖励那些引出参与者推理而非代表他们产生最优答案的提示。大量实验，包括参与者模拟和一项人类受试者研究（N=64），表明IntElicit比专家设计的基线提高了引出的创造性成果。总之，结果表明，交互式引出可以揭示静态FPSP式评估可能遗漏的创造性潜力，为AI中介学习环境中的情境化创造力评估提供了形成性和诊断性视角。
英文摘要：
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 assessing contextualized creativity via dialogue policy optimization. IntElicit functions as a constrained adaptive AI Interviewer: it provides non-directive knowledge and agency scaffolds in multi-turn interaction to reduce non-creative confounders, while preserving participants' responsibility for generating the creative content being evaluated. Specifically, to tackle sparse rewards and potential reward hacking (e.g., answer dictation) in open-ended educational dialogue, IntElicit introduces a decomposed process reward mechanism. This mechanism aligns the policy with pedagogical elicitation, rewarding prompts that draw out participant reasoning rather than producing optimal answers on their behalf. Extensive experiments, including participant simulation and a human subject study (N=64), show that IntElicit improves elicited creative outcomes over expert-designed baselines. Together, the results suggest that interactive elicitation can reveal creative potential that static FPSP-style assessment may miss, providing a formative and diagnostic lens for contextualized creativity assessment in AI-mediated learning contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
