Creativity in Education and Neuroscience

Últimos artículos

• Measuring the Creativity of Frontier LLMs in Automated Research

  Yiheng Zhao, Mengzhuo Chen, Chengming Hu, Pengyi Liao, Yihan Huang, Yiran Pang · 23 de septiembre de 2026

  Frontier LLMs are increasingly capable of conducting automated research, yet their creativity in this setting has not been systematically evaluated. We propose a set of metrics to evaluate creativity along the two dimensions of valueness and novelty. Valueness assesses whether each proposed idea is …

• Is Imagination Derived from Hallucination? A Cross-Taxonomy Evaluation of Imagination and Hallucination in Large Language Models

  Zixuan Tang, Hongzong Li, Shuxin Zhuang, Dapeng Wu, Zi Liang · 22 de septiembre de 2026

  Imagination performs as a high-level function of large language models (LLMs) which determines the potential of how an LLM creates unseen or creative content. While existing works have built a rich family of creativity benchmarks for this ability, they only measure how far an output departs from com…

• What Makes Creation Human? Authorship, Reasons, and Meaningful Human Control in Generative AI

  Yuxi Cao · 11 de septiembre de 2026

  Generative artificial intelligence (GenAI) significantly expands creators' productive capacity, but this does not necessarily entail a corresponding increase in creative agency or authorship. This paper distinguishes creativity at the level of the work from creative agency at the level of the creato…

• Human-AI Co-Creativity: Advances, Opportunities, and Challenges

  Adish Singla, Abhilasha Ravichander, Liwei Jiang, Alexander Spangher, Alice Oh, Jiho Jin, Jun Seong Kim, Changyoon Lee, Manh Hung Nguyen, Chao Wen · 9 de septiembre de 2026

  This survey article has grown out of the human-AI co-creativity workshop organized by the authors at the ICML 2026 conference. We organized this workshop as part of a community-building effort to bring together researchers and practitioners interested in topics of generative AI, creativity, and huma…

• Decoupled Analysis-Judging: An Automated Creativity Evaluator Using LLMs in Complex Multi-step Creativity Tasks

  Xiangyu Wang, Jin Wu, Xiaoyu Li, Chanjin Zheng, Yifeng Zhou · 4 de septiembre de 2026

  Automated evaluation of creativity tasks remains challenging for LLM-as-a-Judge, as LLM is susceptible to biases such as verbosity bias and leniency bias. Such limitations are particularly evident in Contextually-Grounded and Procedurally-Structured Tasks (CGPST), a complex multi-step creativity tas…

• Collective creativity in hybrid societies

  Mason Youngblood, Katie Mudd, Manuel Anglada-Tort, Cameron Jones, Elena Miu, Diana Omigie, Margaret Schedel · 3 de septiembre de 2026

  Generative AI is changing how cultural artifacts are created and circulated, and with it our understanding of creativity itself. Researchers disagree about whether these tools enrich or impoverish culture, and we argue that much of that disagreement comes from conflating two distinct components of c…

• Creative Generation via Multi-Agent Debate: Does Debate Suppress Diversity?

  Tien Anh Nguyen, Khanh-Binh Nguyen, Van Dai Do, Svetha Venkatesh, Hung Le · 2 de septiembre de 2026

  Creative generation tasks, such as narrative writing and scientific ideation, demand both high-quality outputs and distinct responses across independent runs to maximize exploration. Multi-Agent Debate (MAD) has shown strong quality gains on factual and reasoning tasks, making it a natural candidate…

• CLIN: an Objective Framework for Evaluating Creativity in Short Persian Literary Text

  Mohammad Reza Modarres, Armin Tourajmehr, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar · 1 de septiembre de 2026

  Evaluating creativity in large language model (LLM) outputs remains challenging because creativity is multidimensional and human-centered. We examine how reliably LLMs evaluate short literary text in Persian, a low-resource language, across multiple evaluation strategies and prompt formulations. We …

• Using Poly-Encoders for Computationally Efficient Automated Creativity Assessment

  Sam Grouchnikov, Phillip Gregory, Jiho Noh · 28 de agosto de 2026

  Automated creativity assessment has been a long standing challenge, with traditional methods often being resource intensive or lacking practical accuracy. We introduce a novel approach by using Poly-Encoder for computationally efficient and accurate automated creativity assessment. We fine-tuned a P…

• Artificial Intelligence Models Can Predict and Collaboratively Modulate Human Memory Search

  Eric Lacosse, Mariana Duarte, Graham Todd, Peter M. Todd, Daniel C. McNamee · 28 de agosto de 2026

  Large language models (LLMs) exhibit unprecedented natural language generation and many text-based problem-solving capabilities. Indeed, in many language-based tasks, for example routine coding, these artificial intelligence models have reduced, or even eliminated, the need for human input. But rath…

• The Limits of Automatic Evaluation of Creativity in Large Language Models

  Alessandro Tutone, Giorgio Franceschelli, Mirco Musolesi · 26 de agosto de 2026

  Large Language Models (LLMs) are increasingly capable of generating text that challenges human performance in domains requiring creativity, yet evaluating creativity in LLM-generated content remains a significant challenge. Here, we investigate whether current automatic evaluation methods can reliab…

• Are LLMs becoming similarly creative? Evidence from three years of models

  Nirav Patel, Josiah Crossman, Eva Aggarwal, Emily Wenger · 21 de agosto de 2026

  Many benchmarks track Large Language Model (LLM) performance on tasks with verifiable answers, but less is known about how LLM performance is evolving on open-ended tasks, where creativity, originality and diversity may matter as much as quality. As LLMs increasingly support human ideation and creat…

• Harnessing Abundance: A Generativity Perspective on Human-GenAI Collaboration

  Yoram M Kalman, Yun Wan · 11 de agosto de 2026

  Research on human-GenAI collaboration yields conflicting findings: GenAI can enhance creativity yet reduce collective diversity, with uneven benefits across skill levels. Rather than treating these as contradictions, we argue they reflect a core feature of GenAI: abundance. GenAI makes ideas, drafts…

• AI-AI co-creation outperforms human pairs in creative tasks

  Yingyue Luna Luan, Luning Sun, Yeun Joon Kim, Jindong Wang, Xing Xie · 11 de agosto de 2026

  Prior research often finds that AI creativity is limited: single systems rarely outperform humans, and human-AI collaboration does not exceed human output. We argue these conclusions underestimate AI's potential because most studies do not allow iterative, multi-agent exchanges that mirror the socia…

• CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity

  Ananya Sahu, Mohit Bansal, Elias Stengel-Eskin · 10 de agosto de 2026

  While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.g., story generation) as well as those that require it implicitly, e.g., reinforcement learning (…

• Recipes for Creativity: Iterative Generation and Evaluation in Large Language Models

  Rens Anderson, Tessa Verhoef, Amirhossein Zohrehvand · 10 de agosto de 2026

  Generative models are often evaluated through singular artifacts, whereas human creativity typically emerges through iterative generation, appraisal, and refinement. This pilot study examines whether iterative search improves LLM creativity by adapting FunSearch to recipe generation for the 2024 Pil…

• Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding

  Ming Wang, Yuqing Zhang, Tingna Xie, Xiangju Li, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang · 10 de agosto de 2026

  Creative capabilities of MLLMs matter in design, communication, education, and human--AI collaboration, yet remain difficult to evaluate because explicit targets and reward signals are scarce compared with accuracy-oriented tasks. Cross-concept understanding is a core cognitive capacity underlying r…

• Creative Integration: A Decidable Criterion of Creativity

  Yoshinori Nomura · 3 de agosto de 2026

  "Integrative" solutions are widely praised but rarely defined: we lack an operational way to tell a genuine integration -- one that makes the world cheaper to describe -- from a tidy re-description. Building on the lineage that treats creativity and intelligence as compression, we give such a criter…

• Human diversity fuels collective creativity that large language models cannot simulate or sustain

  Mengchen Dong, Hiromu Yakura · 30 de julio de 2026

  Diverse human groups produce diverse ideas, the raw material of innovation. Generative AI challenges this engine twice over: everyday AI assistance may homogenize what diverse people create, and AI-simulated diversity may replace the people altogether. We tested both challenges in a preregistered cr…

• Why Large Language Models and Humans Converge and Diverge in Evaluating Creativity

  Pengzhao Lyu, Yeun Joon Kim, Hanlin Xiao, Yingyue Luna Luan · 27 de julio de 2026

  Despite the growing use of large language models (LLMs) as creativity evaluators, evidence of their alignment with human evaluations remains mixed, raising the question of when and why their judgments converge with or diverge from human judgments. Across three studies and six widely used LLMs, we ad…

• CreativityPrism: A Cross-Domain Evaluation Framework for Large Language Model Creativity

  Zhaoyi Joey Hou, Bowei Alvin Zhang, Yining Lu, Bhiman Kumar Baghel, Anneliese Brei, Ximing Lu, Meng Jiang, Faeze Brahman, Snigdha Chaturvedi, Haw-Shiuan Chang, Daniel Khashabi, Xiang Lorraine Li · 3 de julio de 2026

  Creativity is often seen as a hallmark of human intelligence. While large language models(LLMs) are increasingly perceived as generating creative text, there is still no cross-domain and scalable framework to evaluate their creativity across diverse scenarios. Existing methods of LLM creativity eval…

• CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse

  Samuel Schapiro, Core Francisco Park, Felix Sosa, Lav R. Varshney · 3 de julio de 2026

  Divergent thinking is a crucial aspect of creativity, yet large language models (LLMs) tend to consistently generate similar responses to open-ended questions, in what has been termed the artificial hivemind effect. Here, we introduce CreativityNeuro, a data-free method for enhancing divergent think…

• AGC-Bench: Measuring Artificial General Creativity

  Roger Beaty, Vijeta Deshpande, Clin K. Y. Lai, Anna Attuch, Namrata Shivagunde, Swastik Roy, Rajkumar Pujari, Paul V. DiStefano, Sherin Muckatira, Claire E. Stevenson, Mikhail Gronas, Anna Rumshisky · 2 de julio de 2026

  Creativity research has debated whether creativity is domain-specific (e.g., visual, writing, science), and if it is psychometrically separable from general intelligence. Both questions now apply to LLMs, but a unified benchmark of AI creativity remains elusive. We introduce AGC-Bench, an artificial…

• How LLMs See Creativity: Zero-Shot Scoring of Visual Creativity with Interpretable Reasoning

  William Orwig, Roger E. Beaty · 30 de junio de 2026

  Evaluating the originality of visual images poses enduring challenges for creativity assessment. Automated scoring using AI models has proven effective in the verbal domain, yet key questions remain about evaluating visual creativity and understanding how models arrive at their ratings. The present …

• Creativity Reconsidered: Generative AI and the Problem of Intentional Agency

  James S. Pearson, Matthew J. Dennis, Marc Cheong · 19 de junio de 2026

  Many theorists maintain that conscious intentional agency is a necessary condition of creativity. We argue that this requirement, which we call the Intentional Agency Condition (IAC), should be abandoned. We motivate this by highlighting the problems this criterion encounters in the face of recent a…