The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
Beyond semantic distance: Automated scoring of divergent thinking greatly improves with large language models
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“We measure the originality of each AUT idea with an existing fine-tuned GPT-3 classifier [44].”
From Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking (2025) · cites this paper as [44]
In the citing paper
“We chose these five particular objects as the originality scoring measure had the highest correlation with human judgements for these five objects [44].”
From Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking (2025) · cites this paper as [44]
In the citing paper
“The model was fine-tuned with human judgments of AUT originality (where human raters judged, on a scale of 1 to 5, the originality of an idea given the object) and achieved an 𝑟 = 0.81 overall correlation with human judgments.”
From Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking (2025) · cites this paper as [44]
In the citing paper
“we employ the Alternate Uses Task (AUT; Organisciak et al. (2023)), an established measurement for divergent (as in free-associative) thinking, which possesses a validated and fully automated evaluation pipeline (Organisciak et al., 2025).”
From Within-Model vs Between-Prompt Variability in Large Language Models for Creative Tasks (2026) · cites this paper as Organisciak et al., 2023
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
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Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
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Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 14,137
Research works printed but not linked: 1
- Torrance, 1980, Growing up creatively gifted: A 22-yr longitudinal study, Creative Child & Adult Quarterly, 5, 148
Cited by
Sorted by citations
Artificial Intelligence & Creativity: A Manifesto for Collaboration
Vinchon, F., Lubart, T., Bartolotta, S., Gironnay, V., Botella, M., Bourgeois-Bougrine, S., Burkhardt, J., Bonnardel, N., Corazza, G. E., Glăveanu, V., et al. (2023) · The Journal of Creative Behavior · doi:10.1002/jocb.597 · Cited by 273
Artificial muses: Generative artificial intelligence chatbots have risen to human-level creativity
Haase, J., & Hanel, P. H. P. (2023) · Journal of Creativity · doi:10.1016/j.yjoc.2023.100066 · Cited by 233
Applying large language models and chain-of-thought for automatic scoring
Lee, G.-G., Latif, E., Wu, X., Liu, N., & Zhai, X. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100213 · Cited by 186
Fine-tuning ChatGPT for automatic scoring
Latif, E., & Zhaı, X. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100210 · Cited by 244
The originality of machines: AI takes the Torrance test
Guzik, E. E., Byrge, C., & Gilde, C. (2023) · Journal of Creativity · doi:10.1016/j.yjoc.2023.100065 · Cited by 165
Metadata
- Authors
- Organisciak, P., Acar, S., Dumas, D., & Berthiaume, K.
- Affiliations
- University of Denver (US)
- University of Georgia (US)
- University of North Texas (US)
- Corresponding author
- Organisciak, Peter
- Venue
- Thinking Skills and Creativity
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 49
- Pages
- 101356
- ISSN
- 1871-1871 (ISSN-L)
- OpenAlex
- W4381487157
- Cited by
- 202
- References
- 80
- Citations by year
- 2026: 65 · 2025: 64 · 2024: 56 · 2023: 16
- FWCI
- 52.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Thinking Skills and Creativity (publishedVersion)
- Funders
- Institute of Education Sciences (305A200199)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
- Keywords
- divergent thinking, automated grading, alternative uses task, large language models, creativity assessment, semantic distance, fine-tuning, transfer learning, few-shot prompting, zero-shot prompting, human judgment correlation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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