ChatGPT improves creative problem-solving performance in university students: An experimental study
Open accessAbstract
University students often employ generative artificial intelligence tools such as ChatGPT in resolution of ill-defined problem-solving tasks. However, the experimental evidence about effects of ChatGPT on complex problem-solving performance is still missing. In this preregistered experiment, the impact of ChatGPT on performance in a complex creative problem-solving task was investigated in 77 university students solving a task with ChatGPT in comparison to 68 students solving a task without it. ChatGPT use significantly improved self-efficacy for task resolution (d = 0.65) and enhanced the quality (d = 0.69), elaboration (d = 0.61), and originality (d = 0.55) of solutions. Moreover, participants with ChatGPT assistance perceived task as easier (d = 0.56) and requiring less mental effort (d = 0.58). However, use of ChatGPT did not make task resolution more interesting (d = 0.08), and the impact of ChatGPT on metacognitive monitoring accuracy was unclear. Although there were no significant differences in absolute accuracy between students solving the task with and without the assistance of ChatGPT, the absence of correlation between self-evaluation judgments and performance suggests that participants struggled to calibrate their self-evaluations when using ChatGPT. Notably, the perceived usefulness of ChatGPT appeared to inform self-evaluation judgments, resulting in higher inaccuracy. The implications for hybrid human-AI regulation (HHAIR) theory are discussed. To regulate effectively, students using AI tools should focus on valid metacognitive cues and ignore cues that could lead to inaccurate outcomes.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Urban et al. [18] found that students engaging with ChatGPT exhibit enhanced creative problem-solving skills compared to those who do not use the tool.
“This complements the observations made by Urban et al. [18], who found that students engaging with ChatGPT exhibit enhanced creative problem-solving skills compared to those who do not use the tool.”
From The Mediating Role of Generative AI Self-Regulation on Students’ Critical Thinking and Problem-Solving (2024) · cites this paper as Urban et al. [18]
GenAI plays a constructive role in enhancing students' critical thinking and problem-solving capabilities.
“Our findings demonstrate that GenAI can indeed play a constructive role in enhancing students' critical thinking and problem-solving capabilities, aligning with prior research, such as that by Essien et al. [3] and Urban et al. [18].”
From The Mediating Role of Generative AI Self-Regulation on Students’ Critical Thinking and Problem-Solving (2024) · cites this paper as Urban et al. [18]
More complex tasks involving deeper understanding and application might require more active engagement and critical thinking, where reliance on AI alone may not yield significant improvements in knowledge transfer.
“However, more complex tasks involving deeper understanding and application might require more active engagement and critical thinking, where reliance on AI alone may not yield significant improvements in knowledge transfer (Asare et al., 2023; Soderstrom and Bjork, 2015; Urban et al., 2024).”
From Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance (2025) · cites this paper as Urban et al., 2024
The study's observations of the potential for misjudging performance align with Urban et al.'s (2024) experimental quantitative study.
“Our observations of the potential for misjudging performance aligns with an experimental quantitative study conducted by Urban et al. (2024).”
From How university students work on assessment tasks with generative artificial intelligence: matters of judgement (2026) · cites this paper as Urban et al. (2024)
Participants who thought the task easier also overestimated their performance more.
“the authors observe: 'participants who thought the task easier also overestimated their performance more' (p.11).”
From How university students work on assessment tasks with generative artificial intelligence: matters of judgement (2026) · cites this paper as Urban et al. (2024)
GenAI use can increase self-efficacy but may interfere with estimation of task performance, possibly leading to overestimation.
“This comparative study of performance on a complex creative problem-solving task (with or without ChatGPT) suggests GenAI use can increase self-efficacy but may interfere with estimation of task performance, possibly leading to overestimation.”
From How university students work on assessment tasks with generative artificial intelligence: matters of judgement (2026) · cites this paper as Urban et al. (2024)
Classroom discussions about the broader impacts of AI on education, society, and future careers invite students to engage critically with the technology's role in their lives.
“Classroom discussions about the broader impacts of AI on education, society, and future careers, invite students to engage critically with the technology's role in their lives (Alshamy et al., 2025; Sekli et al., 2024; Urban et al., 2024).”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Urban et al., 2024
Research has shown a wide range of effects of ChatGPT on creativity, including positive, adverse, and negligible or detrimental effects.
“Additionally, research has shown a wide range of effects of ChatGPT on creativity, including positive effects (Urban et al., 2024), entirely adverse impacts (Niloy et al., 2024), and negligible or, if the AI assistance is of low quality, detrimental effects (Medeiros et al., 2025).”
From Human Intelligence, Creativity, and Wisdom in the Age of Generative Artificial Intelligence (2025) · cites this paper as Urban et al., 2024
The perceived usefulness of ChatGPT was associated with learners' self-evaluation judgements, resulting in higher inaccuracies in judgements of learning.
“The same study also found that the perceived usefulness of ChatGPT was associated with learners' self-evaluation judgements, resulting in higher inaccuracies in judgements of learning”
From Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance (2025) · cites this paper as Urban et al. (2024)
Using ChatGPT reduced learners' perceived difficulty, leading to less effort in task solving.
“For example, Urban et al. (2024) found that using ChatGPT reduced learners' perceived difficulty, leading to less effort in task solving.”
From Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance (2025) · cites this paper as Urban et al. (2024)
Participant 12 (P_12) tailored and refined multiple rounds of AI-generated text, though the final solution still required further validation.
“Participant 12 (P_12) similarly tailored and refined multiple rounds of AI-generated text, illustrating the iterative co-creation process advocated by Urban et al. [36], though the final solution still required further validation.”
From Exploring the Impact of AI Tools on Cognitive Skills: A Comparative Analysis (2025) · cites this paper as [36]
Across both groups, participants generally valued the prospect of AI for its capacity to structure content, clarify terminology, facilitate conceptualization and summarization, and perform rapid quantitative analyses.
“Across both groups, participants generally valued the prospect of AI for its capacity to structure content, clarify terminology, facilitate conceptualization and summarization, and perform rapid quantitative analyses, all consistent with reported benefits in the recent literature [30,36].”
From Exploring the Impact of AI Tools on Cognitive Skills: A Comparative Analysis (2025) · cites this paper as [36]
Metacognitive bias in university students solving non-routine problem-solving tasks was linked to their perceived usefulness of ChatGPT rather than their actual performance.
“In the context of generative AI use, Urban et al. (2024) found that metacognitive bias in university students solving non-routine problem-solving tasks was linked to their perceived usefulness of ChatGPT rather than their actual performance.”
From “ <scp>ChatGPT</scp> can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of <scp>ChatGPT</scp> content into academic writing (2025) · cites this paper as Urban et al. (2024)
The sample size was determined a priori using G*Power with α = 0.05, β = 0.80, and an expected effect size of Cohen's d = 0.62, indicating a minimum of 42 participants per group.
“For the between-group comparison, the sample size was determined a priori using G*Power 3.1.9.6, with α = 0.05, β = 0.80, and an expected effect size of Cohen's d = 0.62.This effect size was estimated based on average between-group differences reported by Urban et al. (2024).The calculation indicated that a minimum of 42 participants per group would be required.”
From “ <scp>ChatGPT</scp> can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of <scp>ChatGPT</scp> content into academic writing (2025) · cites this paper as Urban et al. (2024)
Working with generative AI tools such as ChatGPT increases productivity in complex problem-solving tasks in educational and professional settings.
“Working with generative AI tools, such as ChatGPT, increases productivity in complex problem-solving tasks in both educational and professional settings (Noy & Zhang, 2023;Sun & Zhou, 2024;Urban et al., 2024).”
From “ <scp>ChatGPT</scp> can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of <scp>ChatGPT</scp> content into academic writing (2025) · cites this paper as Urban et al., 2024
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148 · see citing works
Self-efficacy: Toward a unifying theory of behavioral change
Bandura, A. (1977) · Psychological Review · doi:10.1037/0033-295x.84.2.191 · Cited by 43,925 · see citing works
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 · see citing works
Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584 · see citing works
Self-Efficacy: An Essential Motive to Learn
Zimmerman, B. J. (2000) · Contemporary Educational Psychology · doi:10.1006/ceps.1999.1016 · Cited by 4,294 · see citing works
CREATIVE SELF-EFFICACY: ITS POTENTIAL ANTECEDENTS AND RELATIONSHIP TO CREATIVE PERFORMANCE.
Tierney, P., & Farmer, S. M. (2002) · Academy of Management Journal · doi:10.2307/3069429 · Cited by 2,787 · see citing works
Handbook of Motivation at School
Wentzel, K. R., & Wigfield, A. (2009) · Routledge eBooks · doi:10.4324/9780203879498 · Cited by 2,482 · see citing works
Paas, F. G. W. C. (1992) · Journal of Educational Psychology · doi:10.1037/0022-0663.84.4.429 · Cited by 2,150 · see citing works
Experimental evidence on the productivity effects of generative artificial intelligence
Noy, S., & Zhang, W. (2023) · Science · doi:10.1126/science.adh2586 · Cited by 2,150 · see citing works
Handbook of Self-Regulation of Learning and Performance
(2017) · Routledge eBooks · doi:10.4324/9781315697048 · Cited by 1,917 · see citing works
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261 · see citing works
Paas, F. G. W. C., & Van Merriënboer, J. J. G. (1994) · Journal of Educational Psychology · doi:10.1037/0022-0663.86.1.122 · Cited by 1,198 · see citing works
Ryan, R. M., Mims, V., & Koestner, R. (1983) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.45.4.736 · Cited by 1,177 · see citing works
Creative self-efficacy development and creative performance over time.
Tierney, P., & Farmer, S. M. (2010) · Journal of Applied Psychology · doi:10.1037/a0020952 · Cited by 1,090 · see citing works
ChatGPT User Experience: Implications for Education
Zhai, X. (2023) · doi:10.2139/ssrn.4312418 · Cited by 935 · see citing works
Models of the Creative Process: Past, Present and Future
Lubart, T. (2001) · Creativity Research Journal · doi:10.1207/s15326934crj1334_07 · Cited by 907 · see citing works
TruthfulQA: Measuring How Models Mimic Human Falsehoods
Lin, S., Hilton, J., & Evans, O. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.229 · Cited by 904 · see citing works
Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts
Zamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B., & Yang, Q. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581388 · Cited by 893 · see citing works
Komarraju, M., & Nadler, D. (2013) · Learning and Individual Differences · doi:10.1016/j.lindif.2013.01.005 · Cited by 851 · see citing works
Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019) · Business & Information Systems Engineering · doi:10.1007/s12599-019-00595-2 · Cited by 826 · see citing works
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100147 · Cited by 745 · see citing works
The self in social psychology.
Baumeister, R. F. (1999) · Psychology Press eBooks · Cited by 730 · see citing works
Metacognition and affect: What can metacognitive experiences tell us about the learning process?
Efklides, A. (2006) · Educational Research Review · doi:10.1016/j.edurev.2005.11.001 · Cited by 724 · see citing works
Process analytic models of creative capacities
Mumford, M. D., Mobley, M. I., Reiter‐Palmon, R., Uhlman, C. E., & Doares, L. M. (1991) · Creativity Research Journal · doi:10.1080/10400419109534380 · Cited by 721 · see citing works
What Is Interesting? Exploring the Appraisal Structure of Interest.
Silvia, P. J. (2005) · Emotion · doi:10.1037/1528-3542.5.1.89 · Cited by 718 · see citing works
Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses
Panadero, E., Jonsson, A., & Botella, J. (2017) · Educational Research Review · doi:10.1016/j.edurev.2017.08.004 · Cited by 712 · see citing works
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.ijcnlp-main.45 · Cited by 682 · see citing works
Walker, A., & Leary, H. (2009) · Interdisciplinary Journal of Problem-Based Learning · doi:10.7771/1541-5015.1061 · Cited by 581 · see citing works
The Cambridge Handbook of Creativity
(2019) · Cambridge University Press eBooks · doi:10.1017/9781316979839 · Cited by 522 · see citing works
When self-efficacy negatively relates to motivation and performance in a learning context.
Vancouver, J., & Kendall, L. N. (2006) · Journal of Applied Psychology · doi:10.1037/0021-9010.91.5.1146 · Cited by 499 · see citing works
A conceptual analysis of five measures of metacognitive monitoring
Schraw, G. J. (2008) · Metacognition and Learning · doi:10.1007/s11409-008-9031-3 · Cited by 491 · see citing works
Improving Measurements of Self-Regulated Learning
Winne, P. H. (2010) · Educational Psychologist · doi:10.1080/00461520.2010.517150 · Cited by 420 · see citing works
Paas, F., Tuovinen, J. E., van Merriënboer, J. J. G., & Aubteen Darabi, A. (2005) · Educational Technology Research and Development · doi:10.1007/bf02504795 · Cited by 401 · see citing works
Liu, Y., & Pásztor, A. (2022) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2022.101069 · Cited by 400 · see citing works
A Meta-Analysis of the Relation between Creative Self-Efficacy and Different Creativity Measurements
Haase, J., Hoff, E. V., Hanel, P. H. P., & Innes-Ker, Å. H. (2018) · Creativity Research Journal · doi:10.1080/10400419.2018.1411436 · Cited by 363 · see citing works
Towards hybrid <scp>human‐AI</scp> learning technologies
Molenaar, I. (2022) · European Journal of Education · doi:10.1111/ejed.12527 · Cited by 348 · see citing works
Human and artificial intelligence collaboration for socially shared regulation in learning
Järvelä, S., Nguyen, A., & Hadwin, A. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13325 · Cited by 342 · see citing works
Situational and Individual Interest
Schiefele, U. (2009) · publish.UP (University of Potsdam) · doi:10.4324/9780203879498-16 · Cited by 341 · see citing works
Li, C., Murad, M., Shahzad, F., Khan, M. A. S., Ashraf, S. F., & Dogbe, C. S. K. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01611 · Cited by 313 · see citing works
Teaching Creativity and Inventive Problem Solving in Science
DeHaan, R. L. (2009) · CBE—Life Sciences Education · doi:10.1187/cbe.08-12-0081 · Cited by 307 · see citing works
SAGE Handbook of Research on Classroom Assessment
McMillan, J. (2013) · Sage eBooks · doi:10.4135/9781452218649 · Cited by 287 · see citing works
Toward Untangling Creative Self-Beliefs
Beghetto, R. A., & Karwowski, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809790-8.00001-7 · Cited by 283 · see citing works
Brown, G., & Harris, L. R. (2013) · Sage eBooks · doi:10.4135/9781452218649.n21 · Cited by 279 · see citing works
Cognition and Metacognition within Self-Regulated Learning
Winne, P. H. (2017) · Handbook of Self-Regulation of Learning and Performance · doi:10.4324/9781315697048-3 · Cited by 274 · see citing works
Jeon, J., Lee, S., & Choe, H. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104898 · Cited by 271 · see citing works
Hitsuwari, J., Ueda, Y., Yun, W., & Nomura, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107502 · Cited by 267 · see citing works
Jonassen, D. H. (2010) · Routledge eBooks · doi:10.4324/9780203847527 · Cited by 242 · see citing works
An analysis of children’ interaction with an AI chatbot and its impact on their interest in reading
Liu, C.-C., Liao, M.-G., Chang, C.-H., & Lin, H.-M. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104576 · Cited by 240 · see citing works
Karwowski, M. (2012) · Europe’s Journal of Psychology · doi:10.5964/ejop.v8i4.513 · Cited by 221 · see citing works
Molenaar, I. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100070 · Cited by 217 · see citing works
Developing an AI-based chatbot for practicing responsive teaching in mathematics
Lee, D., & Yeo, S. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104646 · Cited by 210 · see citing works
Karwowski, M. (2010) · Psychology of Aesthetics Creativity and the Arts · doi:10.1037/a0021427 · Cited by 192 · see citing works
AI and the future of humanity: ChatGPT-4, philosophy and education – Critical responses
Peters, M. A., Jackson, L., Papastephanou, M., Jandrić, P., Lazaroiu, G., Evers, C. W., Cope, B., Kalantzis, M., Araya, D., Tesar, M., et al. (2024) · Educational Philosophy and Theory · doi:10.1080/00131857.2023.2213437 · Cited by 188 · see citing works
Effects of chatbot-assisted in-class debates on students’ argumentation skills and task motivation
Guo, K., Zhong, Y., Li, D., & Chu, S. K. W. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104862 · Cited by 188 · see citing works
Treffinger, D. J., Selby, E. C., & Isaksen, S. G. (2008) · Learning and Individual Differences · doi:10.1016/j.lindif.2007.11.007 · Cited by 180 · see citing works
Karwowski, M., Lebuda, I., & Beghetto, R. A. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316979839.021 · Cited by 171 · see citing works
Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., & Siemens, G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100138 · Cited by 152 · see citing works
Guo, L. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12650 · Cited by 141 · see citing works
Effects of Problem-Based Learning on Attitude: A Meta-analysis Study
Dağyar, M., & Demirel, M. (2016) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.12973/eurasia.2016.1293a · Cited by 138 · see citing works
The Role of Mental Effort in Fostering Self-Regulated Learning with Problem-Solving Tasks
van Gog, T., Hoogerheide, V., & van Harsel, M. (2020) · Educational Psychology Review · doi:10.1007/s10648-020-09544-y · Cited by 119 · see citing works
Decision-guided chatbots and cognitive styles in interdisciplinary learning
Iku-Silan, A., Hwang, G., & Chen, C. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104812 · Cited by 113 · see citing works
The Relation Between Students’ Effort and Monitoring Judgments During Learning: A Meta-analysis
Baars, M., Wijnia, L., de Bruin, A., & Paas, F. (2020) · Educational Psychology Review · doi:10.1007/s10648-020-09569-3 · Cited by 101 · see citing works
Can large language models write reflectively
Li, Y., Sha, L., Yan, L., Lin, J., Raković, M., Galbraith, K., Lyons, K., Gašević, D., & Chen, G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100140 · Cited by 99 · see citing works
Dang, H., Mecke, L., Lehmann, F., Goller, S., & Buschek, D. (2022) · arXiv · doi:10.48550/arxiv.2209.01390 · Cited by 92 · see citing works
Deskilling, Upskilling, and Reskilling: a Case for Hybrid Intelligence
Rafner, J. F., Dellermann, D., Hjorth, A., Verasztó, D., Kampf, C. E., Mackay, W. E., & Sherson, J. F. (2021) · Morals & Machines · doi:10.5771/2747-5174-2021-2-24 · Cited by 89 · see citing works
Puente‐Díaz, R., & Cavazos‐Arroyo, J. (2017) · Creativity Research Journal · doi:10.1080/10400419.2017.1360067 · Cited by 88 · see citing works
Automated reading passage generation with OpenAI's large language model
Bezirhan, U., & von Davier, M. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100161 · Cited by 83 · see citing works
Looking at Mental Effort Appraisals through a Metacognitive Lens: Are they Biased?
Scheiter, K., Ackerman, R., & Hoogerheide, V. (2020) · Educational Psychology Review · doi:10.1007/s10648-020-09555-9 · Cited by 63 · see citing works
Bruns, C. (2018) · The Charleston Advisor · doi:10.5260/chara.20.2.35 · Cited by 62 · see citing works
Fostering creative performance in art and design education via self-regulated learning
Greene, J. A., Freed, R., & Sawyer, R. K. (2018) · Instructional Science · doi:10.1007/s11251-018-9479-8 · Cited by 61 · see citing works
Heuristic Cues for Meta-Reasoning Judgments: Review and Methodology
Ackerman, R. (2019) · Psihologijske teme · doi:10.31820/pt.28.1.1 · Cited by 56 · see citing works
Kuklick, L., Greiff, S., & Lindner, M. A. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104785 · Cited by 54 · see citing works
How can we measure metacognition in creative problem-solving? Standardization of the MCPS scale
Urban, K., & Urban, M. (2023) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2023.101345 · Cited by 52 · see citing works
Self-citationUrban, M., & Urban, K. (2021) · The Journal of Creative Behavior · doi:10.1002/jocb.499 · Cited by 51 · see citing works
Self-citationThe role of failure in learning how to create in art and design
Sawyer, R. (2019) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2018.08.002 · Cited by 45 · see citing works
Woo, D. J., Wang, Y., Susanto, H., & Guo, K. (2023) · Journal of Educational Computing Research · doi:10.1177/07356331231175999 · Cited by 44 · see citing works
Idea Generation, Selection, and Evaluation: A Metacognitive Approach
Puente‐Díaz, R., Cavazos‐Arroyo, J., & Puerta‐Sierra, L. (2021) · The Journal of Creative Behavior · doi:10.1002/jocb.505 · Cited by 41 · see citing works
Functional brain activation patterns of creative metacognitive monitoring
Rominger, C., Benedek, M., Lebuda, I., Perchtold‐Stefan, C. M., Schwerdtfeger, A. R., Papousek, I., & Fink, A. (2022) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2022.108416 · Cited by 41 · see citing works
Hoch, E., Sidi, Y., Ackerman, R., Hoogerheide, V., & Scheiter, K. (2023) · Educational Psychology Review · doi:10.1007/s10648-023-09779-5 · Cited by 39 · see citing works
Do we need metacognition for creativity? A necessary condition analysis of creative metacognition.
Urban, M., & Urban, K. (2023) · Psychology of Aesthetics Creativity and the Arts · doi:10.1037/aca0000647 · Cited by 36 · see citing works
Self-citationDoes Metacognition Matter in Creative Problem‐Solving? A Mixed‐Methods Analysis of Writing
Urban, M., & Urban, K. (2024) · The Journal of Creative Behavior · doi:10.1002/jocb.630 · Cited by 33 · see citing works
Self-citationBias and Bias Remediation in Creative Problem-Solving: Managing Biases through Forecasting
Todd, E. M., Higgs, C. A., & Mumford, M. D. (2019) · Creativity Research Journal · doi:10.1080/10400419.2018.1532268 · Cited by 20 · see citing works
Sensemaking and metacognitive prompting in ill-structured problems
Smy, V., Cahillane, M., & MacLean, P. (2016) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-10-2015-0027 · Cited by 15 · see citing works
Heuristic Cues for Meta-Reasoning Judgments: Review and Methodology
Ackerman, R. (2019) · Psihologijske teme · Cited by 12 · see citing works
Creative Thinking Processes: Managing Innovative Efforts
Mumford, M. D., Martin, R., & Elliott, S. N. (2017) · Oxford University Press eBooks · doi:10.1093/acrefore/9780190224851.013.172 · Cited by 11 · see citing works
Metacognitive Judgment Skills and the Metacognitive Component of Self-Regulated Learning
Dörrenbächer‐Ulrich, L., & Perels, F. (2023) · Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie · doi:10.1026/0049-8637/a000274 · Cited by 10 · see citing works
DeHaan, R. L., & Narayan, K. M. V. (2008) · BRILL eBooks · doi:10.1163/9789087902858_002 · Cited by 1 · see citing works
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.08774
Flagged first, then by citations
1 of the 90 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13544 · Cited by 805 · see citing works
Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025) · Computers & Education · doi:10.1016/j.compedu.2024.105224 · Cited by 531 · see citing works
Wang, J., & Fan, W. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04787-y · Cited by 322 · see citing works
RetractedDerakhshan, A., & Ghiasvand, F. (2024) · International Journal of Applied Linguistics · doi:10.1111/ijal.12561 · Cited by 282 · see citing works
Xu, X., Qiao, L., Cheng, N., Liu, H., & Zhao, W. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13599 · Cited by 284 · see citing works
Guo, Y., & Wang, Y. (2024) · European Journal of Education · doi:10.1111/ejed.12812 · Cited by 274 · see citing works
Higher education students’ perceptions of ChatGPT: A global study of early reactions
Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A. Iahad, N., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., AlHumaid, J., et al. (2025) · PLOS ONE · doi:10.1371/journal.pone.0315011 · Cited by 376 · see citing works
Design and assessment of AI-based learning tools in higher education: a systematic review
Luo, J., Zheng, C., Yin, J., & Teo, H. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00540-2 · Cited by 244 · see citing works
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 · see citing works
Schei, O. M., Møgelvang, A., & Ludvigsen, K. (2024) · Education Sciences · doi:10.3390/educsci14080922 · Cited by 166 · see citing works
Sedkaoui, S., & Benaichouba, R. (2026) · European Journal of Innovation Management · doi:10.1108/ejim-02-2024-0129 · Cited by 142 · see citing works
Balalle, H., & Pannilage, S. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101299 · Cited by 137 · see citing works
Sun, L., & Zhou, L. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241277937 · Cited by 129 · see citing works
Zhou, X., Teng, D., & Al-Samarraie, H. (2024) · Education Sciences · doi:10.3390/educsci14121302 · Cited by 119 · see citing works
“This complements the observations made by Urban et al. [18], who found that students engaging with ChatGPT exhibit enhanced creative problem-solving skills compared to those who do not use the tool.”
AI in the classroom: Exploring students’ interaction with ChatGPT in programming learning
Güner, H., & Er, E. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13337-7 · Cited by 117 · see citing works
Chen, A., Xiang, M., Zhou, J., Jia, J., Shang, J., Li, X., Gašević, D., & Fan, Y. (2025) · Computers & Education · doi:10.1016/j.compedu.2024.105198 · Cited by 115 · see citing works
Chen, S., & Cheung, A. (2025) · Educational Research Review · doi:10.1016/j.edurev.2025.100737 · Cited by 117 · see citing works
Song, Y., Huang, L., Zheng, L., Fan, M., & Liu, Z. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00508-2 · Cited by 98 · see citing works
Ji, Y., Zhong, M., Lyu, S., Li, T., Niu, S., & Zhan, Z. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13437-4 · Cited by 95 · see citing works
Acosta-Enriquez, B. G., Várgas, C. G. A. P., Jordan, O. H., Ballesteros, M. A. A., & Morales, A. E. P. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100320 · Cited by 91 · see citing works
Bai, Y., & Wang, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-08697-6 · Cited by 86 · see citing works
Yu-peng, L., & Yu, Z. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2359222 · Cited by 84 · see citing works
Zhang, X., Zhang, P., Shen, Y., Liu, M., Wang, Q., Gašević, D., & Fan, Y. (2024) · Frontiers of Digital Education · doi:10.1007/s44366-024-0028-5 · Cited by 83 · see citing works
Liu, X., Guo, B., He, W., & Hu, X. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251329185 · Cited by 78 · see citing works
Liu, X., & Zhong, B. (2025) · Educational Research Review · doi:10.1016/j.edurev.2025.100741 · Cited by 78 · see citing works
The Art of Creative Inquiry—From Question Asking to Prompt Engineering
Sasson-Lazovsky, G., Raz, T., & Kenett, Y. N. (2024) · The Journal of Creative Behavior · doi:10.1002/jocb.671 · Cited by 69 · see citing works
Liu, J., Sun, D., Sun, J., Wang, J., & Yu, P. L. H. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100438 · Cited by 67 · see citing works
Shahzad, M. F., Xu, S., Liu, H., & Zahid, H. (2024) · European Journal of Education · doi:10.1111/ejed.12835 · Cited by 64 · see citing works
A Meta‐Analysis of the Impact of Generative Artificial Intelligence on Learning Outcomes
Ma, N., & Zhong, Z. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70117 · Cited by 57 · see citing works
Tang, Q., Deng, W., Huang, Y., Wang, S., & Zhang, H. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70027 · Cited by 55 · see citing works
Li, C. G., Cui, H., & Hagedorn, L. S. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100571 · Cited by 55 · see citing works
Xiao-hua, L., & Xiao, Y. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13117-9 · Cited by 54 · see citing works
Placebo or Assistant? Generative AI Between Externalization and Anthropomorphization
Skulmowski, A. (2024) · Educational Psychology Review · doi:10.1007/s10648-024-09894-x · Cited by 50 · see citing works
Zhao, L., Rahman, M. H., Yeoh, W., Wang, S., & Ooi, K. (2024) · Studies in Higher Education · doi:10.1080/03075079.2024.2427786 · Cited by 49 · see citing works
Effects of ChatGPT use on undergraduate students’ creativity: a threat to creative thinking?
Toma, R. B., & Yánez‐Pérez, I. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00172-x · Cited by 48 · see citing works
Zhao, G., Sheng, H., Wang, Y., Cai, X., & Long, T. (2025) · Education Sciences · doi:10.3390/educsci15050554 · Cited by 48 · see citing works
Hao, Z., Cao, J., Li, R., Yu, J., Liu, Z., & Zhang, Y. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105472 · Cited by 48 · see citing works
Commentary: Reimagining marketing education in the age of generative AI
Acar, O. A. (2024) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2024.06.004 · Cited by 48 · see citing works
ChatGPT in Academia: University Students’ Attitudes Towards the use of ChatGPT and Plagiarism
Fajt, B., & Schiller, E. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-025-09603-5 · Cited by 47 · see citing works
Urban, M., Brom, C., Lukavský, J., Děchtěrenko, F., Hein, V., Svacha, F., Kmoníčková, P., & Urban, K. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13591 · Cited by 47 · see citing works
Self-citationHelal, M. Y. I., Elgendy, I. A., Albashrawi, M. A., Dwivedi, Y. K., Al-Ahmadi, S., & Jeon, I. (2025) · Information Discovery and Delivery · doi:10.1108/idd-05-2025-0125 · Cited by 47 · see citing works
Xia, Q., Li, W., Yang, Y., Weng, X., & Chiu, T. K. F. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100455 · Cited by 45 · see citing works
Song, D., Zhang, P., Zhu, Y., Qi, S., Yang, Y., Gong, L., & Zhou, L. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104549 · Cited by 45 · see citing works
The Impact of <scp>ChatGPT</scp> on Students' Academic Achievement: A Meta‐Analysis
Liu, Z., Zuo, H., & Lu, Y. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70096 · Cited by 44 · see citing works
Hwang, G.-J., Cheng, P.-Y., & Chang, C.-Y. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104266 · Cited by 44 · see citing works
Generative AI Tools in Higher Education: A Meta-Analysis of Cognitive Impact
Qu, X., Sherwood, J., Liu, P., & Aleisa, N. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719841 · Cited by 44 · see citing works
Creativity and artificial intelligence: A study with prospective teachers
Pont, I., Sanz, Y. E., Gómez, P. O., & Ezpeleta, A. M. (2024) · Digital Education Review · doi:10.1344/der.2024.45.91-97 · Cited by 42 · see citing works
He, A. J., Zhang, Z., Anand, P., & McMinn, S. (2024) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2447195 · Cited by 42 · see citing works
Li, T., Zhan, Z., Ji, Y., & Li, T. (2025) · The Internet and Higher Education · doi:10.1016/j.iheduc.2025.101003 · Cited by 40 · see citing works
Liu, J., Zhang, Y., Wei, L., Wang, Q., Niu, P., & Zhang, X. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105473 · Cited by 40 · see citing works
García-Alonso, E. M., León-Mejía, A. C., Sánchez-Cabrero, R., & Guzmán-Ordaz, R. (2024) · Behavioral Sciences · doi:10.3390/bs14070612 · Cited by 53 · see citing works
Student-AI collaborative creative Problem-Solving: The role of human agency
Guoa, W., Liang, Z., Wang, C., Li, X., Hu, H., Chen, S., Yu, Q., & Zhao, Q. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105433 · Cited by 39 · see citing works
Xu, J. H., & Liu, Q. (2025) · Learning and Motivation · doi:10.1016/j.lmot.2025.102100 · Cited by 38 · see citing works
Chun, Z., Ning, Y., Chen, J., & Wijaya, T. T. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13617-2 · Cited by 38 · see citing works
ChatGPT’s impact on student learning outcomes: a meta-analysis of 35 experimental studies
Wu, X., Zhu, P., Zhang, J., Yin, M., & Wang, Y. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-07019-z · Cited by 37 · see citing works
Chugh, R., Turnbull, D., Kutty, S., Sabrina, F., Rashid, M. M., Morshed, A., Azad, S., Kaisar, S., & Subramani, S. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13686-3 · Cited by 37 · see citing works
Research trends in the use of artificial intelligence in higher education
Akhmadieva, R. S., Александровна, К. Н., Belova, T. V., Prokopyev, A. I., Molodozhnikova, N. M., & Spichak, V. Y. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1438715 · Cited by 35 · see citing works
Aladsani, H. K. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2487517 · Cited by 33 · see citing works
Almazrou, S. H., Alanezi, F., Almutairi, S. A., AboAlsamh, H. M., Alsedrah, I. T., Arif, W. M., Alsadhan, A. A., Alsanad, D. S., Alqahtani, N. S., AlShammary, M. H., et al. (2024) · Nutrition and Health · doi:10.1177/02601060241273627 · Cited by 33 · see citing works
Google or ChatGPT: Who is the better helper for university students
Zhang, M., & Yang, X. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13002-5 · Cited by 33 · see citing works
The Responsible Development of Automated Student Feedback with Generative AI
Lindsay, E. D., Zhang, M., Johri, A., & Bjerva, J. (2025) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016572 · Cited by 33 · see citing works
Generative AI Governance Model in Educational Research
de Pinho, I. G., Costa, A. P., & Pinho, C. S. B. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1594343 · Cited by 33 · see citing works
Tang, M., Hofreiter, S., Werner, C. H., Zielińska, A., & Karwowski, M. (2025) · The Journal of Creative Behavior · doi:10.1002/jocb.1519 · Cited by 32 · see citing works
The More Anxious, the More Dependent? The Impact of Math Anxiety on AI‐Assisted Problem‐Solving
Chen, F., Chen, J., & Xu, Y. (2025) · Psychology in the Schools · doi:10.1002/pits.23500 · Cited by 32 · see citing works
Durgungöz, A., & Kharrufa, A. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2509276 · Cited by 32 · see citing works
Xia, Q., Zhang, P., Huang, W., & Chiu, T. K. F. (2025) · Asia Pacific Journal of Education · doi:10.1080/02188791.2025.2530503 · Cited by 31 · see citing works
Kreijkes, P., Kewenig, V. N., Kuvalja, M., Lee, M., Hofman, J. M., Vitello, S., Sellen, A. J., Rintel, S., Goldstein, D. G., Rothschild, D. M., et al. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105514 · Cited by 29 · see citing works
Walton, J., Bearman, M., Crawford, N., Tai, J., & Boud, D. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2570328 · Cited by 28 · see citing works
“Taking both Urban et al. (2024) and our own study into account, we propose that one of the primary risks facing learners when working with GenAI to complete assessment tasks may be the potential to misjudge themselves and their own work.”
A Systematic Review of Research on ChatGPT in Higher Education
Döş, İ. (2025) · The European Educational Researcher · doi:10.31757/euer.824 · Cited by 28 · see citing works
Han, Z., Ying, R., Huang, C., Tsai, C., Wang, X., & He, T. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105422 · Cited by 28 · see citing works
Ahmad, Z., Sultana, A., Latheef, N. A., Siby, N., Sellami, A., & Abbasi, S. A. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105446 · Cited by 28 · see citing works
Hou, C., Zhu, G., Liu, Y., Sudarshan, V., Chong, J. L. L., Zhang, F. Y., Tan, M. Y. H., & Ong, Y. S. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105576 · Cited by 39 · see citing works
Wang, X.-M., Huang, X.-T., & Zhou, W.-Q. (2024) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2024.101658 · Cited by 27 · see citing works
Kejingyun, S., & Rao, M. (2025) · Nurse Educator · doi:10.1097/nne.0000000000001879 · Cited by 27 · see citing works
AI as a creative partner: a PRISMA review of AI’s role in supporting creativity in education
Urmeneta, A., & Roméro, M. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1602151 · Cited by 27 · see citing works
Exploring cognitive strategies in human-AI interaction: ChatGPT's role in creative tasks
Boers, J., Etty, T., Baars, M., & van Broekhoven, K. (2025) · Journal of Creativity · doi:10.1016/j.yjoc.2025.100095 · Cited by 26 · see citing works
ChatGPT and Global Higher Education: Using Artificial Intelligence in Teaching and Learning
Lin, X., Chan, R. Y., Sharma, S., & Bista, K. (2024) · doi:10.32674/rh27qv16 · Cited by 26 · see citing works
Gasaymeh, A.-M. M., Qbeita, A. A. A., Almohtadi, R., & Beirat, M. A. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1580310 · Cited by 26 · see citing works
Liu, Z., Guo, H., Zhou, Z., Ma, F., & Zeng, Y. (2025) · BMC Psychology · doi:10.1186/s40359-025-03630-y · Cited by 26 · see citing works
In search of adequate models of pedagogy
Nussbaum, M., Bekerman, Z., Archambault, L., Rainieri, M., Yılmaz, R., Zou, D., van Braak, J., & Tsai, C. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105345 · Cited by 26 · see citing works
Tsai, C., Lee, L., Lin, M. Y.-C., Cheng, Y.-P., Lin, C.-H., & Tsai, M. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105305 · Cited by 26 · see citing works
The Role of Artificial Intelligence in the Ideation Process
Pescher, C., & Tellis, G. J. (2025) · Journal of Product Innovation Management · doi:10.1111/jpim.12791 · Cited by 25 · see citing works
Selem, K. M., Saghier, E. G., Khreis, S. H. A., & Tan, C. C. (2025) · Journal of Hospitality & Tourism Education · doi:10.1080/10963758.2025.2456638 · Cited by 24 · see citing works
The synergistic effects in an AI-supported online scientific argumentation learning environment
Lin, Y., & Hung, C. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105251 · Cited by 24 · see citing works
Schenck, A. D. (2024) · Education Sciences · doi:10.3390/educsci14060652 · Cited by 24 · see citing works
Jayasinghe, S., Gamage, K. A. A., Yang, D., Cheng, C., Disanayake, C., & Apeji, U. D. (2026) · Education Sciences · doi:10.3390/educsci16010137 · Cited by 23 · see citing works
Reducing interpretative ambiguity in an educational environment with ChatGPT
García-Varela, F., Bekerman, Z., Nussbaum, M., Mendoza, M., & Montero, J. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105182 · Cited by 23 · see citing works
Wang, F., Yin, H., & King, R. B. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105209 · Cited by 23 · see citing works
Am I Still Creative? The Effect of Artificial Intelligence on Creative Self‐Beliefs
Faiella, A., Zielińska, A., Karwowski, M., & Corazza, G. E. (2025) · The Journal of Creative Behavior · doi:10.1002/jocb.70011 · Cited by 23 · see citing works
Martínez-Rivera, Ó. (2024) · European Public & Social Innovation Review · doi:10.31637/epsir-2024-885 · Cited by 22 · see citing works
Seker, O., Kwon, K., & Kocak, O. (2025) · Information Development · doi:10.1177/02666669251336368 · Cited by 21 · see citing works
<scp>ChatGPT</scp> Interventions in Higher Education: A Systematic Review of Experimental Studies
Jin, Y., & Sercu, L. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70072 · Cited by 21 · see citing works
Stephenson, Z., Johnson‐Glauch, N., & Cruchley, S. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2504621 · Cited by 21 · see citing works
Liu, M., Wu, Z., Dai, H. M., Su, Y., Malik, L., Liao, J., Zhang, W., Guo, S., Liu, L., & Zhao, J. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.70005 · Cited by 21 · see citing works
Fadillah, M. A., Usmeldi, U., Lufri, L., Mawardi, M., & Festiyed, F. (2024) · Advances in Mobile Learning Educational Research · doi:10.25082/amler.2024.02.013 · Cited by 21 · see citing works
Nie, X., Tian, Y., Liu, M., Wu, D., & Guo, Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13735-x · Cited by 20 · see citing works
AI as a Helper: Leveraging Generative AI Tools Across Common Parts of the Creative Process
Chompunuch, S., & Lubart, T. (2025) · Journal of Intelligence · doi:10.3390/jintelligence13050057 · Cited by 20 · see citing works
Alkaabi, A. M., Abdallah, A. K., Alblooshi, S., Alomari, F., & Alneaimi, S. (2025) · Journal of Education and e-Learning Research · doi:10.20448/jeelr.v12i2.6746 · Cited by 20 · see citing works
Mentigo: An Intelligent Agent for Mentoring Students in the Creative Problem Solving Process
Zha, S., Liu, Y., Zheng, C., Xu, J., Yu, F., Gong, J., & Xu, Y. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713952 · Cited by 19 · see citing works
Acquah, B. Y. S., Arthur, F., Salifu, I., Mensah, E., Opoku, E., Nortey, S. A., & Tetteh, S. A. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00373-y · Cited by 19 · see citing works
- Authors
- Urban, M., Děchtěrenko, F., Lukavský, J., Hrabalová, V., Svacha, F., Brom, C., & Urban, K.
- Affiliations
- Czech Academy of Sciences (CZ)
- Charles University (CZ)
- Slovak Academy of Sciences (SK)
- Corresponding author
- Urban, Marek
- ORCID
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 215
- Pages
- 105031
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W4392932744
- Semantic Scholar
- df35c2ebe6f3a66f531ac4b9bf89a2377d23fe02
- Cited by
- 396
- References
- 90
- Citations by year
- 2026: 171 · 2025: 181 · 2024: 43 · 2023: 1
- FWCI
- 148.52
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Computers & Education (publishedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Funders
- Vedecká grantová agentúra MŠVVaŠ SR a SAV (2/0026/21)
- Akademie Věd České Republiky (PSU-480/Brno/2023)
- Grantová Agentura České Republiky (GACR 24-11515S)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- ChatGPT, generative AI, creative problem solving, university students, self-efficacy, metacognitive monitoring, self-assessment, perceived usefulness, originality, perceived difficulty, AI-assisted problem solving
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
