Generative AI: A double-edged sword for creative thinking learning — Evidence from facial expressions and fNIRS
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Attempts to suppress persona traits through post hoc steering or ablation either degrade general capabilities (MMLU) or create new pathologies, confirming that sycophancy and related traits are load-bearing rather than removable quirks.
“Their findings show that these persona directions are architecturally integrated in training rather than merely latent: attempts to suppress traits through post hoc steering or ablation either degrade general capabilities (measured by MMLU performance) or create new pathologies, confirming that sycophancy and related traits are load-bearing rather than removable quirks in output patterns.”
From The hidden functions of sycophancy in AI systems: steering, consistency, and cognitive dependency (2026) · cites this paper as [23]
Cites
An argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646 · see citing works
The standard definition of creativity
Runco, M. A., & Jaeger, G. J. (2012) · Creativity Research Journal · doi:10.1080/10400419.2012.650092 · Cited by 3,895 · see citing works
Emotional processing in anterior cingulate and medial prefrontal cortex
Etkin, A., Egner, T., & Kalisch, R. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.11.004 · Cited by 3,059 · see citing works
Sample size determination and power analysis using the G*Power software
Kang, H. (2021) · Journal of Educational Evaluation for Health Professions · doi:10.3352/jeehp.2021.18.17 · Cited by 2,572 · see citing works
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338 · see citing works
The brain basis of emotion: a meta-analytic review
Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Barrett, L. F. (2012) · Behavioral and Brain Sciences · doi:10.1017/s0140525x11000446 · Cited by 2,320 · see citing works
WIDER FACE: A Face Detection Benchmark
Yang, S., Luo, P., Loy, C. C., & Tang, X. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.596 · Cited by 1,907 · see citing works
Strangman, G. E., Culver, J. P., Thompson, J. H., & Boas, D. A. (2002) · NeuroImage · doi:10.1006/nimg.2002.1227 · Cited by 1,421 · see citing works
Generative AI and ChatGPT: applications, challenges, and AI-human collaboration
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023) · Journal of Information Technology Case and Application Research · doi:10.1080/15228053.2023.2233814 · Cited by 1,363 · see citing works
The Cambridge Handbook of Creativity
(2010) · doi:10.1017/cbo9780511763205 · Cited by 1,291 · see citing works
Metrics for measuring ideation effectiveness
Shah, J. J., Smith, S. M., & Vargas-Hernández, N. (2003) · Design Studies · doi:10.1016/s0142-694x(02)00034-0 · Cited by 1,100 · see citing works
Cope, M., & Delpy, D. T. (1988) · Medical & Biological Engineering & Computing · doi:10.1007/bf02447083 · Cited by 1,079 · see citing works
The nature of awe: elicitors, appraisals, and effects on self-concept
Shiota, M. N., Keltner, D., & Mossman, A. (2007) · Cognition and Emotion · doi:10.1080/02699930600923668 · Cited by 1,025 · see citing works
Compound facial expressions of emotion
Du, S., Tao, Y., & Martinez, A. M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1322355111 · Cited by 806 · see citing works
Frontal brain asymmetry and emotional reactivity: A biological substrate of affective style
Wheeler, R. E., Davidson, R. J., & Tomarken, A. J. (1993) · Psychophysiology · doi:10.1111/j.1469-8986.1993.tb03207.x · Cited by 634 · see citing works
Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023) · Entrepreneurial Business and Economics Review · doi:10.15678/eber.2023.110201 · Cited by 584 · see citing works
Promises and challenges of generative artificial intelligence for human learning
Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02004-5 · Cited by 499 · see citing works
Identifying Quality, Novel, and Creative Ideas: Constructs and Scales for Idea Evaluation
Dean, D. L., Hender, J., Rodgers, T., & Santanen, E. L. (2006) · Journal of the Association for Information Systems · doi:10.17705/1jais.00106 · Cited by 478 · see citing works
Sarkar, P., & Chakrabarti, A. (2011) · Design Studies · doi:10.1016/j.destud.2011.01.002 · Cited by 458 · see citing works
Ahern, G. L., & SCHWARTZ, G. E. R. (1985) · Neuropsychologia · doi:10.1016/0028-3932(85)90081-8 · Cited by 439 · see citing works
The Crowdless Future? Generative AI and Creative Problem-Solving
Boussioux, L., Lane, J. N., Zhang, M., Jaćimović, V., & Lakhani, K. R. (2024) · Organization Science · doi:10.1287/orsc.2023.18430 · Cited by 369 · see citing works
How does generative artificial intelligence impact student creativity?
Habib, S., Vogel, T., Anli, X., & Thorne, E. (2024) · Journal of Creativity · doi:10.1016/j.yjoc.2023.100072 · Cited by 364 · see citing works
The Crowdless Future? Generative AI and Creative Problem Solving
Boussioux, L., N. Lane, J., Zhang, M., Jacimovic, V., & Lakhani, K. R. (2023) · doi:10.2139/ssrn.4533642 · Cited by 343 · see citing works
Supena, I., Darmuki, A., IKIP PGRI Bojonegoro, I., Hariyadi, A., & IKIP PGRI Bojonegoro, I. (2021) · International Journal of Instruction · doi:10.29333/iji.2021.14351a · Cited by 338 · see citing works
NIRS-KIT: a MATLAB toolbox for both resting-state and task fNIRS data analysis
Hou, X., Zong, Z., Zhao, C., Duan, L. F., Gong, Y., Li, Z., & Zhu, C. (2021) · Neurophotonics · doi:10.1117/1.nph.8.1.010802 · Cited by 294 · see citing works
Spector, J. M., & Ma, S. (2019) · Smart Learning Environments · doi:10.1186/s40561-019-0088-z · Cited by 275 · see citing works
Critical, Reflective, Creative Thinking and Their Reflections on Academic Achievement
Akpur, U. (2020) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2020.100683 · Cited by 258 · see citing works
Comparison of the CatBoost Classifier with other Machine Learning Methods
Ibrahim, A. A., Raheem, L., Muhammed, M., Rabiat, O., & Ganiyu, A. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0111190 · Cited by 219 · see citing works
Creative interdisciplinary collaboration: A systematic literature review
Moirano, R., Sánchez, M. A., & Štěpánek, L. (2019) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2019.100626 · Cited by 212 · see citing works
Ramírez-Montoya, M., Castillo-Martínez, I. M., Sanabria-Z, J., & Miranda, J. (2022) · Journal of Open Innovation: Technology, Market and Complexity · doi:10.3390/joitmc8010004 · Cited by 210 · see citing works
Py-Feat: Python Facial Expression Analysis Toolbox
Cheong, J. H., Jolly, E., Xie, T., Byrne, S., Kenney, M., & Chang, L. J. (2023) · Affective Science · doi:10.1007/s42761-023-00191-4 · Cited by 199 · see citing works
Emotion-Modulated Performance and Activity in Left Dorsolateral Prefrontal Cortex.
Herrington, J. D., Mohanty, A., Koven, N. S., Fisher, J. E., Stewart, J. L., Banich, M. T., WEBB, A. G., Miller, G. A., & Heller, W. (2005) · Emotion · doi:10.1037/1528-3542.5.2.200 · Cited by 195 · see citing works
Facial Expression Recognition Using Residual Masking Network
Pham, L., Vu, T. H., & Tran, T. A. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9411919 · Cited by 190 · see citing works
Martı́nez, A. M., & Du, S. (2017) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-57021-1_6 · Cited by 173 · see citing works
Cseh, G. M., & Jeffries, K. K. (2019) · Psychology of Aesthetics Creativity and the Arts · doi:10.1037/aca0000220 · Cited by 171 · see citing works
Self-efficacy and performance feedback: Impacts on cognitive load during creative thinking
Redifer, J. L., Bae, C. L. L., & Zhao, Q. (2020) · Learning and Instruction · doi:10.1016/j.learninstruc.2020.101395 · Cited by 167 · see citing works
Obrenovic, B., Gu, X.-C., Wang, G., Godinic, D., & Jakhongirov, I. J. (2024) · Ai & Society · doi:10.1007/s00146-024-01889-0 · Cited by 138 · see citing works
Calavia, M. B., Blanco, T., & Casas, R. (2020) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2020.100761 · Cited by 129 · see citing works
Yılmaz, A. (2020) · Participatory Educational Research · doi:10.17275/per.21.35.8.2 · Cited by 122 · see citing works
Hsia, L., Lin, Y., & Hwang, G. (2021) · British Journal of Educational Technology · doi:10.1111/bjet.13073 · Cited by 122 · see citing works
Khalid, M., Saad, S., Hamid, S. R. A., Abdullah, M. R. T. L., Ibrahim, H., & Shahrıll, M. (2020) · Creativity Studies · doi:10.3846/cs.2020.11027 · Cited by 121 · see citing works
Woo, D. J., Wang, D., Guo, K., & Susanto, H. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12819-4 · Cited by 104 · see citing works
Wijaya, T. T., Yu, Q., Cao, Y., He, Y., & Leung, F. K. S. (2024) · Behavioral Sciences · doi:10.3390/bs14111008 · Cited by 103 · see citing works
Inspired to Create: Awe Enhances Openness to Learning and the Desire for Experiential Creation
Rudd, M., Hildebrand, C., & Vohs, K. D. (2018) · Journal of Marketing Research · doi:10.1177/0022243718802853 · Cited by 101 · see citing works
Compound facial expressions of emotion: from basic research to clinical applications
Du, S., & Martı́nez, A. M. (2015) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2015.17.4/sdu · Cited by 95 · see citing works
Generative Artificial Intelligence and the Emergence of Creative Displacement Anxiety
Caporusso, N. (2023) · Research Directs in Psychology and Behavior · doi:10.53520/rdpb2023.10795 · Cited by 91 · see citing works
The Double Diamond Model: In Pursuit of Simplicity and Flexibility
Kochanowska, M., Gagliardi, W. R., & Ball, W. R. T. J. (2021) · Springer series in design and innovation · doi:10.1007/978-3-030-79879-6_2 · Cited by 86 · see citing works
Li, Y., Grabell, A. S., Wakschlag, L. S., Huppert, T. J., & Perlman, S. B. (2016) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2016.07.002 · Cited by 84 · see citing works
Facial expression recognition from image sequences using twofold random forest classifier
Pu, X., Fan, K., Chen, X., Ji, L., & Zhou, Z. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.05.005 · Cited by 83 · see citing works
Multi-head attention fusion networks for multi-modal speech emotion recognition
Zhang, J., Xing, L., Tan, Z., Wang, H., & Wang, K. (2022) · Computers & Industrial Engineering · doi:10.1016/j.cie.2022.108078 · Cited by 66 · see citing works
Awe in the metaverse: Designing and validating a novel online virtual-reality awe-inspiring training
Pizzolante, M., Borghesi, F., Sarcinella, E. D., Bartolotta, S., Salvi, C., Cipresso, P., Gaggioli, A., & Chirico, A. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107876 · Cited by 62 · see citing works
Restructuring processes and Aha! experiences in insight problem solving
Wiley, J., & Danek, A. H. (2023) · Nature Reviews Psychology · doi:10.1038/s44159-023-00257-x · Cited by 58 · see citing works
Srikongchan, W., Kaewkuekool, S., & Meejaleurn, S. (2021) · International Journal of Instruction · doi:10.29333/iji.2021.14214a · Cited by 56 · see citing works
Facial Expression Recognition for Probing Students’ Emotional Engagement in Science Learning
Tang, X., Gong, Y., Xiao, Y., Xiong, J., & Bao, L. (2024) · Journal of Science Education and Technology · doi:10.1007/s10956-024-10143-7 · Cited by 54 · see citing works
Roles, strategies, and research issues of generative AI in the mobile learning era
Lai, C., & Tu, Y. (2024) · International Journal of Mobile Learning and Organisation · doi:10.1504/ijmlo.2024.141836 · Cited by 49 · see citing works
A systematic quantitative review of divergent thinking assessments.
Saretzki, J., Forthmann, B., & Benedek, M. (2024) · Psychology of Aesthetics Creativity and the Arts · doi:10.1037/aca0000691 · Cited by 40 · see citing works
Istre, G. R., McCoy, M., Moore, B. J., Roper, C., Stephens-Stidham, S., Barnard, J. J., Carlin, D. K., Stowe, M., & Anderson, R. J. (2013) · Injury Prevention · doi:10.1136/injuryprev-2013-040823 · Cited by 38 · see citing works
Concept generation techniques change patterns of brain activation during engineering design
Shealy, T., Gero, J. S., Hu, M., & Milovanovic, J. (2020) · Design Science · doi:10.1017/dsj.2020.30 · Cited by 36 · see citing works
Student Concentration Evaluation Index in an E-learning Context Using Facial Emotion Analysis
Sharma, P., Esengönül, M., Khanal, S. R., Khanal, T. T., Filipe, V. M. D. J., & Reis, M. J. C. S. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-20954-4_40 · Cited by 34 · see citing works
The impact of design thinking and its underlying theoretical mechanisms: A review of the literature
Mayer, S., & Schwemmle, M. (2024) · Creativity and Innovation Management · doi:10.1111/caim.12626 · Cited by 32 · see citing works
Influence of online STEAM hands-on learning on AI learning, creativity, and creative emotions
Chang, Y., Wang, Y.-Y., & Ku, Y.-T. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2023.2205898 · Cited by 31 · see citing works
Creative therapy in health and disease: Inner vision
Khalil, R., & Demarin, V. (2023) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14266 · Cited by 29 · see citing works
Double Diamond Strategy Saves Time of the Design Process
Saad, E., Elekyaby, M., Ali, E., & Hassan, S. (2020) · التصميم الدولية · doi:10.21608/idj.2020.96345 · Cited by 27 · see citing works
Inspired by awe: Awe promotes inspiration via self-transcendence
Dai, Y., & Jiang, T. (2023) · Journal of Positive Psychology · doi:10.1080/17439760.2023.2254737 · Cited by 25 · see citing works
A Reliability Generalization of the Torrance Tests of Creative Thinking-Figural
Acar, S., Lee, L. E., & Scherer, R. (2024) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000819 · Cited by 25 · see citing works
Huang, S., Ko, P., Lin, H.-H., Dai, R.-H., & Chen, H. (2021) · The Journal of Creative Behavior · doi:10.1002/jocb.491 · Cited by 24 · see citing works
Rane, N. L., Mallick, S. K., Kaya, Ö., & Rane, J. (2024) · doi:10.70593/978-81-981271-4-3_5 · Cited by 23 · see citing works
Awe is associated with creative personality, convergent creativity, and everyday creativity.
Zhang, J. W., Howell, R. T., Razavi, P., Shaban-Azad, H., Chai, W. J., Ramis, T., Mello, Z. R., Anderson, C. L., Monroy, M., & Keltner, D. J. (2021) · Psychology of Aesthetics Creativity and the Arts · doi:10.1037/aca0000442 · Cited by 22 · see citing works
Revolutionary artificial intelligence or rogue technology? The promises and pitfalls of ChatGPT
Sieja, M., & Wach, K. (2023) · Przedsiębiorczość Międzynarodowa · doi:10.15678/ier.2023.0904.07 · Cited by 22 · see citing works
The effect of design education on creative design cognition of high school students
Gero, J. S., Yu, R., & Wells, J. G. (2019) · International Journal of Design Creativity and Innovation · doi:10.1080/21650349.2019.1628664 · Cited by 21 · see citing works
Evolving Epistemic Infrastructure: The Role of Scientific Journals in the Age of Generative AI
Yoo, Y. (2024) · Journal of the Association for Information Systems · doi:10.17705/1jais.00870 · Cited by 19 · see citing works
Xie, C., Luchini, S. A., Beaty, R. E., Du, Y., Liu, C., & Li, Y. (2022) · Creativity Research Journal · doi:10.1080/10400419.2022.2108265 · Cited by 17 · see citing works
A Cybernetic Perspective on Generative AI in Education: From Transmission to Coordination.
Griffiths, D., Frı́as-Martı́nez, E., Tlili, A., & Burgos, D. (2024) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2024.02.008 · Cited by 16 · see citing works
Li, H., Xiao, R., Nieu, H., Tseng, Y.-J., & Liao, G. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i28.35187 · Cited by 14 · see citing works
Review on learning framework for facial expression recognition
Borgalli, R. A., & Surve, S. (2022) · The Imaging Science Journal · doi:10.1080/13682199.2023.2172526 · Cited by 12 · see citing works
Özal, Z., Ambrosini, F., Biolcati, R., Trombini, E., Mavroveli, S., & Mancini, G. (2024) · BMC Psychology · doi:10.1186/s40359-024-02094-w · Cited by 11 · see citing works
Sztuka, I. M., & Kühn, S. (2025) · Scientific Reports · doi:10.1038/s41598-024-84988-8 · Cited by 11 · see citing works
Copeland, M. (2023) · Routledge eBooks · doi:10.4324/9781032682396 · Cited by 9 · see citing works
Beyond the basics: Facial expressions of compound emotions
Pallett, P. M., & Martı́nez, A. M. (2014) · Journal of Vision · doi:10.1167/14.10.1401 · Cited by 7 · see citing works
Wu, J., Liu, Y., Du, X., Zhang, X., & Xue, C. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103223 · Cited by 7 · see citing works
Human Emotion Classification using KNN Classifier and Recurrent Neural Networks with Seed Dataset
Satyanarayana, K. N. V., Tejasri, V., Srujitha, Y. S. N., Mounisha, K. N. S., Yerramsetti, S. T., & Darapu, G. D. (2022) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc53470.2022.9754091 · Cited by 6 · see citing works
Khelifa, A., Ghazouani, H., & Barhoumi, W. (2024) · Expert Systems · doi:10.1111/exsy.13724 · Cited by 6 · see citing works
Generative AI Meets Open-Ended Survey Responses: Participant Use of AI and HomogenizationPreprint
Zhang, S., Xu, J., & Alvero, A. (2024) · doi:10.31235/osf.io/4esdp · Cited by 6 · see citing works
Manu, A. (2024) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003450139-15 · Cited by 4 · see citing works
Mehra, P., & Verma, S. K. (2023) · International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) · doi:10.1109/dicct56244.2023.10110282 · Cited by 3 · see citing works
Esquivel, R. V., López, A. A., Lume, J. M., & Cornejo, J. (2024) · International Conference on Engineering and Product Design Education (EPDE) · doi:10.35199/epde.2024.104 · Cited by 3 · see citing works
Human-centered Design Based on the Double Diamond Model for Optimizing Hybrid Game Design
Liang, R., Chu, S., Lawton, D., & Pan, G. (2024) · AHFE international · doi:10.54941/ahfe1005632 · Cited by 3 · see citing works
Camera-Based Stress Detection Using Face-Related and Emotion-Related Features
Ogasawara, R., Bouazizi, M., & Ohtsuki, T. O. (2024) · IEEE International Conference on E-health Networking, Application & Services (HealthCom) · doi:10.1109/healthcom60970.2024.10880805 · Cited by 3 · see citing works
Zhang, X., Pi, Z., Lv, Y., & Hu, W. (2025) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2025.101815 · Cited by 2 · see citing works
Huang, Y., Zhang, Z., Cao, G., Kenett, Y. N., & Xu, Z. (2025) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2025.101821 · Cited by 2 · see citing works
Cisnaulin, D. P., Ghosh, M. M. A., & Putra, A. K. (2025) · Future Space · doi:10.69877/fssge.v2i1.56 · Cited by 1 · see citing works
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- Authors
- Song, X., Zhang, Y., Lu, Z., Xu, L., & Shen, H.
- Affiliations
- Beijing Institute of Technology (CN)
- Hefei University of Technology (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Lu, Zhaolin
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2026-07
- Volume
- 247
- Pages
- 105578
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W7124834265
- Cited by
- 12
- References
- 90
- Citations by year
- 2026: 12
- FWCI
- 94.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers & Education (publishedVersion)
- Funders
- Beijing Institute of Technology (2024CX01023)
- Fundamental Research Funds for the Central Universities (202506030058, 2024CX06123)
- National Natural Science Foundation of China (52275234)
- China Scholarship Council (202106695014)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience, Education and Cognitive Function
- Keywords
- generative AI, creative thinking, TRIZ, prefrontal cortex activation, facial emotion, creativity assessment
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