Exploring the Role of Generative Artificial Intelligence in Creative Thinking Processes: results from a preliminary study.

Autori

  • Eugenia Treglia Università Telematica Pegaso
  • Anna Maria Mariani Università Telematica Pegaso
  • Michele Vindigni Università degli Studi di Brescia

Parole chiave:

Creativity, Generative Artificial Intelligence, Higher-order Thinking Skills

Abstract

Creativity is widely recognized as a key competence in higher education and is closely related to higher-order cognitive processes such as analysis, evaluation, and creation. Recent developments in generative Artificial Intelligence (GenAI) challenge traditional views of creativity as an exclusively human capacity, raising questions about its influence on students’ creative thinking. This preliminary study investigates the impact of GenAI on higher-order thinking processes in university students, with specific reference to the Create dimension of the revised Bloom’s taxonomy. A quasi-experimental design with two independent groups was employed. Fifty-six undergraduate students completed an authentic creative task based on the analysis and reinterpretation of a disciplinary text; only one group was allowed to use GenAI during the analytical phases preceding creative production. Data were collected through a structured creative performance rubric, the creativity module of the A.S.K. test, and a questionnaire assessing familiarity with AI-based tools. Results showed no statistically significant differences in creative performance between students who used GenAI and those who did not, although a small positive trend emerged in the experimental group. Significant positive correlations were found between creative performance and familiarity with AI, particularly in content creation and problem-solving skills. Creative potential measured by the A.S.K. test was also moderately associated with actual performance. Overall, findings suggest that GenAI does not directly enhance creativity but may act as a cognitive amplifier when integrated in a reflective and informed manner, with relevant implications for educational design in higher education.

Riferimenti bibliografici

Amabile, T. M. (1983). The social psychology of creativity: A componential conceptualization. Journal of Personality and Social Psychology, 45(2), 357–376. https://doi.org/10.1037/0022-3514.45.2.357

Anderson, L. W., Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21), 610–623. https://doi.org/10.1145/3442188.3445922

Boden, M. A. (2016). AI: Its nature and future (2nd ed.). Oxford University Press.

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., & al. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems (NeurIPS 2020). arXiv, https://doi.org/10.48550/arXiv.2005.14165

Cosgrove, J. & Cachia, R., (2025). DigComp 3.0: European Digital Competence Framework - Fifth Edition, Publications Office of the European Union, Luxembourg, https://data.europa.eu/doi/10.2760/0001149, JRC144121.

Csikszentmihalyi, M., & Csikszentmihalyi, I. S. (Eds.). (1988). Optimal experience: Psychological studies of flow in consciousness. Cambridge University Press.

Dong, H.-W., Hsiao, W.-Y., Yang, L.-C., & Yang, Y.-H. (2017). MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In Proceedings of the 2017 International Conference on Machine Learning and Applications (ICMLA). arXiv. https://arxiv.org/abs/1709.06298

Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks generating “Art” by learning about styles and deviating from style norms. In A. Goel, A. Jordanous, & A. Pease (Eds.), Proceedings of the 8th International Conference on Computational Creativity (ICCC 2017). Georgia Institute of Technology. https://arxiv.org/abs/1706.07068

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recom-mendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

Gabriel, S. (2024). Generative AI in writing workshops: A path to AI literacy. In Proceedings of the International Conference on AI Research (ICAIR 2024). Academic Conferences International.

Glăveanu, V. P. (2020). A Sociocultural Theory of Creativity: Bridging the Social, the Material, and the Psychological, Review of General Psychology, 24(4) 335–354. https://doi.org/10.1177/10892680209617

Goutham Srinivas, M., Shashank, Thomas T. (2025). Human-AI Collaboration in Creative Endeavours. In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - 3, 485-495, DOI: 10.5220/0013622300004664

Greenfield PM. (2009). Technology and informal education: what is taught, what is learned. Science. 2; 323(5910):69-71. doi: 10.1126/science.1167190.

Minelli (2015). L’economia tribale: quando il non fare diventa abbondanza. https://www.emergenzeweb.it/auth

Patston, T J., Kaufman, J.C., Cropley, AJ., Marrone, R. (2021). What Is Creativity in Education? A Qualitative Study of International Curricula. Journal of Advanced Academics, v32, n2, p207-230

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12, 1-13. https://doi.org/10.1186/s41039-017-0062-8

Reiter-Palmon, R., & Robinson, E. J. (2009). Problem identification and construction: What do we know, what is the future? In R. J. Sternberg (Ed.), Handbook of creativity. Springer.

Remian, D. (2019). Augmenting education: Ethical considerations for incorporating artificial intelligence in education (Master’s thesis). University of Massachusetts Boston. https://scholarworks.umb.edu/instruction_capstone/52

Skilton, M., & Hovsepian, F. (2018). The Fourth Industrial Revolution: Responding to the impact of artificial intelligence on business. Springer. https://doi.org/10.1007/978-3-319-62479-2

Shuler, H., Hell, B. (2009). Test di Pensiero inferenziale e creativo (ASK). Adattamento italiano a cura di Faraci e Clarotti. Giunti O.S, Firenze.Treglia, E. (2020). Processi creativi ed educazione. Roma: La Pecora nera editore.

World Bank. (2018). Artificial intelligence and development: Towards a research agenda. World Bank Group. http://documents.worldbank.org/curated/en/099757104152527995/AI-revolution-in-higher-education-what-you-need-to-know

Zawacki-Richter, O., Marín, V.I., Bond, M. et al. Systematic review of research on artificial intelligence applications in higher education – where are the educators?. Int J Educ Technol High Educ 16, 39 (2019). https://doi.org/10.1186/s41239-019-0171-0

##submission.downloads##

Pubblicato

2026-03-29

Come citare

Treglia, E., Mariani, A. M. ., & Vindigni, M. (2026). Exploring the Role of Generative Artificial Intelligence in Creative Thinking Processes: results from a preliminary study . Journal of Inclusive Methodology and Technology in Learning and Teaching, 6(1). Recuperato da https://inclusiveteaching.it/index.php/inclusiveteaching/article/view/537

Fascicolo

Sezione

Articoli