Abstract
The rapid advancements in artificial intelligence (AI) have transformed various domains, including education. Generative AI models have garnered significant attention for their potential in educational settings, but image-generative AI models need to be more utilized. This study explores the potential of integrating generative AI, specifically Stable Diffusion, into art-focused STEAM classes. To this end, we combined STEAM education with image-generative AI. We designed and implemented a learning activity in which students used image-generative AI to generate creative images and wrote imaginative diaries inspired by AI-generated pictures. Using a mixed-methods approach, we collected and analyzed data from surveys, interviews, and prompt datasets to gain insights into students' perceptions, motivations, and behaviors concerning the integration of generative AI in art education. Our study found that incorporating generative AI into art-focused STEAM classes may reduce the gap between male and female students' interest in art practices. We also found that the number of subjects in students' prompt datasets is linked to their divergent and convergent thinking. Finally, we discovered a meaningful correlation between the number of unique words in students' diaries and the image scores that researchers assessed. Our findings shed light on the relationship between students' divergent and convergent behaviors, offering valuable insights into their creative thinking processes. This study underscores the potential of integrating image-generative AI models in STEAM education.
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References
Aguilera, D., & Ortiz-Revilla, J. (2021). STEM vs. STEAM education and student creativity: A systematic literature review. Education Sciences, 11(7), 331. https://doi.org/10.3390/educsci11070331
Ali, S., DiPaola, D., & Breazeal, C. (2021). What are GANs?: Introducing generative adversarial networks to middle school students. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 15472–15479). https://doi.org/10.1609/aaai.v35i17.17821
Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Available at SSRN 4337484.
Bailey, C. R., & Bailey, C. A. (2017). A guide to qualitative field research. Sage Publications.
Brown, A., & Cariveau, T. (2022). A systematic review of simultaneous prompting and prompt delay procedures. Journal of Behavioral Education, 1–22. https://doi.org/10.1007/s10864-022-09481-6
Bull, C., & Kharrufa, A. (2023). Generative AI assistants in software development education. arXiv preprint arXiv:2303.13936
Castronovo, F., Van Meter, P. N., & Messner, J. I. (2022). Leveraging metacognitive prompts in construction educational games for higher educational gains. International Journal of Construction Management, 22(1), 19–30. https://doi.org/10.1080/15623599.2018.1492760
Clelland, A. G. (2009). STEAM - Science, Technology, Engineering, Arts, Mathematics. http://www.handshake20.com/2009/04/steam.html
Cooper, G. (2023). Examining science education in chatgpt: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 1–9. https://doi.org/10.1007/s10956-023-10039-y
Crescenzi-Lanna, L. (2020). Multimodal learning analytics research with young children: A systematic review. British Journal of Educational Technology, 51(5), 1485–1504. https://doi.org/10.1111/bjet.12959
Creswell, J., & Poth, C. (2017). Qualitative Inquiry and Research Design: Choosing among Five Approaches. Sage.
Davis, S. (1995). Considering gender equity policy and recent debates in relation to arts education. Australian Art Education, 18(2), 52–60.
Dehouche, N., & Dehouche, K. (2023). What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education. Heliyon. https://doi.org/10.1016/j.heliyon.2023.e16757
DeJarnette, N. K. (2018). Implementing STEAM in the early childhood classroom. European Journal of STEM Education, 3(3), 18. https://doi.org/10.20897/ejsteme/3878
Drachsler, H., & Goldhammer, F. (2020). Learning analytics and eAssessment—Towards computational psychometrics by combining psychometrics with learning analytics. Radical solutions and learning analytics: Personalized learning and teaching through big data, 67–80. https://doi.org/10.1007/978-981-15-4526-9_5
Dumais, S., Jeffries, R., Russell, D. M., Tang, D., & Teevan, J. (2014). Understanding user behavior through log data and analysis. Ways of Knowing in HCI, 349–372. https://doi.org/10.1007/978-1-4939-0378-8_14
Guilford, J. P. (1967). The nature of human intelligence. McGraw-Hill.
Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840–6851. https://doi.org/10.48550/ARXIV.2006.11239
Hoffman, B., & Spatariu, A. (2008). The influence of self-efficacy and metacognitive prompting on math problem-solving efficiency. Contemporary Educational Psychology, 33(4), 875–893. https://doi.org/10.1016/j.cedpsych.2007.07.002
Hornberg, J., & Reiter-Palmon, R. (2017). Creativity and the big five personality traits: Is the relationship dependent on the creativity measure? Cambridge University Press. https://doi.org/10.1017/9781316228036.015
How, M. L., & Hung, W. L. D. (2019). Educing AI-thinking in science, technology, engineering, arts, and mathematics (STEAM) education. Education Sciences, 9(3), 184. https://doi.org/10.3390/educsci9030184
Jang, J., Jeon, J., & Jung, S. K. (2022). Development of STEM-based AI education program for sustainable improvement of elementary learners. Sustainability, 14(22), 15178. https://doi.org/10.3390/su142215178
Jovanovic, M., & Campbell, M. (2022). Generative artificial intelligence: Trends and prospects. Computer, 55, 107–112.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kauffman, D. F., Ge, X., Xie, K., & Chen, C. H. (2008). Prompting in web-based environments: Supporting self-monitoring and problem solving skills in college students. Journal of Educational Computing Research, 38(2), 115–137. https://doi.org/10.2190/EC.38.2.a
Kim, K. H. (2006). Can we trust creativity tests? A review of the Torrance Tests of Creative Thinking (TTCT). Creativity Research Journal, 18(1), 3–14. https://doi.org/10.1207/s15326934crj1801_2
Kim, K. H. (2011). Proven reliability and validity of the Torrance Tests of Creative Thinking (TTCT). Psychology of Aesthetics, Creativity, and the Arts, 5(4), 314–315. https://doi.org/10.1037/a0021916
Land, M. H. (2013). Full STEAM ahead: The benefits of integrating the arts into STEM. Procedia Computer Science, 20, 547–552. https://doi.org/10.1016/j.procs.2013.09.317
Lang, C., Siemens, G., Wise, A. F., Gašević, D., & Merceron, A. (2022). The Handbook of Learning Analytics - Second edition. SOLAR, Society for Learning Analytics and Research. NY: SOLAR. https://doi.org/10.18608/hla22
Law, V., & Chen, C. H. (2016). Promoting science learning in game-based learning with question prompts and feedback. Computers & Education, 103, 134–143. https://doi.org/10.1016/j.compedu.2016.10.005
Li, B. & Han, L. (2013). Distance weighted cosine similarity measure for text classification. In Intelligent Data Engineering and Automated Learning–IDEAL 2013: 14th International Conference, IDEAL 2013, Hefei, China, October 20–23, 2013. Proceedings 14 (pp. 611–618). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_74
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790
Liu, V., & Chilton, L. B. (2022). Design guidelines for prompt engineering text-to-image generative models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1–23). https://doi.org/10.1145/3491102.3501825
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1–35. https://doi.org/10.1145/3560815
Maeda, J. (2013). Stem+ art= steam. The STEAM Journal, 1(1), 34. https://doi.org/10.5642/steam.201301.34
Maes, B. (2010). Stop talking about “STEM” education! “TEAMS” is way cooler. Retrieved from http://bertmaes.wordpress.com/2010/10/21/teams/
Markelz, A., Scheeler, M. C., Riccomini, P., & Taylor, J. C. (2020). A systematic review of tactile prompting in teacher education. Teacher Education and Special Education, 43(4), 296–313. https://doi.org/10.1177/0888406419877500
Meece, J. L., Glienke, B. B., & Burg, S. (2006). Gender and motivation. Journal of School Psychology, 44(5), 351–373. https://doi.org/10.1016/j.jsp.2006.04.004
Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: A guidance for policymakers. UNESCO Publishing. https://doi.org/10.54675/PCSP7350
Michalsky, T., & Kramarski, B. (2015). Prompting reflections for integrating self-regulation into teacher technology education. Teachers College Record, 117(5), 1–38. https://doi.org/10.1177/016146811511700507
Microsoft Azure Azure Documentation (2023). Introduction to prompt engineering. Retrieved from https://learn.microsoft.com/en-us/azure/cognitive-services/openai/concepts/prompt-engineering
Mohana, M., Nandhini, K., & Subashini, P. (2022). Review on Artificial Intelligence and Robots in STEAM Education for Early Childhood Development: The State-of-the-Art Tools and Applications. In Handbook of Research on Innovative Approaches to Early Childhood Development and School Readiness (pp. 468–498). IGI Global. https://doi.org/10.4018/978-1-7998-8649-5.ch019
Oliner, A., & Stearley, J. (2007). What supercomputers say: A study of five system logs. 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN’07). https://doi.org/10.1109/dsn.2007.103
Pavlik, J. V. (2023). Collaborating with CHATGPT: Considering the implications of Generative Artificial Intelligence for Journalism and Media Education. Journalism & Mass Communication Educator, 78(1), 84–93. https://doi.org/10.1177/10776958221149577
Pennington, R. C., Stenhoff, D. M., Gibson, J., & Ballou, K. (2012). Using simultaneous prompting to teach computer-based story writing to a student with autism. Education & Treatment of Children, 35(3), 389–406. https://doi.org/10.1353/etc.2012.0022
Platz, J. (2007). How do you turn STEM into STEAM? Add the arts (pp. 1–5). Columbus: Ohio Alliance for Arts Education. http://www.ikzadvisors.com/wp-content/uploads/2009/09/STEM-+-ARTS-STEAM.pdf
Qadir, J. (2022). Engineering Education in the Era of Chatgpt: Promise and Pitfalls of Generative AI for Education. https://doi.org/10.36227/techrxiv.21789434.v1
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748–8763). PMLR. https://doi.org/10.48550/arXiv.2103.00020
Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084
Runco, M. A. (1992). Children’s divergent thinking and creative ideation. Developmental Review, 12(3), 233–264. https://doi.org/10.1016/0273-2297(92)90010-Y
Sanders, M. (2009). STEM, STEM education, STEMAnia. The Technology Teacher, 68(4), 20–27.
Savoie, A. (2009). Boys’ lack of interest in fine arts in a coeducational setting: A review of sex-related cognitive traits studies. International Journal of Art & Design Education, 28(1), 25–36. https://doi.org/10.1111/j.1476-8070.2009.01590.x
Shatunova, O., Anisimova, T., Sabirova, F., & Kalimullina, O. (2019). STEAM as an innovative educational technology. Journal of Social Studies Education Research, 10(2), 131–144.
Shawbitz, K. N., & Brock, M. E. (2023). A systematic review of training educators to implement response prompting. Teacher Education and Special Education, 46(2), 127–145. https://doi.org/10.1177/08884064221114130
Stokel-Walker, C. (2022). AI bot ChatGPT writes smart essays-should academics worry? Nature. https://doi.org/10.1038/d41586-022-04397-7
Torrance, E. P. (1966). Torrance tests of creative thinking: Norms-technical manual: Verbal tests, forms a and b: Figural tests, forms a and b. Personal Press.
Torrance, E. P. (1998). Torrance tests of creative thinking: Norms-technical manual: Figural (streamlined) forms A & B. Scholastic Testing Service.
Treffers-Daller, J. (2013). Measuring lexical diversity among L2 learners of French. Vocabulary knowledge: Human ratings and automated measures, 47. https://doi.org/10.1075/sibil.47.05ch3
Treffinger, D. J. (1995). Creative problem solving: Overview and educational implications. Educational Psychology Review, 7, 301–312. https://doi.org/10.1007/BF02213375
Treffinger, D. J., Isaksen, S. G., & Stead-Dorval, K. B. (2006). Creative problem solving: An introduction. Prufrock Press Inc.
Trisnayanti, Y., Khoiri, A., Miterianifa, & Ayu, H. D. (2019). Development of Torrance test creativity thinking (TTCT) instrument in science learning. In AIP Conference Proceedings (Vol. 2194, No. 1, p. 020129). https://doi.org/10.1063/1.5139861
Vartiainen, H., & Tedre, M. (2023). Using artificial intelligence in craft education: crafting with text-to-image generative models. Digital Creativity, 1–21. https://doi.org/10.1080/14626268.2023.2174557
Wang, Y., Shen, S., & Lim, B. Y. (2023). RePrompt: Automatic prompt editing to refine AI-generative art towards precise expressions. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581402
Wikberg, S. (2013). Art education–mostly for girls? A gender perspective on the Art subject in Swedish compulsory school. Education Inquiry, 4(3), 22630. https://doi.org/10.3402/edui.v4i3.22630
Wu, S. P. W., & Rau, M. A. (2018). Effectiveness and efficiency of adding drawing prompts to an interactive educational technology when learning with visual representations. Learning and Instruction, 55, 93–104. https://doi.org/10.1016/j.learninstruc.2017.09.010
Yang, K.-H., Chu, H.-C., & Chiang, L.-Y. (2018). Effects of a progressive prompting-based educational game on second graders’ mathematics learning performance and behavioral patterns. Journal of Educational Technology & Society, 21(2), 322–334. http://www.jstor.org/stable/26388410
Yu, G. (2010). Lexical diversity in writing and speaking task performances. Applied Linguistics, 31(2), 236–259. https://doi.org/10.1093/applin/amp024
Zhai, X. (2022). CHATGPT user experience: Implications for education. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4312418
Zhai, X. (2023). Chatgpt for next generation science learning. XRDS: Crossroads, The ACM Magazine for Students, 29(3), 42–46. https://doi.org/10.1145/3589649
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Lee, U., Han, A., Lee, J. et al. Prompt Aloud!: Incorporating image-generative AI into STEAM class with learning analytics using prompt data. Educ Inf Technol 29, 9575–9605 (2024). https://doi.org/10.1007/s10639-023-12150-4
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DOI: https://doi.org/10.1007/s10639-023-12150-4