Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, Aleman FL, Almeida D, Altenschmidt J, Altman S, Anadkat S (2023) Gpt-4 technical report. arXiv preprint arXiv:2303.08774
Avello R, Gajderowicz T, Gómez-Rodríguez VG (2024) Is ChatGPT helpful for graduate students in acquiring knowledge about digital storytelling and reducing their cognitive load? an experiment. Rev Educ Dist 24 (78)
Alter F (2009) Understanding the role of critical and creative thinking in australian primary school visual arts education. Int Art Early Child Res J 1(1):1–12
Ahmad N, Murugesan S, Kshetri N (2023) Generative artificial intelligence and the education sector. Computer 56(6):72–76
Google Scholar
Allingham JU, Ren J, Dusenberry MW, Gu X, Cui Y, Tran D, Liu JZ, Lakshminarayanan B (2023) A simple zero-shot prompt weighting technique to improve prompt ensembling in text-image models. In: Proceedings of the International Conference on Machine Learning
Anderson T, Shattuck J (2012) Design-based research: A decade of progress in education research? Educ Res 41(1):16–25
Google Scholar
Baytak A (2023) The acceptance and diffusion of generative artificial intelligence in education: A literature review. Curr Perspect Educ Res 6(1):7–18
Google Scholar
Bandura A, Freeman WH, Lightsey R (1999) Self-efficacy: The exercise of control. J Cogn Psychother 13(2):158–166
Google Scholar
Borji A (2022) Generated faces in the wild: Quantitative comparison of stable diffusion, midjourney and DALL-E 2. arXiv preprint arXiv:2210.00586
Bruner JS (1974) Toward a Theory of Instruction. Harvard University Press, London
Burger K, Winner E (2000) Instruction in visual art: Can it help children learn to read? J Aesthet Educ 34(3/4):277–293
Google Scholar
Chen J, An J, Lyu H, Kanan C, Luo J (2024) Learning to evaluate the artness of AI-generated images. IEEE Trans Multimed 26:10731–10740
Google Scholar
Cao Y, Li S, Liu Y, Yan Z, Dai Y, Yu PS, Sun L (2023) A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to ChatGPT. arXiv preprint arXiv:2303.04226
Chandler P, Sweller J (1991) Cognitive load theory and the format of instruction. Cogn Instr 8(4):293–332
Google Scholar
Chen C-M, Wu C-H (2015) Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance. Comput Educ 80:108–121
Google Scholar
Dehouche N, Dehouche K (2023) What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education. Heliyon 9(6):16757
Google Scholar
Dewey J (1986) Experience and education. Educ Forum 50(3):241–252
Google Scholar
Doshi AR, Hauser OP (2024) Generative AI enhances individual creativity but reduces the collective diversity of novel content. Sci Adv 10(28):5290
Google Scholar
Dhanapal S, Kanapathy R, Mastan J (2014) A study to understand the role of visual arts in the teaching and learning of science. In: Asia-Pacific Forum on Science Learning & Teaching, vol. 15, pp. 1–25
Du C, Li Y, Qiu Z, Xu C (2024) Stable diffusion is unstable. In: Proceedings of the Advances in Neural Information Processing Systems
Dhariwal P, Nichol A (2021) Diffusion models beat GANs on image synthesis. In: Proceedings of the Advances in Neural Information Processing Systems
Deci EL, Ryan RM (2012) Self-determination theory. Handb Theories Soc Psychol 1(20):416–436
Google Scholar
Diwan C, Srinivasa S, Suri G, Agarwal S, Ram P (2023) AI-based learning content generation and learning pathway augmentation to increase learner engagement. Comput Educ: Artif Intell 4:100110
Epstein Z, Hertzmann A, Herman L, Mahari R, Frank M, Groh M, Schroeder H, Smith A, Akten M, Fjeld J (2023) Art and the science of generative AI: A deeper dive. arxiv 2023. arXiv preprint arXiv:2306.04141
Eisner EW (2003) The arts and the creation of mind. Lang Arts 80(5):340–344
Google Scholar
Eisner EW (2004) What can education learn from the arts about the practice of education? Int J Educ Arts 5(4):1–13
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: Proceedings of the International Conference on Computational Creativity
Ellington AJ (2006) The effects of non-cas graphing calculators on student achievement and attitude levels in mathematics: A meta-analysis. Sch Sci Math 106(1):16–26
Google Scholar
Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Gafni O, Polyak A, Ashual O, Sheynin S, Parikh D, Taigman Y (2022) Make-a-scene: Scene-based text-to-image generation with human priors. In: Proceedings of the European Conference on Computer Vision
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139–144
Google Scholar
Haas C (2013) Writing Technology: Studies on the Materiality of Literacy. Routledge, New York and London
Google Scholar
Hsu Y-C, Ching Y-H (2023) Generative artificial intelligence in education, part one: the dynamic frontier. TechTrends 67(4):603–607
Google Scholar
Ha AYJ, Passananti J, Bhaskar R, Shan S, Southen R, Zheng H, Zhao BY (2024) Organic or diffused: Can we distinguish human art from AI-generated images? arXiv preprint arXiv:2402.03214
Huang R (2019) Educational Technology a Primer for the 21st Century. Springer, Singapore
Google Scholar
Hitsuwari J, Ueda Y, Yun W, Nomura M (2023) Does human–ai collaboration lead to more creative art? aesthetic evaluation of human-made and AI-generated haiku poetry. Comput Hum Behav 139:107502
Google Scholar
Huang Y, Wu W, Wu P, Teng Y (2024) Investigating the impact of integrating prompting strategies in ChatGPT on students learning achievement and cognitive load. In: Proceedings of the International Conference on Innovative Technologies and Learning
Hwang G-J, Xie H, Wah BW, Gašević D (2020) Vision, challenges, roles and research issues of artificial intelligence in education. Comput Educ: Artif Intell 1:100001
Hwang G, Yang L, Wang S (2013) A concept map-embedded educational computer game for improving students’ learning performance in natural science courses. Comput Educ 69:121–130
Google Scholar
Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Jiang HH, Brown L, Cheng J, Khan M, Gupta A, Workman D, Hanna A, Flowers J, Gebru T (2023) AI art and its impact on artists. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
Jeon J, Lee S (2023) Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT. Educ Inf Technol 28(12):15873–15892
Google Scholar
Jia N, Luo X, Fang Z, Liao C (2024) When and how artificial intelligence augments employee creativity. Acad Manag J 67(1):5–32
Google Scholar
Javaheri Pour I, Abbasi E, Kian M, Hasanpour M (2021) A comparative study of primary school visual arts curriculum in Australia, Canada, Iran and Ireland. Iran J Comp Educ 4(2):1097–1116
Jin Y, Yoon J, Andrew Self J, Lee K (2024) AI as a catalyst for creativity: Exploring the use of a generative approach in fashion design for improving their inspiration. In: Proceedings of the DRS Biennial Conference, pp. 1–27
Keller JM (1987) Strategies for stimulating the motivation to learn. Perform Instr 26(8):1–7
Google Scholar
Kumar P, Gupta V, Grover M (2024) Dual attention and channel transformer-based generative adversarial network for restoration of the damaged artwork. Eng Appl Artif Intell 128:107457
Google Scholar
Kwegyiriba A, Mensah RO, Ewusi E (2022) The use of audio-visual materials in teaching and learning process in Effia Junior High Schools. Tech Soc Sci J 31:106
Kasneci E, Seßler K, Küchemann S, Bannert M, Dementieva D, Fischer F, Gasser U, Groh G, Günnemann S, Hüllermeier E et al. (2023) Chatgpt for good? on opportunities and challenges of large language models for education. Learn Individ Differ 103:102274
Google Scholar
Kim J, Yu S, Detrick R, Li N (2025) Exploring students’ perspectives on generative ai-assisted academic writing. Educ Inf Technol 30(1):1265–1300
Google Scholar
Lee U, Han A, Lee J, Lee E, Kim J, Kim H, Lim C (2024) Prompt aloud!: Incorporating image-generative AI into STEAM class with learning analytics using prompt data. Educ Inf Technol 29(8):9575–9605
Google Scholar
Lyu M, Li W, Xie Y (2019) The influences of family background and structural factors on children’s academic performances: A cross-country comparative study. Chin J Sociol 5(2):173–192
Google Scholar
Lawan AA, Muhammad BR, Tahir AM, Yarima KI, Zakari A, Abdullahi II AH, Hussaini A, Kademi HI, Danlami AA, Sani MA et al. (2023) Modified flipped learning as an approach to mitigate the adverse effects of generative artificial intelligence on education. Educ J 12(4):136–43
Lambert C, Philp J, Nakamura S (2017) Learner-generated content and engagement in second language task performance. Lang Teach Res 21(6):665–680
Google Scholar
Luong T-T, Tran M-T (2024) Designing a gai-assisted pedagogical task for teaching negotiation skills: A design thinking approach. In: Proceedings of the International Conference on Advances in Education and Information Technology
Lu Y, Yang X, Li X, Wang XE, Wang WY (2024) Llmscore: Unveiling the power of large language models in text-to-image synthesis evaluation. In: Proceddings of the Advances in Neural Information Processing Systems
Mayer RE (2002) Multimedia learning. In: Psychology of Learning and Motivation vol. 41, pp. 85–139. Elsevier
Mao J, Chen B, Liu JC (2024) Generative artificial intelligence in education and its implications for assessment. TechTrends 68(1):58–66
Google Scholar
Marrone R, Cropley D, Medeiros K (2024) How does narrow AI impact human creativity? Creativity Research Journal (0), 1–11
Mishra P, Koehler MJ (2006) Technological pedagogical content knowledge: A framework for teacher knowledge. Teach Coll Rec 108(6):1017–1054
Google Scholar
Margolis H, McCabe PP (2003) Self-efficacy: A key to improving the motivation of struggling learners. Prev Sch Fail: Altern Educ Child Youth 47(4):162–169
Google Scholar
Midgley C, Maehr ML, Hruda LZ, Anderman E, Anderman L, Freeman KE, Urdan T (2000) Manual for the patterns of adaptive learning scales. Ann Arbor: University of Michigan, 734–763
Mordvintsev A, Olah C, Tyka M (2015) Deepdream-a code example for visualizing neural networks. Google Research 2(5)
Mahajan S, Rahman T, Yi KM, Sigal L (2024) Prompting hard or hardly prompting: Prompt inversion for text-to-image diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Mulian H, Shlomov S, Limonad L, Noccaro A, Buscaglione S (2024) Mimicking the maestro: Exploring the efficacy of a virtual ai teacher in fine motor skill acquisition. In: Proceedings of the AAAI Conference on Artificial Intelligence
Nichol AQ, Dhariwal P, Ramesh A, Shyam P, Mishkin P, Mcgrew B, Sutskever I, Chen M (2022) Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In: Proceedings of the International Conference on Machine Learning (2022)
Ngo TTA (2023) The perception by university students of the use of ChatGPT in education. Int J Emerg Technol Learn 18(17):4
Google Scholar
Noy S, Zhang W (2023) Experimental evidence on the productivity effects of generative artificial intelligence. Science 381(6654):187–192
Google Scholar
Papert SA (2020) Mindstorms: Children, Computers, and Powerful Ideas. Basic Books, New York
Peppler KA (2010) Media arts: Arts education for a digital age. Teach Coll Rec 112(8):2118–2153
Google Scholar
Pavlik JV, Pavlik OM (2024) Art education and generative ai: An exploratory study in constructivist learning and visualization automation for the classroom. Creative Educ 15(4):601–616
Google Scholar
Paas F, Van Merrienboer JJ (2020) Cognitive-load theory: Methods to manage working memory load in the learning of complex tasks. Curr Direct Psychol Sci 29(4):394–398
Google Scholar
Reddy MDM, Basha MSM, Hari MMC, Penchalaiah MN (2021) Dall-e: Creating images from text. UGC Care Group I J 8(14):71–75
Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B (2022) High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Ramesh A, Dhariwal P, Nichol A, Chu C, Chen M (2022) Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 (2022)
Renninger KA, Hidi S (2015) The Power of Interest for Motivation and Engagement. Routledge, New York
Google Scholar
Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J (2021) Learning transferable visual models from natural language supervision. In: Proceedings of the International Conference on Machine Learning
Ramesh A, Pavlov M, Goh G, Gray S, Voss C, Radford A, Chen M, Sutskever I (2021) Zero-shot text-to-image generation. In: Proceedings of the International Conference on Machine Learning
Ruiz-Rojas LI, Acosta-Vargas P, De-Moreta-Llovet J, Gonzalez-Rodriguez M (2023) Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix. Sustainability 15(15):11524
Google Scholar
Ruskov M (2023) Grimm in Wonderland: Prompt engineering with MidJourney to illustrate fairytales. arXiv preprint arXiv:2302.08961
Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, Scales N, Tanwani A, Cole-Lewis H, Pfohl S et al. (2023) Large language models encode clinical knowledge. Nature 620(7972):172–180
Google Scholar
Skjuve M, Følstad A, Brandtzaeg PB (2023) The user experience of ChatGPT: findings from a questionnaire study of early users. In: Proceedings of the International Conference on Conversational User Interfaces
Staddon RV (2020) Bringing technology to the mature classroom: age differences in use and attitudes. Int J Educ Technol High Educ 17(1):11
Google Scholar
Sullivan G (2010) Art Practice as Research: Inquiry in Visual Arts. Sage, California
Sweller J, Van Merriënboer JJ, Paas F (2019) Cognitive architecture and instructional design: 20 years later. Educ Psychol Rev 31:261–292
Google Scholar
Sweller J (1988) Cognitive load during problem solving: Effects on learning. Cogn Sci 12(2):257–285
Google Scholar
Sweller J (2011) Cognitive load theory. Psychol Learn Motiv 55:37–76
Google Scholar
Tan WR, Chan CS, Aguirre HE, Tanaka K (2017) Artgan: Artwork synthesis with conditional categorical GANs. In: Proceedings of the IEEE International Conference on Image Processing
Tyler CW, Likova LT (2012) The role of the visual arts in enhancing the learning process. Front Hum Neurosci 6:8
Google Scholar
Uswatun K, Mariani S et al. (2020) Comparison between generative learning and discovery learning in improving written mathematical communication ability. Int J Instr 13(3):729–744
Vygotsky LS (1978) Mind in Society: The Development of Higher Psychological Processes. Harvard University Press, London
Wang Z, Bergin C, Bergin DA (2014) Measuring engagement in fourth to twelfth grade classrooms: The classroom engagement inventory. Sch Psychol Q 29(4):517
Google Scholar
Wang X, Liu X-Q (2023) Potential and limitations of ChatGPT and generative artificial intelligence in medical safety education. World J Clin Cases 11(32):7935
Google Scholar
Wu Y, Mou Y, Li Z, Xu K (2020) Investigating American and Chinese subjects’ explicit and implicit perceptions of ai-generated artistic work. Comput Hum Behav 104:106186
Google Scholar
Walsh JN, O’Brien MP, Costin Y (2021) Investigating student engagement with intentional content: An exploratory study of instructional videos. Int J Manag Educ 19(2):100505
Yu H, Guo Y (2023) Generative artificial intelligence empowers educational reform: current status, issues, and prospects. Front Educ 8:1183162
Google Scholar
Zhang B (2023) Preparing educators and students for ChatGPT and AI technology in higher education. ResearchGate
Zhou E, Lee D (2024) Generative artificial intelligence, human creativity, and art. PNAS nexus 3(3):052
Google Scholar
Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision
Zylinska J (2020) AI Art: Machine Visions and Warped Dreams. Open Humanities Press, London
link

