Prompt Aloud!: Incorporating image-generative AI into STEAM class with learning analytics using prompt data
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?
Using AI to generate images and write inspired diaries reduced gender disparities in interest in art during STEAM classes.
“For instance, students can create imaginative visuals with AI and write inspired diaries based on these images, a practice found to reduce gender disparities in interest in art during Science, Technology, Engineering, the Arts and Mathematics (STEAM) classes [28].”
From Promises and challenges of generative artificial intelligence for human learning (2024) · cites this paper as [28]
Exploratory work such as Lee and colleagues' 'Prompt aloud!' has yet to conduct a prolonged engagement of learners on a more qualitative footing.
“Further, exploratory work such as Lee and colleagues' 'Prompt aloud!' [54] has yet to conduct a prolonged engagement of learners on a more qualitative footing.”
From Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Study (2024) · cites this paper as [54]
In-depth and prolonged hands-on engagement with generative AI technologies is needed to really pursue the development of competencies.
“Specifically, the instantiation of our exploratory study phases (see Figure 1) show that, as highlighted in other work, indepth and prolonged hands-on engagement with generative AI technologies is needed to really pursue the development of competencies.”
From Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Study (2024) · cites this paper as [54]
Factors of time and exploration are yet to be explored in emerging approaches to generative AI education, such as Lee et al.'s 'Prompt aloud!' project lasting a single 90 minute lesson.
“which are factors yet to be explored in the emerging approaches to generative AI education (e.g., the 'Prompt aloud!' project by Lee and colleagues [54] lasting for a single 90 minute lesson).”
From Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Study (2024) · cites this paper as [54]
Cites
(2017) · Cited by 16,036 · see citing works
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N., & Gurevych, I. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1410 · Cited by 12,778 · see citing works
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712 · see citing works
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 1× · see citing works
“…in artificial intelligence (AI) have revolutionized various domains, including education, by providing opportunities to enrich the learning experiences and foster creativity, critical thinking, and motivation among students (Baidoo-Anu & Owusu Ansah, 2023; Kasneci et al., 2023; Qadir, 2022).”
The Nature of Human Intelligence
Carroll, J. B., & Guilford, J. P. (1968) · American Educational Research Journal · doi:10.2307/1161820 · Cited by 4,657 · see citing works
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · cited 2× · see citing works
“When students use image-generative AI, language proficiency is important to write appropriate prompts for generating images that the user wants (Liu et al., 2023; Wang et al., 2023).”
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · cited 2× · see citing works
“When students use image-generative AI, language proficiency is important to write appropriate prompts for generating images that the user wants (Liu et al., 2023; Wang et al., 2023).”
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004 · see citing works
Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence
Cooper, G. (2023) · Journal of Science Education and Technology · doi:10.1007/s10956-023-10039-y · Cited by 1,263 · cited 1× · see citing works
“For example, Cooper (2023) and Pavlik (2023) discuss the implications of using ChatGPT in science education and journalism and media education, respectively.”
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
Pavlik, J. V. (2023) · Journalism & Mass Communication Educator · doi:10.1177/10776958221149577 · Cited by 972 · cited 1× · see citing works
“For example, Cooper (2023) and Pavlik (2023) discuss the implications of using ChatGPT in science education and journalism and media education, respectively.”
ChatGPT User Experience: Implications for Education
Zhai, X. (2023) · doi:10.2139/ssrn.4312418 · Cited by 935 · cited 1× · see citing works
“Zhai (2022) also highlighted that students should be able to use AI tools to conduct subject-specific tasks, and education should focus on improving students’ creativity and critical thinking.”
AI and education: guidance for policy-makers
UNESCO (2021) · doi:10.54675/pcsp7350 · Cited by 929 · see citing works
Can We Trust Creativity Tests? A Review of the Torrance Tests of Creative Thinking (TTCT)
Kim, K. H. (2006) · Creativity Research Journal · doi:10.1207/s15326934crj1801_2 · Cited by 830 · cited 1× · see citing works
“Previous research used test-based assessment tools, such as TTCT, to estimate divergent and convergent thinking (Kim, 2006, 2011; Trisnayanti et al., 2019).”
Meece, J. L., Glienke, B. B., & Burg, S. (2006) · Journal of School Psychology · doi:10.1016/j.jsp.2006.04.004 · Cited by 805 · see citing works
What Supercomputers Say: A Study of Five System Logs
Oliner, A., & Stearley, J. (2007) · Dependable Systems and Networks · doi:10.1109/dsn.2007.103 · Cited by 721 · see citing works
Design Guidelines for Prompt Engineering Text-to-Image Generative Models
Liu, V., & Chilton, L. B. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501825 · Cited by 639 · cited 1× · see citing works
“This was inspired by the previous study (Liu & Chilton, 2022), which points out the challenge of Prompt Engineering (Liu et al., 2023)—it is challenging even for adult users to input prompts appropriate for generating images as a user intended.”
AI bot ChatGPT writes smart essays — should professors worry?
Stokel-Walker, C. (2022) · Nature · doi:10.1038/d41586-022-04397-7 · Cited by 543 · see citing works
STEM vs. STEAM Education and Student Creativity: A Systematic Literature Review
Aguilera, D., & Ortiz-Revilla, J. (2021) · Education Sciences · doi:10.3390/educsci11070331 · Cited by 455 · see citing works
Full STEAM Ahead: The Benefits of Integrating the Arts Into STEM
Land, M. H. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.09.317 · Cited by 398 · see citing works
Generative Artificial Intelligence: Trends and Prospects
Jovanovic, M., & Campbell, M. (2022) · Computer · doi:10.1109/mc.2022.3192720 · Cited by 357 · see citing works
Distance Weighted Cosine Similarity Measure for Text Classification
Li, B., & Han, L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41278-3_74 · Cited by 332 · see citing works
Lexical Diversity in Writing and Speaking Task Performances
Yu, G. (2009) · Applied Linguistics · doi:10.1093/applin/amp024 · Cited by 324 · see citing works
Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education
Qadir, J. (2022) · doi:10.36227/techrxiv.21789434.v1 · Cited by 264 · see citing works
Using artificial intelligence in craft education: crafting with text-to-image generative models
Vartiainen, H., & Tedre, M. (2023) · Digital Creativity · doi:10.1080/14626268.2023.2174557 · Cited by 263 · see citing works
Creative Problem Solving: Overview and educational implications
Treffinger, D. J. (1995) · Educational Psychology Review · doi:10.1007/bf02213375 · Cited by 237 · see citing works
Implementing STEAM in the Early Childhood Classroom
DeJarnette, N. K. (2018) · European Journal of STEM Education · doi:10.20897/ejsteme/3878 · Cited by 232 · see citing works
What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education
Dehouche, N., & Dehouche, K. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16757 · Cited by 217 · see citing works
The influence of self-efficacy and metacognitive prompting on math problem-solving efficiency
Hoffman, B. H., & Spatariu, A. (2007) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2007.07.002 · Cited by 198 · see citing works
Chatgpt for next generation science learning
Zhai, X. (2023) · XRDS Crossroads The ACM Magazine for Students · doi:10.1145/3589649 · Cited by 178 · cited 1× · see citing works
“In another research work, Zhai (2023) reported the potential of ChatGPT in education and discussed ways to adapt to its infiltration in pedagogical environments.”
A Guide to Qualitative Field Research
(2018) · doi:10.4135/9781071909614 · Cited by 164 · see citing works
Maeda, J. (2013) · STEAM · doi:10.5642/steam.201301.34 · Cited by 157 · see citing works
Educing AI-thinking in science, technology, engineering, arts, and mathematics (STEAM) education
How, M.-L., & Hung, W. L. D. (2019) · Education Sciences · doi:10.3390/educsci9030184 · Cited by 135 · see citing works
Understanding User Behavior Through Log Data and Analysis
Dumais, S., Jeffries, R., Russell, D. M., Tang, D., & Teevan, J. (2014) · Ways of Knowing in HCI · doi:10.1007/978-1-4939-0378-8_14 · Cited by 131 · see citing works
Kauffman, D. F., Ge, X., Xie, K., & Chen, C. (2008) · Journal of Educational Computing Research · doi:10.2190/ec.38.2.a · Cited by 121 · see citing works
RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions
Wang, Y., Shen, S., & Lim, B. Y. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581402 · Cited by 120 · see citing works
Multimodal Learning Analytics research with young children: A systematic review
Lanna, L. C. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.12959 · Cited by 118 · see citing works
The Handbook of Learning Analytics
Gašević, D., & Merceron, A. (2022) · Solar eBooks · doi:10.18608/hla22 · Cited by 114 · see citing works
Children's divergent thinking and creative ideation
Runco, M. A. (1992) · Developmental Review · doi:10.1016/0273-2297(92)90010-y · Cited by 101 · see citing works
Proven reliability and validity of the Torrance Tests of Creative Thinking (TTCT).
Kim, K. H. (2011) · Psychology of Aesthetics Creativity and the Arts · doi:10.1037/a0021916 · Cited by 69 · see citing works
Promoting science learning in game-based learning with question prompts and feedback
Law, V., & Chen, C. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.10.005 · Cited by 62 · see citing works
Wu, S. P. W., & Rau, M. A. (2017) · Learning and Instruction · doi:10.1016/j.learninstruc.2017.09.010 · Cited by 61 · see citing works
Using Simultaneous Prompting to Teach Computer-based Story Writing to a Student with Autism
Pennington, R. C., Stenhoff, D. M., Gibson, J. L., & Ballou, K. (2012) · Education and Treatment of Children · doi:10.1353/etc.2012.0022 · Cited by 54 · see citing works
What are GANs?: Introducing Generative Adversarial Networks to Middle School Students
Ali, S., DiPaola, D., & Breazeal, C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i17.17821 · Cited by 46 · see citing works
Leveraging metacognitive prompts in construction educational games for higher educational gains
Castronovo, F., Meter, P. N. V., & Messner, J. (2018) · International Journal of Construction Management · doi:10.1080/15623599.2018.1492760 · Cited by 45 · see citing works
Development of STEM-Based AI Education Program for Sustainable Improvement of Elementary Learners
Jang, J.-H., Jeon, J., & Jung, S. K. (2022) · Sustainability · doi:10.3390/su142215178 · Cited by 45 · see citing works
Creativity and the Big Five Personality Traits
Hornberg, J., & Reiter‐Palmon, R. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316228036.015 · Cited by 40 · see citing works
Chapter 3. Measuring lexical diversity among L2 learners of French
Treffers‐Daller, J. (2013) · Studies in bilingualism · doi:10.1075/sibil.47.05ch3 · Cited by 36 · see citing works
Drachsler, H., & Goldhammer, F. (2020) · Lecture notes in educational technology · doi:10.1007/978-981-15-4526-9_5 · Cited by 24 · see citing works
Prompting Reflections for Integrating Self-Regulation into Teacher Technology Education
Michalsky, T., & Kramarski, B. (2015) · Teachers College Record The Voice of Scholarship in Education · doi:10.1177/016146811511700507 · Cited by 24 · see citing works
Yang, K.-H., Chu, H., & Chiang, L.-Y. (2018) · Educational Technology & Society · Cited by 21 · see citing works
Development of Torrance test creativity thinking (TTCT) instrument in science learning
Trisnayanti, Y., Khoiri, A., Miterianifa, M., & Ayu, H. D. (2019) · AIP conference proceedings · doi:10.1063/1.5139861 · Cited by 16 · see citing works
A Systematic Review of Simultaneous Prompting and Prompt Delay Procedures
Brown, A., & Cariveau, T. (2022) · Journal of Behavioral Education · doi:10.1007/s10864-022-09481-6 · Cited by 12 · see citing works
Art education – mostly for girls?
Wikberg, S. (2013) · Education Inquiry · doi:10.3402/edui.v4i3.22630 · Cited by 11 · see citing works
A Systematic Review of Tactile Prompting in Teacher Education
Markelz, A. M., Scheeler, M. C., Riccomini, P. J., & Taylor, J. (2019) · Teacher Education and Special Education The Journal of the Teacher Education Division of the Council for Exceptional Children · doi:10.1177/0888406419877500 · Cited by 9 · see citing works
Savoie, A. (2009) · International Journal of Art & Design Education · doi:10.1111/j.1476-8070.2009.01590.x · Cited by 9 · see citing works
A Systematic Review of Training Educators to Implement Response Prompting
Shawbitz, K. N., & Brock, M. E. (2022) · Teacher Education and Special Education The Journal of the Teacher Education Division of the Council for Exceptional Children · doi:10.1177/08884064221114130 · Cited by 8 · see citing works
Review on Artificial Intelligence and Robots in STEAM Education for Early Childhood Development
Mohana, M., Nandhini, K., & Subashini, P. (2022) · Advances in early childhood and K-12 education · doi:10.4018/978-1-7998-8649-5.ch019 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
“For instance, students can create imaginative visuals with AI and write inspired diaries based on these images—a practice found to reduce gender disparities in interest in art during science, technology, engi neering, the arts and mathematics classes 28 .”
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
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
Liu, M., Zhang, L. J., & Zhang, D. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108569 · Cited by 43 · see citing works
Wang, L., & Ren, B. (2024) · Education Sciences · doi:10.3390/educsci14121329 · Cited by 38 · see citing works
Using ChatGPT for medical education: the technical perspective
Chan, K. Y., Yuen, T. H., & Co, M. (2025) · BMC Medical Education · doi:10.1186/s12909-025-06785-9 · Cited by 34 · see citing works
Benjamin, J. J., Lindley, J., Edwards, E., Rubegni, E., Korjakow, T., Grist, D., & Sharkey, R. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660677 · Cited by 32 · see citing works
Bian, C., Wang, X., Huang, Y., Zhou, S., & Lu, W. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05860-2 · Cited by 30 · see citing works
- Authors
- Lee, U., Han, A., Lee, J., Lee, E., Kim, J., Kim, H., & Lim, C.
- Affiliations
- Korea University (KR)
- University of California, Irvine (US)
- Seoul National University (KR)
- Corresponding author
- Kim, Hyeoncheol
- Lim, Cheolil
- Venue
- Education and Information Technologies
- Published
- 2023-09-18
- Volume
- 29
- Issue
- 8
- Pages
- 9575-9605
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4386836433
- Cited by
- 107
- References
- 58
- Citations by year
- 2026: 34 · 2025: 56 · 2024: 17
- FWCI
- 19.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Education and Information Technologies (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art Education and Development
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
- Keywords
- Stable Diffusion, STEAM education, art education, generative AI in education, learning analytics, divergent thinking, convergent thinking, creative thinking, student diaries, student perceptions of AI
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
