31, July 2025

Learning from Machines: A literature review of AI pattern recognition as a reverse framework for Fashion design pedagogy

VOLUME-11; ISSUE-7; JULY-2025

Author(s): Rajiv Sengupta, Dr. Bhakti Bakshi

Authors Affiliations:

1PhD Research Scholar, Jaipur School of Design, JECRC University, Jaipur, Rajasthan, India

2Assistant Professor-I, Jaipur School of Design, JECRC University, Jaipur, Rajasthan, India

DOIs:10.2015/IJIRMF/202507051     |     Paper ID: IJIRMF202507051


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This literature review explores Artificial Intelligence (AI) pattern recognition as a novel teaching-learning model in fashion design education, shifting the traditional perspective by proposing that educators and students learn from machines, rather than solely teaching ‘the machines’. It uniquely shifts focus from AI as merely a design tool to its structured logic and refinement, examining how AI's data analysis and pattern extraction can enhance human learning and creativity. It critically analyses current AI uses, particularly generative AI and machine learning, to reveal how their underlying pattern recognition logic can inform novel pedagogical approaches. Drawing from experiential and situated learning theories, it presents AI’s systematic pattern analysis as a reverse framework for studio-based pedagogy. A dedicated methodology section explains the systematic study selection. By integrating insights from scholarly and technical literature, this review provides a comprehensive understanding of how learning from machines relates to evolving fashion pedagogy, highlighting opportunities, challenges, and future research areas.

Artificial Intelligence, Pattern Recognition, Fashion Design Pedagogy, Design Education, Creative Cognition, Learning Models.

Rajiv Sengupta, Dr. Bhakti Bakshi(2025); Learning from Machines: A literature review of AI pattern recognition as a reverse framework for Fashion design pedagogy, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-7, Pp.342-352.          Available on –   https://www.ijirmf.com/

  1. Anantrasirichai, N., Zhang & Bull (2025): Artificial intelligence in creative industries: Advances prior to 2025. arXiv. Available at: https://arxiv.org/pdf/2501.02725v1
  2. Balasubramanian, M. & Anderson, R. (2025), Generative artificial intelligence in Fashion curriculum accentuates design process, International Textile and Apparel Association Annual Conference Proceedings, 81(1). https://doi.org/10.31274/itaa.18763
  3. Orr, S. & Shreeve, A. (2019). Art and design pedagogy in higher education: Knowledge, values and ambiguity in the creative curriculum. 1st ed. London: Routledge. Available at: https://www.routledge.com/Art-and-Design-Pedagogy-in-Higher-Education-Knowledge-Values-and-Ambiguity-in-the-Creative-Curriculum/Orr-Shreeve/p/book/9780367192501
  4. BabadoÄŸan, M. C. (2025): Mind mapping as a learning tool: A course design example for higher education. Psycho-Educational Research Reviews, 14(1), 1-14. https://doi.org/10.52963/PERR_Biruni_V14.N1.01
  5. Culpepper, M.K. (2013), The telling line: The relationship between cognitive style and Fashion design sketching, Master of Science Thesis, State University of New York, College at Buffalo. Available at: https://digitalcommons.buffalostate.edu/creativetheses/20
  6. Bansal, P. (2025): AI pattern recognition and its features. International Journal of Engineering and Advanced Technology, 14(3), p.18. https://doi.org/10.35940/ijeat.C4562.14030225
  7. Sakovich, N. (2025): Pattern recognition in AI: A comprehensive guide. SaM Solutions [online]. Available at: https://sam-solutions.com/blog/pattern-recognition-in-ai/
  8. Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep learning. Cambridge, MA: The MIT Press. Available at: https://www.deeplearningbook.org
  9. Bommasani, R. et al. (2021): On the opportunities and risks of foundation models. Stanford Institute for Human-Centered Artificial Intelligence. Available at: https://arxiv.org/abs/2108.07258
  10. Russell, S. & Norvig, P. (2010). Artificial intelligence: A modern approach. 3rd ed. Upper Saddle River, NJ: Prentice Hall. Available at: https://aima.cs.berkeley.edu/
  11. Peckham, O. et al. (2025): Artificial intelligence in generative design: A structured review of trends and opportunities in techniques and applications. Designs, 9(4), 79. https://doi.org/10.3390/designs9040079
  12. Kyambade, M., Namatovu, A.N. & Ssentumbwe, A.M. (2025): Exploring the evolution of artificial intelligence in education: From AI-guided learning to learner-personalized paradigms. Cogent Education, 12(1), 2505297. https://doi.org/10.1080/2331186X.2505297
  13. Glickman, M. & Sharot, T. (2024): How human-AI feedback loops alter human perceptual, emotional and social judgements. Nature Human Behaviour, 9(2), 345-359. https://doi.org/10.1038/s41562-024-02077-2
  14. Kolb, D.A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice-Hall. Available at: https://books.google.com/books/about/Experiential_Learning.html?id=zXruAAAAMAAJ
  15. Lave, J. & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511815355
  16. Educatia 21 Journal (2024): Epistemological Foundations of Fashion Pedagogy: Investigating Knowledge and Emerging Issues. Educatia 21 Journal. Available at: https://educatia21.reviste.ubbcluj.ro/data/uploads/article/2024/ed21-no28-art02.pdf
  17. Bishop, C.M. (2006). Pattern recognition and machine learning. New York: Springer. Available at: https://www.microsoft.com/en-us/research/wp-content/uploads/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
  18. Loft, G. (2025): How AI uses feedback loops to learn from its mistakes. Zendesk [online]. Available at: https://www.zendesk.com/in/blog/ai-feedback-loop/
  19. Very Engineering Team (2024): Machine learning vs neural networks: Understanding their differences. Very Technology [online]. Available at: https://www.verytechnology.com/insights/machine-learning-vs-neural-networks
  20. Deisenroth, M.P., Faisal, A.A. & Ong, C.S. (2020). Mathematics for machine learning. Cambridge: Cambridge University Press. Available at: https://www.cambridge.org/9781108470049
  21. Hastie, T., Tibshirani, R. & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. 2nd ed. New York: Springer. Available at: https://books.google.com/books/about/The_Elements_of_Statistical_Learning.html?id=eBSgoAEACAAJ
  22. GeeksforGeeks (2025): Flowchart for basic Machine Learning models. GeeksforGeeks. Available at: https://www.geeksforgeeks.org/machine-learning/flowchart-for-basic-machine-learning-models/
  23. Bransford, J.D., Brown, A.L. & Cocking, R.R. (2000). Learning and Transfer. In: how people learn: brain, mind, experience, and school (Expanded ed.) (pp. 63-84). Washington, DC: National Academy Press. Available at: https://nap.nationalacademies.org/read/10067/chapter/7
  24. Masný, A. (2024): Learning Styles: VARK, Kolb, Honey and Mumford Comparison. Available at: https://www.academia.edu/118663127
  25. Lave, J. (2008): Situating learning in communities of practice. In: Ecology of Design [online]. Available at: https://ecologyofdesigninhumansystems.com
  26. Schön, D.A. (1987). Educating the reflective practitioner: Toward a new design for teaching and learning in the professions. San Francisco, CA: Jossey-Bass. Available at: https://web.eecs.umich.edu/~mjguz/csl/home.cc.gatech.edu/allison/uploads/4/schon1987c.pdf
  27. school (2010): An Introduction to Design Thinking PROCESS GUIDE. Hasso Plattner Institute of Design at Stanford. Available at: https://s3-eu-west-1.amazonaws.com/ih-materials/uploads/Introduction-to-design-thinking.pdf
  28. Jeon, Y., Jin, S., Shih, P.C. & Han, K. (2021), FashionQ: An AI-driven creativity support tool for facilitating ideation in Fashion design, In CHI Conference on Human Factors in Computing Systems. [Online] Available at: https://www.researchgate.net/publication/345324055
  29. Selkee, C. (2025), AI-driven innovation in Fashion: Enhancing the design process through technology, University of Lapland Thesis. [Online] Available at: https://lauda.ulapland.fi/bitstream/handle/10024/66758/Selkee_Charlotta.pdf
  30. Samaniego, M., Usca, N., Salguero, J. & Quevedo, W. (2024): Creative thinking in art and design education: A systematic review. Education Sciences, 14(2), 192. DOI: 3390/educsci14020192
  31. Kim, M., Joo & Han, K. (2025): Understanding the differences in an AI-based creativity support tool between creativity types in Fashion design. International Journal of Human-Computer Interaction. DOI: 1080/10447318.2024.2448484
  32. Jin, Y., Yoon, J., Self, J. & Lee, K. (2024): Understanding Fashion designers’ behavior using generative AI for early-stage concept ideation and revision. Archives of Design Research. [Online] Available at: https://www.researchgate.net/publication/382942142
  33. Lee, Y.K. (2022): How complex systems get engaged in Fashion design creation: Using artificial intelligence. Thinking Skills and Creativity, 44, 101005. DOI: 1016/j.tsc.2022.101005
  34. Kim, S.J. (2024): Generative artificial intelligence in collaborative ideation: Educational insight from Fashion students. IEEE Access. DOI:1109/ACCESS.2024.3382194
  35. Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York: Basic Books. Available at: https://books.google.com/books/about/Frames_of_Mind.html?id=ObgOAAAAQAAJ
  36. Kaelbling, L.P., Littman, M.L. & Moore, A.W. (1996): Reinforcement learning: A survey. Journal of Artificial Intelligence Research, MIT Press. https://doi.org/10.48550/arXiv.cs/9605103
  37. von Luxburg, U. & Schölkopf, B. (2008): Statistical learning theory: Models, concepts, and results. Springer. https://doi.org/10.48550/arXiv.0810.4752
  38. Bengio, Y., Courville, A. & Vincent, P. (2012): Representation learning: A review and new perspectives. Foundations and Trends in Machine Learning. https://doi.org/10.48550/arXiv.1206.5538
  39. Happer, C. (2025): Creating feedback loops between human experts and AI systems. Available at: https://www.researchgate.net/publication/391398367_CREATING_FEEDBACK_LOOPS_BETWEEN_HUMAN_EXPERTS_AND_AI_SYSTEMS
  40. Cunningham, C.V., Radvansky & Brockmole, J.R. (2025): Human creativity versus artificial intelligence: source attribution, observer attitudes, and eye movements while viewing visual art. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1509974
  41. Ok, E. (2025): Comparing Human Creativity and AI-Generated Image Design. Available at: https://www.researchgate.net/publication/388849201_Comparing_Human_Creativity_and_AI-_Generated_Image_Design
  42. Mohri, M., Rostamizadeh, A. & Talwalkar, A. (2012). Foundations of machine learning. Cambridge, MA: MIT Press. Available at: https://mitpress.mit.edu/9780262039406
  43. James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. New York: Springer. Available at: https://www.stat.berkeley.edu/~rabbee/s154/ISLR_First_Printing.pdf
  44. Prince, S. (2024). Understanding deep learning. Cambridge, MA: MIT Press. Available at: https://anthology-of-data.science/resources/prince2023udl.pdf
  45. Md Khairulanwar, A.B. & Jamaluddin, K.A. (2025): Artificial Intelligence in Fashion Design Education: A Phenomenological Exploration of Certificate-Level Learning. International Journal of Academic Research in Business and Social Sciences, 15(3), 313–331. DOI: 6007/IJARBSS/v15-i3/24604
  46. Gkintoni, E., Antonopoulou, H., Sortwell, A. & Halkiopoulos, C. (2025): Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain Sciences, 15, 203. https://doi.org/10.3390/brainsci15020203
  47. Sweller, J. (1988): Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. Available at: https://mrbartonmaths.com/resourcesnew/8.%20Research/Explicit%20Instruction/Cognitive%20Load%20during%20problem%20solving.pdf
  48. Oakley, B. (2014): Learning how to learn. [online course and book]. Available at: https://www.coursera.org/learn/learning-how-to-learn
  49. Marku, E. (2023): AI-Artificial Intelligence and the growth of the creative potential of designers in the Fashion industry. Forum A+P. [Online] Available at: https://www.researchgate.net/publication/379573623
  50. Zhang, J. (2024): Artificial intelligence vs. human intelligence: Which excels where and what will never be matched. UTHealth Houston McWilliams School of Biomedical Informatics [online]. Available at: https://sbmi.uth.edu/blog/2024/artificial-intelligence-versus-human-intelligence.htm
  51. JITE (2015): A Systematic Review of Machine Learning Techniques for Predicting Student Engagement in Higher Education Online Learning. Journal of Information Technology Education: Research, 24, 1-20. Available at: https://www.jite.org/documents/Vol24/JITE-Rv24Art005Chong10955.pdf
  52. Lombrozo, T. (2024): Learning by thinking in natural and artificial minds. Trends in Cognitive Sciences, 28(11). https://doi.org/10.1016/j.tics.2024.07.007
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