HELLENIC JOURNAL OF STEM EDUCATION
Research Article
Apr 09, 2026 6 3

The Interactive Design Process Framework (IDPF): Utilizing GenAI as a Collaborative Agent for Creating STEAM Projects

Dimitrios Sotiropoulos 1 * ,
Apostolis Xenakis 1,
Michail Kalogiannakis 1,
Mehmet Fatih Taşar 2
1 University of Thessaly2 Georgia State University* Corresponding Author
Hellenic Journal of STEM Education, 5(1), 2026, 1-18, https://doi.org/10.51724/hjstemed.v5i1.76
Publication date: Apr 09, 2026
Full Text (PDF)

ABSTRACT

Increasingly, teachers are considering using Generative AI (GenAI) and large language models (LLMs) as new tools for developing lessons and creating resources. Interdisciplinary STEAM project design specifically continues to be problematic because it requires many professionals to work together in integrative ways when developing successful STEAM project designs. Collaborating closely to develop authentic problems, establish a high level of pedagogical quality, use principles of inclusive design, and create valid and reliable assessment instruments often exceeds the individual teacher’s professional expertise and available time. The purpose of this conceptual paper is to lay the foundation of what we call “the Interactive Design Process Framework (IDPF)” and propose its use in instruction. IDPF has four steps: Co-design, Analysis, Expert Review, and Synthesis. Using these four iterative (cyclical) steps, the IDPF framework positions GenAI as a collaborative designer to work with both classroom teachers and subject area expert reviewers. As a conceptual contribution, this paper does not present empirical data; rather, it articulates the theoretical rationale, structural logic, and design principles of the framework as a foundation for future empirical investigation. In addition to a primary education example of illustration, implications for practice, limitations of the study, and recommendations for future research are discussed.

KEYWORDS

generative artificial intelligence STEAM education human-AI interaction TPACK universal design for learning engineering design process collaborative project design

CITATION (APA)

Sotiropoulos, D., Xenakis, A., Kalogiannakis, M., & Taşar, M. F. (2026). The Interactive Design Process Framework (IDPF): Utilizing GenAI as a Collaborative Agent for Creating STEAM Projects. Hellenic Journal of STEM Education, 5(1), 1-18. https://doi.org/10.51724/hjstemed.v5i1.76
Harvard
Sotiropoulos, D., Xenakis, A., Kalogiannakis, M., and Taşar, M. F. (2026). The Interactive Design Process Framework (IDPF): Utilizing GenAI as a Collaborative Agent for Creating STEAM Projects. Hellenic Journal of STEM Education, 5(1), pp. 1-18. https://doi.org/10.51724/hjstemed.v5i1.76
Vancouver
Sotiropoulos D, Xenakis A, Kalogiannakis M, Taşar MF. The Interactive Design Process Framework (IDPF): Utilizing GenAI as a Collaborative Agent for Creating STEAM Projects. Hellenic Journal of STEM Education. 2026;5(1):1-18. https://doi.org/10.51724/hjstemed.v5i1.76
AMA
Sotiropoulos D, Xenakis A, Kalogiannakis M, Taşar MF. The Interactive Design Process Framework (IDPF): Utilizing GenAI as a Collaborative Agent for Creating STEAM Projects. Hellenic Journal of STEM Education. 2026;5(1), 1-18. https://doi.org/10.51724/hjstemed.v5i1.76
Chicago
Sotiropoulos, Dimitrios, Apostolis Xenakis, Michail Kalogiannakis, and Mehmet Fatih Taşar. "The Interactive Design Process Framework (IDPF): Utilizing GenAI as a Collaborative Agent for Creating STEAM Projects". Hellenic Journal of STEM Education 2026 5 no. 1 (2026): 1-18. https://doi.org/10.51724/hjstemed.v5i1.76
MLA
Sotiropoulos, Dimitrios et al. "The Interactive Design Process Framework (IDPF): Utilizing GenAI as a Collaborative Agent for Creating STEAM Projects". Hellenic Journal of STEM Education, vol. 5, no. 1, 2026, pp. 1-18. https://doi.org/10.51724/hjstemed.v5i1.76

REFERENCES

  1. Achieve, Inc. (2016). EQuIP rubric for science: Version 3.0. Next Generation Science Standards. https://www.nextgenscience.org/resources/equip-rubric-science
  2. Abdulmajid, M., Alrashaidi, E., Alayar, M., & Alrashidi, N. (2025). AI as a pedagogical partner: Enhancing art history education and quality assurance through AI-generated critiques. Quality Assurance in Education. https://doi.org/10.1108/QAE-03-2025-0074
  3. Anderson, J., & Tully, D. (2024). STEM teacher collaboration in curriculum design. In R. Tytler et al. (Eds.), Integrated STEM curriculum design. Springer.
  4. Anh-Hoang, T., Tran, T., & Nguyen, N. (2025). Survey and analysis of hallucinations in large language models. Frontiers in Artificial Intelligence, 8, 1622292. https://doi.org/10.3389/frai.2025.1622292
  5. Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. University of Rhode Island. https://digitalcommons.uri.edu/cba_facpubs/548
  6. Bequette, J. W., & Bequette, M. B. (2012). A place for art and design education in the STEM conversation. Art Education, 65(2), 40-47. https://doi.org/10.1080/00043125.2012.11519167
  7. Blonder, R., & Feldman-Maggor, Y. (2024). AI for chemistry teaching: Responsible AI and ethical considerations. Chemistry Teacher International, 6(4), 385-395. https://doi.org/10.1515/cti-2024-0040
  8. Boice, K. L., Alemdar, M., Jackson, J. R., et al. (2024). Exploring teachers' understanding and implementation of STEAM: One size does not fit all. Frontiers in Education, 9, 1401191. https://doi.org/10.3389/feduc.2024.1401191
  9. Bryan, L., & Guzey, S. S. (2020). K-12 STEM Education: An Overview of Perspectives and Considerations. Hellenic Journal of STEM Education, 1(1), 5-15. https://doi.org/10.51724/hjstemed.v1i1.5
  10. Carlson, J., Daehler, K. R., Alonzo, A. C., et al. (2019). The refined consensus model of pedagogical content knowledge in science education. In A. Hume, R. Cooper, & A. Borowski (Eds.), Repositioning pedagogical content knowledge in teachers' knowledge for teaching science (pp. 77-94). Springer. https://doi.org/10.1007/978-981-13-5898-2_2
  11. CAST (2024). Universal Design for Learning Guidelines version 3.0. https://udlguidelines.cast.org
  12. Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers' professional knowledge to ethically integrate AI-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
  13. Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66, 616-630. https://doi.org/10.1007/s11528-022-00715-y
  14. Chaitidou, M., Spyrtou, A., Kariotoglou, P., & Dimitriadou, C. (2018). Professional development in inquiry-oriented pedagogical content knowledge among primary school teachers. The International Journal of Science, Mathematics and Technology Learning, 25(2), 17-36.
  15. Chappell, K., Hetherington, L., Juillard, S., Aguirre, C., & Duca, E. (2025). A framework for effective STEAM education: Pedagogy for responding to wicked problems. International Journal of Educational Research Open, 9, https://doi.org/10.1016/j.ijedro.2025.100474
  16. Chatzopoulos, A., Xenakis, A., Papoutsidakis, M., Kalovrektis, K., Kalogiannakis, M., & Psycharis, S. (2024). Proposing and testing an open-source and low-cost drone under the engineering design process for higher education. In 2024 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-7). IEEE. https://doi.org/10.1109/EDUCON60312.2024.10578677
  17. Clark, C. H., & van Kessel, C. (2024). “I, for one, welcome our new computer overlords”: Using artificial intelligence as a lesson planning resource for social studies. Contemporary Issues in Technology and Teacher Education, 24(2), 151-183. https://citejournal.org/wp-content/uploads/2024/05/v24i2SocialStudies1.pdf
  18. Cohen, N., & Bronshtein, O. (2025). Bridging disciplines: Exploring interdisciplinary curriculum development in STEM teacher education. Education Sciences, 15(8), 1064. https://doi.org/10.3390/educsci15081064
  19. Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444-452. https://doi.org/10.1007/s10956-023-10039-y
  20. Education Endowment Foundation (EEF). (2024). Using Generative AI (ChatGPT) for KS3 Science Lesson Preparation: Teacher Choices Trial. Education Endowment Foundation. https://educationendowmentfoundation.org.uk/projects-and-evaluation/projects/choices-in-edtech-using-generative-ai -chatgpt-for-ks3-science-lesson-preparation-2024-teacher-choices-trial
  21. European University Association (EUA). (2025). Assuring higher education’s quality in the age of AI is no easy task. https://www.eua.eu/our-work/expert-voices/
  22. Feldman-Maggor, Y., Blonder, R., & Alexandron, G. (2025). Perspectives of generative AI in chemistry education within the TPACK framework. Journal of Science Education and Technology, 34, 1-12. https://doi.org/10.1007/s10956-024-10148-2
  23. Google. (2026). Do you agree with this claim: The fundamental problem is that GenAI does not have Pedagogical Content Knowledge (PCK), or the integration of content knowledge and pedagogical knowledge, necessary for teachers to determine whether a representation, explanation, or activity will be effective for a certain group of students (Lee Shulman, 1986 (Attached. It defines what PCK is)). Can you create PCK?[Large language model]. https://gemini.google.com/share/1ef6d2a2cd4f
  24. Gess-Newsome, J. (2015). A model of teacher professional knowledge and skill including PCK. In A. Berry, P. Friedrichsen, & J. Loughran (Eds.), Reexamining pedagogical content knowledge in science education (pp. 28-42). Routledge.
  25. Gözüm, A. İ. C., Papadakis, S., & Kalogiannakis, M. (2022). Preschool teachers' STEM Pedagogical Content Knowledge: A comparative study of teachers in Greece and Turkey. Frontiers in Psychology, 13, 996338. https://doi.org/10.3389/fpsyg.2022.996338
  26. Großmann, L., Koberstein-Schwarz, M., Krüger, D., & Krell, M. (2025). Lesson planning with ChatGPT for inquiry-based biology instruction – A(I) roll of the dice? International Journal of Science Education. https://doi.org/10.1080/09500693.2025.2567509
  27. Hamilton, E. R., Rosenberg, J. M., & Akcaoglu, M. (2016). The Substitution Augmentation Modification Redefinition (SAMR) Model: A critical review and suggestions for its use. TechTrends, 60(5), 433–441. https://doi.org/10.1007/s11528-016-0091-y
  28. Henriksen, D. (2014). Full STEAM ahead: Creativity in excellent STEM teaching practices. The STEAM Journal, 1(2), Article 15. https://doi.org/10.5642/steam.20140102.15
  29. Herro, D., Quigley, C., & Cian, H. (2019). The challenges of STEAM instruction: Lessons from the field. Action in Teacher Education, 41(2), 172-190. https://doi.org/10.1080/01626620.2018.1551159
  30. Kalogiannakis, M., & Sotiropoulos, D. (Eds.). (2026a). Harnessing Generative Artificial Intelligence for Science Education. IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-2913-0
  31. Kalogiannakis, M., & Sotiropoulos, D. (Eds.). (2026b). Generative AI-Enhanced Science Education From Early Childhood to Higher Education. IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-9074-1
  32. Kaplan-Rakowski, R., et al. (2023). Generative AI and teachers' perspectives. In Proceedings of AACE EdMedia.
  33. Karpouzis, K., Pantazatos, D., Taouki, J., & Meli, K. (2024). Tailoring education with GenAI: A new horizon in lesson planning. In 2024 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-10). IEEE. https://doi.org/10.1109/EDUCON60312.2024.10578690
  34. Kasneci, E., Sessler, K., et al. (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
  35. Kim, S., & Martin, S. N. (2024). Developing an evaluation rubric for planning and assessing SSI-based STEAM programs. Research in Science Education, 54, 457-482. https://doi.org/10.1007/s11165-023-10123-8
  36. Land, M. H. (2013). Full STEAM ahead: The benefits of integrating the arts into STEM. Procedia Computer Science, 20, 547-552.
  37. Li, J. (2024). Effective strategies for interdisciplinary integration in STEAM curriculum design. Transactions on Social Science, Education and Humanities Research, 8, 99-105. https://doi.org/10.62051/gvesha87
  38. Lin, C.-L., & Tsai, C.-Y. (2025). LUPDA: A comprehensive rubrics-based assessment model for STEAM education. International Journal of STEM Education, 12, Article 570. https://doi.org/10.1186/s40594-025-00570-2
  39. Liu, D., & Bates, T. (2025). A collaborative model for integrating teacher and GenAI into future education. TechTrends. https://doi.org/10.1007/s11528-025-01105-w
  40. Mishra, P., & Koehler, M. J. (2006). Technological Pedagogical Content Knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
  41. Mehddi, F., Kazi, A. S., & Butt, A. I. (2025). From theory to practice: How STEAM professional development shapes teacher beliefs and perceptions about design thinking activities. SAGE Open, 15(1). https://doi.org/10.1177/21582440251355779
  42. Mishra, P., Warr, M., & Islam, R. (2023). TPACK in the age of ChatGPT and Generative AI. Journal of Digital Learning in Teacher Education, 39(4), 235-251. https://doi.org/10.1080/21532974.2023.2247480
  43. Mollick, E. R., & Mollick, L. (2024). Instructors as Innovators: A Future-focused Approach to New AI Learning Opportunities, With Prompts. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4802463
  44. Moundridou, M., Matzakos, N., & Doukakis, S. (2024). Generative AI tools as educators' assistants: Designing and implementing inquiry-based lesson plans. Computers and Education: Artificial Intelligence, 7, 100277. https://doi.org/10.1016/j.caeai.2024.100277
  45. National Research Council. (2012). A framework for K-12 science education. National Academies Press. https://doi.org/10.17226/13165
  46. Nyaaba, M., Zhai, X., & Aydin, H. (2025). Developing a theory-grounded AI tool for the generation of culturally responsive lesson plans. Computers and Education: Artificial Intelligence, 8, 100346. https://doi.org/10.1016/j.caeai.2025.100346
  47. NGSS Lead States. (2013). Next Generation Science Standards. The National Academies Press. https://www.nextgenscience.org
  48. OECD. (2026). Digital Education Outlook 2026: Generative AI and Education. OECD Publishing.
  49. Otto, C. A., & Everett, S. A. (2013). An instructional strategy to introduce pedagogical content knowledge using Venn diagrams. Journal of Science Teacher Education, 24(2), 391-403. https://doi.org/10.1007/s10972-011-9275-3
  50. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.
  51. Peikos, G., & Stavrou, D. (2025). ChatGPT for science lesson planning: An exploratory study based on pedagogical content knowledge. Education Sciences, 15(3), 338. https://doi.org/10.3390/educsci15030338
  52. Penuel, W. R., Fishman, B. J., Yamaguchi, R., & Gallagher, L. P. (2007). What makes professional development effective? American Educational Research Journal, 44(4), 921-958. https://doi.org/10.3102/0002831207308221
  53. Psycharis, S., Iatrou, P., Kalovrektis, K., & Xenakis, A. (2023). The impact of the computational pedagogy STEAM model. In Lecture Notes in Networks and Systems (pp. 400-411). https://doi.org/10.1007/978-3-031-26190-9_41
  54. Puentedura, R. R. (2006). Transformation, technology, and education. http://hippasus.com/resources/tte/
  55. Sakamoto, Y., et al. (2024). Cross-cultural evaluation of AI-generated math lesson plans. In Proceedings of AIED 2024.
  56. Saseendran, A., & Thomas, M. V. (2025). Design thinking in science and integrated STEM/STEAM education: A systematic review. STEM Education, 5(6), 1058-1101. https://doi.org/10.3934/steme.2025046
  57. Shulman, L. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4-14. https://doi.org/10.3102/0013189X015002004
  58. Sotiropoulos, D., & Kalogiannakis, M. (2025). Using GenAI tools to enhance teaching and learning in Science. In Proceedings of the 14th International Conference New Perspectives in Science Education (pp. 293-301). Florence: Filodiritto-Pixel. https://doi.org/10.26352/L319_2420-9732
  59. Spasopoulos, T., Sotiropoulos, D., & Kalogiannakis, M. (2025). Generative AI in pre-service science teacher education: A systematic review. Advances in Mobile Learning Educational Research, 5(2), 1501-1523. https://doi.org/10.25082/AMLER.2025.02.007
  60. Spyropoulou, N., & Kameas, A. (2024). Augmenting the impact of STEAM education by developing a competence framework for educators. Education Sciences, 14(1), 25. https://doi.org/10.3390/educsci14010025
  61. Sun, Y., et al. (2025). Exploring the role of GenAI in collaborative lesson planning. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.70098
  62. UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
  63. Taşar, M. F., & Yılmaz Ergül, D. (2023). Technological pedagogical content knowledge (TPACK) in physics education. In M. F. Taşar, & P. R. L. Heron (Eds.), The International Handbook of Physics Education Research: Teaching Physics. AIP Publishing, Melville, New York, pp. 1-30. https://doi.org/10.1063/9780735425712_001
  64. Tomlinson, C. A., Brighton, C., Hertberg, H., Callahan, C. M., Moon, T. R., Brimijoin, K., Conover, L. A., & Reynolds, T. (2003). Differentiating instruction in response to student readiness, interest, and learning profile in academically diverse classrooms: A review of literature. Journal for the Education of the Gifted, 27(2-3), 119-145. https://doi.org/10.1177/016235320302700203
  65. Vlasopoulou, M., Kalogiannakis, M., & Sifaki, E. (2021). Investigating Teachers' Attitude and Behavioral Intentions for the Impending Integration of STEM Education in Primary School. In S. Papadakis, & M. Kalogiannakis (Eds.), Handbook of Research on Using Education Robotics to Facilitate Student Learning, 235-256, Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-7998-6717-3.ch009
  66. Uğraş, H., Uğraş, M., Papadakis, S., & Kalogiannakis, M. (2025). Innovative early childhood STEM education with ChatGPT: Teacher perspectives. Technology, Knowledge and Learning, 30, 809-831. https://doi.org/10.1007/s10758-024-09804-8
  67. Voicu, C. D., Ampartzaki, M., Dogan, Z. Y., & Kalogiannakis, M. (2022). STEAM implementation in preschool and primary school education: Experiences from six countries. In M. Ampartzaki, & M. Kalogiannakis (Eds.), Early childhood education: Innovative pedagogical approaches in the post-modern era (pp. 1-23). IntechOpen. https://doi.org/10.5772/intechopen.107886
  68. Wardak, D., et al. (2025). Reshaping higher education designs and futures: Postdigital co-design with GenAI. Postdigital Science and Education. https://doi.org/10.1007/s42438-025-00595-4
  69. Yakman, G. (2008). STEAM education: An overview of creating a model of integrative education. Purdue University STEM Colloquium.
  70. Xenakis, A. (2025). AI-Based Models for Assessing STEAM Engineering Literacy for University Students: The Case of Digital Systems for Precision Agriculture. Hellenic Journal of STEM Education, 4(1), 1-9. https://doi.org/10.51724/hjstemed.v4i1.37

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