Home All Issues Issue articles Previous article

Scaling Complex Thinking: A Conceptual Framework for AI-Supported Inquiry-Based Learning

Lori Doyle 1 * ,
Jill Swisher 2 *
1 Concordia University Irvine, UNITED STATES2 Northwestern College in Iowa, UNITED STATES* Corresponding Author
Educational Point, 3(3), 2026, e183, https://doi.org/10.71176/edup/19054
Publication date: Aug 04, 2026
Conceptual Article
Open Access Open Access
Download PDF

ABSTRACT

As higher education faces the realities of technological advancements, institutions face the challenge of fostering high-level cognitive competencies while maintaining scalability. This paper proposes a holistic framework that integrates inquiry-based learning (IBL) with artificial intelligence (AI) to support learning. While traditional IBL is pedagogical and resource-intensive, the proposed model utilizes AI as a scaffolding layer, transitioning its use from an output generator to a metacognitive coach. Utilizing a conceptual framework methodology, the study maps the approaches between the stages of inquiry and AI interactions. In addition, the paper explores the institutional implications for teacher training and technological governance, arguing that the sustainability of such a system depends on shifting assessment from content mastery to measurable complex thinking skills. This contribution provides a cohesive foundation for administrators and educators seeking to implement active pedagogies in this era of AI.

KEYWORDS

inquiry-based learning AI metacognition scaffolding

CITATION (APA)

Doyle, L., & Swisher, J. (2026). Scaling Complex Thinking: A Conceptual Framework for AI-Supported Inquiry-Based Learning. Educational Point, 3(3), e183. https://doi.org/10.71176/edup/19054
Harvard
Doyle, L., and Swisher, J. (2026). Scaling Complex Thinking: A Conceptual Framework for AI-Supported Inquiry-Based Learning. Educational Point, 3(3), e183. https://doi.org/10.71176/edup/19054
Vancouver
Doyle L, Swisher J. Scaling Complex Thinking: A Conceptual Framework for AI-Supported Inquiry-Based Learning. Educational Point. 2026;3(3):e183. https://doi.org/10.71176/edup/19054
AMA
Doyle L, Swisher J. Scaling Complex Thinking: A Conceptual Framework for AI-Supported Inquiry-Based Learning. Educational Point. 2026;3(3), e183. https://doi.org/10.71176/edup/19054
Chicago
Doyle, Lori, and Jill Swisher. "Scaling Complex Thinking: A Conceptual Framework for AI-Supported Inquiry-Based Learning". Educational Point 2026 3 no. 3 (2026): e183. https://doi.org/10.71176/edup/19054
MLA
Doyle, Lori et al. "Scaling Complex Thinking: A Conceptual Framework for AI-Supported Inquiry-Based Learning". Educational Point, vol. 3, no. 3, 2026, e183. https://doi.org/10.71176/edup/19054

REFERENCES

  1. Adeyele, V. O., & Ramnarain, U. (2024). Exploring the integration of ChatGPT in inquiry-based learning: Teacher perspectives. International Journal of Technology in Education, 7(2), 200-217. https://files.eric.ed.gov/fulltext/EJ1426699.pdf
  2. Aditomo, A., Goodyear, P., Bliuc, A. M., & Ellis, R. A. (2013). Inquiry-based learning in higher education: Principal forms, educational objectives, and disciplinary variations. Studies in Higher Education, 38(9), 1239-1258. http://dx.doi.org/10.1080/03075079.2011.616584
  3. Álvarez-Icaza, I., Miranda, J., Martínez-Arboleda, A., Suárez-Brito, P., & Ramírez-Montoya, M. S. (2025). Driving complex thinking and technological entrepreneurship with artificial intelligence: A mixed methods study. Sustainable Futures, 10, Article 101312. https://doi.org/10.1016/j.sftr.2025.101312
  4. Bell, T., Urhahne, D., Schanze, S., & Ploetzner, R. (2010). Collaborative inquiry learning: Models, tools, and challenges. International Journal of Science Education, 32(3), 349-377. https://doi.org/10.1080/09500690802582241
  5. Bruce, B. C., & Bishop, A. P. (2001). Using the web to support inquiry-based language learning. In Chuang, Y. S. & Wang, P. L. (Eds.) (2001). Proceedings of ROCMELIA 2001, the Fifth International Conference on Multimedia Language Instruction. Taipei: Crane. https://www.ideals.illinois.edu/items/13423
  6. Clark, L. A. (2025). Reframing Bloom’s for the age of AI: A white paper for future-ready educators. Anthology. https://backstage.anthology.com/sites/default/files/2025-09/ReframingBloomsForTheAgeOfAI_WhitePaper_v1.pdf
  7. Doo, M. Y., Bonk, C. J., & Heo, H. (2020). A meta-analysis of scaffolding effects in online learning in higher education. International Review of Research in Open and Distributed Learning, 21(3), 61-80. https://files.eric.ed.gov/fulltext/EJ1267049.pdf
  8. Ernst, D. C., Hodge, A., & Yoshinobu, S. (2017). What is inquiry-based learning. Notices of the AMS, 64(6), 570-574. http://dx.doi.org/10.1090/noti1536
  9. Hattie, J. (2023). Visible learning: The sequel - A synthesis of over 2,100 meta-analyses relating to achievement. Routledge. https://doi.org/10.4324/9781003380542
  10. Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2007). Educational Psychologist, 42(2), 99-107. https://doi.org/10.1080/00461520701263368
  11. Jabareen, Y. (2009). Building a conceptual framework: Philosophy, definitions, and procedure. International Journal of Qualitative Methods, 8(4), 49-62. https://doi.org/10.1177/160940690900800406
  12. Lazonder, A. W., & Harmsen, R. (2016). Meta-analysis of inquiry-based learning: Effects of guidance. Review of Educational Research, 86(3), 681-718. https://doi.org/10.3102/0034654315627366
  13. Lelasari, T., Yohanita, A. M., & Damopolii, I. (2021). Effect of inquiry science learning on students’ metacognitive skill. Journal of Research in Instructional, 1(1), 53–60. https://www.researchgate.net/publication/353144889_Effect_of_inquiry_science_learning_on_students'_metacognitive_skill
  14. Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL IOE Press. https://eric.ed.gov/?id=ED584840
  15. Pambudi, G. D., Winangsih, F., Nunaki, J. H., Nusantari, E., & Damopolii, I. (2022). Encouraging students’ metacognitive skills through inquiry learning. Inornatus: Biology Education Journal, 2(1), 43-52. https://doi.org/10.30862/inornatus.v2i1.272
  16. Pedaste, M., Mäeots, M., Siiman, L. A., de Jong, T., van Riesen, S. A. N., Kamp, E. T., Manoli, C. C., Zacharia, A. C., & Tsourlidaki, E. (2015). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational Research Review, 14, 47-61. https://doi.org/10.1016/j.edurev.2015.02.003
  17. Ramírez-Montoya, M. S., Castillo-Martínez, I. M., Sanabria-Z, J., & Miranda, J. (2022). Complex thinking in the framework of 4.0 and open innovation – A systematic literature review. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), Article 4. https://doi.org/10.3390/joitmc8010004
  18. Sam, R. (2024). Systematic review of inquiry-based learning: assessing impact and best practices in education. F1000Research, 13, Article 1045. https://doi.org/10.12688/f1000research.155367.1
  19. UNESCO. (2022). Reimagining our futures together: A new social contract for education. International Commission on the Futures of Education. https://doi.org/10.54675/ASRB4722
  20. Wade, S., & Kidd, C. (2019). The role of prior knowledge and curiosity in learning. Psychonomic Bulletin & Review, 26(4), 1377-1387. https://doi.org/10.3758/s13423-019-01598-6
  21. Yu, J. H., & Chauhan, D. (2024). Trends in NLP for personalized learning: LDA and sentiment analysis insights. Education and Information Technologies, 30, 4307-4348. https://doi.org/10.1007/s10639-024-12988-2

LICENSE

Creative Commons License
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.