Artificial intelligence in interdisciplinary project-based learning

Student perceptions across five dimensions

Authors

DOI:

https://doi.org/10.32674/hb1g4f42

Keywords:

Artificial Intelligence in Education, Project-Based Learning (PBL), Non-STEM Higher Education, Interdisciplinary Integration , Collaboration Challenges, Digital Literacy, Functional Interaction, Epistemic Engagement

Abstract

This study explores undergraduate perceptions of generative artificial intelligence (AI) within interdisciplinary Project-Based Learning (PBL) in biological sciences courses for non-STEM majors at a Latin-Caribbean university. Using a mixed-methods design, a multidimensional questionnaire assessed five dimensions of AI interaction—cognitive processes, functional interaction, social mediation, personal empowerment, and bridging disciplines—while anecdotal observation cards provided qualitative insights. Analyses confirmed the robustness of the framework, with Functional Interaction valued highest and Social Mediation lowest. Narratives revealed heterogeneous practices, from critical validation to uncritical dependency, and highlighted creative resistance to AI. Findings suggest that AI enhances technical accuracy and student autonomy but requires deliberate pedagogical scaffolding to strengthen collaboration and foster meaningful interdisciplinary integration in higher education contexts.

Author Biographies

  • Bonny M Ortiz-Andrade, Department of Biological Sciences, General Studies College, University of Puerto Rico, Río Piedras Campus

    Dr. Bonny M. Ortiz-Andrade is an Assistant Professor at the University of Puerto Rico, Río Piedras Campus. Her research focuses on project-based learning, interdisciplinarity, and the design of experimental activities for non-STEM students, as well as the study of bird diversity and their floral resources. She is also an experienced science communicator, dedicated to integrating scientific concepts into everyday life and fostering a deeper understanding of the natural world among her students.

    In addition to her teaching and research, Dr. Ortiz-Andrade has a strong background in program coordination and project management, ensuring the effective implementation of educational initiatives. Her interdisciplinary expertise bridges scientific knowledge and pedagogical innovation, supporting academic growth and strengthening university–community connections.

  • Arnulfo Rojas-Pérez, Department of Chemistry, Natural Sciences College, University of Puerto Rico, Río Piedras Campus

    Arnulfo Rojas-Pérez, PhD, is a Professor of Chemistry at the University of Puerto Rico, Río Piedras Campus. He earned his doctorate in Analytical Chemistry from the same institution, following a strong background in education and undergraduate studies in Biology and Chemistry. His teaching focuses on chemistry, where he integrates innovative strategies to foster student engagement and learning. Dr. Rojas-Pérez’s research experience spans bioelectrochemistry, nanomaterials, and environmental applications, and he has co-authored multiple peer-reviewed publications. He is committed to advancing inclusive and high-quality education through both teaching and research.

  • María P Ortiz-Andrade, Department of Psychology, Social Sciences College, University of Puerto Rico, Río Piedras Campus

    María Ortiz-Andrade is a clinical psychology doctoral student at the University of Puerto Rico, Río Piedras Campus. She earned her undergraduate degree in Psychology from Universidad Surcolombiana in Colombia. Her work has focused on adolescents and the cultural dynamics surrounding minority groups, with a strong commitment to advancing equity and inclusion. She is dedicated to interdisciplinary approaches that bridge psychology with broader social and educational contexts.

References

Baudin, P. V., Sacksteder, R. E., Worthington, A. K., Voitiuk, K., Ly, V. T., Hoffman, R. N., ... & Mostajo-Radji, M. A. (2022). Cloud-controlled microscopy enables remote project-based biology education in underserved Latinx communities. Heliyon, 8(11), e11596. https://doi.org/10.1016/j.heliyon.2022.e11596

Bell, S. (2010). Project-based learning for the 21st century: Skills for the future. The Clearing House, 83(2), 39–43. https://doi.org/10.1080/00098650903505415

Blumenfeld, P. C., Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M., & Palincsar, A. (1991). Motivating project-based learning: Sustaining the doing, supporting the learning. Educational Psychologist, 26(3–4), 369–398. https://doi.org/10.1080/00461520.1991.9653139

Boix Mansilla, V. (2010). Learning to synthesize: The development of interdisciplinary understanding. In R. Frodeman, J. T. Klein, C. Mitcham, & J. B. Holbrook (Eds.), Oxford Handbook of Interdisciplinarity (pp. 288–306). Oxford University Press.

Boix Mansilla, V. (2016). Interdisciplinary learning: A cognitive-epistemological foundation. In R. Frodeman & J. Klein (Eds.), Oxford Handbook of Interdisciplinarity (2nd ed., pp. 261–275). Oxford University Press.

Boix Mansilla, V., & Duraisingh, E. D. (2007). Targeted assessment of students' interdisciplinary work: An empirically grounded framework proposed. The Journal of Higher Education, 78(2), 215–237. https://doi.org/10.1080/00221546.2007.11780874

Boix Mansilla, V., & Gardner, H. (2003). Assessing interdisciplinary work at the frontier: An empirical exploration of symptoms of quality. Interdisciplinary Studies Project, Project Zero. Harvard Graduate School of Education.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Bybee, R. W. (2013). The case for STEM education: Challenges and opportunities. NSTA Press.

Cai, C., Zhu, G, & Ma, M. (2024). A systematic review of AI for interdisciplinary learning: Application contexts, roles, and influences. Education and Information Technologies, 1-47. Advance online publication. https://doi.org/10.1007/s10639-024-13193-x

Clark, L. A., & Watson, D. (2016). Constructing validity: Basic issues in objective scale development. In A. E. Kazdin (Ed.), Methodological issues and strategies in clinical research (4th ed., pp. 187–203). American Psychological Association. https://doi.org/10.1037/14805-012

Costello, A. B. & Osborne, J., (2005) “Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis”, Practical Assessment, Research, and Evaluation 10(1): 7. doi: https://doi.org/10.7275/jyj1-4868

Dai, Y., Xiao, J.-Y., Huang, Y., Zhai, X., Wai, F.-C., & Zhang, M. (2025). How Generative AI Enables an Online Project-Based Learning Platform: An Applied Study of Learning Behavior Analysis in Undergraduate Students. Applied Sciences, 15(5), 2369. https://doi.org/10.3390/app15052369

DeVellis, R. F. & Thorpe, C. T. (2022). Scale development: Theory and applications (5th ed.). SAGE Publications.

Dias, M., & Brantley-Dias, L. (2017). Setting the standard for project-based learning: A proven approach to rigorous classroom instruction. Interdisciplinary Journal of Problem-Based Learning, 11(2). https://doi.org/10.7771/1541-5015.1721

Donelan, H. & Kear, K. (2024). Online group projects in higher education: persistent challenges and implications for practice. Information Systems Education Journal, 18(5), 75-88., 13(2), 123. https://doi.org/10.1007/s12528-023-09360-7

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological methods, 4(3), 272.

Guo, P., Saab, N., Post, L. S., & Admiraal, W. (2020). A review of project-based learning in higher education: Student outcomes and measures. International Journal of Educational Research, 102, 101586. https://doi.org/10.1016/j.ijer.2020.101586

Hew, K. F., & Brush, T. (2007). Integrating technology into K–12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55(3), 223–252. https://doi.org/10.1007/s11423-006-9022-5

Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266. https://doi.org/10.1023/B:EDPR.0000034022.16470.f3

Hussein, M. J. (2021). Addressing collaboration challenges in project-based learning: The student’s perspective. Education Sciences, 11(8), 434. https://doi.org/10.3390/educsci11080434

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (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

Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., ... & Ryan, M. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting Learning by Design™ into practice. Journal of the Learning Sciences, 12(4), 495–547. https://doi.org/10.1207/S15327809JLS1204_2

Krajcik, J., & Blumenfeld, P. (2012). Project-based learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 317–333). Cambridge University Press.

Kwon, H., & Lee, Y. (2025). A meta-analysis of STEM project-based learning on creativity. STEM Education, 5(2), 275–290. https://doi.org/10.3934/steme.2025014

Lattuca, L. R., Voigt, L. J., & Fath, K. Q. (2004). Does interdisciplinarity promote learning? Theoretical support and researchable questions. The review of higher education, 28(1), 23-48. https://dx.doi.org/10.1353/rhe.2004.0028.

Lavado-Anguera, S. L., Velasco-Quintana, P. J. , Terrón-López, M. J., & Marínez-Requejo, S. (2024). Implementación de IA en el aprendizaje basado en proyectos en ingeniería universitaria. Una revisión sistemática. In Jiménez-García & Velasco-Quintana, Construyendo el futuro de la educación superior en la era digital, Dykinson (pp. 445-454).

Moalagh, M. (2024). Which teamwork challenges do computing students face in project-based learning research methods courses? Proceedings of the 55th ACM Technical Symposium on Computer Science Education V.2, 1487–1488. https://doi.org/10.1145/3699538.3699563

Niu, W., Zhao, L., & Li, H. (2024). The role of artificial intelligence autonomy in higher education. Sustainability, 16(3), 1276. https://doi.org/10.3390/su16031276

Ortiz-Andrade, B. (2025). Enseñanza interdisciplinaria de las Ciencias Biológicas a estudiantes no STEM mediante aprendizaje basado en proyectos. Cuaderno de Pedagogía Universitaria, 22(43), 25-43. https://doi.org/10.29197/cpu.v22i43.641

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 4. https://doi.org/10.1186/s41039-017-0062-8

Selwyn, N. (2019). Should robots replace teachers? AI and the Future of Education. (1st ed.) Polity Press.

Shata, A. (2025). “Opting Out of AI”: Exploring Perceptions, Reasons, and Concerns Behind Faculty Resistance to Generative AI. Frontiers in Communication, 10, 1614804 https://doi.org/10.3389/fcomm.2025.1614804

Simamora, A. M. (2024). A decade of science technology, engineering, and mathematics (STEM) project-based learning (PjBL): A systematic literature review. Journal of Computers for Science and Mathematics Learning, 1(1), 58–78. https://doi.org/10.70232/pn3nek61

Strobel, J., & van Barneveld, A. (2009). When is PBL more effective? A meta-synthesis of meta-analyses comparing PBL to conventional classrooms. Interdisciplinary Journal of Problem-Based Learning, 3(1), 44–58. https://doi.org/10.7771/1541-5015.1046

Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd

Thomas, J. W. (2000). A review of research on project-based learning. Autodesk Foundation.

Venter, J., Coetzee, S. A., & Schmulian, A. (2025). Exploring the use of artificial intelligence (AI) in the delivery of effective feedback. Assessment & Evaluation in Higher Education, 50(4), 516–536. https://doi.org/10.1080/02602938.2024.2415649

Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom. International Journal of Educational Technology in Higher Education, 21(1), 55. https://doi.org/10.1186/s41239-024-00448-3

Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: Insights from a meta-analysis. Humanities and Social Sciences Communications, 12, 1–21. https://doi.org/10.1057/s41599-025-04787-y

Xu, M. (2025). Interaction between students and artificial intelligence in the context of creative potential development. Interactive Learning Environments, 33(7), 4460–4475. https://doi.org/10.1080/10494820.2025.2465439

Yang, Y., Zhang, Y., Sun, D., He, W., & Wei, Y. (2025). Navigating the landscape of AI literacy education: insights from a decade of research (2014–2024). Humanities and Social Sciences Communications, 12(1), 1-12. https://doi.org/10.1057/s41599-025-04583-8

Yonggang, L., Awang, H., & Mansor, N. S. (2024). Exploring Barriers to the Adoption of Generative Artificial Intelligence Tools among University Students: A Perspective from Innovation Resistance Theory. Educational Research (IJMCER) Vol, 6, 193-201.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7

Zhai, J., Sudiarta, I. G. P., Santosa, M. H., & Astawa, I. W. P. (2025). Research on intelligent regulation mechanisms of learner cognitive load in digital learning environments. Future Technology, 4(4), 205–215. https://fupubco.com/futech/article/view/484

Zhang, J., Tian, Y., Yuan, G., & Tao, D. (2022). Epistemic agency for costructuring expansive knowledge‐building practices. Science Education, 106(4), 890-923. https://doi.org/10.1002/sce.21717

Zhang, J., Shi, Y., & Zhang, X. (2023). Research on the quality of collaboration in project-based learning based on group awareness. Sustainability, 15(15), 11901. https://doi.org/10.3390/su151511901

Additional Files

Published

2026-03-06

Issue

Vol. 20 (2026): American Journal of STEM Education

Section

STEM Education (regular)

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Copyright (c) 2026 American Journal of STEM Education

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How to Cite

Ortiz-Andrade, B. M., Rojas-Pérez, A., & Ortiz-Andrade, M. P. (2026). Artificial intelligence in interdisciplinary project-based learning: Student perceptions across five dimensions. American Journal of STEM Education, 20, 1-24. https://doi.org/10.32674/hb1g4f42