.jpg)
Email this article 
.png)
.png)
AI Scaffolding for Evidence-Based Critical Thinking in a Microcontroller Learning-Media Design Project
Abstract
The rapid integration of artificial intelligence (AI) into education has increased interest in how digital tools can support higher-order thinking rather than simply automate tasks. Within STEAM learning, this issue is especially important because students must combine evidence, reasoning, and design decisions in authentic project work. Generative AI tools based on large language models were used in this study as learning scaffolds to assist students with keyword expansion, scientific query construction, journal abstract summarization, hypothesis development, and revision of written explanations. The study examined whether such AI-supported learning improved students’ critical thinking during a STEAM microcontroller media-design project. Two intact classes completed the same project workflow: one class used AI support, while the other completed the project without AI. Student performance was evaluated using a five-dimension critical-thinking rubric covering information and keywords, concepts and logic, journal abstract interpretation, hypothesis and reasoning, and academic formatting and compliance. Results showed that the AI-supported class performed better overall, scoring about five points higher on average and showing a large overall advantage. Improvements appeared across all rubric dimensions, with the strongest gains found in journal abstract interpretation and hypothesis and reasoning, suggesting that AI was most helpful when students had to interpret evidence, connect ideas, and justify decisions. The weakest-link bottleneck analysis, defined as the rubric dimension in which each student performed worst, showed that journal abstract interpretation remained the main constraint in both groups. These findings indicate that AI can serve as a productive scaffold for critical thinking and decision-making in STEAM projects, especially by supporting evidence use and iterative reasoning, while also highlighting the need for explicit instruction in reading and interpreting scientific abstracts.
Keywords
Full Text:
PDFReferences
Albadarin, Y., Saqr, M., Pope, N., & Tukiainen, M. (2024). A systematic literature review of empirical research on ChatGPT in education. Discover Education, 3, Article 60. https://doi.org/10.1007/s44217-024-00138-2
Berland, L. K., & Reiser, B. J. (2009). Making sense of argumentation and explanation. Science Education, 93(1), 26–55. https://doi.org/10.1002/sce.20286
Blikstein, P. (2013). Digital fabrication and “making” in education: The democratization of invention. In J. Walter-Herrmann & C. Büching (Eds.), FabLabs: Of machines, makers and inventors (pp. 1–22). Transcript Verlag. https://share.google/76G60acxkrzaFKJsi
Brookhart, S. M. (2010). How to assess higher-order thinking skills in your classroom. ASCD. https://share.google/1BgX7tSXmRR8pckeS
Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281. https://doi.org/10.3102/00346543065003245
Cavagnetto, A. R. (2010). Argument to foster scientific literacy: A review of argument interventions in K–12 science contexts. Review of Educational Research, 80(3), 336–371. https://doi.org/10.3102/0034654310376953
Chernozhukov, V., Fernández-Val, I., & Melly, B. (2013). Inference on counterfactual distributions. Econometrica, 81(6), 2205–2268. https://doi.org/10.3982/ECTA10582
Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7–29. https://doi.org/10.1177/0956797613504966
Dignath, C., Büttner, G., & Langfeldt, H. P. (2008). How can primary school students learn self-regulated learning strategies most effectively? A meta-analysis on self-regulation training programmes. Educational Research Review, 3(2), 101–129. https://doi.org/10.1016/j.edurev.2008.02.003
Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26. https://doi.org/10.1214/aos/1176344552
Ennis, R. H. (1993). Critical thinking assessment. Theory Into Practice, 32(3), 179–186. https://doi.org/10.1080/00405849309543594
Facione, P. A. (1990). Critical thinking: A statement of expert consensus for purposes of educational assessment and instruction (The Delphi Report). American Philosophical Association. https://share.google/V6BILm5KABRlwY3Db
Firpo, S. (2007). Efficient semiparametric estimation of quantile treatment effects. Econometrica, 75(1), 259–286. https://doi.org/10.1111/j.1468-0262.2007.00738.x
Halpern, D. F. (2014). Thought and knowledge: An introduction to critical thinking (5th ed.). Psychology Press. https://doi.org/10.4324/9781315885278
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Hedges, L. V. (1981). Distribution theory for Glass’s estimator of effect size and related estimators. Journal of Educational and Behavioral Statistics, 6(2), 107–128. https://doi.org/10.3102/10769986006002107
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://share.google/Em6IhOHThMk1kV4Vm
Honey, M., Pearson, G., & Schweingruber, H. (Eds.). (2014). STEM integration in K–12 education: Status, prospects, and an agenda for research. National Academies Press. https://share.google/Xe8Y8R8v2usXbmX3X
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., & 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
Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50. https://doi.org/10.2307/1913643
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, Article 863. https://doi.org/10.3389/fpsyg.2013.00863
Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10.1177/2515245918770963
McNeill, K. L., & Krajcik, J. (2008). Inquiry and scientific explanations: Helping students use evidence and reasoning. Science Education, 92(2), 223–254. https://doi.org/10.1002/sce.20205
National Research Council. (2012). A framework for K–12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press. https://share.google/iCVajQWmDPdVIA9NW
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090
Osborne, J., Erduran, S., & Simon, S. (2004). Enhancing the quality of argumentation in school science. Journal of Research in Science Teaching, 41(10), 994–1020. https://doi.org/10.1002/tea.20035
Panadero, E., & Jonsson, A. (2013). The use of scoring rubrics for formative assessment purposes revisited: A review. Educational Research Review, 9, 129–144. https://doi.org/10.1016/j.edurev.2013.01.002
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books. https://share.google/r62JMhoM89licb8b2
Paul, R., & Elder, L. (2006). The miniature guide to critical thinking: Concepts & tools (4th ed.). Foundation for Critical Thinking. https://share.google/Sijbdb8redRwOfBd0
Sandoval, W. A., & Millwood, K. A. (2005). The quality of students’ use of evidence in written scientific explanations. Cognition and Instruction, 23(1), 23–55. https://doi.org/10.1207/s1532690xci2301_2
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer. https://doi.org/10.1007/978-1-4419-8126-4
Thomas, J. W. (2000). A review of research on project-based learning. Autodesk Foundation. https://share.google/n9PuFKu4bh6ZLHGnF
Toulmin, S. (1958). The uses of argument. Cambridge University Press. https://doi.org/10.1017/CBO9780511840005
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO. https://share.google/057C8b8YaNYkI0wre
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. https://share.google/UBqDTqbkWcTD3H3f9
van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147–177. https://doi.org/10.1007/s10648-005-3951-0
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. https://share.google/IlYf9CvWuhmioBv5V
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. https://doi.org/10.1080/00031305.2016.1154108DOI: https://doi.org/10.20961/ijpte.v10i1.115698
Refbacks
- There are currently no refbacks.
Copyright (c) 2026 Harjito, Wahyu Hardyanto, Woro Sumarni, Sri Wardani
This work is licensed under a Creative Commons Attribution 4.0 International License.
 | International Journal of Pedagogy and Teacher Education Print ISSN: 2597-7792 |