DeepCrit: A Deep Learning–Driven Intelligent Tutoring System to Enhance Critical Action Skills in Science Learning
Keywords:
Critical action; Critical consciousness; Deep learning; Intelligent tutoring system; Socio-scientific issues
Abstract
The present study reports the design, development, and evaluation of DeepCrit, a deep learning (DL)–driven intelligent tutoring system (ITS) intended to enhance students’ critical action skills in science education. While ITS research has expanded rapidly alongside advances in adaptive learning and learner modeling, few systems explicitly target higher-order skills such as critical action—a dimension of critical consciousness involving the capability to analyze socio-scientific issues, design evidence-based solutions, and enact transformative actions. This study addresses this gap through a multiphase mixed-method evaluation integrated with Design-Based Research (DBR). The research involved preliminary needs analysis, conceptual design, prototype development, expert validation, and classroom implementation with 62 secondary school students. DeepCrit integrates deep knowledge tracing, multi-task learner profiling, a knowledge graph–based domain model, and a pedagogical engine driven by a deep Q-network to provide adaptive and dialogic scaffolding around socio-scientific issues. Quantitative results demonstrate significant improvements in critical reflection, critical motivation, and critical action, alongside gains in conceptual science mastery. Qualitative findings reveal that DeepCrit supports students’ movement through praxis cycles—reflection, decision-making, and action—thereby strengthening their scientific agency. This study contributes a pedagogical and technical framework for designing ITS that support transformative science learning aligned with the demands of the 21st century.
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References
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Pinedo, A., Frisby, M., Kubi, G., Vezaldenos, V., Diemer, M. A., McAlister, S., & Harris, E. (2024). Charting the longitudinal trajectories and interplay of critical consciousness among youth activists. Child Development, 95(1), 296–312. https://doi.org/10.1111/cdev.13977
Prihantini, P., Sutarto, S., Apriliyani, E. S., Stavinibelia, S., Arsyad, M., & Mukhtar, D. (2025). Deep Learning Approaches in Education: A Literature Review on Their Role in Addressing Future Challenge. TOFEDU: The Future of Education Journal, 4(5), 1213–1220. https://doi.org/10.61445/tofedu.v4i5.532
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Rapa, L. J., & Geldhof, G. J. (2020). Critical consciousness: New directions for understanding its development during adolescence. Journal of Applied Developmental Psychology, 70, 101187. https://doi.org/10.1016/j.appdev.2020.101187
Schoute, E. C., Bailey, J. M., & Lombardi, D. (2024). Learning about science topics of social relevance using lower and higher autonomy-supportive scaffolds. Contemporary Educational Psychology, 78, 102284. https://doi.org/10.1016/j.cedpsych.2024.102284
Tong, C., & Ren, C. (2025). Deep knowledge tracing and cognitive load estimation for personalized learning path generation using neural network architecture. Scientific Reports, 15(1), 24925. https://doi.org/10.1038/s41598-025-10497-x
VanLEHN, K. (2011). The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
Viehmann, C., Fernández Cárdenas, J. M., & Reynaga Peña, C. G. (2024). The Use of Socioscientific Issues in Science Lessons: A Scoping Review. Sustainability, 16(14), 5827. https://doi.org/10.3390/su16145827
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Zhou, X., Zhang, Z., Xie, X., & Zhang, J. (2025). Deep learning based knowledge tracing in intelligent tutoring systems. Scientific Reports, 15(1), 21395. https://doi.org/10.1038/s41598-025-07422-7
Zimmerman, B. J. (1990). Self-Regulated Learning and Academic Achievement: An Overview. Educational Psychologist, 25(1), 3–17. https://doi.org/10.1207/s15326985ep2501_2
Boone, K., Roets, G., & Roose, R. (2019). Raising critical consciousness in the struggle against poverty: Breaking a culture of silence. Critical Social Policy, 39(3), 434–454. https://doi.org/10.1177/0261018318820233
Castro, S. K., Brown, J. C., & Crippen, K. J. (2025). Fostering critical consciousness: A systematic review of K-12 teachers’ integrations of sociopolitical issues in science and mathematics classrooms. Disciplinary and Interdisciplinary Science Education Research, 7(1), 7. https://doi.org/10.1186/s43031-025-00126-y
Chen, J., & Singh, C. K. S. (2024). A Systematic Review on Deep Learning in Education: Concepts, Factors, Models and Measurements. Journal of Education and Educational Research, 7(1), 125–129. https://doi.org/10.54097/gzk2yd38
Chen, X., Xie, H., Qin, S. J., Chai, Y., Tao, X., & Wang, F. L. (2024). Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis. Cognitive Computation, 16(6), 3518–3556. https://doi.org/10.1007/s12559-024-10331-y
Creswell, J. W. (2024). My 35 Years in Mixed Methods Research. Journal of Mixed Methods Research, 18(3), 203–215. https://doi.org/10.1177/15586898241253892
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (Sixth edition, international student edition). Sage.
Dayan, E., & Tsybulsky, D. (2025). Designing and teaching socio-scientific issues online: Digital curation in the science classroom. International Journal of Science Education, 47(17), 2219–2238. https://doi.org/10.1080/09500693.2024.2381133
Dermeval, D., Paiva, R., Bittencourt, I. I., Vassileva, J., & Borges, D. (2018). Authoring Tools for Designing Intelligent Tutoring Systems: A Systematic Review of the Literature. International Journal of Artificial Intelligence in Education, 28(3), 336–384. https://doi.org/10.1007/s40593-017-0157-9
Diemer, M. A., Pinedo, A., Bañales, J., Mathews, C. J., Frisby, M. B., Harris, E. M., & McAlister, S. (2021). Recentering Action in Critical Consciousness. Child Development Perspectives, 15(1), 12–17. https://doi.org/10.1111/cdep.12393
Elahi, M., Afolaranmi, S. O., Martinez Lastra, J. L., & Perez Garcia, J. A. (2023). A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discover Artificial Intelligence, 3(1), 43. https://doi.org/10.1007/s44163-023-00089-x
Fraenkel, J., Wallen, N., & Hyun, H. (2023). How to Design and Evaluate Research in Education (Eleventh edition. International Student Edition). MCGRAW-HILL EDUCATION.
Freire, P. (2021). Education for Critical Consciousness. Bloomsbury Academic.
Friedrichsen, P. J., Sadler, T. D., Graham, K., & Brown, P. (2016). Design of a Socio-scientific Issue Curriculum Unit: Antibiotic Resistance, Natural Selection, and Modeling. International Journal of Designs for Learning, 7(1). https://doi.org/10.14434/ijdl.v7i1.19325
Giroux, H. A. (2020). On critical pedagogy (2nd edition). Bloomsbury Academic.
Grimalt-Álvaro, C., López-Simó, V., & Tena, È. (2025). How Do Secondary-School Teachers Design STEM Teaching–Learning Sequences? A Mixed Methods Study for Identifying Design Profiles. International Journal of Science and Mathematics Education, 23(1), 235–260. https://doi.org/10.1007/s10763-024-10457-3
Han, J., Li, Y., & Qin, Y. (2023). Applying DBR to design protocols for synchronous online Chinese learning: An activity theoretic perspective. System, 116, 103092. https://doi.org/10.1016/j.system.2023.103092
Hoadley, C., & Campos, F. C. (2022). Design-based research: What it is and why it matters to studying online learning. Educational Psychologist, 57(3), 207–220. https://doi.org/10.1080/00461520.2022.2079128
Högström, P., Gericke, N., Wallin, J., & Bergman, E. (2025). Teaching Socioscientific Issues: A Systematic Review. Science & Education, 34(5), 3079–3122. https://doi.org/10.1007/s11191-024-00542-y
Ichikawa, H. (2022). A theory of hope in critical pedagogy: An interpretation of Henry Giroux. Educational Philosophy and Theory, 54(4), 384–394. https://doi.org/10.1080/00131857.2020.1840973
Kinskey, M., & Zeidler, D. (2024). Elementary preservice teachers’ pedagogical decisions about socioscientific issues instruction. Journal of Research in Science Teaching, 61(8), 1890–1924. https://doi.org/10.1002/tea.21932
Morris, D. L. (2025). Rethinking Science Education Practices: Shifting from Investigation-Centric to Comprehensive Inquiry-Based Instruction. Education Sciences, 15(1), 73. https://doi.org/10.3390/educsci15010073
Mulyani, A., Indriyanti, D. R., & Madnasri, S. (2023). Research Trend of 21st Century Skills in Science Education Through Bibliometrics. Jurnal Pendidikan Sains Indonesia, 11(4), 897–916. https://doi.org/10.24815/jpsi.v11i4.32527
OECD. (2019). OECD Future of Education and Skills 2030: Conceptual Learning Framework. OECD. https://www.oecd.org/education/2030-project/teaching-and-learning/learning/skills/Skills_for_2030_concept_note.pdf
Owens, D. C., & Sadler, T. D. (2024). Socio‐scientific issues instruction for scientific literacy: 5E Framing to enhance teaching practice. School Science and Mathematics, 124(3), 203–210. https://doi.org/10.1111/ssm.12626
Pinedo, A., Frisby, M., Kubi, G., Vezaldenos, V., Diemer, M. A., McAlister, S., & Harris, E. (2024). Charting the longitudinal trajectories and interplay of critical consciousness among youth activists. Child Development, 95(1), 296–312. https://doi.org/10.1111/cdev.13977
Prihantini, P., Sutarto, S., Apriliyani, E. S., Stavinibelia, S., Arsyad, M., & Mukhtar, D. (2025). Deep Learning Approaches in Education: A Literature Review on Their Role in Addressing Future Challenge. TOFEDU: The Future of Education Journal, 4(5), 1213–1220. https://doi.org/10.61445/tofedu.v4i5.532
Rapa, L. J., Diemer, M. A., & Roseth, C. J. (2020). Can a values-affirmation intervention bolster academic achievement and raise critical consciousness? Results from a small-scale field experiment. Social Psychology of Education, 23(2), 537–557. https://doi.org/10.1007/s11218-020-09546-2
Rapa, L. J., & Geldhof, G. J. (2020). Critical consciousness: New directions for understanding its development during adolescence. Journal of Applied Developmental Psychology, 70, 101187. https://doi.org/10.1016/j.appdev.2020.101187
Schoute, E. C., Bailey, J. M., & Lombardi, D. (2024). Learning about science topics of social relevance using lower and higher autonomy-supportive scaffolds. Contemporary Educational Psychology, 78, 102284. https://doi.org/10.1016/j.cedpsych.2024.102284
Tong, C., & Ren, C. (2025). Deep knowledge tracing and cognitive load estimation for personalized learning path generation using neural network architecture. Scientific Reports, 15(1), 24925. https://doi.org/10.1038/s41598-025-10497-x
VanLEHN, K. (2011). The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
Viehmann, C., Fernández Cárdenas, J. M., & Reynaga Peña, C. G. (2024). The Use of Socioscientific Issues in Science Lessons: A Scoping Review. Sustainability, 16(14), 5827. https://doi.org/10.3390/su16145827
Villegas-Ch, W., Buenano-Fernandez, D., Navarro, A. M., & Mera-Navarrete, A. (2025). Adaptive intelligent tutoring systems for STEM education: Analysis of the learning impact and effectiveness of personalized feedback. Smart Learning Environments, 12(1), 41. https://doi.org/10.1186/s40561-025-00389-y
Watts, R. J., Diemer, M. A., & Voight, A. M. (2011). Critical consciousness: Current status and future directions. New Directions for Child and Adolescent Development, 2011(134), 43–57. https://doi.org/10.1002/cd.310
World Economic Forum. (2020). Schools of the Future: Defining New Models of Education for the Fourth Industrial Revolution. World Economic Forum.
World Economic Forum. (2024). The Global Risks Report 2024 (19th edn). World Economic Forum. https://www.weforum.org/publications/ global-risks-report-2024/
Zeidler, D. L., Herman, B. C., & Sadler, T. D. (2019). New directions in socioscientific issues research. Disciplinary and Interdisciplinary Science Education Research, 1(1), 11. https://doi.org/10.1186/s43031-019-0008-7
Zhou, X., Zhang, Z., Xie, X., & Zhang, J. (2025). Deep learning based knowledge tracing in intelligent tutoring systems. Scientific Reports, 15(1), 21395. https://doi.org/10.1038/s41598-025-07422-7
Zimmerman, B. J. (1990). Self-Regulated Learning and Academic Achievement: An Overview. Educational Psychologist, 25(1), 3–17. https://doi.org/10.1207/s15326985ep2501_2
Published
2026-05-07
How to Cite
Supriyadi, & Kholid, I. (2026). DeepCrit: A Deep Learning–Driven Intelligent Tutoring System to Enhance Critical Action Skills in Science Learning. ISEJ : Indonesian Science Education Journal, 7(2), 18-35. https://doi.org/10.62159/isej.v7i2.2021
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Copyright (c) 2026 Idham Kholid, Supriyadi
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