Reasoning-Based Learning in Higher Education
A New Era of Higher Education in the Age of Artificial Intelligence
Keywords:
AI, Reasoning, Education, UniversityAbstract
This whitepaper introduces Reasoning-Based Learning (RBL), the pedagogical paradigm operationalized at Continents International University. Unlike traditional models that reward memorization and long-form reproduction of information, RBL trains students to think in a structured, evidence grounded, multi step manner. Students are no longer asked to produce long essays as a proxy for understanding. Instead, they advance through a sequence of verified reasoning steps inside each instructional unit, where every decision, justification, and citation is checked against an institutional knowledge corpus built from peer reviewed, Q1 ranked, Scopus indexed scholarship.
The institution operates a closed, evidence grounded learning environment. Curriculum, tutoring, and grading are produced behind a mandatory retrieval step that anchors every output in a curated source corpus. The system is engineered for continuous, auditable academic governance, where every reasoning step is observable in real time rather than reconstructed retrospectively.
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References
Anderson, L. W., and Krathwohl, D. R. (2001). A Taxonomy for Learning, Teaching, and Assessing. New York: Longman.
Bjork, R. A., and Bjork, E. L. (2011). Making Things Hard on Yourself, but in a Good Way: Creating Desirable Difficulties to Enhance Learning. Psychology and the Real World, 56 to 64.
Bommasani, R., Hudson, D. A., Adeli, E., et al. (2021). On the Opportunities and Risks of Foundation Models. Stanford Center for Research on Foundation Models.
Brown, P. C., Roediger, H. L., and McDaniel, M. A. (2014). Make It Stick: The Science of Successful Learning. Cambridge, MA: Harvard University Press.
Ericsson, K. A., and Pool, R. (2016). Peak: Secrets from the New Science of Expertise. Boston: Houghton Mifflin Harcourt.
Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
Lewis, P., Perez, E., Piktus, A., et al. (2020). Retrieval Augmented Generation for Knowledge Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33.
Meyer, J. H. F., and Land, R. (2006). Overcoming Barriers to Student Understanding: Threshold Concepts and Troublesome Knowledge. London: Routledge.
Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. New York: Farrar, Straus and Giroux.
National Academies of Sciences, Engineering, and Medicine (2018). How People Learn II: Learners, Contexts, and Cultures. Washington, DC: The National Academies Press.
Pellegrino, J. W., and Hilton, M. L. (Eds.) (2012). Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. Washington, DC: The National Academies Press.
SCImago (2024). SCImago Journal and Country Rank. Retrieved from scimagojr.com.
Scopus (2024). Indexed Sources Database. Elsevier B.V.
Wei, J., Wang, X., Schuurmans, D., et al. (2022). Chain of Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems, 35.
Wiggins, G., and McTighe, J. (2005). Understanding by Design, Expanded Second Edition. Alexandria, VA: ASCD.
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