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Physics Education in the Age of Artificial Intelligence | ||
| Physics Journal | Farhangian University | ||
| مقاله 2، دوره 2، شماره 3، مهر 2025 اصل مقاله (747.07 K) | ||
| نوع مقاله: Original Paper | ||
| شناسه دیجیتال (DOI): 10.48310/esip.2025.20919.1021 | ||
| نویسنده | ||
| Ghadir Jafari* | ||
| Department of Physics Education, Farhangian University | ||
| چکیده | ||
| The intrinsic angular momentum, or spin, is a cornerstone of modern physics with profound applications from nuclear magnetic resonance to spintronics. While its mathematical structure within quantum theory is well-defined, its fundamental origin is often less emphasized. This paper revisits the genesis of spin by examining its emergence in relativistic wave equations, its role in the Thomas precession, and its formulation for massless photons in electrodynamics. It is argued that these foundational elements collectively demonstrate that spin is inherently a consequence of relativistic spacetime symmetry, and its full manifestation requires the quantum framework. Consequently, the term "rest angular momentum" offers a more conceptually accurate description, highlighting its origin as an intrinsic property manifest even in an object's rest frame, as dictated by the Poincaré group. Spin, as an invariant property of any object, is the angular momentum an elementary particle possesses in its rest frame, where its orbital angular momentum is zero. This perspective aims to bridge the gap between advanced theoretical concepts and pedagogical clarity, emphasizing that relativity is not merely about high-speed phenomena but is fundamental to the structure of matter itself. | ||
| کلیدواژهها | ||
| Education؛ Artificial Intelligence؛ Physics Education؛ Machine Learning | ||
| مراجع | ||
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[1] Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign..
[2] Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL Institute of Education Press.
[3] Meltzer, D. E., & Thornton, R. K. (2012). Resource letter ALIP–1: Active-learning instruction in physics. American Journal of Physics, 80(6), 478–496.
[4] Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. Farrar, Straus and Giroux.
[5] Redish, E. F. (2003). Teaching physics with the physics suite. John Wiley & Sons.
[6] 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(39).
[7] Chollet, F. (2021). Deep learning with Python (2nd ed.). Manning.
[8] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
[9] Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260
[10] Nilsson, N. J. (2010). The quest for artificial intelligence: A history of ideas and achievements. Cambridge University Press.
[11] Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
[12] Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How learning works: Seven research-based principles for smart teaching. San Francisco, CA: Jossey-Bass.
[13] Bishop, C. M. (2006). Pattern recognition and machine learning. New York, NY: Springer.
[14] Brown, P. C., Roediger III, H. L., & McDaniel, M. A. (2014). Make it stick: The science of successful learning. Cambridge, MA: Harvard University Press.
[15] Carey, B. (2014). How we learn: The surprising truth about when, where, and why it happens. New York, NY: Random House.
[16] Epstein, D. (2021). Range: Why generalists triumph in a specialized world. New York, NY: Penguin.
[17] Krashen, S. D. (1982). Principles and practice in second language acquisition. Oxford: Pergamon Press.
[18] Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge: Cambridge University Press.
[19] National Research Council. (2000). How people learn: Brain, mind, experience, and school (Expanded ed.). Washington, DC: National Academies Press.
[20] Nussbaum, M. C. (2013). Creating capabilities: The human development approach. Cambridge, MA: Harvard University Press.
[21] Wiggins, G., & McTighe, J. (2005). Understanding by design (2nd ed.). Alexandria, VA: ASCD.
[22] Docktor, J. L., & Mestre, J. P. (2014). Synthesis of discipline-based education research in physics. Physical Review Special Topics - Physics Education Research, 10(2), 020119.
[23] Feyerabend, P. (2010). Against method (4th ed.). London: Verso.
[24] Hestenes, D. (1992). Modeling games in the Newtonian world. American Journal of Physics, 60(8), 732–748.
[25] Kortemeyer, G. (2025). The boiling-frog problem of physics education. arXiv preprint. https://arxiv.org/abs/2508.08842.
[26] Redish, E. F. (2005). Problem solving and the use of math in physics courses. Proceedings of the World View on Physics Education 2005 Conference.
[27] Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147.
[28] Janiak, A., & Schliesser, E. (Eds.). (2012). Interpreting Newton: Critical Essays. Cambridge University Press.
[29] DiSalle, R. (2006). Understanding Space-Time: The Philosophical Development of Physics from Newton to Einstein. Cambridge University Press.
[30] Facione, P. A. (2015). Critical Thinking: What It Is and Why It Counts. Insight Assessment.
[31] Koyré, A. (1957). From the Closed World to the Infinite Universe, Baltimore: Johns Hopkins Press.
[32] Steiner, M. (1998). The Applicability of Mathematics as a Philosophical Problem. Harvard University Press
[33] Strogatz, S. (2015). Nonlinear Dynamics and Chaos. Westview Press.
[34] Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Computational Thinking in Science, Technology, Engineering, and Mathematics. Journal of Science Education and Technology, 25(1), 127–147.
[35] Wing, J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33–35.
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آمار تعداد مشاهده مقاله: 136 تعداد دریافت فایل اصل مقاله: 30 |
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