AI-Powered Tutors as a Catalyst for Conceptual Understanding in Einsteinian Physics Education
Abstract
Purpose of the study: The objective of this study is to investigate the role of AI-powered tutors in assisting students to rectify Newtonian misconceptions and attain a conceptual comprehension of Einsteinian physics concepts, including spacetime curvature, time dilation, and gravity as geometry.
Methodology: A conceptual and narrative literature review was performed utilizing databases such as Scopus, Web of Science, ERIC, SpringerLink, and Google Scholar. The utilized tools and frameworks encompass conceptual change theory, constructivism, cognitive load theory, Bayesian Knowledge Tracing, reinforcement learning, virtual simulations, and natural language processing.
Main Findings: AI-driven tutors proficiently identify misconceptions, deliver tailored feedback, and present multimodal simulations of relativistic phenomena. They augment conceptual comprehension, diminish cognitive load, elevate student engagement and motivation, and facilitate inquiry-based learning. Recently researches indicates enhanced conceptual precision and acceptance of Einsteinian models when artificial intelligence is incorporated with guided instruction.
Novelty/Originality of this study: This study integrates artificial intelligence technologies with conceptual change theory and Einsteinian physics education to propose a systematic pedagogical framework. It enhances understanding by demonstrating how AI operates as a cognitive collaborator, improving conceptual restructuring, metacognition, and accessibility to contemporary physics instead of supplanting educators.
References
J. Horvath, “Should we teach general relativity in high school? Why and how,” Int. J. Astron. Educ., vol. 8, pp. 30–46, 2021, doi: 10.32374/AEJ.2021.1.1.008.
T. Kaur, D. Blair, J. Moschilla, W. Stannard, and M. Zadnik, “Teaching Einsteinian physics at schools: Part 1, models and analogies for relativity,” Phys. Educ., vol. 52, no. 5, art. no. 055008, 2017, doi: 10.1088/1361-6552/aa83e4.
T. Kaur, D. Blair, J. Moschilla, and M. Zadnik, “Teaching Einsteinian physics at schools: Part 2, models and analogies for quantum physics,” Phys. Educ., vol. 52, no. 6, art. no. 065013, 2017, doi: 10.1088/1361-6552/aa83e1.
I. M. Greca and M. A. Moreira, “Mental models, conceptual models, and modeling ability: Clarification and measurement,” Int. J. Sci. Educ., vol. 24, no. 1, pp. 61–79, 2002, doi: 10.1080/09500690110098819.
A. Foppoli, R. Choudhary, D. Blair, T. Kaur, J. Moschilla, and M. Zadnik, “Public and teacher response to Einsteinian physics in schools,” Phys. Educ., vol. 54, no. 1, art. no. 015001, 2018, doi: 10.1088/1361-6552/aae4a4.
S. Vosniadou, “The development of students’ understanding of science,” Front. Educ., vol. 4, art. no. 32, 2019, doi: 10.3389/feduc.2019.00032.
R. Duit and D. F. Treagust, “Conceptual change: A powerful framework for improving science teaching and learning,” Int. J. Sci. Educ., vol. 25, no. 6, pp. 671–688, 2003, doi: 10.1080/09500690305016.
A. M. Leonardi, S. Mobilio, and C. Fazio, “An analysis of students’ misconceptions on special relativity,” in J. Phys.: Conf. Ser., vol. 2750, no. 1, art. no. 012018, 2024, doi: 10.1088/1742-6596/2750/1/012018.
R. Luckin and M. Cukurova, “Designing educational technologies in the age of AI: A learning-sciences-driven approach,” Br. J. Educ. Technol., vol. 50, no. 6, pp. 2824–2838, 2019, doi: 10.1111/bjet.12861.
W. Holmes, M. Bialik, and C. Fadel, Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston, MA, USA: Center for Curriculum Redesign, 2019.
L. S. Vygotsky, Mind in Society: The Development of Higher Psychological Processes. Cambridge, MA, USA: Harvard Univ. Press, 1978.
M. T. H. Chi and R. Wylie, “The ICAP framework: Linking cognitive engagement to active learning outcomes,” Educ. Psychol., vol. 49, no. 4, pp. 219–243, 2014, doi: 10.1080/00461520.2014.965823.
P. Alstein, K. Krijtenburg-Lewerissa, and W. R. van Joolingen, “Teaching and learning special relativity theory in secondary and lower undergraduate education: A literature review,” Phys. Rev. Phys. Educ. Res., vol. 17, no. 2, art. no. 023101, 2021, doi: 10.1103/PhysRevPhysEducRes.17.023101.
G. Vakarou, G. Stylos, and K. T. Kotsis, “Effect of didactic interventions in Einsteinian physics on students’ interest in physics,” Eur. J. Sci. Math. Educ., vol. 12, no. 2, pp. 200–210, 2024, doi: 10.30935/scimath/14303.
K. T. Kotsis, “Artificial intelligence for physics education in STEM classrooms: A narrative review within a pedagogy–technology–policy framework,” Schrödinger: J. Phys. Educ., vol. 6, no. 3, pp. 204–211, 2025, doi: 10.37251/sjpe.v6i3.2148.
R. Driver, H. Asoko, J. Leach, P. Scott, and E. Mortimer, “Constructing scientific knowledge in the classroom,” Educ. Res., vol. 23, no. 7, pp. 5–12, 1994, doi: 10.3102/0013189X023007005.
N. Mercer and C. Howe, “Explaining the dialogic processes of teaching and learning,” Learn., Cult. Soc. Interact., vol. 1, no. 1, pp. 12–21, 2012, doi: 10.1016/j.lcsi.2012.03.001.
J. Sweller, P. Ayres, and S. Kalyuga, Cognitive Load Theory. New York, NY, USA: Springer, 2011, doi: 10.1007/978-1-4419-8126-4.
R. E. Mayer, Multimedia Learning, 3rd ed. Cambridge, U.K.: Cambridge Univ. Press, 2021.
M. G. de Souza, M. Won, D. Treagust, and A. Serrano, “Visualising relativity: Assessing high school students’ understanding of complex physics concepts through AI-generated images,” Phys. Educ., vol. 59, no. 2, art. no. 025018, 2024, doi: 10.1088/1361-6552/ad1e71.
B. J. Zimmerman, “Becoming a self-regulated learner: An overview,” Theory Pract., vol. 41, no. 2, pp. 64–70, 2002, doi: 10.1207/s15430421tip4102_2.
R. Azevedo and D. Gašević, “Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: Issues and challenges,” Comput. Hum. Behav., vol. 96, pp. 207–210, 2019, doi: 10.1016/j.chb.2019.03.025.
H. Snyder, “Literature review as a research methodology: An overview and guidelines,” J. Bus. Res., vol. 104, pp. 333–339, 2019, doi: 10.1016/j.jbusres.2019.07.039.
T. Greenhalgh, J. R. Malterud, and J. Gruer, “The nature of evidence in qualitative research,” Qual. Health Res., vol. 28, no. 8, pp. 1203–1206, 2018, doi: 10.1177/1049732318780198.
S. Boublil, D. Blair, and D. F. Treagust, “Design and implementation of an Einsteinian energy learning module,” Int. J. Sci. Math. Educ., vol. 22, no. 1, pp. 49–72, 2024, doi: 10.1007/s10763-022-10348-5.
S. Boyzaqova, S. Ermatova, and K. Egamberdiyeva, “Integrating elements of quantum physics into physics education in academic lyceums,” Int. J. Artif. Intell., vol. 1, no. 4, pp. 723–730, 2025. [Online]. Available: https://inlibrary.uz/index.php/ijai/article/view/98943
P. Crosthwaite, S. Smala, and F. Spinelli, “Prompting for pedagogy? Australian F–10 teachers’ generative AI prompting use cases,” Aust. Educ. Res., vol. 52, pp. 1795–1825, 2025, doi: 10.1007/s13384-024-00787-0.
M. G. de Souza, A. Serrano, and D. Treagust, “Exploring the relationship between mental representations and conceptual understanding of special relativity by high school students,” Res. Sci. Technol. Educ., pp. 1–26, 2024, doi: 10.1080/02635143.2024.2446801.
M. G. de Souza, A. Serrano, and D. Treagust, “External mediation educational resources for teaching general relativity: A systematic review,” J. Turk. Sci. Educ., vol. 22, no. 3, pp. 561–583, 2025, doi: 10.36681/tused.2025.029.
W. J. Fassbender, “Of teachers and centaurs: Exploring the interactions and intra-actions of educators on AI education platforms,” Learn. Media Technol., vol. 50, no. 3, pp. 352–364, 2025, doi: 10.1080/17439884.2024.2447946.
V. Fu, “AI for science: Opportunities, challenges, and future directions,” TechRxiv, Feb. 2025, doi: 10.36227/techrxiv.173949768.84003950/v1.
D. Gousopoulos, “Educational approach of special relativity using a custom GPT as a teaching assistant,” EIKI J. Eff. Teach. Methods, vol. 2, no. 4, 2024, doi: 10.59652/jetm.v2i4.328.
T. Kaur et al., “Developing and implementing an Einsteinian science curriculum from years 3–10: Concepts, rationale and learning outcomes,” Phys. Educ., vol. 59, no. 6, art. no. 065008, 2024, doi: 10.1088/1361-6552/ad66a7.
K. Kotsis, “Integrating artificial intelligence into the higher education of physics: Theoretical frameworks and pedagogical strategies,” Gabaldon Int. J. Educ. Methodol., vol. 1, no. 1, pp. 1–9, 2025, doi: 10.5281/zenodo.17114953.
K. T. Kotsis, “Artificial intelligence helps primary school teachers to plan and execute physics classroom experiments,” EIKI J. Eff. Teach. Methods, vol. 2, no. 2, 2024, doi: 10.59652/jetm.v2i2.158.
K. T. Kotsis, “Correcting students’ misconceptions in physics using experiments designed by ChatGPT,” Eur. J. Contemp. Educ. E-Learn., vol. 2, no. 2, pp. 83–100, 2024, doi: 10.59324/ejceel.2024.2(2).07.
K. T. Kotsis, “Dialogic vs. structured AI in physics education: Theoretical frameworks and pedagogical implications,” Eur. J. Contemp. Educ. E-Learn., vol. 3, no. 5, pp. 18–31, 2025, doi: 10.59324/ejceel.2025.3(5).02.
K. T. Kotsis and G. Vakarou, “Bridging the gap between Newtonian and Einsteinian thinking: The impact of AI-powered teachers on physics education,” Eur. J. Contemp. Educ. E-Learn., vol. 3, no. 4, pp. 32–45, 2025, doi: 10.59324/ejceel.2025.3(4).03.
H. A. Mustofa, M. R. Bilad, and N. W. B. Grendis, “Utilizing AI for physics problem solving: A literature review and ChatGPT experience,” Lensa: J. Kependidikan Fisika, vol. 12, no. 1, pp. 78–97, 2024, doi: 10.33394/j-lkf.v12i1.11748.
M. Shafiq, M. A. Sami, N. Bano, R. Bano, and M. Rashid, “Artificial intelligence in physics education: Transforming learning from primary to university level,” Indus J. Soc. Sci., vol. 3, no. 1, pp. 717–733, 2025, doi: 10.59075/ijss.v3i1.807.
M. Serio et al., “School–university collaboration to train teachers on new topics and new tools in physics education,” J. Phys.: Conf. Ser., vol. 2950, no. 1, art. no. 012048, 2025, doi: 10.1088/1742-6596/2950/1/012048.
G. Vakarou, G. Stylos, and K. T. Kotsis, “AI for enhancing physics education: Practical tools and lesson plans,” Int. J. Sci. Math. Technol. Learn., vol. 31, no. 2, pp. 159–176, 2024, doi: 10.18848/2327-7971/CGP/v31i02/159-176.
G. Vakarou, G. Stylos, and K. T. Kotsis, “Probing students’ understanding of Einsteinian physics concepts: A study in primary and secondary Greek schools,” Phys. Educ., vol. 59, no. 2, art. no. 025004, 2024, doi: 10.1088/1361-6552/ad1768.
L. Yan, S. Greiff, Z. Teuber, and D. Gašević, “Promises and challenges of generative artificial intelligence for human learning,” Nat. Hum. Behav., vol. 8, no. 10, pp. 1839–1850, 2024, doi: 10.1038/s41562-024-02004-5.
G. J. Posner, K. A. Strike, P. W. Hewson, and W. A. Gertzog, “Accommodation of a scientific conception: Toward a theory of conceptual change,” Sci. Educ., vol. 66, no. 2, pp. 211–227, 1982, doi: 10.1002/sce.3730660207.
J. H. Flavell, “Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry,” Am. Psychol., vol. 34, no. 10, pp. 906–911, 1979, doi: 10.1037/0003-066X.34.10.906.
R. M. Ryan and E. L. Deci, “Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being,” Am. Psychol., vol. 55, no. 1, pp. 68–78, 2000, doi: 10.1037/0003-066X.55.1.68.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and acknowledge that the Schrödinger: Journal of Physics Education is the first publisher licensed under a Creative Commons Attribution 4.0 International License.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges and earlier and greater citation of published work.
.png)
.png)
.png)
.png)
.png)
