Generative AI Scaffolding in Physics Education: A Phenomenological Analysis of Its Role and Implications in STEM Learning
Abstract
Purpose of the study: This study investigates how generative AI tools—especially video generation—scaffold high school students’ understanding of Newtonian mechanics, focusing on female learners in a STEM Honors Physics class. It explores how these tools impact conceptual mastery, critical thinking, creativity, and students’ perceptions of AI use in education.
Methodology: Using a phenomenological qualitative design, the study involved 17 female students. It followed a three-phase structure—preparatory, scaffolding, and post-discourse—with tools like AI-generated videos, simulations, TAM-based surveys, and reflective journals, grounded in Constructivist Learning Theory and the Technology Acceptance Model.
Main Findings: AI-enhanced visualizations improved students’ conceptual understanding and learning efficiency. Students gained critical thinking through prompt refinement and creativity. Ethical concerns and AI accuracy issues were noted. Overall, students showed moderate satisfaction, ease of use, and usefulness perceptions, but cautious intentions toward future AI use.
Novelty/Originality of this study: This is among the first studies to apply generative AI hypermedia in high school physics education through a structured, theory-driven framework. It uniquely highlights gender-specific engagement, ethical considerations, and practical integration of AI in fostering deeper conceptual and creative STEM learning.
References
E. Chng, A. L. Tan, and S. C. Tan, “Examining the use of emerging technologies in schools: a review of artificial intelligence and immersive technologies in STEM education,” Journal for STEM Education Research, vol. 6, no. 3, p. 385, Apr. 2023, doi: 10.1007/s41979-023-00092-y.
F. Mahligawati, E. Allanas, M. H. Butarbutar, and N. A. N. Nordin, “Artificial intelligence in physics education: a comprehensive literature review,” Journal of Physics Conference Series, vol. 2596, no. 1, p. 12080, Sep. 2023, doi: 10.1088/1742-6596/2596/1/012080.
W. Xu and F. Ouyang, “The application of AI technologies in STEM education: a systematic review from 2011 to 2021,” International Journal of STEM Education, vol. 9, no. 1, Sep. 2022, doi: 10.1186/s40594-022-00377-5.
R. J. Collado, “Reducing the gender gap in spatial skills in high school physics,” Journal of Physics Conference Series, vol. 1286, no. 1, p. 12013, Aug. 2019, doi: 10.1088/1742-6596/1286/1/012013.
Mordor Intelligence, “AI In Education Market Analysis.” [Online]. Available: https://www.mordorintelligence.com/industry-reports/ai-in-education-market
J. Lambert and M. Stevens, “ChatGPT and generative AI technology: A mixed bag of concerns and new opportunities,” Computers in the Schools, vol. 41, no. 4, p. 559, Sep. 2023, doi: 10.1080/07380569.2023.2256710.
N. Kshetri, Y. Dwivedi, T. Davenport, and N. Panteli, “Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda,” International Journal of Information Management, vol. 75, p. 102716, Oct. 2023, doi: 10.1016/j.ijinfomgt.2023.102716.
Y. Song, K. Wu, and J. Ding, “Developing an immersive game-based learning platform with generative artificial intelligence and virtual reality technologies – ‘LearningverseVR,’” Computers & Education X Reality, vol. 4, p. 100069, Jan. 2024, doi: 10.1016/j.cexr.2024.100069.
E. Hussein, M. A. Hussein, and M. Al-Hendawi, “Investigation into the applications of Artificial Intelligence (AI) in special education: a literature review,” Social Sciences, vol. 14, no. 5, p. 288, May 2025, doi: 10.3390/socsci14050288.
E. Kasneci et al., “ChatGPT for good? on opportunities and challenges of large language models for education,” Learning and Individual Differences, vol. 103, p. 102274, Mar. 2023, doi: 10.1016/j.lindif.2023.102274.
I. T. Sanusi, S. S. Oyelere, H. Vartiainen, J. Suhonen, and M. Tukiainen, “A systematic review of teaching and learning machine learning in K-12 education,” Education and Information Technologies, vol. 28, no. 5, p. 5967, Nov. 2022, doi: 10.1007/s10639-022-11416-7.
G. Lee et al., “Multimodality of AI for education: towards artificial general intelligence,” IEEE Transactions on Learning Technologies, p. 1, Jan. 2025, doi: 10.1109/tlt.2025.3574466.
M. Bond et al., “A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour,” International Journal of Educational Technology in Higher Education, vol. 21, no. 1, Jan. 2024, doi: 10.1186/s41239-023-00436-z.
A. Alamäki, C. Nyberg, A. Kimberley, and A. O. Salonen, “Artificial intelligence literacy in sustainable development: a learning experiment in higher education,” Frontiers in Education, vol. 9, Mar. 2024, doi: 10.3389/feduc.2024.1343406.
C. K. Y. Chan, “A comprehensive AI policy education framework for university teaching and learning,” International Journal of Educational Technology in Higher Education, vol. 20, no. 1, Jul. 2023, doi: 10.1186/s41239-023-00408-3.
A. Ateeq, M. Alzoraiki, M. Milhem, and R. A. Ateeq, “Artificial intelligence in education: implications for academic integrity and the shift toward holistic assessment,” Frontiers in Education, vol. 9, Oct. 2024, doi: 10.3389/feduc.2024.1470979.
S. Kohnke and T. Zaugg, “Artificial intelligence: an untapped opportunity for equity and access in STEM education,” Education Sciences, vol. 15, no. 1, p. 68, Jan. 2025, doi: 10.3390/educsci15010068.
R. Toma, I. Yánez-Pérez, and J. Meneses-Villagrá, “Towards a socio-constructivist didactic model for integrated STEM education,” Interchange, vol. 55, no. 1, p. 75, Feb. 2024, doi: 10.1007/s10780-024-09513-2.
S. Wibowo, M. Nur Wangid, and F. M. Firdaus, “The relevance of Vygotsky’s constructivism learning theory with the differentiated learning primary schools,” Journal of Education and Learning (EduLearn), vol. 19, no. 1, p. 431, Jul. 2024, doi: 10.11591/edulearn.v19i1.21197.
T. A. Hà, “Pretend play and early language development—relationships and impacts: a comprehensive literature review,” Journal of Education, vol. 202, no. 1, p. 122, Nov. 2020, doi: 10.1177/0022057420966761.
C. Pacaci, U. Ustun, and O. Faruk Ozdemir, “Effectiveness of conceptual change strategies in science education: A meta‐analysis,” Journal of Research in Science Teaching, Jul. 2023, doi: 10.1002/tea.21887.
K. Lambright, “The effect of a teacher’s mindset on the cascading zones of proximal development: a systematic review,” Technology Knowledge and Learning, vol. 29, no. 3, p. 1313, Oct. 2023, doi: 10.1007/s10758-023-09696-0.
M. M. Msambwa, Z. Wen, and K. Daniel, “The impact of AI on the personal and collaborative learning environments in higher education,” European Journal of Education, vol. 60, no. 1, Jan. 2025, doi: 10.1111/ejed.12909.
Q. Xia, X. Weng, F. Ouyang, T. Lin, and T. K. F. Chiu, “A scoping review on how generative artificial intelligence transforms assessment in higher education,” International Journal of Educational Technology in Higher Education, vol. 21, no. 1, May 2024, doi: 10.1186/s41239-024-00468-z.
M. Shafie Rosli, N. Shela Saleh, A. Md. Ali, S. Abu Bakar, and L. Mohd Tahir, “A systematic review of the technology acceptance model for the sustainability of higher education during the COVID-19 Pandemic and identified research gaps,” Sustainability, vol. 14, no. 18, p. 11389, Sep. 2022, doi: 10.3390/su141811389.
F. de O. Santini et al., “Understanding students’ technology acceptance behaviour : a meta-analytic study,” Technology in Society, p. 102798, Dec. 2024, doi: 10.1016/j.techsoc.2024.102798.
L. Yan, S. Greiff, Z. Teuber, and D. Gašević, “Promises and challenges of generative artificial intelligence for human learning,” Nature Human Behaviour, vol. 8, no. 10, p. 1839, Oct. 2024, doi: 10.1038/s41562-024-02004-5.
H. W. Chua and Z. A. Yu, “A systematic literature review of the acceptability of the use of Metaverse in education over 16 years,” Journal of Computers in Education, vol. 11, no. 2, p. 615, May 2023, doi: 10.1007/s40692-023-00273-z.
E. S. Vorm and D. J. Y. Combs, “Integrating transparency, trust, and acceptance: the Intelligent Systems Technology Acceptance Model (ISTAM),” International Journal of Human-Computer Interaction, vol. 38, p. 1828, May 2022, doi: 10.1080/10447318.2022.2070107.
H. Taherdoost, “A Critical Review of Blockchain Acceptance Models—Blockchain Technology Adoption Frameworks and Applications,” Computers, vol. 11, no. 2, p. 24, Feb. 2022, doi: 10.3390/computers11020024.
J. David Bryan and T. Zuva, “A review on TAM and TOE framework progression and how these models integrate,” Advances in Science Technology and Engineering Systems Journal, vol. 6, no. 3, p. 137, May 2021, doi: 10.25046/aj060316.
M. M. Cioc, Ș. C. Popa, A. A. Olariu, C. F. Popa, and C.-B. Nica, “Behavioral intentions to use energy efficiency smart solutions under the impact of social influence: an extended TAM approach,” Applied Sciences, vol. 13, no. 18, p. 10241, Sep. 2023, doi: 10.3390/app131810241.
A. S. Al-Adwan, N. Li, A. Al-Adwan, G. Ali Abbasi, N. A. Albelbisi, and A. Habibi, “Extending the Technology Acceptance Model (TAM) to predict university students’ intentions to use metaverse-based learning platforms”, Education and Information Technologies, vol. 28, no. 11, p. 15381, Apr. 2023, doi: 10.1007/s10639-023-11816-3.
A. Hooda, P. Gupta, A. Jeyaraj, M. Giannakis, and Y. K. Dwivedi, “The effects of trust on behavioral intention and use behavior within e-government contexts,” International Journal of Information Management, vol. 67, p. 102553, Aug. 2022, doi: 10.1016/j.ijinfomgt.2022.102553.
O. T. Adigun, N. Mpofu, and M. C. Maphalala, “Fostering self‐directed learning in blended learning environments: a constructivist perspective in higher education,” Higher Education Quarterly, Sep. 2024, doi: 10.1111/hequ.12572.
M. Al-kfairy, “Factors impacting the adoption and acceptance of ChatGPT in educational settings: a narrative review of empirical studies,” Applied System Innovation, vol. 7, no. 6, p. 110, Nov. 2024, doi: 10.3390/asi7060110.
N. Carter, D. Bryant‐Lukosius, A. DiCenso, J. Blythe, and A. J. Neville, “The use of triangulation in qualitative research,” Oncology nursing forum, vol. 41, no. 5, p. 545, Aug. 2014, doi: 10.1188/14.onf.545-547.
H. Noble and J. Smith, “Issues of validity and reliability in qualitative research,” Evidence-Based Nursing, vol. 18, no. 2, p. 34, Feb. 2015, doi: 10.1136/eb-2015-102054.
U. Flick, “Doing Triangulation and Mixed Methods,” Jan. 2018, doi: 10.4135/9781529716634.
D. P. Panagoulias, E. Sarmas, V. Marinakis, M. Virvou, G. A. Tsihrintzis, and H. Doukas, “Intelligent decision support for energy management: a methodology for tailored explainability of artificial intelligence analytics,” Electronics, vol. 12, no. 21, p. 4430, Oct. 2023, doi: 10.3390/electronics12214430.
C. Ferguson, E. L. van den Broek, and H. van Oostendorp, “AI-induced guidance: preserving the optimal zone of proximal development,” Computers and Education Artificial Intelligence, vol. 3, p. 100089, Jan. 2022, doi: 10.1016/j.caeai.2022.100089.
L. Cai, M. M. Msambwa, and D. Kangwa, “Exploring the impact of integrating AI tools in higher education using the zone of proximal development,” Education and Information Technologies, Oct. 2024, doi: 10.1007/s10639-024-13112-0.
C. Chen and C.-L. Chang, “Effectiveness of AI-assisted game-based learning on science learning outcomes, intrinsic motivation, cognitive load, and learning behavior,” Education and Information Technologies, vol. 29, no. 14, p. 18621, Mar. 2024, doi: 10.1007/s10639-024-12553-x.
R. Weijers et al., “From Intuition to Understanding: Using AI Peers to Overcome Physics Misconceptions,” 2025, doi: 10.48550/ARXIV.2504.00408.
T. Li, Y. Ji, and Z. Zhan, “Expert or machine? Comparing the effect of pairing student teacher with in-service teacher and ChatGPT on their critical thinking, learning performance, and cognitive load in an integrated-STEM course,” Asia Pacific Journal of Education, vol. 44, no. 1, p. 45, Jan. 2024, doi: 10.1080/02188791.2024.2305163.
R. AlShaikh, N. Al-Malki, and M. Almasre, “The implementation of the cognitive theory of multimedia learning in the design and evaluation of an AI educational video assistant utilizing large language models,” Heliyon, vol. 10, no. 3, Feb. 2024, doi: 10.1016/j.heliyon.2024.e25361.
M. M. North and S. North, “Dynamic immersive visualisation environments: enhancing pedagogical techniques,” Australian Journal of Information Systems, vol. 23, May 2019, doi: 10.3127/ajis.v23i0.2109.
H.-P. Hsu, “From programming to prompting: developing computational thinking through large language model-based generative artificial intelligence,” TechTrends, Feb. 2025, doi: 10.1007/s11528-025-01052-6.
A. M. Al-Abdullatif, “modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of ai literacy, intelligent TPACK, and perceived trust,” Education Sciences, vol. 14, no. 11, p. 1209, Nov. 2024, doi: 10.3390/educsci14111209.
J. N. Montes and J. Elizondo-García, “Faculty acceptance and use of generative artificial intelligence in their practice,” Frontiers in Education, vol. 10, Feb. 2025, doi: 10.3389/feduc.2025.1427450.
K. Stolpe and J. Hällström, “Artificial intelligence literacy for technology education,” Computers and Education Open, vol. 6, p. 100159, Jan. 2024, doi: 10.1016/j.caeo.2024.100159.
E. Bauer, S. Greiff, A. C. Graesser, K. Scheiter, and M. Sailer, “Looking beyond the hype: understanding the effects of AI on learning,” Educational Psychology Review, vol. 37, no. 2, Apr. 2025, doi: 10.1007/s10648-025-10020-8.
J. Kamali, M. F. Alpat, and A. Bozkurt, “AI ethics as a complex and multifaceted challenge: decoding educators’ AI ethics alignment through the lens of activity theory,” International Journal of Educational Technology in Higher Education, vol. 21, no. 1, Dec. 2024, doi: 10.1186/s41239-024-00496-9.
N. Z. Bako, “Ethical AI in schools: balancing automation, privacy, and human oversight,” World Journal of Advanced Engineering Technology and Sciences, vol. 15, no. 1, p. 924, Jan. 2025, doi: 10.30574/wjaets.2025.15.1.0262.
N. N. Varghese, B. Jose, T. Bindhumol, A. Cleetus, and S. B. Nair, “The power duo: unleashing cognitive potential through human-AI synergy in STEM and non-STEM education,” Frontiers in Education, vol. 10, Mar. 2025, doi: 10.3389/feduc.2025.1534582.
J. Lademann, J. Henze, and S. Becker-Genschow, “Augmenting learning environments using AI custom chatbots: Effects on learning performance, cognitive load, and affective variables,” Physical Review Physics Education Research, vol. 21, no. 1, May 2025, doi: 10.1103/physrevphyseducres.21.010147.
J. W. Hur, “Fostering AI literacy: overcoming concerns and nurturing confidence among preservice teachers,” Information and Learning Sciences, Jul. 2024, doi: 10.1108/ils-11-2023-0170.
L. K. Allen and P. Kendeou, “ED-AI Lit: an interdisciplinary framework for AI literacy in education,” Policy Insights from the Behavioral and Brain Sciences, vol. 11, no. 1, p. 3, Dec. 2023, doi: 10.1177/23727322231220339.
M. Cukurova, “The interplay of learning, analytics and artificial intelligence in education: a vision for hybrid intelligence,” British Journal of Educational Technology, Aug. 2024, doi: 10.1111/bjet.13514.
S. Nurjanah, N. A. Martaputri, Z. Zamzami, I. K. Suardi, and H. K. Hulu, “Artificial intelligence in physics education research in two decades: a bibliometric study from scopus database,” Jurnal Pendidikan Fisika, vol. 12, no. 2, p. 53, Apr. 2024, doi: 10.26618/jpf.v12i2.14745.
S. V. Chinta et al., “FairAIED: navigating fairness, bias, and ethics in educational AI applications,” arXiv (Cornell University), Jul. 2024, doi: 10.48550/arxiv.2407.18745.
Y. Meng, W. Xu, Z. Liu, and Z.-G. Yu, “Scientometric analyses of digital inequity in education: problems and solutions,” Humanities and Social Sciences Communications, vol. 11, no. 1, Aug. 2024, doi: 10.1057/s41599-024-03480-w.
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