Deep Learning in Physics Education: Exploring the Potential of Mindful, Meaningful, and Joyful for a Better Learning Experience



Ria Asep Sumarni(1*), Indica Yona Okyranida(2),

(1) Scopus ID: 57205056664 - Universitas Indraprasta PGRI
(2) Universitas Indraprasta PGRI
(*) Corresponding Author

Abstract


This article explores the synergistic potential of integrating the Deep Learning  approach with the Mindful, Meaningful, and Joyful learning paradigm to enhance physics education. Traditional physics instruction often faces "fundamental barriers" in human learning, leading to a lack of student engagement and a decline in expert-like confidence. Despite efforts to improve teaching methods, significant progress in student learning outcomes remains difficult to achieve. We argue that Deep Learning , with Intelligent Tutoring System (ITS) capabilities in personalization, adaptation, and interactive simulation, can act as a powerful driver to foster mindful, meaningful, and joyful learning experiences in physics. Mindful learning enhances cognitive and emotional well-being, meaningful learning promotes deep understanding and relevance, and joyful learning nurtures intrinsic motivation and creativity.  A comprehensive review of the latest literature (2015-2025) reveals that intelligent Deep Learning -powered tutoring systems, adaptive learning environments, virtual laboratories, personalized feedback mechanisms, and gamification strategies can collectively transform physics pedagogy. This integration encourages increased student engagement, better conceptual understanding, critical thinking, problem-solving skills, and more positive emotional involvement, thereby creating a more effective and sustainable learning journey. It is concluded that the holistic framework integrating Deep Learning with this pedagogical philosophy offers a promising path to address long-standing challenges in physics education. 


Keywords


Deep Learning; Physics Learning; Literature Revew, Intelligent Tutoring System (ITS)

Full Text:

PDF

References


Akyuz, Y. (2020). Effects of intelligent tutoring systems (ITS) on personalized learning (PL). Creative Education, 11(06), 953.

Davis, J. P., & Price, W. A. (2017). Deep Learning for teaching university physics to computers. American Journal of Physics, 85(4), 311-312.

Dai, C. P., Ke, F., Pan, Y., Moon, J., & Liu, Z. (2024). Effects of artificial intelligence-powered virtual agents on learning outcomes in computer-based simulations: A meta-analysis. Educational Psychology Review, 36(1), 31.

Davis, J. P., & Price, W. A. (2017). Deep Learning for teaching university physics to computers. American Journal of Physics, 85(4), 311-312.

Feriyanto, F., & Anjariyah, D. (2024). Deep Learning Approach Through Meaningful, Mindful, and Joyful Learning: A Library Research. Electronic Journal of Education, Social Economics and Technology, 5(2), 208-212.

Ghazali, N., Nordin, M. S., Abdullah, A., & Ayub, A. F. M. (2020). The Relationship between Students' MOOC-Efficacy and Meaningful Learning. Asian Journal of University Education, 16(3), 89-101.

Guo, X., & Depaynos, J. L. (2023). Physics Experiment Teaching Based on Deep Learning . International Journal of New Developments in Education, 5(9).

Lara, J. (2024). AI-powered laboratory diagnostics technology. In Recent Advancements in the Diagnosis of Human Disease (pp. 1-45). CRC Press.

Madsen, A., McKagan, S. B., & Sayre, E. C. (2015). How physics instruction impacts students’ beliefs about learning physics: A meta-analysis of 24 studies. Physical Review Special Topics-Physics Education Research, 11(1), 010115.

Maftuh, M. S. J., Lawal, U. S., Ade, M., Lama, A. V., & Adzim, A. F. (2023). Understanding learning strategies: a comparison between contextual learning and problem-based learning. Educazione: Journal of Education and Learning, 1(1), 54-65.

Nurulwati, N., Khairina, L., & Huda, I. (2020, February). The effect of students self-efficacy on the learning outcomes in learning physics. In Journal of Physics: Conference Series (Vol. 1460, No. 1, p. 012113). IOP Publishing.

Prabowo, J. (2025). Enhancing Writing Skills through Joyful Learning: A Comparative Study of Extroverted and Introverted Fourth-Semester English Department Students at a Public University in Banten. Journal of Linguistics, Literacy, and Pedagogy, 4(1), 26-39.

Prince, S. J. (2023). Understanding Deep Learning . MIT press.

Richter, K., & Kickmeier-Rust, M. (2025). Gamification in physics education: play your way to better learning. International Journal of Serious Games, 12(1), 59-81.

Rus, V., Maharjan, N., & Banjade, R. (2016). Dialogue act classification in human-to-human tutorial dialogues. In Innovations in smart learning (pp. 185-188). Singapore: Springer Singapore.

Shafiq, M., Sami, M. A., Bano, N., Bano, R., & Rashid, M. (2025). Artificial intelligence in physics education: Transforming learning from primary to university level. Indus Journal of Social Sciences, 3(1), 717-733.

Shelach Inbar, O., & Tarrasch, R. (2025). The Effects of Integrating Mindfulness Exercises into the Elementary Science Curriculum: A Cluster, Randomized, Controlled Trial. Education Sciences, 15(4), 478.

Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI‐driven adaptive learning for sustainable educational transformation. Sustainable Development, 33(2), 1921-1947.

Suresh, R., Bishnoi, H., Kuklin, A. V., Parikh, A., Molokeev, M., Harinarayanan, R., ... & Hiba, P. (2024). Revolutionizing physics: a comprehensive survey of machine learning applications. Frontiers in Physics, 12, 1322162.

Tuyboyov, O., Sharipova, N., Ergasheva, L., & Nasirdinova, S. (2025, February). The role and impact of AI-enhanced virtual laboratories in mechanical engineering education. In AIP Conference Proceedings (Vol. 3268, No. 1, p. 070019). AIP Publishing LLC.

Wen, X., Zhang, Q., Liu, X., Du, J., & Xu, W. (2021). Momentary and longitudinal relationships of mindfulness to stress and anxiety among Chinese elementary school students: mediations of cognitive flexibility, self-awareness, and social environment. Journal of Affective Disorders, 293, 197-204.




DOI: https://doi.org/10.30998/npjpe.v7i1.4215

Article Metrics

Abstract Views : 616 | PDF Views : 451

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Ria Asep Sumarni, Indica Yona Okyranida

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Department of Physics Education
Faculty of Mathematics and Sciences
Universitas Indraprasta PGRI

Address: Jl. Raya Tengah No. 80, Kel. Gedong, Kec. Pasar Rebo, Jakarta Timur 13760 , Jakarta, Indonesia. 
Phone: +62 (021) 7818718 – 78835283 | Close in sunday and public holidays in Indonesia
Work Hours: 09.00 AM – 08.00 PM
Best hours to visit: From 9 am to 11 am or after 3 pm. The busiest times are between 11 am and 3 pm. 

Creative Commons License
Navigation Physics: Journal of Physics Education is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License