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With the rapid advancements in technology, the educational landscape is witnessing significant transformations in pedagogy and classroom dynamics. Two prominent technologies, Artificial Intelligence (AI) and Augmented Reality (AR), are gaining prominence in the field of education, promising to revolutionize the way teaching and learning take place. This article explores the potential benefits, challenges, and practical applications of integrating AI and AR into the teaching process to enhance student engagement and learning outcomes. The integration of AI in education brings forth personalized learning experiences. AI-powered algorithms analyze vast amounts of student data, including learning patterns, strengths, and weaknesses, to create tailored learning paths. This individualized approach helps educators identify students’ unique needs and provide targeted support, ensuring that no student is left behind. More-over, AI-based chatbots and virtual teaching assistants are increasingly being used to address student queries promptly, providing real-time support and fostering a more interactive learning environment. AR, on the other hand, enables the overlay of virtual objects and information in the real-world environment. Students can explore complex concepts through visualizations, simulations, and interactive demonstrations, facilitating a deeper understanding of abstract topics. AR also fosters collaboration and teamwork among students, promoting active learning and peer-to-peer knowledge sharing. Combining AI and AR technologies offers a powerful synergy in the educational realm. AI can analyze AR-generated data and adapt instructional strategies in real time, responding to individual students’ progress. This synergy not only enhances learning outcomes but also empowers teachers with data-driven insights, enabling them to make informed decisions about their teaching methodologies. However, successfully implementing AI and AR in education comes with its challenges. Issues related to data privacy, ethical considerations, and the need for effective teacher training in utilizing these technologies require careful attention. Additionally, the digital divide can exacerbate educational inequalities, as not all students have equal access to technology outside the classroom. Collaboration between educators, researchers, and technology developers is crucial to overcome these challenges. The development of user-friendly, accessible, and ethically sound AI and AR tools can ensure inclusivity and maximize the potential benefits of these technologies in education. The aim is to investigate whether the use of AR technology can enhance students’ understanding and mastery of physics concepts through visualizations and simulations. Spatial intelligence plays a crucial role in various subjects, including physics, as it enables students to create mental models and representations of objects and expressions. While spatial intelligence is not an innate skill, it can be developed through interactions with real and virtual objects. ARas a cutting-edge technology, has the potential to illustrate physical applications and significantly aid students in visualizing and comprehending complex physics concepts.
Rocznik
Tom
Strony
57--61
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
autor
- Sidi Mohammed Ben Abdellah University, Faculty of Sciences Dhar El Mahraz, LISAC Laboratory, Fez, Morocco
autor
- Sidi Mohammed Ben Abdellah University, High Normal School (ENS), Fez, Morocco
Bibliografia
- [1] E. Klopfer, K. Squire, “Environmental Detectives—the development of an augmented reality platform for environmental simulations,”Education Tech Research Dev, vol. 56, 2008, pp. 203–228. doi: 10.1007/s11423-007-9037-6.
- [2] H.K. Wu, S.W.Y. Lee, H.Y. Chang, J.C. Liang, “Current status, opportunities and challenges of augmented reality in education,” Computers & Education, vol. 62, 2013, pp. 41–49. doi: 10.1016/j.compedu.2012.10.024.
- [3] M. Akçayır, G. Akçayır, “Advantages and challenges associated with augmented reality foreducation: A systematic review of the literature,” Educational Research Review, vol. 20, 2017, pp. 1–11. doi: 10.1016/j.edurev.2016.11.002.
- [4] S. Kucuk, N. Sener, “A meta-analysis on the effects of augmented reality applications in learning physics,” Computers & Education, vol. 125, 2018, pp. 390–399. doi: 10.1002/cae.22628.
- [5] M. Karal, “The effect of using augmented reality and sensing technology to teach magnetism in high school physics,” Technology Pedagogy and Education, vol. 29, no. 1, 2020, pp. 1–18. doi: 10.1080/1475939X.2020.1766550.
- [6] J. Wang, Z. Lei, C. Sun, “The effects of augmented reality on student engagement, achievement, and immersion in science education,” Journal of Educational Technology & Society, vol. 21, no. 3, 2018, pp. 222–234. doi: 10.30935/cet.444119.
- [7] I. Radu, X. Huang, G. Kestin, B. Schneider, “Enhancing physics learning through augmented, How augmented reality infuences student learning and inquiry styles: A study of 1-1 physics remote AR tutoring,” Computers & Education: X Reality, vol. 2, 2023, p. 100011. doi: 10.1016/j.cexr.2023.100011.
- [8] P. Zaharias, D, Michael, “Effects of Augmented Reality on Student Achievement and Self-Efficacy in Vocational Education and Training,” Journal of Science Education and Technology, vol. 27, no. 6, pp. 551-564. doi: 10.13152/IJRVET.5.1.1.
- [9] M. Sirakaya, E.K. Cakmak, “Augmented reality as a tool to improve students’ spatial ability: An empirical study,” International Journal ForResearch In Vocational Education And Training, vol. 5, 2018, pp. 1–18. doi: 10.13152/IJRVET. 5.1.1.
- [10] J. Cabero-Almenara, “Educational Uses of Augmented Reality (AR): Experiences in EducationalScience,” Sustainability, vol. 11, no. 18, p. 4990.doi: 10.3390/su11184990.
- [11] Y. Lin, Z. Yu, “A meta-analysis of the effects of augmented reality technologies in interactive learning environments,” Computer Application And Engineering Education, 02 April 2023. doi:10.1002/cae.22628.
- [12] S. Özeren, E. Top, “The effects of augmentedreality applications on the academic achievement and motivation of secondary school students,” Malaysian Online Journal of Educational Technology, vol. 11, no. 1, 2023, pp. 25-40. doi: 10.52380/mojet.2023.11.1.425.
- [13] A. Vidak, I. Movre Šapić, V. Mešić, “AR in teaching about physics: first findings from a systematic review,” Journal of Physics: Conference Series, vol. 2415. doi 10.1088/1742-6596/2415/1/012008.
- [14] A. Ismail, I. Festiana, T.I. Hartini, Y. Yusal, A. Malik, “Enhancing students’ conceptual understanding of electricity using learning media-based augmented reality,” Journal of Physics Conference Series, vol. 1157, no. 3. doi: 10.1088/1742-6596/1157/3/032049.
- [15] El Mallahi, I., Riffi, J., Tairi, H., Ez-Zahout, A., & Mahraz, M. A. (2023). A Distributed Big Data Analytics Models for Traffic Accidents Classification and Recognition based SparkMlLib Cores. Journal of Automation, Mobile Robotics and Intelligent Systems, 16(4), 62–71. doi: 10.14313/JAMRIS/4-2022/34.
- [16] Rahman Shafique, Furqan Rustam, Sheriff Murtala, Anca Delia Jurcut, Gyu Sang Choi, “Advancing Autonomous Vehicle Safety: Machine Learning to Predict Sensor-Related Accident Severity”, IEEE Access, vol. 12, pp. 25933–25948, 2024.
- [17] Nassim Sohaee, Shahram Bohluli, “Nonlinear Analysis of the Effects of Socioeconomic, Demographic, and Technological Factors on the Number of Fatal Traffic Accidents”, Safety, vol. 10, no. 1, pp. 11, 2024
- [18] I. E. Mallahi, A. Dlia, J. Riffi, M. A. Mahraz and H. Tairi, “Prediction of Traffic Accidents using Random Forest Model,” 2022 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, 2022, pp. 1–7, doi: 10.1109/ISCV54655.2022.9806099.
- [19] Nasry, A., Ezzahout, A., and Omary, F. (2023). People Tracking in Video Surveillance Systems Based on Artificial Intelligence. Journal of Automation, Mobile Robotics and Intelligent Systems, 17(1), 59–68. doi: 10.14313/JAMRIS/1-2023/8.
- [20] Ndayikengurukiye, A., Ez-zahout, A., Aboubakr, A., Charkaoui, Y., & Fouzia, O. (2022). Resource Optimisation in Cloud Computing: Comparative Study of Algorithms Applied to Recommendations in a Big Data Analysis Architecture. Journal of Automation, Mobile Robotics and Intelligent Systems, 15(4), 65–75. doi: 10.14313/JAMRIS/4-2021/28
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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