Enhancing Student Learning Autonomously: Exploring the Global Impact of Artificial Intelligence
DOI:
https://doi.org/10.12928/eltej.v6i2.9100Keywords:
Artificial Intelligence (AI), Autonomous Learning, Teacher Perceptions, Educational TechnologyAbstract
This study investigates the global impact of Artificial Intelligence (AI) on enhancing student learning autonomously through a mixed-method approach. By combining both qualitative and quantitative data collection and analysis methods, this research provides a comprehensive understanding of the role of AI in autonomous learning as perceived by teachers. The study involves 25 teachers from SD Muhammadiyah Kebumen as participants, representing a diverse educational context. The qualitative analysis delves into the rich tapestry of educators' experiences and perspectives, shedding light on the multifaceted nature of their interactions with AI in the classroom. This qualitative component allows for an in-depth exploration of how teachers perceive and engage with AI in their teaching practices. Additionally, the quantitative analysis quantifies teachers' perceptions and offers statistical evidence of the impact of AI on student learning outcomes. Through surveys and data-driven analysis, the study assesses the extent to which AI influences student learning autonomously. The triangulation of these findings validates and complements each other, reinforcing the positive perception of AI's role in education. However, the research also highlights the need for addressing ethical concerns surrounding AI implementation and the importance of providing comprehensive support mechanisms for teachers navigating the integration of AI in the classroom. These findings contribute to the ongoing discourse on AI in education, offering insights into its potential benefits and challenges while emphasizing the importance of teacher training and ethical considerations in leveraging AI for autonomous student learning.
References
Ali, Z. (2020). Artificial Intelligence (AI): A Review of Its Uses in Language Teaching and Learning. IOP Conference Series: Materials Science and Engineering. https://doi.org/10.1088/1757-899x/769/1/012043
An-nisa, N., Astika, G. A., & Suwartono, T. (2021). Millennials, Technology, and English Language Teaching. Tarling Journal of Language Education. https://doi.org/10.24090/tarling.v5i1.4072
Chun, D. M., Smith, B. R., & Kern, R. (2016). Technology in Language Use, Language Teaching, and Language Learning. Modern Language Journal. https://doi.org/10.1111/modl.12302
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V, Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fu, S., Gu, H., & Yang, B. (2020). The Affordances of AI‐enabled Automatic Scoring Applications on Learners’ Continuous Learning Intention: An Empirical Study in China. British Journal of Educational Technology. https://doi.org/10.1111/bjet.12995
Gacanin, H. (2019). Autonomous Wireless Systems With Artificial Intelligence: A Knowledge Management Perspective. Ieee Vehicular Technology Magazine. https://doi.org/10.1109/mvt.2019.2920162
Haristiani, N. (2019). Artificial Intelligence (AI) Chatbot as Language Learning Medium: An Inquiry. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1387/1/012020
Hoopingarner, D. (2009). Best Practices in Technology and Language Teaching. Language and Linguistics Compass. https://doi.org/10.1111/j.1749-818x.2008.00123.x
Huang, S. (2020). Research on the Application of Artificial Intelligence in Teaching Chinese as a Foreign Language. https://doi.org/10.2991/assehr.k.201030.041
Kaldirim, A., & Tavşanli, Ö. F. (2021). A Thematic Review of Using Digital Teaching Technologies in Turkish Language Teaching. Journal of Educational Technology and Online Learning. https://doi.org/10.31681/jetol.898014
Kawinkoonlasate, P. (2019). Integration in Flipped Classroom Technology Approach to Develop English Language Skills of Thai EFL Learners. English Language Teaching. https://doi.org/10.5539/elt.v12n11p23
Khan, F., Pasha, M. F., & Masud, S. (2021). Advancements in Microprocessor Architecture for Ubiquitous AI—An Overview on History, Evolution, and Upcoming Challenges in AI Implementation. Micromachines. https://doi.org/10.3390/mi12060665
Kim, J. M., Huh, J.-H., Jung, S.-H., & Sim, C.-B. (2021). A Study on an Enhanced Autonomous Driving Simulation Model Based on Reinforcement Learning Using a Collision Prevention Model. Electronics. https://doi.org/10.3390/electronics10182271
Li, R. (2020). Using Artificial Intelligence in Learning English as a Foreign Language: An Examination of IELTS LIULISHUO as an Online Platform. Journal of Higher Education Research. https://doi.org/10.32629/jher.v1i2.178
Li, X. (2017). The Construction of Intelligent English Teaching Model Based on Artificial Intelligence. International Journal of Emerging Technologies in Learning (Ijet). https://doi.org/10.3991/ijet.v12i12.7963
Martinez-Marroquin, E., Chau, M., Turner, M., Haxhimolla, H., & Paterson, C. (2023). Use of artificial intelligence in discerning the need for prostate biopsy and readiness for clinical practice: a systematic review protocol. Systematic Reviews, 12(1). https://doi.org/10.1186/s13643-023-02282-6
Pérez-Gil, Ó., Barea, R., López-Guillén, E., Bergasa, L. M., Gómez-Huélamo, C., Gutiérrez, R. A., & Díaz-Díaz, A. (2022). Deep Reinforcement Learning Based Control for Autonomous Vehicles in CARLA. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-021-11437-3
Riskin, D., Cady, R., Shroff, A., Hindiyeh, N. A., Smith, T., & Kymes, S. (2023). Using artificial intelligence to identify patients with migraine and associated symptoms and conditions within electronic health records. BMC Medical Informatics and Decision Making, 23(1). https://doi.org/10.1186/s12911-023-02190-8
Sathya, K. B. S., Jebamani, B. J. A., & Fowjiya, S. (2022). Deep Learning. https://doi.org/10.4018/978-1-6684-6001-6.ch001
Schlaeger, S., Shit, S., Eichinger, P., Hamann, M., Opfer, R., Krüger, J., Dieckmeyer, M., Schön, S., Mühlau, M., Zimmer, C., Kirschke, J. S., Wiestler, B., & Hedderich, D. M. (2023). AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis. Insights into Imaging, 14(1). https://doi.org/10.1186/s13244-023-01460-3
Seng, K. P., Ang, L.-M., Peter, E. E., & Mmonyi, A. C. (2022). Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing. Electronics. https://doi.org/10.3390/electronics11142239
Siebert, L. C., Lupetti, M. L., Aizenberg, E., Beckers, N., Zgonnikov, A., Veluwenkamp, H., Abbink, D. A., Giaccardi, E., Houben, G.-J., Jonker, C. M., Hoven, J. van den, Forster, D., & Lagendijk, R. L. (2022). Meaningful Human Control: Actionable Properties for AI System Development. Ai and Ethics. https://doi.org/10.1007/s43681-022-00167-3
Smith, B. R., & González-Lloret, M. (2020). Technology-Mediated Task-Based Language Teaching: A Research Agenda. Language Teaching. https://doi.org/10.1017/s0261444820000233
Sun, X., Yin, Y., Yang, Q., & Huo, T. (2023). Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. European Journal of Medical Research, 28(1). https://doi.org/10.1186/s40001-023-01065-y
Sutrisno, D. (2022). Fostering student’s critical reading through technology integrated instruction. Teaching English as a Foreign Language Journal, 1(2), 125–134.
Yin, N. (2021). Research on the Impacts of Artificial Intelligence Technology on Language Teaching Innovation. Frontiers in Educational Research. https://doi.org/10.25236/fer.2021.040706
Zhang, C., Wang, J., Yen, G. G., Zhao, C., Sun, Q., Tang, Y., Qian, F., & Kurths, J. (2020). When Autonomous Systems Meet Accuracy and Transferability Through AI: A Survey. Patterns. https://doi.org/10.1016/j.patter.2020.100050
Zhang, X., Lu, C., Tian, J., Zeng, L., Wang, Y., Sun, W., Han, H., & Kang, J. (2024). Artificial intelligence optimization and controllable slow-release iron sulfide realizes efficient separation of copper and arsenic in strongly acidic wastewater. Journal of Environmental Sciences (China), 139, 293–307. https://doi.org/10.1016/j.jes.2023.05.038
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Djoko Sutrisno, Iin Inawati, Hermanto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish in ELTEJ agree to the following terms: Authors retain copyright and grant the ELTEJ right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) the work for any purpose, even commercially with an acknowledgement of the work's authorship and initial publication in ELTEJ. 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 acknowledgement of its initial publication in ELTEJ. 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, as well as earlier and greater citation of published work (See The Effect of Open Access).