Evaluating the Impact of Artificial Intelligence-Based Learning Methods on Students' Motivation and Academic Achievement
DOI:
https://doi.org/10.59944/postaxial.v2i1.279Keywords:
Evaluating, Artificial Intelligence, Learning Methods, Students' Motivation, Academic AchievementAbstract
This research explores the transformative potential of artificial intelligence (AI) in elementary education, focusing on its impact on student motivation, academic achievement, and overall learning experiences. Through a comprehensive examination of existing literature, empirical evidence, and qualitative interviews with educators and administrators, this study sheds light on the benefits and challenges of AI integration in educational settings. The findings highlight a positive correlation between AI-based learning methods and student motivation levels, suggesting that technology can engage students in meaningful ways by fostering curiosity, autonomy, and intrinsic motivation in their learning journey. Moreover, significant improvements in students' academic achievement following exposure to AI-enhanced instruction underscore the efficacy of these innovative approaches in facilitating deeper conceptual understanding and mastery of academic content. However, addressing challenges such as equity, inclusion, and ethical considerations requires a multifaceted approach involving ongoing professional development, investments in infrastructure, and the establishment of ethical safeguards. Further research is needed to explore the nuanced dynamics of AI integration in education, including its impact on teacher-student interactions, classroom dynamics, and the broader socio-cultural context. Overall, interdisciplinary collaborations and a commitment to ethical innovation are essential for harnessing the transformative power of AI to create more equitable, inclusive, and effective learning environments for all student.
References
Abdeldayem, M. M., & Aldulaimi, S. H. (2020). Trends and opportunities of artificial intelligence in human resource management: Aspirations for public sector in Bahrain. International Journal of Scientific and Technology Research, 9(1), 3867–3871.
Alexander, V. D., Thomas, H., Cronin, A., Fielding, J., & Moran-Ellis, J. (2008). Mixed methods. Researching Social Life, 3, 125–144.
Bewersdorff, A., Seßler, K., Baur, A., Kasneci, E., & Nerdel, C. (2023). Assessing student errors in experimentation using artificial intelligence and large language models: A comparative study with human raters. Computers and Education: Artificial Intelligence, 5, 100177. https://doi.org/10.1016/j.caeai.2023.100177
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8
de la Calle-Arroyo, C., Amo-Salas, M., López-Fidalgo, J., Rodríguez-Aragón, L. J., & Wong, W. K. (2023). A methodology to D-augment experimental designs. Chemometrics and Intelligent Laboratory Systems, 237, 104822. https://doi.org/10.1016/j.chemolab.2023.104822
Fitrianto, I., & Aimmah, S. (2023). Tathwir al-Wasail al-Ta’limiyah fi Ta’lim al-Qashr Muassasan’ala Barnamaj Sketchware. Prosiding Pertemuan Ilmiah Internasional Bahasa Arab, 14(1), 732–740.
Fitrianto, I., Hamid, R., & Mulalic, A. (2023). The effectiveness of the learning strategy" think, talk, write" and snowball for improving learning achievement in lessons insya’at Islamic Boarding School Arisalah. International Journal of Post Axial: Futuristic Teaching and Learning, 13–22.
Ibnu Fitrianto, F. M. A. (2018). MODEL PEMBELAJARAN PROGAM PEMANTAPAN BAHASA ARAB DAN SHAHSIAH (KEMBARA) KE 4 MAHASISWA KOLEJ UNIVERSITI ISLAM ANTAR BANGSA SELANGOR (KUIS) TAHUN 2018. Nasional Conference on Islamic Civilization University of Darussalam Gontor, 1(First Publishing), 121–135.
Ivankova, N. V, & Creswell, J. W. (2009). Mixed methods. Qualitative Research in Applied Linguistics: A Practical Introduction, 23, 135–161.
Keshav, M., Julien, L., & Miezel, J. (2022). The Role Of Technology In Era 5.0 In The Development Of Arabic Language In The World Of Education. Journal International of Lingua and Technology, 1(2), 79–98. https://doi.org/10.55849/jiltech.v1i2.85
Mahfud, C. (2019). Evaluation of Islamic Education Curriculum Policy in Indonesia. Premiere Educandum : Jurnal Pendidikan Dasar Dan Pembelajaran, 9(1), 34. https://doi.org/10.25273/pe.v9i1.4016
Muniasamy, A., & Alasiry, A. (2020). Deep Learning: The Impact on Future eLearning. International Journal of Emerging Technologies in Learning (IJET), 15(01), 188. https://doi.org/10.3991/ijet.v15i01.11435
Muttaqin, I. (2023). CHALLENGES OF ISLAMIC EDUCATION MANAGEMENT IN THE DIGITAL ERA. Ta’allum: Jurnal Pendidikan Islam, 10(2), 343–364. https://doi.org/10.21274/taalum.2022.10.2.343-364
Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2023). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 33(1), 100857. https://doi.org/10.1016/j.hrmr.2021.100857
Rabie, D. R. M. (2023). The Future of Education with Artificial Intelligence and Machine Learning in the Arab World: A Systemat. العلوم التربوية, 31(3), 1–35. https://doi.org/10.21608/ssj.2023.344574
Reddy, P., Chaudhary, K., & Hussein, S. (2023). A digital literacy model to narrow the digital literacy skills gap. Heliyon, 9(4), e14878. https://doi.org/10.1016/j.heliyon.2023.e14878
Rukajat, A. (2018). Pendekatan Penelitian Kualitatif (Qualitative Research Approach). Deepublish. https://books.google.co.id/books?id=qy1qDwAAQBAJ
Strauss, A., & Corbin, J. (1998). Basics of qualitative research techniques.
Suryadarma, Y., & Fitriyanto, I. (2020). Taá1bÄ«q Al-Manhaj Al-DirÄsi fi Taâ€TMlÄ«m Al-Lugah Al-Arabiyyah li Al-Daurah Al-Mukaá1¡á1¡af bi Markaz Al-DaurÄt wa Al-TadribÄt JÄmiâ€TMah DÄr Al-Salam Gontor. ALSINATUNA, 5(1), 72–87. https://doi.org/10.28918/alsinatuna.v5i1.2467
Suwartono, T., & Aniuranti, A. (2019). Digital Teaching Tools in 21st Century EFL Classroom: Are Our Teachers Ready? ELLITE: Journal of English Language, Literature, and Teaching, 3(2), 57–62. https://doi.org/10.32528/ellite.v3i2.1916
Tapalova, O., & Zhiyenbayeva, N. (2022). Artificial intelligence in education: AIEd for personalised learning pathways. Electronic Journal of E-Learning, 20(5), 639–653.
Weng, X., & Chiu, T. K. F. (2023). Instructional design and learning outcomes of intelligent computer assisted language learning: Systematic review in the field. Computers and Education: Artificial Intelligence, 4, 100117. https://doi.org/10.1016/j.caeai.2022.100117
Woodward, K., Kanjo, E., Oikonomou, A., & Chamberlain, A. (2020). LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach. Personal and Ubiquitous Computing, 24(5), 709–722. https://doi.org/10.1007/s00779-020-01427-x
Yan, Y., Rosales, R., Fung, G., Schmidt, M., Hermosillo, G., Bogoni, L., Moy, L., & Dy, J. (2010). Modeling annotator expertise: Learning when everybody knows a bit of something. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 932–939.
Zhang, J., Mazurowski, M. A., Allen, B. C., & Wildman-Tobriner, B. (2023). Multistep Automated Data Labelling Procedure (MADLaP) for thyroid nodules on ultrasound: An artificial intelligence approach for automating image annotation. Artificial Intelligence in Medicine, 141, 102553. https://doi.org/10.1016/j.artmed.2023.102553
Zhao, Y., Chen, Y., Cheng, K., & Huang, W. (2023). Artificial intelligence based multimodal language decoding from brain activity: A review. Brain Research Bulletin, 201, 110713. https://doi.org/10.1016/j.brainresbull.2023.110713
Zuiderwijk, A., Chen, Y.-C., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3), 101577. https://doi.org/10.1016/j.giq.2021.101577
