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نقش فناوریهای نوین و هوش مصنوعی در بهبود فرایندهای آموزشی: یک مطالعه مروری در آموزش ابتدایی | ||
| پژوهش در مطالعات برنامه درسی تربیت معلم | ||
| مقاله 3، دوره 4، شماره 2 - شماره پیاپی 7، اسفند 1403، صفحه 42-58 اصل مقاله (794.92 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.48310/jcdr.2025.18664.1138 | ||
| نویسندگان | ||
| رزا حسین زاده* 1؛ پرستو جودی2 | ||
| 1دانشجوی کارشناسی ارشد تحقیقات آموزشی، گروه ﻋﻠﻮم ﺗﺮﺑﯿتی، داﻧشکده ﻋﻠﻮم ﺗﺮﺑﯿتی و روانﺷﻨﺎسی، داﻧشگاه ﻣﺤﻘﻖ اردﺑﯿلی، | ||
| 2دانشجوی کارشناسی ارشد روانشناسی تربیتی، دانشکده علوم تربیتی و روانشناسی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| چکیده | ||
| پیشینه و اهداف: آموزش ابتدایی یکی از مهمترین مراحل در فرآیند یادگیری و شکلدهی به تواناییها و مهارتهای پایه دانشآموزان است. در سالهای اخیر، فناوریهای نوین و بهویژه هوش مصنوعی (AI) نقش بهسزایی در تحول و بهبود فرایند های آموزشی در مقطع ابتدایی ایفا کردهاند. بنابراین، هدف پژوهش حاضر، بررسی نقش فناوریهای نوین و هوش مصنوعی در بهبود فرایندهای آموزشی در آموزش ابتدایی می باشد. روشها: روش پژوهش حاضر کیفی و در قالب مروری ـ توصیفی می باشد. برای گردآوری داده ها، از منابع معتبر علمی، اسناد موجود و مطالعات کتابخانه ای استفاده شد. این اطلاعات به صورت توصیفی، تحلیل و تفسیر شد. یافتهها: یافته های پژوهش حاضر نشان می دهد که فناوری های نوین و هوش مصنوعی در بهبود فرایندهای آموزشی در6 بعد نقش موثری دارد. این ابعاد شامل توانمند کردن دانش آموزان با و بدون چالش های فیزیکی و یادگیری، یادگیری عمیق تر و معتبر مسائل آموزشی توسط هوش مصنوعی، بهبود آمادگی دانش آموزان برای ورود به مقاطع بالاتر، آموزش معلمان و توسعه ی حرفه ای آنها، رفتار و مدیریت کلاس، بازخورد و امتیازدهی است. نتیجهگیری: نتایج پژوهش نشان داد که توانمند کردن دانش آموزان با و بدون چالش های فیزیکی و یادگیری توسط هوش مصنوعی منجر به یادگیری عمیق تر و معتبر مسائل آموزشی شده و در راستای آن آمادگی دانش آموزان را برای ورود به مقاطع بالاتر بهبود می بخشد. همچنین عملکرد حرفه ای معلمان، رفتار و مدیریت کلاس توسط آنها و در نهایت بازخورد و امتیازدهی در توسعه ی روش های تدریس آموزشی از طریق هوش مصنوعی ارتقاء می یابد. نتایج به دست آمده از این مطالعه، اهمیت روزافزون فناوریهای نوین و هوش مصنوعی در تحول نظام آموزشی ابتدایی را تأیید میکند و زمینه را برای استفاده هوشمندانه و بهینه از این ابزارها در جهت ارتقای کیفیت آموزش فراهم میسازد. | ||
| کلیدواژهها | ||
| فناوری های نوین'؛ هوش مصنوعی'؛ فرایندهای آموزشی'؛ آموزش ابتدایی'؛ '؛ رویکرد مروری' | ||
| عنوان مقاله [English] | ||
| The Role of Modern Technologies and Artificial Intelligence in Improving Educational Processes: A Review Study in Primary Education | ||
| نویسندگان [English] | ||
| Roza Hosseinzadeh1؛ Parastoo Joudi2 | ||
| 1Master's student in Educational Research, Department of Educational Sciences, Faculty of Educational Sciences and Psychology, University of Mohaghegh Ardabili, Ardabil, Iran. | ||
| 2Master’s Student in Educational Psychology, Department of Educational Sciences, Faculty of Educational Sciences and Psychology,University of Mohaghegh Ardabili, Ardabil, Iran. | ||
| چکیده [English] | ||
| Background and Objectives: Primary education is one of the most crucial stages in the learning process, shaping students' foundational abilities and skills. In recent years, modern technologies, particularly artificial intelligence (AI), have played a significant role in transforming and enhancing educational processes at the primary level. Therefore, The aim of the present study is to examine the role of modern technologies and artificial intelligence in improving educational processes in primary education. Methods: This research is qualitative and conducted in a descriptive-review framework. For data collection, reliable scientific sources, available documents, and library studies were used. The information was analyzed and interpreted descriptively. Findings: Findings of the study indicate that modern technologies and artificial intelligence play a significant role in improving educational processes in six key areas: empowering students with and without physical and learning challenges, enabling deeper and more authentic learning through AI, enhancing students’ readiness for higher education levels, teacher training and professional development, classroom behavior and management, and feedback and assessment. Conclusion: The results demonstrate that empowering students with and without physical and learning challenges through AI leads to deeper and more authentic learning, consequently improving their readiness for higher education levels. Additionally, AI enhances teachers’ professional performance, classroom behavior and management, and, ultimately, feedback and assessment, contributing to the development of innovative teaching methods. The findings of this study confirm the increasing importance of modern technologies and artificial intelligence in transforming the primary education system, paving the way for the intelligent and optimal use of these tools to enhance the quality of education. | ||
| کلیدواژهها [English] | ||
| New technologies', Artificial intelligence', Educational Processes', Primary education', ', Review approach' | ||
| مراجع | ||
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Al-Azawei, A., Parslow, P., & Lundqvist, K. (2016). Barriers and Opportunities of E-Learning Implementation in Iraq: A Case of Public Universities. The International Review of Research in Open and Distributed Learning, 17(5), 1-15. Anderson, P., & Murphy, J. (2019). Ethical considerations in AI-driven education. Educational Technology Journal, 35(4), 45-60. Arora , V.(2022). Artificial Intelligence in Schools A Guide for Teachers, Administrators, and Technology Leaders. ISBN 9781032009056. 264 Pages 34 B/W Illustrations Published December 31, by Eye On Education. Attali, Y., & Van der Kleij, F. (2017). Effects of feedback elaboration and feedback timing during computer-based practice in mathematics problem solving. Computers & Education, 110, 154–169. https://doi.org/10.1016/j.compedu.2017.03.012. Augusto, J. C. (2009). Ambient intelligence: Opportunities and consequences of its use in smart classrooms. Innovation in Teaching and Learning in Information and Computer Sciences, 8(2), 53–63 Azad, S., Chen, B., Fowler, M., West, M., & Zilles, C. (2020). Strategies for deploying unreliable AI graders in high-transparency high-stakes exams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bingham, A. J., Pane, J. F., Steiner, E. D., & Hamilton, L. S. (2018). Ahead of the Curve: Implementation Challenges in Personalized Learning School Models. Educational Policy. Chen, N. S., Cheng, I. L., & Chew, S. W. (2016). Evolution is not enough: Revolutionizing current learning environments to smart learning environments. International Journal of Artificial Intelligence in Education, 26(2), 561–581. Chen, X., & Liu, Y. (2022). AI-Driven Personalized Curriculum Development. Journal of Educational Technology & Society, 25(1), 78-92. Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory of learning analytics in smart learning environments: A systematic review. Computers & Education, 156, 103940. https://doi.org/10.1016/j.compedu.2020.103940 Chevalier, M., Riedo, F., & Mondada, F. (2016). Pedagogical Uses of Thymio II: How Do Teachers Perceive Educational Robots in Formal Education? IEEE Robotics and Automation Magazine. Chin, H., Chew, C.M. & Lim, H.L. (2021). Incorporating feedback in online cognitive diagnostic assessment for enhancing grade five students’ achievement in ‘time’. J. Comput. Educ. 8, 183–212. https://doi.org/10.1007/s40692-020-00176-3. Chintalapati, S., & Raghunadh, M. V. (2013). Automated attendance management system based on face recognition algorithms. In 2013 IEEE International conference on computational intelligence and computing research (pp. 1–5). Chowdhury, S., Nath, S., Dey, A., & Das, A. (2020). Development of an automatic class attendance system using cnn-based face recognition. In 2020 Emerging Technology in Computing, Communication and Electronics (pp. 1–5). Chu, Shih-Ting & Hwang, Gwo-Jen & Tu, Yun-Fang. (2022). Artificial intelligence-based robots in education: A systematic review of selected SSCI publications. Computers and Education: Artificial Intelligence. 3. 100091. 10.1016/j.caeai.2022.100091. Crimmins, M & Midkiff, B. (2017). High Structure Active Learning Pedagogy for the Teaching of Organic Chemistry: Assessing the Impact on Academic Outcomes. Journal of chemical education. web. 10.1021/acs.jchemed.6b00663. CUI, L., & LI, J. (2019). Study on Data Fields Grading Category Labeling for ERP Practical Skills Intelligent Assessment System. DEStech Transactions on Computer Science and Engineering. Cui, W., Xue, Z., & Thai, K. P. (2019). Performance Comparison of an AI-Based Adaptive Learning System in China. Proceedings 2018 Chinese Automation Congress, CAC 2018. Despina ,V & Konstantinos, K,(2021). Examining the impact of self-assessment with the use of rubrics on primary school students’ performance, International Journal of Educational Research Open,Volume 2, 2021,100031, ISSN 2666-3740, https://doi.org/10.1016/j.ijedro.2021.100031. Dimitriadou, E., Lanitis, A. A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learn. Environ. 10, 12 (2023). https://doi.org/10.1186/s40561-023-00231 Dishon, G. (2017). New data, old tensions: Big data, personalized learning, and the challenges of progressive education. Theory and Research in Education. Garcia, L., & Rodriguez, M. (2023). AI-Powered Personalized Academic Advising for Student Transition to Higher Education. Journal of Educational Data Mining, 15(2), 78-93. Gui, M., Gerosa, T., Argentin, G., Losi, L. (2023). Mobile media education as a tool to reduce problematic smartphone use: Results of a randomised impact evaluation. Computers & Education, 194, 104705, https://doi.org/10.1016/j.compedu.2022. 104705. Gupta, S. K., Ashwin, T. S., & Guddeti, R. M. R. (2018). Cvucams: Computer vision based unobtrusive classroom attendance management system. In 2018 IEEE 18th International Conference on Advanced Learning Technologies (pp. 101–102). Hernandez, K., & Patel, R. (2021). Social skills development using AI chatbots in special education. Educational Robotics Review, 22(4), 55-69. Holmes, N. (2017). Engaging with assessment: increasing student engagement through continuous assessment. Active Learning in Higher Education, 19 (1), 23-34. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Routledge. ISBN: 9780367335579 Holstein, K., McLaren, B. M., & Aleven, V. (2017). Intelligent tutors as teachers’ aides: Exploring teacher needs for real-time analytics in blended classrooms. ACM International Conference Proceeding Series. Huang, L. S., Su, J. Y., & Pao, T. L. (2019). A context aware smart classroom architecture for smart campuses. Applied Sciences, 9(9), 1837. Ip, H. H. S., Li, C., Leoni, S., Chen, Y., Ma, K. F., Wong, C. H. to, & Li, Q. (2019). Design and Evaluate Immersive Learning Experience for Massive Open Online Courses (MOOCs). IEEE Transactions on Learning Technologies Jackson, B., & Rivera, D. (2022). The digitaldivide and AI accessibility in education. Technology and Society, 14(2), 112-127. Jiahong, S., Weipeng, Y. (2023). A systematic review of integrating computational thinking in early childhood education, Computers and Education Open, 4, 100122. https://doi.org/10.1016/j.caeo.2023.100122. Jiahong, S., Weipeng, Y. (2022). Artificial intelligence in early childhood education: A scoping review, Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100049. Kawaguchi, Y., Shoji, T., Lin, W., Kakusho, K., & Minoh, M. (2005). Face recognition-based lecture attendance system. In The 3rd AEARU workshop on network education (pp. 70–75). Kim, S., & Lee, J. (2021). AI-Powered Curriculum Alignment with Labor Market Needs. Journal of Vocational Education & Training, 73(4), 567-582. Lau, T. B., Ong, A. C., & Putra, F. A. (2014). Non-invasive monitoring of people with disabilities via motion detection. International Journal of Signal Processing Systems, 2(1), 37–41. Lee, D., Yeo, S. (2022). Developing an AI-based chatbot for practicing responsive teaching in mathematics, Computers & Education,. 191, 104646, https://doi.org/10.1016/j.compedu.2022.104646. Li, W., & Zhang, Y. (2021). Adaptive Learning Systems Powered by AI: Enhancing Student Readiness for Higher Education. Educational Technology Research and Development, 69(3), 1234-1250. Luckin, R & Holmes, W. (2016). Intelligence Unleashed: An argument for AI in Education. Lui, M., & Slotta, J. D. (2014). Immersive simulations for smart classrooms: exploring evolutionary concepts in secondary science. Technology, Pedagogy and Education. Marin, V & Pereira, T & Sridharan, S & Rivero, C. (2017). Automated Personalized Feedback in Introductory Java Programming MOOCs. 1259-1270. 10.1109/ICDE.2017.169. Mery, D., Mackenney, I., & Villalobos, E. (2019). Student attendance system in crowded classrooms using a smartphone camera. In 2019 IEEE Winter Conference on Applications of Computer Vision (pp. 857–866). Mircea, M., Stoica, M., & Ghilic-Micu, B. (2021). Investigating the impact of the internet of things in higher education environment. IEEE Access, 9, 33396–33409. Mizumoto, A., Eguchi, M. (2023). Exploring the potential of using an AI language model for automated essay scoring, Research Methods in Applied Linguistics, Vol. 2, 100050, https://doi.org/10.1016/j.rmal.2023.100050. Mollick, E., Mollick, L. (2023). Using AI to Implement Effective Teaching Strategies in Classrooms: Five Strategies, Including Prompts, Wharton School of the University of Pennsylvania & Wharton Interactive, March 16, https://ssrn.com/abstract=4391243 Murphy, R. (2019). Artificial Intelligence Applications to Support K–12 Teachers and Teaching: A Review of Promising Applications, Challenges, and Risks. Artificial Intelligence Applications to Support K–12 Teachers and Teaching: A Review of Promising Applications, Challenges, and Risks. Nguyen, Q & Rienties, B, Toetenel, L, Ferguson, R, Whitelock, D ,(2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates, Computers in Human Behavior, Volume 76,2017,Pages 703-714,ISSN 0747-5632, https://doi.org/10.1016/j.chb.2017.03.028.
Palanisamy, P., Paavizhi, K., & Saravanakumar, A. R. (2020). Techno pedagogical skills for teaching-learning process in smart class. Talent Development & Excellence, 12(1), 4984–4994. Parambil, M. M. A., Ali, L., Alnajjar, F., & Gochoo, M. (2022). Smart classroom: A deep learning approach towards attention assessment through class behavior detection. Advances in Science and Engineering Technology International Conferences (ASET), 2022, 1–6. https://doi.org/10.1109/ASET53988.2022.9735018 Qiu, Y. (2020). Education Informationization. Proceedings of the 2020 6th International Conference on Education and Training Technologies (pp. 40–43). New York, NY, USA: ACM. Quer, G., Muse, E. D., Nikzad, N., Topol, E. J., & Steinhubl, S. R. (2017). Augmenting diagnostic vision with AI. Lancet(London, England). Robinson, K. A. (2019). Enhancing student engagement through collaborative technologies. Journal of Educational Technology, 15(2), 45-60. Selwyn, N. (2019). Should robots replace teachers? AI and the Future of Education. (1st ed.) Polity Press. Siemens, G & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review. 5. 30-32. 10.17471/2499-4324/195. Smith, E., & Brown, K. (2018). Automated Grading and Feedback: Enhancing Efficiency and Student Satisfaction. Journal of Educational Technology Integration, 42(1),67-82. Tapalova, O., & Zhiyenbayeva, N. (2022). Artificial Intelligence in Education: AIEd for Personalised Learning Pathways. Electronic Journal of e-Learning, 20, 639-653. Tenenbaum et al. (2018). Building machines that learn & think like people—Prof. Josh Tenenbaum ICML2018. Retrieved July 15, Thomas, C., & Jayagopi, D. B. (2017). Predicting student engagement in classrooms using facial behavioral cues. In Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for education (pp. 33–40). Umar A., (2018). The impact of assessment for learning on students’ achievement in English for specific purposes A case study of pre-medical students at Khartoum university: Sudan,” English Language Teaching, vol. 11, no. 2, Wang, R., Zhang, G., Zhang, F., Dong, Z., & Qi, M. (2021). Student Behavior Recognition in Remote Video Classrooms. In Advances in Intelligent Information Hiding and Multimedia Signal Processing (pp. 496–504). Williamson, B. (2018). The hidden architecture of higher education: building a big data infrastructure for the‘smarter university.’ International Journal of Educational Technology in Higher Education. Wilson, T. (2020). AI-Powered Educational Resource Development. Computers & Education, 150, 103827. Woolf, B. (2008). Building Intelligent Interactive Tutors, Student-Centered Strategies for Revolutionizing E-Learning. | ||
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