مطالعات بین رشته ای در آموزش

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بر اساس نویسندگان

بر اساس موضوعات

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درباره نشریه

اهداف و چشم انداز

اعضای هیات تحریریه

همکاران دفتر نشریه

اصول اخلاقی انتشار مقاله

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آیین‌نامه‌ها و مصوبات

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داوران

پیوندهای مفید

بررسی قابلیت‌های هوش مصنوعی در فرآیند شخصی‌سازی آموزش، ارتقای کیفیت یادگیری و توسعه مهارت‌های قرن بیست و یکم

نوع مقاله : مروری

نویسندگان

1 دانشجو دکتری، مدیریت و برنامه ریزی آموزش، دانشکده علوم تربیتی و روان شناسی، دانشگاه شیراز، شیراز، ایران

2 استادیار، مدیریت و برنامه ریزی آموزش، دانشکده علوم تربیتی و روان شناسی، دانشگاه شیراز، شیراز، ایران

3 دانشجوی دکتری، مدیریت و برنامه ریزی آموزش، دانشکده علوم تربیتی و روان شناسی، دانشگاه شیراز، شیراز، ایران

چکیده

زمینه و هدف: با ظهور هوش مصنوعی و گسترش ابزارهای دیجیتال، نظام‌های آموزشی در سراسر جهان با تحولی بنیادین روبه‌رو شده‌اند. این مطالعه با هدف بررسی نقش هوش مصنوعی در تحقق سه هدف اصلی آموزشی انجام شده است: شخصی‌سازی تجربۀ یادگیری بر اساس ویژگی‌های فردی فراگیران، ارتقای کیفیت و کارایی فرایند آموزش و یادگیری، و پرورش مهارت‌های ضروری قرن بیست‌ویکم نظیر تفکر انتقادی، خلاقیت، ارتباط و همکاری. همچنین، مطالعۀ حاضر تلاش دارد زیرساخت‌ها و الزامات سیاستی مورد نیاز برای اجرای موفق و اخلاق‌محور هوش مصنوعی در محیط‌های آموزشی را شناسایی کند.
داده­ها و روش­ها: این پژوهش با بهره‌گیری از روش مرور نظام‌مند ادبیات انجام شده است. منابع علمی فارسی و انگلیسی منتشرشده بین سال‌های ۲۰۱۰ تا ۲۰۲۳ بررسی شده‌اند. با استفاده از کلیدواژه‌هایی مانند «هوش مصنوعی»، «آموزش»، «یادگیری شخصی‌سازی‌شده» و «مهارت‌های قرن بیست‌ویکم»، جست‌وجو در پایگاه‌های معتبری همچون اسکپوس، وب آو ساینس، اریک و گوگل اسکالر صورت گرفت. پس از غربال‌گری مطالعات بر اساس معیارهای ازپیش‌تعیین‌شده، مقالات منتخب با روش تحلیل محتوای کیفی بررسی شدند تا مضامین و شکاف‌های پژوهشی شناسایی شوند.
یافته­ها: یافته‌ها نشان دادند که هوش مصنوعی با به‌کارگیری الگوریتم‌های تطبیقی، سامانه‌های آموزش هوشمند، ربات‌های گفت‌وگو و تحلیل‌های یادگیری، تجربۀ یادگیری را برای هر فرد تنظیم می‌کند. همچنین، ابزارهای خودکار ارزیابی، تحلیل پیش‌بینی، دستیاران مجازی و بازخورد لحظه‌ای، آموزش را از حالت سنتی به یادگیری فعال تبدیل کرده‌اند. افزون بر این، هوش مصنوعی بستری برای تمرین مهارت‌هایی مانند حل مسئله و ارتباط مؤثر فراهم می‌سازد. با این حال، چالش‌هایی چون نگرانی‌های اخلاقی، شکاف دیجیتال، و فقدان آموزش معلمان وجود دارد.
نتیجه­گیری: هوش مصنوعی می‌تواند آموزش را متحول سازد؛ اما این تحول نیازمند زیرساخت فنی قوی، رهنمودهای اخلاقی روشن، دسترسی برابر و توانمندسازی معلمان است.

کلیدواژه‌ها

عنوان مقاله [English]

Investigating the role of artificial intelligence in personalizing education, improving the quality of learning and developing 21st century skills

نویسندگان [English]

  • zahra rafatjoo 1
  • Masumeh Mohtaram 2
  • Mehdi Behzadi 3

1 PHD student، Educational Manegement and planning، Faculty of Educational university sciences and psychology، shiraz،Iran

2 Assistant Professor, Educational Management and Planning, Faculty of Educational Sciences and Psychology Shiraz University, Shiraz, Iran

3 PHD student، Educational Manegement and planning، Faculty of Educational university sciences and psychology، shiraz،Iran

چکیده [English]

Introduction
With the advent of artificial intelligence and the expansion of digital tools, education systems worldwide are undergoing a paradigm shift. This study aims to examine the contributions of AI to three primary educational goals: individualizing the learning experience based on learner profiles, enhancing the overall quality and efficiency of teaching and learning processes, and cultivating critical 21st-century skills, including critical thinking, creativity, communication, and collaboration. Furthermore, the study seeks to identify the infrastructure and policy requirements necessary for the successful and ethical implementation of AI in educational environments.
Methods and Data
This research adopted a systematic literature review methodology. The review encompassed Persian and English academic publications from 2010 to 2023. Using specific keywords-“Artificial Intelligence,” “Education,” “Personalized Learning,” and “21st Century Skills”-a broad search was conducted across leading scholarly databases such as Scopus, Web of Science, ERIC, and Google Scholar. Studies were screened based on relevance and adherence to predefined inclusion criteria. The selected articles were then analyzed using qualitative content analysis to extract meaningful insights. This method allowed for the identification of prevailing themes, trends, challenges, and research gaps in the current literature on AI in education.
Findings
The review uncovered several major findings. First, AI significantly contributes to personalized learning by employing adaptive algorithms, intelligent tutoring systems, chatbots, and learning analytics to tailor content and learning pace to individual student needs. These technologies enhance learner engagement, improve retention, and support autonomy in the learning process. Second, AI plays a central role in enhancing learning quality. Automated assessment tools, predictive analytics, virtual teaching assistants, and real-time feedback systems shift classroom instruction from static lectures to dynamic, student-centered experiences. Such changes lead to more effective learning and improved academic outcomes. Third, AI-supported environments are instrumental in the development of 21st-century competencies. Through interactive and collaborative platforms, students are able to practice critical thinking, problem-solving, creativity, and interpersonal communication in meaningful and real-world scenarios. These capabilities are crucial in today’s interconnected and fast-evolving society. However, the review also revealed notable challenges and limitations. Among them are ethical concerns regarding data privacy, algorithmic bias, and transparency, as well as the risk of over-reliance on technology at the expense of human connection and emotional intelligence in teaching. Additionally, disparities in access to AI tools-often referred to as the digital divide-pose a threat to educational equity, especially in under-resourced regions. Finally, the lack of sufficient teacher training and digital infrastructure remains a critical barrier to the effective integration of AI in many educational systems.
Conclusion and Discussion
This study confirms the transformative potential of artificial intelligence in modern education. By enabling personalized learning, increasing instructional quality, and promoting the development of vital cognitive and social skills, AI can help build more inclusive and future-ready learning environments. Nonetheless, for AI to be effectively and ethically deployed, several foundational elements must be in place: robust technical infrastructure, clear ethical guidelines, equitable access to technology, and comprehensive training for educators. Policymakers and educational stakeholders are encouraged to address these foundational requirements to harness AI’s full potential. Emphasis should also be placed on aligning AI applications with pedagogical goals and maintaining a balanced relationship between human and machine in the learning process. If implemented thoughtfully, AI can act as a powerful enabler of educational innovation, adaptability, and equity in the 21st century and beyond.

کلیدواژه‌ها [English]

  • artificial intelligence
  • education personalization
  • learning quality
  • 21st century skills
  • educational technology
  • digital education
  • learning personalization
مراجع
آراسته، حمیدرضا و خباره، کبری . (1402). نقش هوش مصنوعی و تحول در آموزش عالی. نشاء علم،14 (1)، 2-8.
سند ملی هوش مصنوعی جمهوری اسلامی ایران (1403). شورای عالی انقلاب فرهنگی.
شیخ شعاعی، حمزه. (1400). چالش ها، نقش ها و سیاستگذاری پژوهش های هوش مصنوعی در آموزش و پرورش. کنفرانس بین المللی مدیریت، گردشگری و تکنولوژی. پنانگ. مالزی.
مطلبی نژاد، علیرضا ، فاضلی، فرزانه و نوائی، الهام . (1402). بررسی نظام مند نویدها و چالش‌های هوش مصنوعی برای معلمان. فناوری و دانش پژوهی در تعلیم و تربیت، 3 (1)، 23-44.
Abdurohman, N. R. (2024). Artificial intelligence in higher education: Opportunities and challenges. Eurasian Science Review: An International Peer-Reviewed Multidisciplinary Journal, 2(Special Issue), 1683–1695.
Acuna, G. E., Alvarez, L. A., Miraflores, J., & Samonte, M. J. (2021, June). Towards the development of an adaptive e-learning system with chatbot using personalized e-learning model. In Proceedings of the 7th International Conference on Frontiers of Educational Technologies (pp. 120–125). Association for Computing Machinery.
Aoun, J. E. (2017). Robot-proof: Higher education in the age of artificial intelligence. MIT Press.
Bajaj, R., & Sharma, V. (2018). Smart education with artificial intelligence based determination of learning styles. Procedia Computer Science, 132, 834–842.
Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26(2), 600–614.
Basham, J. D., Hall, T. E., Carter, R. A., Jr., & Stahl, W. M. (2016). An operationalized understanding of personalized learning. Journal of Special Education Technology, 31(3), 126–136.
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17, Article 42. https://doi.org/10.1186/s41239-020-00218-x
Benvenuti, M., Cangelosi, A., Weinberger, A., Mazzoni, E., Benassi, M., Barbaresi, M., & Orsoni, M. (2023). Artificial intelligence and human behavioral development: A perspective on new skills and competences acquisition for the educational context. Computers in Human Behavior, 148, Article 107903.
Boumediene, H., & Bouakkaz, M. (2025). Artificial intelligence in education: Emerging trends, ethical considerations, and future directions. In Next-generation AI methodologies in education (pp. 407–438). IGI Global.
Brusilovsky, P., Karagiannidis, C., & Sampson, D. (2004). Layered evaluation of adaptive learning systems. International Journal of Continuing Engineering Education and Life Long Learning, 14(4–5), 402–421.
Cain, E. J., & Class, S. (2023). Exploring the college enrollment of students from rural areas: Considerations for scholarly practitioners. Georgia Journal of College Student Affairs, 39(1), Article 1.
Chandrasekera, T., Hosseini, Z., Perera, U., & Bazhaw Hyscher, A. (2024). Generative artificial intelligence tools for diverse learning styles in design education. International Journal of Architectural Computing. Advance online publication. https://doi.org/10.1177/14780771241287345
Channa, F. R., Sarhandi, P. S. A., Bugti, F., & Pathan, H. (2021). Harnessing artificial intelligence in education for preparing learners for the 21st century. Elementary Education Online, 20(5), 3186.
Charmatz, M. (2021). Improving the ADA and 504 for colleges, universities, and students and employees with disabilities. Disability Compliance for Higher Education, 26(6), 3–15.
Chatti, M. A., & Muslim, A. (2019). The PERLA framework: Blending personalization and learning analytics. International Review of Research in Open and Distributed Learning, 20(1).
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278.
Chichekian, T., & Benteux, B. (2022). The potential of learning with (and not from) artificial intelligence in education. Frontiers in Artificial Intelligence, 5, Article 903051.
Davies, J. N., Verovko, M., Verovko, O., & Solomakha, I. (2020, June). Personalization of e-learning process using AI-powered chatbot integration. In International Scientific-Practical Conference (pp. 209–216). Springer.
Debuse, J. C., Lawley, M., & Shibl, R. (2008). Educators’ perceptions of automated feedback systems. Australasian Journal of Educational Technology, 24(4).
Den Hengst, F., Grua, E. M., el Hassouni, A., & Hoogendoorn, M. (2020). Reinforcement learning for personalization: A systematic literature review. Data Science, 3(2), 107–147.
Eckerson, W. W. (2007). Predictive analytics: Extending the value of your data warehousing investment. TDWI Best Practices Report, 1, 1–36.
Elhossiny, M., Eladly, R., & Saber, A. (2022). The integration of psychology and artificial intelligence in e-learning systems to guide the learning path according to the learner’s style and thinking. International Journal of Advanced and Applied Sciences, 9(12), 162–169.
Ertel, W. (2018). Introduction to artificial intelligence. Springer.
Fabijanic Gagro, S. (2024). Artificial intelligence in education—Current challenges. Annals of the Faculty of Law in Belgrade – International Edition, 72, 725.
Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., ... Rienties, B. (2024). The promise and challenges of generative AI in education. Behaviour & Information Technology, 1–27. Advance online publication.
Goldenberg, D., Kofman, K., Albert, J., Mizrachi, S., Horowitz, A., & Teinemaa, I. (2021, March). Personalization in practice: Methods and applications. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 1123–1126). Association for Computing Machinery.
Goli, A., Reiley, D. H., & Zhang, H. (2024). Personalizing ad load to optimize subscription and ad revenues: Product strategies constructed from experiments on Pandora. Marketing Science. Advance online publication.
Goulart, V. G., Liboni, L. B., & Cezarino, L. O. (2022). Balancing skills in the digital transformation era: The future of jobs and the role of higher education. Industry and Higher Education, 36(2), 118–127.
Graesser, A. C., Conley, M. W., & Olney, A. (2012). Intelligent tutoring systems. In K. R. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook: Vol. 3. Application to learning and teaching (pp. 451–473). American Psychological Association. https://doi.org/10.1037/13275-018
Gubareva, R., & Lopes, R. P. (2020). Virtual assistants for learning: A systematic literature review. In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) (Vol. 1, pp. 97–103). SciTePress.
Gumabay, C. A. N., & Gumabay, M. V. N. (2024). Opportunities and challenges for information technology and business educators in implementing generative artificial intelligence in instruction.
Hassan, A., Elrahman, M. G. S. A., Ali, S. A., Abdulkhaleq, N. M. S., Dahlan, M., & Shaker, G. (2023). New teaching methods in universities using artificial intelligence. In From Industry 4.0 to Industry 5.0: Mapping the transitions (pp. 689–698). Springer.
Holmes, J., Sacchi, L., & Bellazzi, R. (2004). Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England, 86(5), 334–338.
Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher-AI complementarity. Journal of Learning Analytics, 6(2), 27–52. https://doi.org/10.18608/jla.2019.62.3
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. WIREs Data Mining and Knowledge Discovery, 9(4), Article e1312. https://doi.org/10.1002/widm.1312
Hussain, S., Al-Hashmi, S. H., Malik, M. H., & Kazmi, S. I. A. (2023). Chatbot in e-learning. In SHS Web of Conferences (Vol. 156, Article 01002). EDP Sciences.
Iftene, A., & Vanderdonckt, J. (2016). MOOCBuddy: A chatbot for personalized learning with MOOCs. In RoCHI – International Conference on Human-Computer Interaction (p. 91).
Johnsen, S. K. (2016). Implementing personalized learning. Gifted Child Today, 39(2), 73.
Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, Article 100017. https://doi.org/10.1016/j.caeai.2021.100017
Kamruzzaman, M. M., Alanazi, S., Alruwaili, M., Alshammari, N., Elaiwat, S., Abu-Zanona, M., ... Ahmed Alanazi, B. (2023). AI- and IoT-assisted sustainable education systems during pandemics, such as COVID-19, for smart cities. Sustainability, 15(10), Article 8354. https://doi.org/10.3390/su15108354
Kanchon, M. K. H., Sadman, M., Nabila, K. F., Tarannum, R., & Khan, R. (2024). Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies. International Journal of Cognitive Computing in Engineering, 5, 269–278.
Kaswan, K. S., Dhatterwal, J. S., & Ojha, R. P. (2024). AI in personalized learning. In Advances in technological innovations in higher education (pp. 103–117). CRC Press.
Kennedy, T. J., & Sundberg, C. W. (2020). 21st century skills. In Science education in theory and practice: An introductory guide to learning theory (pp. 479–496). Springer.
Khalifa, M. (2022, June). The role of artificial intelligence in the education process of political science field. In 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) (pp. 409–416). IEEE.
Khosravi, H., Sadiq, S., & Gasevic, D. (2020, February). Development and adoption of an adaptive learning system: Reflections and lessons learned. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 58–64). Association for Computing Machinery.
Kizilcec, R. F., Reich, J., Yeomans, M., Dann, C., Brunskill, E., Lopez, G., ... Tingley, D. (2020). Scaling up behavioral science interventions in online education. Proceedings of the National Academy of Sciences, 117(26), 14900–14905. https://doi.org/10.1073/pnas.1921417117
Klašnja-Milićević, A., & Ivanović, M. (2021). E-learning personalization systems and sustainable education. Sustainability, 13(12), Article 6713. https://doi.org/10.3390/su13126713
Konstantinos, K. T. (2024). Integrating artificial intelligence in science education: Benefits and challenges.
Krause, A., & Horvitz, E. (2010). A utility-theoretic approach to privacy in online services. Journal of Artificial Intelligence Research, 39, 633–662.
Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78. https://doi.org/10.3102/0034654315581420
Lapek, J. (2018). Promoting 21st century skills in problem-based learning environments. CTETE-Research Monograph Series, 1(1), 66–85.
Lodge, J. M., Thompson, K., & Corrin, L. (2023). Mapping out a research agenda for generative artificial intelligence in tertiary education. Australasian Journal of Educational Technology, 39(1), 1–8. https://doi.org/10.14742/ajet.8695
Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Markowska-Kaczmar, U., Kwasnicka, H., & Paradowski, M. (2010). Intelligent techniques in personalization of learning in e-learning systems. In Computational intelligence for technology enhanced learning (pp. 1–23). Springer.
Mauti, J. M., & Ayieko, D. S. O. (2024). Ethical implications of artificial intelligence in university education. East African Journal of Education Studies, 8(1), 159–167.
Mittal, U., Sai, S., & Chamola, V. (2024). A comprehensive review on generative AI for education. IEEE Access. Advance online publication.
Mohammadian, H. D., Langari, Z. G., Castro, M., & Wittberg, V. (2022, March). Smart governance for educational sustainability: Hybrid SMEs and the 5th wave theory towards mapping the future education in post-COVID era. In 2022 IEEE Global Engineering Education Conference (EDUCON) (pp. 1916–1926). IEEE.
Moreno-Guerrero, A. J., López-Belmonte, J., Marín-Marín, J. A., & Soler-Costa, R. (2020). Scientific development of educational artificial intelligence in Web of Science. Future Internet, 12(8), Article 124. https://doi.org/10.3390/fi12080124
Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-based personalized e-learning systems: Issues, challenges, and solutions. IEEE Access, 10, 81323–81342.
Nanjundaswamy, C., Baskaran, S., & Leela, M. H. (2021). Digital pedagogy for sustainable learning. Shanlax International Journal of Education, 9(3), 179–185.
Nemorin, S., Vlachidis, A., Ayerakwa, H. M., & Andriotis, P. (2023). AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development. Learning, Media and Technology, 48(1), 38–51. https://doi.org/10.1080/17439884.2022.2095568
Ng, D. T. K., Su, J., Leung, J. K. L., & Chu, S. K. W. (2024). Artificial intelligence (AI) literacy education in secondary schools: A review. Interactive Learning Environments, 32(10), 6204–6224.
Pane, J. F. (2018). Strategies for implementing personalized learning while evidence and resources are underdeveloped (Report No. PE-314-RC). RAND Corporation.
Perminova, L., Vasylyuk-Zaitseva, S., Shapka, I., & Savastru, N. (2023). The role of artificial intelligence in improving the quality of education and research. Futurity Education, 3(4), 46–59.
Popkova, E. G., & Gulzat, K. (2020). Technological revolution in the 21st century: Digital society vs. artificial intelligence. In The 21st century from the positions of modern science: Intellectual, digital and innovative aspects (pp. 339–345). Springer.
Rafieian, O., & Yoganarasimhan, H. (2023). AI and personalization. In Artificial intelligence in marketing (pp. 77–102). Emerald Publishing.
Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
Roumate, F. (2023). Ethics of artificial intelligence, higher education, and scientific research. In Artificial intelligence in higher education and scientific research: Future development (pp. 129–144). Springer.
Saavedra, A. R., & Opfer, V. D. (2012). Teaching and learning 21st century skills: Lessons from the learning sciences. Asia Society.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Senter, M. S. (2023). The impact of social relationships on college student learning during the pandemic: Implications for sociologists. Teaching Sociology, 51(1), 73–91. https://doi.org/10.1177/0092055X221134594
Shemshack, A., & Spector, J. M. (2020). A systematic literature review of personalized learning terms. Smart Learning Environments, 7, Article 33. https://doi.org/10.1186/s40561-020-00140-9
Shemshack, A., Kinshuk, & Spector, J. M. (2021). A comprehensive analysis of personalized learning components. Journal of Computers in Education, 8(4), 485–503. https://doi.org/10.1007/s40692-021-00188-7
Shishakly, R., Almaiah, M., Lutfi, A., & Alrawad, M. (2024). The influence of using smart technologies for sustainable development in higher education institutions. International Journal of Data and Network Science, 8(1), 77–90.
Skrebeca, J., Kalniete, P., Goldbergs, J., Pitkevica, L., Tihomirova, D., & Romanovs, A. (2021, October). Modern development trends of chatbots using artificial intelligence (AI). In 2021 62nd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS) (pp. 1–6). IEEE.
Smith Morest, V. (2013). From access to opportunity: The evolving social roles of community colleges. The American Sociologist, 44(4), 319–328.
Spector, J. M., & Ma, S. (2019). Inquiry and critical thinking skills for the next generation: From artificial intelligence back to human intelligence. Smart Learning Environments, 6, Article 8. https://doi.org/10.1186/s40561-019-0088-z
Thornhill-Miller, B., Camarda, A., Mercier, M., Burkhardt, J. M., Morisseau, T., Bourgeois-Bougrine, S., ... Lubart, T. (2023). Creativity, critical thinking, communication, and collaboration: Assessment, certification, and promotion of 21st century skills for the future of work and education. Journal of Intelligence, 11(3), Article 54. https://doi.org/10.3390/jintelligence11030054
Tian, F., Zheng, Q., Gong, Z., Du, J., & Li, R. (2007, April). Personalized learning strategies in an intelligent e-learning environment. In 2007 11th International Conference on Computer Supported Cooperative Work in Design (pp. 973–978). IEEE.
Trisnawati, W., Putra, R. E., & Balti, L. (2023). The impact of artificial intelligence in education toward 21st century skills: A literature review. PPSDP International Journal of Education, 2(2), 501–513.
Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior, 55, 1185–1193. https://doi.org/10.1016/j.chb.2015.02.014
Tuomi, I. (2022). Artificial intelligence, 21st century competences, and socio-emotional learning in education: More than high-risk? European Journal of Education, 57(4), 601–619. https://doi.org/10.1111/ejed.12531
University of Arkansas. (2023). Professors use AI to improve communication tools for people with limited speech.
Uskov, V. L., Howlett, R. J., Jain, L. C., & Vlacic, L. (Eds.). (2018). Smart education and e-learning 2016 (Vol. 99). Springer.
Van Dijk, J. A. G. M. (2017). Digital divide: Impact of access. In The international encyclopedia of media effects (pp. 1–11). Wiley.
Van Laar, E., Van Deursen, A. J., Van Dijk, J. A., & De Haan, J. (2017). The relation between 21st-century skills and digital skills: A systematic literature review. Computers in Human Behavior, 72, 577–588. https://doi.org/10.1016/j.chb.2017.03.010
Villatoro Moral, S., & de-Benito Crosseti, B. (2022). Self-regulation of learning and the co-design of personalized learning pathways in higher education: A theoretical model approach. Journal of Interactive Media in Education, 2022(1), Article 6.
Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2023). When adaptive learning is effective learning: Comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31(2), 793–803. https://doi.org/10.1080/10494820.2020.1808794
Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE.
Wogu, I. A. P., Misra, S., Olu-Owolabi, E. F., Assibong, P. A., Udoh, O. D., Ogiri, S. O., & Damasevicius, R. (2018). Artificial intelligence, artificial teachers and the fate of learners in the 21st century education sector: Implications for theory and practice. International Journal of Pure and Applied Mathematics, 119(16), 2245–2259.
Wu, J., Thorne-Large, J., & Zhang, P. (2022). Safety first: The risk of over-reliance on technology in navigation. Journal of Transportation Safety & Security, 14(7), 1220–1246.
Yoganarasimhan, H. (2020). Search personalization using machine learning. Management Science, 66(3), 1045–1070. https://doi.org/10.1287/mnsc.2018.3255
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Zhang, L., Basham, J. D., & Yang, S. (2020). Understanding the implementation of personalized learning: A research synthesis. Educational Research Review, 31, Article 100339. https://doi.org/10.1016/j.edurev.2020.100339
Zhong, L., Wei, Y., Yao, H., Deng, W., Wang, Z., & Tong, M. (2020, January). Review of deep learning-based personalized learning recommendation. In Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning (pp. 145–149). Association for Computing Machinery.
Zhu, Z. T., Yu, M. H., & Riezebos, P. (2016). A research framework of smart education. Smart Learning Environments, 3, Article 4. https://doi.org/10.1186/s40561-016-0026-2
Zulfa, S., Dewi, R. S., Hidayat, D. N., Hamid, F., & Defianty, M. (2023, August). The use of AI and technology tools in developing students’ English academic writing skills. In Proceedings of the International Conference on Education (pp. 47–63)
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دوره 4، شماره 1 - شماره پیاپی 10
خرداد 1404
صفحه 69-98
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