کارراهه‌های آموزشی معلمان آینده بر مدار هوش مصنوعی و ارائه الگو

هدف: در عصر دیجیتال، دانش‌آموزان نیازمند رویکردهای نوین آموزشی هستند. معلمان آینده کارراهه های متفاوتی از معلمان سنتی درمسیر پیشرفت شغلی پیش رو دارند چنانکه باید کلیه فعالیت های آموزشی و یادگیری را با استفاده از فناوری هوش مصنوعی به پیش ببرند. ا ز این منظر هدف این پژوهش شناسایی کارراهه های معلمان آینده بر مدار هوش مصنوعی و ارائه الگو بوده است.روش: این پژوهش یک مطالعه کیفی با رویکرد فراترکیب بود. میدان پژوهش مشتمل بر کلیه مقالات منتشر شده سال‌های 2014-2024، و گزینش نمونه‌ها بطور هدفمند تا حد اشباع داده ها به تعداد 30 مقاله صورت پذیرفت. ابزار پژوهش متن‌کاوی و برای جمع‌آوری اطلاعات از پایگاه های معتبر علمی داخلی(اس آی دی، ایرانداک، مگ ایران ) و پایگاههای معتبر خارجی(امرآلد، اشپرینگر، الزویر، گوگل اسکالر و ..) استفاده شد. برای تامین روایی و اعتباربخشی داده ها از روش اطمینان پذیری،تایید پذیری و انتقال پذیری استفاده شد. نتایج: الگوی شناسایی کارراهه‌های آموزشی معلمان آینده بر مدار هوش مصنوعی بر اساس ابعاد نُه گانه(داشتن ارتباطات فناورانه، جذب فناورانه دانش‌آموزان ، بکارگیری ابزارهای هوش مصنوعی، استفاده فناورانه منابع آموزش، فعالیت در محیط فناورانه آموزشی، داشتن خلاقیت های فناورانه، تحلیل محیط فناورانه آموزش، برنامه سازی فناورانه آموزش، ارزشیابی فناورانه آموزش) تدوین شد. هوش مصنوعی به معلمان کمک می‌کند تا نیازهای فردی دانش‌آموزان را بهتر شناسایی کرده و تجربه یادگیری شخصی‌سازی شده‌ای را برای آن‌ها فراهم کنند.

educational career paths of future teachers based on artificial intelligence and model presentation

education has moved beyond imparting information to fostering critical thinking, problem solving, and creativity skills. future teachers have different educational career paths from traditional teachers in the path of career development, as they have to advance all educational and learning activities using artificial intelligence technology. teachers will continue to be at the heart of the learning process, but their role will gradually change from a transmitter of information to a guide, facilitator, and coach. future teachers will act as facilitators of learning, guides and advisors to students and, in addition to technical skills, will have the ability to develop deep connections with students and support their social and emotional development. using artificial intelligence, we can create intelligent learning systems that respond to each student individually and provide them with personalized learning experiences. however, there are challenges such as maintaining the privacy of students' data that need to be addressed. education in the digital age is a continuous process based on lifelong learning. using artificial intelligence and innovative educational approaches, we can envision a future where all students reach their full potential.familiarity with digital technologies, data analysis, and the ability to manage and use artificial intelligence tools in the classroom will become part of the essential characteristics of teachers. artificial intelligence can adapt educational content based on the individual needs and interests of each student by analyzing data about students. this personalization can make learning more efficient and motivating. by using these technologies, teachers can plan more precisely for the progress of students. however, few researches have investigated the educational methods of future teachers in connection with artificial intelligence. the main goal of this research was to identify the educational methods of future teachers based on artificial intelligence and provide a model. in terms of the practical purpose, the present research has been carried out with a meta-synthesis approach in terms of a qualitative method. choosing a meta-synthesis approach is very suitable for examining a subject such as the educational methods of future teachers in the field of artificial intelligence. this approach comprehensively and systematically analyzes and combines the findings of various researches. the research field included all articles published between 2014 and 2024. the selection of samples has been targeted to the extent of data saturation, which is a screening method from the initial selection of 56 articles based on input criteria (year of publication, profile in reliable scientific databases, study method, and richness of data in terms of depth of analysis) 30 articles in the end was selected for data extraction. the richness of the data was evaluated according to the existence of sufficient details about the subject under study and the use of various qualitative data collection methods (interview, observation, document analysis). single-authored articles were less selected due to a greater focus on the perspective of a single researcher, and articles with a large number of authors were less likely to be analyzed in depth. 24% of single-authored articles, 43% of two-authored articles, and 33% of more than two-authored articles.