علوم ترویج و آموزش کشاورزی ایران

علوم ترویج و آموزش کشاورزی ایران

توسعه مدل پذیرش هوش مصنوعی در نظام آموزش عالی کشاورزی ایران

نوع مقاله : مقاله پژوهشی

نویسندگان
1 استادیار گروه ترویج و آموزش کشاورزی، دانشکده مهندسی زراعی و عمران روستایی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ملاثانی، ایران
2 دانشیار گروه مهندسی ماشین‌های کشاورزی و مکانیزاسیون، دانشکده مهندسی زراعی و عمران روستایی، دانشگاه علوم کشاورزی و منابع طبیعی، ملاثانی، ایران
چکیده
همگام با پیشرفت‌های سریع فناوری‌های مبتنی بر هوش مصنوعی، درک عوامل موثر بر تمایل به پذیرش کاربران نیز به طور فزاینده اهمیت یافته است. در حوزه کشاورزی، هوش مصنوعی به عنوان ابزاری یادگیری تحول‌آفرین در نظام آموزشی می‌تواند تحولات قابل توجهی به همراه داشته باشد و به افزایش بهره‌وری، کاهش هزینه‌ها و بهبود دقت در پژوهش و آموزش منجر شود. از این رو، پژوهش حاضر با ادغام نظریه تناسب وظیفه-فناوری و نظریه یکپارچه پذیرش و استفاده از فناوری، سازه‌های موثر بر استفاده از ابزارهای هوش مصنوعی در میان دانشجویان کشاورزی را تحلیل کرد. جامعه آماری این پژوهش شامل کلیه دانشجویان تحصیلات تکمیلی مراکز آموزش عالی کشاورزی بود که با استفاده از شیوه نمونه‌گیری چندمرحله‌ای در سطوح دانشگاه و مقطع تحصیلی، به روش ریشه مربع معکوس، تعداد ۳۸۵ نفر به صورت تصادفی انتخاب شدند. برای جمع‌آوری داده‌ها از پرسشنامه‌ای استفاده شد که روایی صوری آن توسط متخصصان و پایایی سازه‌های آن با محاسبه ضریب آلفای کرونباخ و پایایی ترکیبی در حد قابل قبول ارزیابی شد. برای تجزیه و تحلیل روابط بین متغیرها از مدل‌سازی معادلات ساختاری با رویکرد حداقل مربعات جزئی استفاده شد. نتایج نشان داد که سازه‌های انتظار عملکرد، انتظار تلاش و نفوذ اجتماعی به طور معناداری بر تمایل رفتاری دانشجویان موثر بودند. همچنین شرایط تسهیل‌گری و تمایل رفتاری تاثیر مثبت و معناداری بر رفتار استفاده واقعی فراگیران داشتند. علاوه بر این، تناسب وظیفه-فناوری تاثیر مثبت و معناداری بر تمایل و استفاده واقعی دانشجویان داشت. یافته‌های پژوهش ضمن ارتقای ادبیات نظری، درک موجود از پذیرش ابزارهای هوش مصنوعی توسط دانشجویان را عمیق‌تر کرد و پیشنهادهای کاربردی را برای توسعه‌دهندگان، سیاست‌گذاران آموزشی، اساتید و پژوهشگران ارائه داد.
کلیدواژه‌ها

عنوان مقاله English

Developing an Artificial Intelligence Adoption Model in Iranian Agricultural Higher Education

نویسندگان English

Seyed Mohammad Javad Sobhani 1
Morteza Taki 2
1 Assistant Professor, Department of Agricultural Extension and Education, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
2 Associate Professor, Department of Agricultural Machinery and Mechanization, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
چکیده English

With the rapid advancement of artificial intelligence (AI) technologies, understanding the factors influencing user adoption has become increasingly critical. In agriculture, AI can serve as a transformative learning tool within educational systems, driving improvements in productivity, cost efficiency, and research and teaching accuracy. This study integrates the Task-Technology Fit (TTF) and Unified Theory of Acceptance and Use of Technology (UTAUT) frameworks to examine the key constructs influencing AI tool adoption among agricultural students. The statistical population comprised 3,243 graduate students from Iranian agricultural higher education institutions. A multistage sampling approach, stratified by university and academic level, was employed to select a random sample of 385 students using the inverse square root method. Data were collected via a validated questionnaire, with reliability assessed through Cronbach’s alpha and composite reliability indices. Structural equation modeling (PLS-SEM) was used to analyze relationships among variables. Results indicated that performance expectancy, effort expectancy, and social influence significantly impacted students’ behavioral intention to adopt AI tools, while facilitating conditions and behavioral intention had strong positive effects on actual usage behavior. Furthermore, task-technology fit demonstrated a significant positive influence on both behavioral intention and actual use. These findings enhance the theoretical understanding of AI adoption in educational contexts and provide practical recommendations for developers, policymakers, educators, and researchers to optimize AI integration within agricultural curricula.

کلیدواژه‌ها English

Artificial intelligence
Unified theory of acceptance and use of technology (UTAUT)
Task-technology fit (TTF)
Agricultural higher education institutions
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