توسعۀ مهارت‏ های استخدامی نرم و سخت مبتنی بر هوش مصنوعی در دانشجویان مهندسی: مطالعۀ فراترکیب نظریه ‏ساز

نوع مقاله : مقاله علمی - پژوهشی

نویسندگان

1 دانشگاه شیراز

2 دکتری مدیریت آموزشی، بخش مدیریت و برنامه‌ریزی آموزشی، دانشکده علوم تربیتی و روان‌شناسی، دانشگاه شیراز، شیراز، ایران

10.22047/ijee.2026.575317.2240
چکیده
با گسترش سریع فنّاوری ‏های هوش مصنوعی و دگرگونی انتظارهای بازار کار مهندسی، بازنگری در رویکردهای آموزش دانشگاهی بیش ‏ازپیش ضروری شده است. هدف این مطالعه، شناسایی پیشایندها، فرایندها و پیامدهای آموزش «تفکر هوش مصنوعی» در راستای توسعۀ مهارت‏ های اشتغال‏پذیری دانشجویان مهندسی با بهره ‏گیری از رویکرد فراترکیب نظریه ‏ساز است. این پژوهش با روش کیفیِ فراترکیب نظریه‏ ساز و رویکرد استقرایی، بر اساس مدل هفت‏ مرحله ‏ای فینفگلد-کانت (Finfgeld-Connett) انجام شد. مرور ادبیات به‏ صورت نظام‏ مند صورت گرفت و جست‏وجو با کلیدواژه‏ های هدایت‏گر در پایگاه‏ های فارسی و بین ‏المللی انجام شد. سپس مطالعات مرتبط برمبنای تناسب عنوان و کلیدواژه ‏ها گردآوری و پس از غربالگری، 50 مقاله برای تحلیل و استخراج مؤلفه ‏ها انتخاب شد. داده ‏ها با تحلیل کیفی و کدگذاری باز، محوری و گزینشی سامان‏دهی شد و برای مدل حاصل، اعتباریابی توصیفی، تفسیری، نظری و مصلحت ‏گرایانه انجام گرفت. یافته‏ ها نشان داد پیشایندها در قالب دانش، مهارت ‏ها و نگرش ‏های موردنیاز دانشجویان و اعضای هیئت علمی قابل تبیین است. در بُعد فرایندی، سازوکارهای آموزش مهارت ‏های تفکر هوش مصنوعی شناسایی شد. درنهایت، پیامدها در قالب توسعۀ مهارت‏ های اشتغال‏ پذیریِ سخت و نرم در دانش‏ آموختگان مهندسی، به‏ ویژه در تعامل با بن‏سازه ‏ها و ابزارهای هوش مصنوعی صورت‏ بندی شد. مدل استخراج ‏شده می‏تواند به‏ عنوان چارچوبی راهبردی برای بازطراحی آموزش مهندسی با هدف ارتقای اشتغال ‏پذیری دانش ‏آموختگان به کار گرفته شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

DEVELOPING AI-ENABLED HARD AND SOFT EMPLOYABILITY SKILLS IN ENGINEERING STUDENTS: A THEORY-BUILDING META-SYNTHESIS

نویسندگان English

Mehdi Mohammadi 1
Fateme Mirghafari 2
1 Shiraz University
2 Ph.D. in Educational Administration, Department of Administration and Educational Planning, Faculty of Educational Sciences and Psychology, Shiraz University, Shiraz, Iran
چکیده English

With the rapid expansion of Artificial Intelligence (AI) technologies and the changing expectations of the engineering labor market, revisiting university teaching and learning approaches has become increasingly necessary. This study aims to identify the antecedents, processes, and outcomes of teaching AI thinking to foster engineering students’ employability skills, using a theory-building meta-synthesis approach. A qualitative, inductive meta-synthesis was conducted based on the seven-step Finfgeld-Connett model. A systematic literature review was performed through structured searches in major Persian and international databases using guiding keywords. Studies were initially retrieved based on title–keyword relevance and then screened; ultimately, 50 articles were included for analysis and component extraction. Qualitative data were synthesized through open, axial, and selective coding, and the resulting model was subjected to descriptive, interpretive, theoretical, and pragmatic validation. The findings indicated that antecedents can be conceptualized as the knowledge, skills, and attitudes required of both students and faculty members. The process dimension comprised instructional mechanisms for developing AI thinking skills. Outcomes were articulated as the development of hard and soft employability skills among engineering graduates, particularly through engagement with AI tools and platforms. The proposed model provides a strategic framework for redesigning engineering education to enhance graduates’ employability.

کلیدواژه‌ها English

Artificial intelligence thinking
employability skills
hard skills
soft skills
engineering students
theory-building meta-synthesis
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