فصلنامه روندها و دستاوردها در فناوری یادگیری

فصلنامه روندها و دستاوردها در فناوری یادگیری

بررسی اثربخشی کلاس درس طراحی آموزشی مبتنی بر هوش مصنوعی بر خودکارآمدی تحصیلی، انگیزش پیشرفت و یادگیری خودتنظیمی دانشجویان

نوع مقاله : پژوهشی اصیل

نویسندگان
سید علی مسلمی 1
رضا علم 2
مرضیه علم 2
زینب اعلمشاهی 3
1 تکنولوژی آموزشی، دانشگاه علامه طباطبائی ، تهران، ایران
2 دانشجوی دکترای برنامه ریزی درسی، گروه علوم تربیتی، دانشگاه آزاد اسلامی، آزادشهر، ایران.
3 کارشناسی ارشد، روان‌شناسی بالینی، دانشگاه آزاد اسلامی، بهشهر، ایران.
10.22034/jlt.2026.2082107.1078
چکیده
هدف این مطالعه بررسی اثربخشی «درس طراحی آموزشی مبتنی بر هوش مصنوعی» بر خودکارآمدی تحصیلی، انگیزش پیشرفت و یادگیری خودتنظیمی دانشجویان دانشگاه آزاد اسلامی واحد آزادشهر بود. پژوهش از نوع کاربردی و با طرح نیمه‌آزمایشی پیش‌آزمون–پس‌آزمون با گروه کنترل انجام شد. جامعه، دانشجویان کارشناسی علوم تربیتی و رشته‌های مرتبطِ اخذکننده درس طراحی آموزشی بودند که ۶۰ نفر به روش خوشه‌ای چندمرحله‌ای انتخاب و به‌صورت تصادفی در دو گروه آزمایش (طراحی آموزشی مبتنی بر هوش مصنوعی) و کنترل (طراحی آموزشی مرسوم) گمارده شدند. ابزارها شامل پرسشنامه خودکارآمدی تحصیلی، پرسشنامه انگیزش پیشرفت هرمنس و پرسشنامه یادگیری خودتنظیمی بوفارد و همکاران بود. داده‌ها با آمار توصیفی و تحلیل کوواریانس (ANCOVA) تحلیل شدند. نتایج نشان داد با کنترل نمرات پیش‌آزمون، میانگین خودکارآمدی تحصیلی، انگیزش پیشرفت و یادگیری خودتنظیمی در گروه آزمایش به‌طور معناداری بالاتر از گروه کنترل است و نوع آموزش سهم قابل‌توجهی از واریانس هر سه متغیر را تبیین می‌کند؛ بنابراین، گذراندن درس مبتنی بر هوش مصنوعی به تقویت باور به توانمندی تحصیلی، افزایش تمایل به تلاش و موفقیت، و ارتقای مهارت‌های برنامه‌ریزی، نظارت و خودارزیابی انجامیده است. محدودیت‌ها شامل استفاده از نمونه یک واحد دانشگاهی، بازه زمانی محدود و تکیه بر خودگزارش‌دهی است. از نظر عملی، پیشنهاد می‌شود برنامه‌های تربیت معلم و علوم تربیتی با ادغام ساختاریافته ابزارهای هوش مصنوعی در سرفصل‌ها، تکالیف و ارزشیابی بازطراحی شوند. این مطالعه با تأکید بر بهره‌گیری مسئولانه از هوش مصنوعی برای تقویت سرمایه انسانی، با SDG4 و SDG9 همسو است و از حیث اصالت، درس طراحی آموزشی مبتنی بر هوش مصنوعی را به‌عنوان یک «مداخله منسجم» (نه صرفاً پذیرش فناوری) در بافت بومی آموزش عالی ایران می‌آزماید.
کلیدواژه‌ها
طراحی آموزشی
هوش مصنوعی
خودکارآمدی تحصیلی
انگیزش پیشرفت
یادگیری خودتنظیمی
موضوعات

عنوان مقاله English

Effectiveness of an AI-enhanced Instructional Design Course on Academic Self-Efficacy, Achievement Motivation, and Self-Regulated Learning among Undergraduate Students

نویسندگان English

seyed ali mosallami 1
Reza Alam 2
Marzieh Alam 2
Zeinab Aalamshahi 3
1 Educational Technology,Allameh Tabataba’i University, Tehran, Iran
2 Phd student of Curriculum Planning, Azad Islamic University, Azadshahr, Iran.
3 M.A. in Clinical Psychology, Islamic Azad University, Behshahr, Iran.
چکیده English

This study examined the effectiveness of an artificial intelligence (AI)–based instructional design course on academic self-efficacy, achievement motivation, and self-regulated learning among students at Islamic Azad University, Azadshahr Branch. The research was applied in purpose and employed a quasi-experimental pretest–posttest design with a control group. The population comprised undergraduate students in Educational Sciences and related majors enrolled in the instructional design course. Sixty students were selected through multistage cluster sampling and randomly assigned to an experimental group (AI-based instructional design) or a control group (conventional instructional design). Measures included an Academic Self-Efficacy Questionnaire, Hermans’ Achievement Motivation Questionnaire, and the Self-Regulated Learning Questionnaire by Bouffard and colleagues. Data were analyzed using descriptive statistics and analysis of covariance (ANCOVA). After controlling for pretest scores, the experimental group achieved significantly higher posttest means in academic self-efficacy, achievement motivation, and self-regulated learning than the control group, and the instructional approach accounted for a substantial share of variance in all three outcomes. Accordingly, completing the AI-based course strengthened students’ beliefs in their academic capabilities, increased their motivation to strive for success, and enhanced key self-regulation processes such as planning, monitoring, and self-evaluation. Limitations include sampling from a single university branch, a limited time frame, and reliance on self-report data, which warrants cautious generalization. Practically, teacher education and educational science programs are encouraged to redesign instructional design courses by integrating AI tools in a structured manner across syllabi, assignments, and assessment. Emphasizing responsible AI use to build academic human capital, the study aligns with SDG 4 (Quality Education) and SDG 9 (Industry, Innovation and Infrastructure).

کلیدواژه‌ها English

instructional design
artificial intelligence
academic self-efficacy
achievement motivation
self-regulated learning
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  • تاریخ دریافت 05 دی 1404
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