سواد هوش مصنوعی برای یادگیرندگان قرن بیست و یکم: چارچوبی برای ارزیابی و اعتبارسنجی
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند
1 - دانشجوی دکتری، تکنولوژی آموزشی، دانشگاه علامه طباطبائی، تهران، ایران
کلید واژه: هوش مصنوعی, سواد هوش مصنوعی, روان سنجی, دانشجویان.,
چکیده مقاله :
چکیده: با کاربرد هوش مصنوعی در بخش‌های مختلفی مانند آموزش، پزشکی، مهندسی و امور مالی، تقاضا برای سواد هوش مصنوعی که شامل ارزیابی انتقادی، همکاری مؤثر و درک مفاهیم و پیامدهای اخلاقی هوش مصنوعی می‌شود شدت گرفته است. در آموزش عالی، سواد هوش مصنوعی به عنوان ستونی اساسی شناخته می‌شود که دانش و مهارت‌های لازم برای انجام مؤثر وظایف عملی مورد انتظار از هر دو مدرس و دانشجو در بستر آموزشی با استفاده از هوش مصنوعی را در بر می‌گیرد. سواد هوش مصنوعی (AI) امروزه در صدر دستور کار آموزش برای توسعه دانش، مهارت، نگرش و ارزش‌های دانش‌آموزان در قرن بیست و یکم قرار دارد. با این حال، ابزارهای تحقیقاتی تایید شده کمی برای ارزیابان آموزشی جهت بررسی چگونگی توسعه و درک دانشجویان از نتایج یادگیری‌شان وجود دارد. هدف از پژوهش حاضر ساخت و اعتباریابی مقیاس سواد هوش مصنوعی در دانشجویان بود. روش تحقیق توصیفی-پیمایشی بود. جامعه آماری شامل 150 نفر از دانشجویان کارشناسی رشته علوم تربیتی دانشگاه پیام نور کرمان می باشد که با روش نمونه گیری در دسترس انتخاب شدند. ابزار گردآوری پژوهش شامل پرسشنامه 32 سوالی سواد هوش مصنوعی ان جی و همکاران (2023) بود . داده ها با استفاده از تحلیل عاملی تاییدی تجزیه و تحلیل شدند. یافته های پژوهش نشان داد با توجه به نتایج حاصل از بررسی شاخص‌های ارزیابی براش مدل که همگی در دامنه قابل قبول قرار دارند، مدل سواد هوش مصنوعی از برازش لازم برخوردار است (RMSEA=0.06،NFI=0.94،CFI=0.92). لذا با توجه به این یافته‌ها می‌توان گفت که مدل آزمون شده از برازش مناسبی برخوردار است و پرسشنامه پژوهش از روایی سازه مطلوبی برخوردار است. نتیجه‌گیری ما این است که سواد هوش مصنوعی مستلزم برخورداری دانشجویان از نه تنها دانش و مهارت های فنی، بلکه همچنین دانش و مهارت های عاطفی، رفتاری و اخلاقی است.
چکیده انگلیسی:
Introduction: With the application of AI in various sectors such as education, medicine, engineering, and finance, the demand for AI literacy - which includes critical evaluation, effective collaboration, and understanding the concepts and ethical implications of AI - has intensified. In higher education, AI literacy is recognized as a fundamental pillar encompassing the knowledge and skills required to effectively perform practical tasks expected of both instructors and students in AI-enhanced learning environments. AI literacy is now at the forefront of educational agendas to develop the knowledge, skills, attitudes, and values of 21st-century learners. However, there are few validated research tools for educational evaluators to assess how students develop and understand their learning outcomes. The aim of this research was AI Literacy for 21st-Century Learners: A Framework for Assessment and Validation. The research method was a descriptive-survey.
Method: The research method was descriptive survey. The statistical population consisted of 150 undergraduate students of Educational Sciences at Payame Noor University of Kerman, who were selected using an available sampling method. The research data collection tool was a 32-item AI literacy questionnaire developed by Ng and colleagues (2023).
Results: Data analyzed using confirmatory factor analysis .Findings indicated that the AI literacy model exhibited a good fit (RMSEA=0.06, NFI=0.94, CFI=0.92), suggesting that the questionnaire possessed sound construct validity.
Discussion: The study concludes that achieving AI literacy in today's world requires more than just knowing how to use AI and having a basic understanding of it. Students must also acquire socio-emotional, metacognitive, and higher-order thinking skills. A critical ethical mindset is essential for students' use of AI. AI literacy necessitates that students possess not only technical knowledge and skills but also emotional, behavioral, and ethical knowledge and abilities.
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[3] Banele, S. D. “The students' cognitive discrepancies in artificial intelligences utilization: A case of higher learning institutions”. SocioEdu: Sociological Education, 4(2), 2023; 53-61.
[4] Ng DT, Chu SK. “Motivating Students to Learn AI Through Social Networking Sites: A Case Study in Hong Kong”. Online Learning. 2021 Mar;25(1):195-208.
[5] Kong SC, Cheung WM, Zhang G. “Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds”. Computers and Education: Artificial Intelligence. 2021 Jan 1;2:100026.
[6] Southworth J, Migliaccio K, Glover J, Reed D, McCarty C, Brendemuhl J, Thomas A. “Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy”. Computers and Education: Artificial Intelligence. 2023 Jan 1;4:100127.
[7] Chu HC, Hwang GH, Tu YF, Yang KH. “Roles and research trends of artificial intelligence in higher education: A systematic review of the top 50 most-cited articles”. Australasian Journal of Educational Technology. 2022 Apr 19;38(3):22-42.
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