الزامات توسعه سیستم پشتیبان تصمیم با هوش مصنوعی مبتنی بر نیازهای کاربر تربیت و آموزش

نبی زاده شهربابک, سمیه; جواد زاده, محمد علی; یزدانی, سمانه

الزامات توسعه سیستم پشتیبان تصمیم با هوش مصنوعی مبتنی بر نیازهای کاربر تربیت و آموزش

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ دانشجوی دکتری هوش مصنوعی، گروه مهندسی کامپیوتر، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران.

² استادیار، گروه مهندسی کامپیوتر، دانشگاه جامع امام حسین(ع)، تهران، ایران.

³ استادیار، گروه مهندسی کامپیوتر، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران

چکیده

در عصر نوین داده‌محور، تصمیم‌گیری اثربخش در سازمان‌های تربیتی و آموزشی با چالش‌هایی نظیر حجم عظیم اطلاعات، پویایی نیازها و پیچیدگی رفتار انسانی روبروست. پژوهش حاضر با هدف توسعه و اعتبارسنجی یک سیستم پشتیبان تصمیم هوشمند (DSS) مبتنی بر نیازها و اهداف خاص کاربران آموزشی، با رویکردی چندلایه و انسان‌محور انجام شد. روش‌ پژوهش به صورت ترکیبی (پرسشنامه، مصاحبه، تحلیل داده) بود که با به‌کارگیری الگوریتم‌های یادگیری ماشین و پردازش زبان طبیعی، نمونه عملیاتی سامانه توسعه یافته و در محیط واقعی مورد آزمون قرار گرفت. نتایج تحلیل آماری نشان داد که DSS پیشنهادی موجب بهبود معنادار کیفیت تصمیم‌گیری، افزایش رضایت کاربران، و ارتقاء سرعت و دقت فرآیند تصمیم‌سازی نسبت به مدل‌های سنتی گردیده و قابلیت تطبیق سامانه با نیازهای متغیر و داده‌های جدید نیز تأیید شد. این دستاوردها زمینه‌ای برای بومی‌سازی و ارتقاء سامانه‌های هوشمند پشتیبان تصمیم در مراکز تربیت و آموزش را فراهم ساخته و مسیر تحقیقات آینده را روشن می‌نماید.

کلیدواژه‌ها

موضوعات

پژوهش در مسائل تعلیم و تربیت اسلامی

عنوان مقاله [English]

Requirements for developing a decision support system with artificial intelligence based on user needs. Training and education

نویسندگان [English]

somaye nabizadeh shahrebabak ¹
Mohammad Ali Javadzadeh ²
Samaneh Yazdani ³

¹ PhD student in Artificial Intelligence, Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.

² Assistant Professor, Department of Computer Engineering, Imam Hossein University, Tehran, Iran

³ Assistant Professor, Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

چکیده [English]

In the modern data-driven era, effective decision-making in educational and training organizations faces challenges such as a huge amount of information, dynamic needs, and the complexity of human behavior. The present study aimed to develop and validate an intelligent decision support system (DSS) based on the specific needs and goals of educational users, with a multi-layered and human-centered approach. The research method was a combination (questionnaire, interview, data analysis), and by using machine learning algorithms and natural language processing, an operational prototype of the system was developed and tested in a real environment. The results of statistical analysis showed that the proposed DSS significantly improved the quality of decision-making, increased user satisfaction, and improved the speed and accuracy of the decision-making process compared to traditional models, and the system's ability to adapt to changing needs and new data was also confirmed. These achievements provide a basis for the localization and promotion of intelligent decision support systems in training and training centers and illuminate the path of future research.

کلیدواژه‌ها [English]

Artificial intelligence
educational needs assessment
educational goal setting
education personalization
decision support system

مراجع

Amritraj, S., & Soni, M. (2021). AI-Powered Decision Support Systems for Dynamic Environments. Springer.

Angern, A., et al. (1990). Intelligent decision support systems: An interactive visual approach. International Journal of Decision Support Systems, 7(2), 112-125.

Bødker, S. (2021). Human-Computer Interaction: A Practical Guide for Designers. Springer.

Booviduo, J., et al. (2023). AI-based decision support systems for educational management in higher education. Educational Technology and Decision Support, 29(2), 72-88.

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589-597.

Bryman, A. (2016). Social Research Methods. Oxford University Press.

Cohn, M. (2020). User Stories Applied: For Agile Software Development. Addison-Wesley.

Creswell, J. W. (2018). Research Design. SAGE.

Creswell, J. W. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). SAGE.

Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th Ed.). SAGE Publications.

Dai, Y., Wang, Y., & Liu, J. (2022). Artificial intelligence in decision support systems: Trends and opportunities. Journal of Decision Support Systems, 28(3), 222-235.

Erfanirad, Ruhollah. (2015). Database and Decision Support Systems, Third National Conference on Business Management and First International Conference on Accounting and Resistance Economics. [In Persian]

Fonda, V., et al. (2024). AI-based decision support system for operational decision-making in ICT sector. African Journal of Decision Support Systems, 6(3), 44-58.

Goli Roshan, Marzieh. (1404). The role of data mining and data analysis in educational management decision-making, Third International Conference on Law, Management, Educational Sciences, Psychology and Educational Planning Management. [In Persian]

González, R., & Li, J. (2025). Advanced decision support systems for e-commerce. Journal of E-Commerce and Artificial Intelligence, 15(2), 134-150.

Gorry, G. A., & Scott Morton, M. S. (2020). A Framework for Decision Support Systems. Journal of Management Information Systems, 34(2), 45-63.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). Multivariate Data Analysis (8th Ed.). Cengage Learning.

Hauser, J. R., & Clausing, D. (2021). The House of Quality. Harvard Business Review, 59(3), 63-73.

Hossain, M. A., Khan, M. S., & Alam, S. (2020). A review on the role of decision support systems in sustainable development. International Journal of Sustainable Development, 15(2), 45-58.

Hossain, M. A., Khan, M. S., & Alam, S. (2021). Artificial intelligence in decision support systems: A review. Journal of Decision Support Systems, 28(4), 100-110.

Ivankova, N. V. & Creswell, J. W. (2009). Mixed Methods Research: A Guide to the Field. SAGE.

Ivankova, N. V., & Creswell, J. W. (2009). Mixed methods research: A guide to the field. SAGE.

Jain, A., & Rathi, R. (2021). Decision support systems: A modern approach. Management Decision, 59(4), 702-718.

Jain, A., & Rathi, R. (2022). Machine learning and deep learning in decision support systems. Management Science Letters, 12(8), 307-315.

Johnson, L., & Lee, Y. (2022). Integration of AI in decision support systems for business applications. Journal of Business Analytics and AI, 21(3), 90-105.

Kano, N. (2020). Attractive Quality and Must-Be Quality. Journal of the Japanese Society for Quality Control, 29(1), 1-21.

Kim, S., & Park, J. (2020). Real-time decision support systems in healthcare. Healthcare Decision Systems Journal, 17(1), 34-48.

Kostopoulos, G., et al. (2024). Explainable AI-based decision support systems: A comprehensive review. Journal of Artificial Intelligence and Decision Systems, 18(1), 23-45.

Kovari, A. (2024). Artificial intelligence for decision support: Balancing accuracy, transparency, and trust in various sectors. International Journal of Decision Support Systems, 19(2), 112-128.

Liu, X., et al. (2021). The role of natural language processing in decision support systems. Journal of Natural Language Processing and Decision Making, 12(3), 55-72.

Maleki, Hassan. (2017). Lesson Planning (Practical Guide). Mashhad: Payam Andisheh Publications. [In Persian]

Miller, T. (2021). Explainable AI: A guide for building trust in machine learning models. Journal of AI and Society, 36(1), 39-52.

Müller, A., & Davis, P. (2025). Deep learning integration in decision support systems for healthcare. Journal of Medical Decision Support Systems, 11(3), 45-60.

Preece, J., Rogers, Y., & Sharp, H. (2020). Interaction Design: Beyond Human-Computer Interaction. Wiley.

Resnik, D. B. (2020). The ethics of research with human subjects. Springer.

Saunders, M., Lewis, P., & Thornhill, A. (2019). Research Methods for Business Students (8th Ed.). Pearson.

Sekaran, U., & Bougie, R. (2020). Research Methods for Business: A Skill-building Approach (8th Ed.). Wiley.

Sharma, P., Kumar, R., & Verma, A. (2021). Emerging technologies in decision support systems: Current trends and future outlook. International Journal of Technology Management, 58(2), 112-130.

Smith, M., & Davis, T. (2022). Evaluating decision support systems in crisis management. Crisis and Decision Support Journal, 19(2), 123-140.

Sprague, R. H., & Carlson, E. D. (2021). Building effective decision support systems. Prentice Hal

Sutton, R. S., & Barto, A. G. (2019). Reinforcement Learning: An Introduction. MIT Press.

Tashakkori, A., & Teddlie, C. (2010). Handbook of mixed methods in social & behavioral research. SAGE.

Tobias, J. (2021). Applications of machine learning in decision support systems. Journal of Machine Learning and Decision Support, 23(4), 67-85.

Turban, E., & Volonino, L. (2020). Information Technology for Management: Digital Strategies for Insight, Action, and Sustainable Performance. Wiley.

Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative–quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS Quarterly, 37(1), 21–54.

Wang, H., et al. (2023). Decision support systems for sustainable development. Sustainable Development and AI, 14(5), 200-215.

Zhang, L., et al. (2023). Deep learning in decision support systems: A review. AI & Decision Systems Journal, 17(4), 103-119.

Zhang, Q., & Wang, Z. (2025). Real-time decision support systems for crisis management. Journal of Crisis Management and Decision Making, 22(4), 78-95.

Zhou, M., Chen, H., & Zhang, L. (2021). Natural language processing for decision support systems. Journal of Information Systems, 35(6), 75-82.

Zhou, W., et al. (2024). AI in military decision-making systems: Supporting human decisions, not replacing them. Journal of Military Decision Support, 8(3), 33-50.