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واعظی, سید کمال, پاشایی, فرانک. (1404). کاربرد هوش مصنوعی برای تولید تلنگرهای رفتاری هوشمند. , 17(4), 938-962. doi: 10.22059/jipa.2025.395752.3700
سید کمال واعظی; فرانک پاشایی. "کاربرد هوش مصنوعی برای تولید تلنگرهای رفتاری هوشمند". , 17, 4, 1404, 938-962. doi: 10.22059/jipa.2025.395752.3700
واعظی, سید کمال, پاشایی, فرانک. (1404). 'کاربرد هوش مصنوعی برای تولید تلنگرهای رفتاری هوشمند', , 17(4), pp. 938-962. doi: 10.22059/jipa.2025.395752.3700
واعظی, سید کمال, پاشایی, فرانک. کاربرد هوش مصنوعی برای تولید تلنگرهای رفتاری هوشمند. , 1404; 17(4): 938-962. doi: 10.22059/jipa.2025.395752.3700

کاربرد هوش مصنوعی برای تولید تلنگرهای رفتاری هوشمند

مدیریت دولتی
دوره 17، شماره 4، 1404، صفحه 938-962    اصل مقاله (613.52 K)
نوع مقاله: مقاله علمی پژوهشی
شناسه دیجیتال (DOI): 10.22059/jipa.2025.395752.3700
نویسندگان
سید کمال واعظی* 1؛ فرانک پاشایی2
1دانشیار، گروه رهبری و سرمایۀ انسانی، دانشکدۀ مدیریت دولتی و علوم سازمانی، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران.
2دانشجوی دکتری، گروه خط‌مشی‌گذاری عمومی، پردیس بین‌الملل کیش، دانشگاه تهران، کیش، ایران.
چکیده
هدف: هدف این پژوهش بررسی ظرفیت‌های هوش مصنوعی در تولید تلنگرهای رفتاری هوشمند، در راستای بهبود فرایندهای خط‌مشی‌گذاری رفتاری است. در این راستا تلاش شد تا با استفاده از چارچوب نظری معماری سامانه تلنگر هوشمند، الگویی طراحی شود که بتواند با تکیه بر ابزارهای فناورانه و شخصی‌سازی تصمیم‌ها، اثربخشی مداخلات رفتاری را ارتقا بخشد.
روش: این پژوهش از نوع کیفی و مبتنی بر تحلیل مضمون داده‌هاست که با استفاده از مصاحبه‌های نیمه‌ساختاریافته با ۱۲ نفر از خبرگان حوزه‌های خط‌مشی‌گذاری، اقتصاد رفتاری و هوش مصنوعی اجرا شده است. نمونه‌گیری به‌روش گلولۀ برفی انجام شد و گردآوری داده‌ها تا رسیدن به اشباع نظری ادامه یافت. داده‌های حاصل از مصاحبه‌ها با بهره‌گیری از روش کدگذاری باز و محوری تحلیل و در قالب مدل مفهومی ارائه شدند. همچنین از یافته‌های نظری و تحلیل‌های کتابخانه‌ای نیز در طراحی مدل استفاده شد.
یافته‌ها: یافته‌های نظری پژوهش نشان داد که از ابزارهای مداخله‌ای مانند کلان‌داده، یادگیری ماشین، رویکرد الگوریتمی، عامل نرم‌افزار هوشمند، اینترنت اشیا و فناوری‌های شناختی، به‌عنوان اجزای اصلی سامانۀ تلنگر هوشمند می‌توان بهره برد. نتایج حاصل از مصاحبه‌ها حاکی است که ۹ ابزار جدید سیستم‌های توصیه‌گر، یادگیری تقویتی، شبکه‎های عصبی، تحلیل پیش‌بینی‌کننده، نوتیفیکیشن، منطق فازی، پردازش زبان طبیعی، پلتفرم‌های یادگیری سفارشی و سیستم‌های پشتیبانی تصمیم‌گیری، می‌توانند قابلیت شخصی‌سازی تلنگرها را تقویت کنند. در مدل مفهومی طراحی‌شده، پردازندۀ مرکزی به‌عنوان یادگیرنده نمایه‌ای تعریف شد که با تحلیل داده‌های مرتبط با علایق، رفتارها و توانمندی‌های کاربران، امکان تولید تلنگرهای متناسب با موقعیت‌های خاص را فراهم می‌سازد.
نتیجه‌گیری: تلنگرهای رفتاری مبتنی بر هوش مصنوعی، برای ارتقای اثربخشی مداخلات در خط‌مشی‌گذاری رفتاری ظرفیت چشمگیری دارند و به‌جای اتکا به راه‌کارهای کلی، قادرند با پردازش داده‌های فردی، مداخلاتی شخصی‌سازی‌شده و به‌موقع ارائه دهند. استفاده از معماری سامانه‌های هوشمند و یادگیری نمایه‌ای، امکان تلفیق داده‌های چندمنبعی و تحلیل آن‌ها را برای طراحی تلنگر فراهم می‌سازد. یافته‌های این پژوهش می‌تواند به خط‌مشی‌گذاران، طراحان سیستم‌های هوشمند و محققان حوزۀ علوم رفتاری در مسیر طراحی مداخلات اثربخش‌تر کمک کند و گامی در جهت توسعه خط‌مشی‌گذاری داده‌محور و رفتارمحور باشد.
کلیدواژه‌ها
تلنگر رفتاری؛ خط‌‌مشی‌‌گذاری رفتاری؛ شخصی‌‌سازی؛ علوم رفتاری؛ هوش مصنوعی
عنوان مقاله [English]
Application of artificial intelligence for Generating Smart Behavioral Nudges
نویسندگان [English]
Sayed Kamal Vaezi1؛ faranak pashaei2
1Associate Prof., Department of Leadership and Human Capital, Faculty of Public Administration and Organizational Sciences, College of Management, University of Tehran, Tehran, Iran.
2Ph.D. Candidate, Department of Public Policy, Kish International Campus, University of Tehran, Kish, Iran.
چکیده [English]
Objective
The primary aim of this study is to examine the capacities and capabilities of artificial intelligence in designing and implementing intelligent, personalized behavioral nudges. These nudges, viewed as the second generation of behavioral interventions, can enhance the effectiveness of behavioral policymaking by relying on technology-driven and data-based tools. The central research question guiding this study is: How can AI-based behavioral nudges, grounded in choice architecture and nudge theory, contribute to public policymaking and influence both individual and collective behaviors? By integrating big data analytics, machine learning algorithms, and cognitive technologies, the study seeks to demonstrate how behavioral interventions can be elevated from a generalized and impersonal level to one that is precise, individualized, and adaptive.
Methods
This research follows a qualitative approach using thematic analysis. Data were collected through semi-structured interviews with 12 experts in public policy, behavioral economics, and artificial intelligence. Sampling was conducted using the snowball method until theoretical saturation was achieved. The data were analyzed through open and axial coding, and the results were synthesized into a conceptual model. To enrich the model, findings from library research and international literature were incorporated. This combination of empirical and theoretical data provided the foundation for developing a comprehensive conceptual architecture of an intelligent nudge system.
Results
The theoretical exploration identified technological tools such as big data, machine learning, the Internet of Things, intelligent software agents, algorithmic methods, and cognitive technologies as the central components of an intelligent nudge system. Expert interviews led to the recognition of nine complementary tools—predictive analytics, reinforcement learning, neural networks, recommender systems, notifications, fuzzy logic, natural language processing, adaptive learning platforms, and decision-support systems—that strengthen the personalization of nudges. The proposed conceptual model is built around several key components: (1) a user profile containing descriptive data (age, gender, health, location), preferences, past behaviors, and individual capabilities, gathered explicitly (e.g., user responses) or implicitly (e.g., online behavior, wearable devices); (2) a profile learner that functions as the central processor, analyzing user data to detect behavioral patterns and design context-appropriate nudges; (3) a data collection and analysis process that uses big data and algorithms such as predictive analytics and natural language processing to transform information into actionable insights; (4) nudge design, where tailored interventions are created; and (5) evaluation of user response, where feedback and behavioral changes are measured, and new data are reintegrated into the system’s learning cycle to ensure continuous refinement. The findings highlight that unlike traditional nudges—which are uniform and general—AI-based nudges can be precisely tailored to individuals and delivered at the right time and in the right context. This capacity allows policymakers to move beyond broad, often inefficient interventions toward adaptive, data-driven tools. However, risks were also identified, including privacy violations, the reproduction of human biases in AI systems, and the possibility of “dark nudges.” Addressing these risks requires regulatory safeguards, algorithmic transparency, and ethical oversight.
Conclusion
The integration of artificial intelligence with behavioral sciences creates new capacities for data-driven policymaking. Intelligent nudge systems not only increase the effectiveness of interventions but also provide opportunities for continuous learning, refinement, and long-term policy improvement. By presenting a conceptual model grounded in a profile learner, this study offers policymakers, developers, and behavioral researchers a roadmap for using AI to design interventions that are more efficient, equitable, and adaptive. Ultimately, AI-based nudges represent not only tools for influencing individual behavior but also a transformative step toward reimagining public policymaking in the era of big data and intelligent decision-making.
کلیدواژه‌ها [English]
Artificial intelligence, Behavioral nudges, Behavioral policymaking, Personalization, Behavioral science
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