Application of artificial intelligence for Generating Smart Behavioral Nudges

Document Type : Research Paper

Authors

1 Associate Prof., Department of Leadership and Human Capital, Faculty of Public Administration and Organizational Sciences, College of Management, University of Tehran, Tehran, Iran.

2 Ph.D. Candidate, Department of Public Policy, Kish International Campus, University of Tehran, Kish, Iran.

10.22059/jipa.2025.395752.3700

Abstract

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.

Keywords

Main Subjects

References

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Journal of Public Administration
Volume 17, Issue 4
2025
Pages 938-962

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