Accepted for/Published in: JMIR Human Factors
Date Submitted: Dec 2, 2024
Date Accepted: Jul 30, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Toward Human-centered Artificial Intelligence for Users’ Digital Well-being: Systematic Review, Synthesis, and Future Directions
ABSTRACT
Background:
As Information and Communication Technologies (ICTs) and Artificial Intelligence (AI) become deeply integrated into daily life, the focus on users’ digital well-being has grown across academic and industrial fields. However, fragmented perspectives and approaches to digital well-being in AI-powered systems hinder a holistic understanding, leaving researchers and practitioners struggling to design truly human-centered AI systems.
Objective:
This paper aims to address the fragmentation by synthesizing diverse perspectives and approaches to digital well-being through a systematic literature review. Using the Stimulus-Organism-Response (SOR) framework as a guiding lens, the study seeks to conceptualize a comprehensive model for designing human-centered AI systems that enhance digital well-being.
Methods:
A systematic review of 240 multidisciplinary publications was conducted to explore the intersection of AI, digital well-being, and human-centered design. The analysis involved identifying key themes, frameworks, and approaches, with the SOR model serving as an overarching perspective to organize findings and inform model development.
Results:
The review resulted in the Human-Centered AI for Digital Well-Being (HCAI-DW) model, a conceptual framework consolidating current knowledge on designing AI systems that support digital well-being and influence human behavior positively. The proposed model integrates insights from cross-disciplinary research, providing a structured understanding of how stimuli (AI system features) affect users’ internal states (perceptions, emotions) and lead to behavioral responses and changes. Additionally, the paper highlights emerging challenges and opportunities, including ethical considerations, scalability, and practical guidelines for applying the model in long-term research and practice.
Conclusions:
This study contributes to advancing the field by presenting an overarching framework for fostering digital well-being through human-centered AI systems. By addressing gaps in the fragmented literature and proposing a unifying model, the findings offer actionable insights for researchers and practitioners. The HCAI-DW model serves as a foundation for future exploration and practical application in creating intelligent computing systems that improve users' digital well-being in everyday life.
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