Artificial intelligence addiction: exploring the emerging phenomenon of addiction in the AI age

This comprehensive scoping review looks at how AI addiction could be spread among AI users. It evaluates prior research on AI addiction using different applications, with an emphasis on improving the well-being of users. The research questions explored in this review focus on the description of the frameworks, the target samples, the research methods, and research findings related to the topic of AI addiction. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) is the methodology employed to identify and analyze relevant research articles. This scoping review concentrates on little but diverse global 14 studies between 2020 and 2025. The results demonstrate that research on AI addiction has addressed issues like the definition of AI addiction, the potential causes of AI addiction that directly impact researchers, how students have become overly dependent on generative chatbots in the classroom, how teachers heavily rely on generative AI, the responsible role of AI designers, online instruction, how generative chatbot addiction significantly reduced social media addiction, the impact of generative AI on older adults, and, finally, how loneliness and reliance on short-form videos moderate the relationship between narcissism and sincere self-disclosure to AI. It also show the main factors that lead to AI addiction such as innate desire for competence and relatedness, the role of designs and emotional attachment, in addition to exaggeration of using AI. The findings further show that the majority of AI engagement is motivated by functional needs like cognitive immersion and perceived utility, even though emotional over-use of generative AI might occasionally resemble behavioral addiction. Finally, the findings demonstrate that the samples include children, teenagers, elderly people, college students, teachers, experienced users, and researchers. Based on the results obtained, some pedagogical implications are put forward. Finally, collaboration between researchers, legislators, educators, and tech developers can guarantee the development of moral AI systems that put the welfare of users first.