Reducing Alcohol and Smoking Dependency through Artificial Intelligence: A Critical Analysis


  • Lakshya Tanwar


Alcohol Dependency; Smoking Dependency; Artificial Intelligence; Respondent


This study report critically analyses the efficacy of using Artificial Intelligence (AI) to reduce alcohol and smoking addictions. The main goal is to evaluate the effectiveness of AI technology in delivering individualized and focused assistance for addiction cessation. The study used a combined-technique approach, using both qualitative and quantitative data gathering techniques. A structured survey was conducted with 100 participants to collect demographic data, evaluate the extent of alcohol and smoking addiction, and explore the participants' perspectives on AI-driven therapies. Qualitative data was collected via detailed interviews and focus group discussions to provide a nuanced insight into individual experiences and perspectives. The research employs sophisticated AI algorithms to examine the gathered data, detecting patterns and relationships among demographic characteristics, dependence levels, and reactions to AI interventions. The results emphasize the need of customized treatments, using artificial intelligence to adjust treatment strategies according to individual requirements and preferences. It is determined that 95.0% of individuals agree or strongly agree on the significance of AI integration. The Cronbach's Alpha value of 0.871 indicates that the survey questions effectively assess the desired constructs of addiction treatment effectiveness in a reliable manner.


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How to Cite

Tanwar, L. . (2024). Reducing Alcohol and Smoking Dependency through Artificial Intelligence: A Critical Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3538 –. Retrieved from



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