<![CDATA[The development of modern Artificial Intelligence (AI) technology has enhanced human interaction with intelligent systems in daily life; however, excessive use of AI can lead to AI Addiction, particularly among university students. This study aims to design and develop a web-based expert system using the Certainty Factor (CF) method to identify early symptoms of AI addiction and calculate the likelihood level of dependence based on user input. The case study was conducted on students of the Informatics Student Association (HIMAGIRI), Universitas Sebelas Maret (UNS), with symptom data obtained from the adaptation of the AI Addiction Scale (AIAS-21) and interviews with psychological experts specializing in addiction, anxiety, and mood disorders. Testing using Black Box Testing and White Box Testing demonstrated that all system functions operated properly and produced consistent diagnostic calculations. From 77 respondents, addiction tendencies were dominated by the Continued Use Despite Harm category (38%), followed by Compulsive Use/Loss of Control (33%) and Withdrawal (29%). These results indicate that the Certainty Factor method is effective in detecting AI addiction tendencies and providing relevant treatment recommendations, making this expert system a useful early detection tool as well as an educational medium to increase students’ self-awareness of their dependence on AI. Keywords: Expert System; Certainty Factor; Artificial Intelligence Addiction; Diagnosis, Web-based; Students; HIMAGIRI UNS]]>
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