The idea of personalization through the Artificial Intelligence (AI) has become one of the most popular elements of digital platforms, enabling the making of hyper-targeted recommendations, dynamic interfaces, and personalized consumer experiences. Though there is a possibility of increasing relevance and convenience when it is personalized, there are also concerns of the emergence of issues to do with surveillance, data misuse, and intrusion of privacy as well. The present paper will research the dual-response model, according to which the experience of the user with either positive (based on value) or negative (based on privacy threat) reactions to AI-based personalization is considered. The current study develops an extensive framework based on the most recent psychological, technological, and consumer-behavior theories to present in which the perceived relevance, perceived trust, data transparency, and autonomy positively influence user acceptance, and the perceived risk, perceived privacy, and perceived algorithmic obscurity, negatively influence opposition. According to a descriptive research methodology, using the help of the secondary data, the article will categorize the available literature and find an emotional-cognitive contradiction that users feel when interacting with AI-driven systems. The results indicate that transparency, informed consent and explainable AI could be relevant in regulating the privacy issue whereas personalization is observed to facilitate the engagement. The study also has the gaps in the privacy regulation, the ethical design of AI, and the mechanisms of user awareness. The proposed model of dual-response is the middle ground viewpoint in order to enable the platform designers, policymakers and researchers to optimise AI personalisation without compromising user autonomy...