As digital media companies pursue sustainable revenue, AI-based strategies like personalized advertising and dynamic paywalls have become prevalent. These monetization models involve different forms of consumer data collection, raising distinct privacy concerns. This study investigates how digital news users perceive privacy trade-offs between these two AI-driven models. Based on Communication Privacy Management Theory and Privacy Calculus Theory, we conducted a survey of 336 Korean news consumers. Findings indicate that perceived control and risk significantly affect users’ willingness to disclose data. Moreover, users with different privacy orientations prefer different monetization models. Those favoring dynamic paywalls tend to be more privacy-sensitive and show a higher willingness to pay for personalized, ad-free content. While personalization benefits are broadly acknowledged, the effectiveness of privacy control mechanisms remains limited. These insights highlight the importance of ethical, user-centered AI monetization strategies in journalism and contribute to theoretical discussions around algorithmic personalization and digital news consumption.

Jump rope exercise requires a fast tempo and breathing, which often leads to the problem of users forgetting their jump count during the workout. To address this issue, we propose a jump rope exercise assistance program that recognizes the user’s jump rope motions and analyzes the impact of joint coordinates on these motions. The proposed solution extracts frame-by-frame joint coordinate data from jump rope performance videos. It then utilizes artificial intelligence models to recognize jump rope motions and measure the jump count through motion recognition. We employed five machine learning models and two deep learning models to validate the jump rope motion recognition and count measurement. We analyzed the joint coordinates significantly influencing each jump rope motion using SHAP. Furthermore, we used Odds Ratios to analyze the jump rope motion occurrence probability based on joint coordinate values. Through these methods, we confirmed that the proposed solution effectively performs jump rope motion recognition and joint coordinate impact analysis for jump rope motions.