This paper introduces TranSlider, an AI-powered tool for effectively communicating scientific content to the general public. TranSlider provides personalized translations of scientific texts based on a user's profile (e.g., interests, residence, and education level). Users can adjust the level of personalization via a slider ranging from 0 (weak relevance) to 100 (strong relevance), and the translations are generated using a large-scale language model (LLM). An exploratory study with 15 participants examined the utility of AI-based personalized translation and the impact of interactive reading features on user comprehension and the reading experience. Results showed that participants who preferred a high level of personalization valued relevant and contextually relevant translations, while those who preferred a low level of personalization preferred concise and subtly contextualized translations. Furthermore, participants reported that reading multiple translations in parallel enhanced their understanding of scientific content. Based on these findings, we discuss several implications for designing adaptive interfaces that facilitate science communication and support human-AI harmony.