CARefully Prompt Your AI v1.0 Release This is the first full release of "CARefully prompt your AI" - a comprehensive prompt library for AI-assisted academic writing with ethical guidance. This initial version establishes an initial collection of useful and academically oriented prompt templates, with labels of ethical guidance assigned to the prompts based on the specific writing goal. ✨ Core Features Prompt Library: 14 carefully crafted prompt templates across three essential categories: Improve Existing Text: Clarity enhancement, conciseness, flow improvement, and more Drafting Assistance: Starting sentences, transitions, conclusions, and expansion tools Maintain Voice & Precision: Style matching, terminology preservation, and avoiding generic AI patterns Ethical Framework Integration: All prompts are categorized using the CARe Framework and three-tier ethical system proposed by Cheng et al. (2025) Tier 1: Ethically Acceptable - Simple tasks like grammar checking Tier 2: Ethically Contingent - Tasks requiring human oversight Tier 3: Ethically Suspect - Included for awareness but not recommended Interactive Examples: Each prompt template includes an example for testing its effectiveness with your GenAI chatbot model, or simply additional guidance on its application 📱 Responsive Design Fully optimized for both desktop and mobile screens Adapted layouts for various device sizes with careful attention to spacing and readability Accessible on all modern browsers and devices 👩‍🏫 Educational Components Clear explanation of the CARe Framework (Collect, Articulate, Refine, evaluate) Ethical guidance integrated throughout the application This initial release is intended for academic writers, writing instructors, and anyone interested in using GenAI writing tools ethically and effectively. I believe this prompt library provides a solid foundation for thoughtful, intentional, and responsible collaboration with GenAI assistants in academic contexts. Based on the CARe framework developed by Oliveira (2025) and the ethical tiers proposed by Cheng et al. (2025).