Understanding AI Ethics and Integrity in Research

The rapid integration of generative AI tools presents unprecedented challenges to academic integrity, research ethics, and scholarly publication standards. This course provides the foundational understanding necessary to responsibly integrate AI into your scholarly work while maintaining ethical standards and ensuring accountability in research design and communication. What you'll learn: * Understand the fundamental ethical frameworks applied to AI development and deployment in academic settings. * Analyze the complex issues of intellectual property, authorship, and plagiarism when utilizing large language models (LLMs). * Evaluate the risks of bias, lack of transparency, and reproducibility in AI-assisted research and data analysis. * Apply best practices for documenting AI usage and ensuring accountability in experimental design and data generation. * Master current methods used by anti-plagiarism tools to detect AI-generated text and how to maintain original work standards. * Identify and mitigate ethical pitfalls related to data privacy and the misuse of academic datasets by AI systems. We begin by defining core AI concepts and ethical theories, then move into practical scenarios covering research integrity, publication standards, and future governance models for AI in academia. This course is designed for beginners—students, researchers, educators, and academic professionals—who need to establish a strong ethical foundation for using AI in their scholarly work. No prior knowledge of advanced AI or ethics is required. Start building your ethical framework for the age of artificial intelligence today.