When using AI, how can we ensure that the quality of the generated content meets academic requirements?

Using AI to generate academic content requires rigorous human supervision to ensure quality and integrity, as raw AI outputs frequently include inaccuracies or lack appropriate scholarly depth. Therefore, verification and refinement are essential for meeting academic standards. Key principles include maintaining human oversight throughout the generation and review process, critically evaluating AI-sourced claims against peer-reviewed literature, and ensuring factual accuracy and logical coherence. Adequate source validation and thorough paraphrasing to avoid plagiarism are crucial. Moreover, content must adhere strictly to the academic discipline's conventions regarding structure, style, and terminology. Verification against established datasets and academic knowledge bases is vital for credibility and reliability. To implement effectively, integrate AI tools as assistants primarily for drafts or ideation rather than final products. Develop a robust editing process involving subject-matter experts for factual validation, structural refinement, argument strengthening, and stringent plagiarism checks using specialized software. Crucially, all AI assistance must be disclosed transparently according to institutional or publisher policies on authorship and ethical conduct.