Course descriptions play a critical role in higher education quality assurance (QA), curriculum transparency, and learner understanding, yet they often vary widely in clarity and structure. While artificial intelligence (AI) is increasingly proposed as a tool to support course design and review processes, empirical work examining how AI can improve course documentation, without altering course intent or learning outcomes, remains limited. This concise paper reports findings from an exploratory pilot analysis examining AI-supported course description improvement using publicly available materials. The study analyzed sixteen university-level courses (n = 16) from computer science and related disciplines. Using a locked prompt restricted to public data only, the AI system generated revised course descriptions based on existing descriptions, Course Learning Outcomes (CLOs), and content outlines. The analysis did not evaluate course quality; instead, it examined whether AI-generated revisions improved clarity, structural coherence, and reviewer confidence. Across all cases, AI-generated revisions consistently produced clearer and more coherent course descriptions. Improvements were most evident in clearer articulation of course purpose, improved narrative flow, and more transparent representation of course scope. These gains were achieved without introducing new learning outcomes, assessments, or assumptions, demonstrating that meaningful clarity improvements can be realized without content invention. Improvement magnitude varied with the quality of the original documentation. Courses with minimal or fragmented descriptions showed the largest gains, while verbose or disorganized descriptions benefited from consolidation and improved structure. Dense technical courses exhibited more moderate improvements, and courses with already strong descriptions showed only marginal gains. These findings suggest that AI can serve as a responsible, human-centered support tool for improving course documentation, with potential applications for program review, accreditation preparation, and learner-facing consistency.