In the context of rapidly advancing information technology, Artificial Intelligence (AI) has profoundly impacted various industries, presenting new challenges and opportunities for higher education, particularly in computer science experiment teaching. Despite covering fundamental topics such as programming basics and algorithm design, current computer science experiment courses often suffer from a disconnect between content and real-world applications, with outdated materials that fail to keep pace with industry developments. This gap leaves students ill-prepared to navigate rapidly evolving technological landscapes. Additionally, traditional teaching methods and assessment models limit students' opportunities for independent exploration and innovation, while outdated laboratory facilities further hinder the quality of experimental teaching. To address these challenges, this study proposes AI-enabled reforms in experimental teaching. The strategies include establishing a "multi-dimensional, practice-oriented" curriculum system, implementing a "data-driven, precision-guided" teaching model, promoting "self-directed, flexible progression" learning paths, and building a "collaborativ
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