Author(s)

Yu Zhang

Affiliation(s)

Beihai Campus of Guilin University of Electronic Technology, Beihai 536000, Guangxi, China.

Corresponding Author

Yu Zhang

ABSTRACT

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computer science experiment courses often suffer from a disconnect between content and real-world applications, with outdated materials that fail to keep pace with industry developments.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teaching methods and assessment models limit students' opportunities for independent exploration and innovation, while outdated laboratory facilities further hinder the quality of experimental teaching.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 "collaborative innovation and open-sharing" experimental teaching environment. These reforms aim to enhance students' practical skills, increase the practical relevance of courses, and comprehensively improve the quality of experimental teaching, ultimately preparing students to meet the demands of future industry needs.

KEYWORDS

Artificial intelligence; Computer science; Experimental teaching; Multi-dimensional Curriculum System; Personalized teaching

CITE THIS PAPER

Yu Zhang. Exploration of AI empowered experimental teaching reform in computer science. Journal of Computer Science and Electrical Engineering. 2024, 6(3): 8-12. DOI: 10.61784/jcsee3011.

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