Exploring the effectiveness of micro-credentials in artificial intelligence teaching and learning: an empirical study based on AI+X program in China

This empirical study investigates the effectiveness of micro-credentials (M-Cs) in artificial intelligence (AI) education through a mixed-methods approach. A systematic literature review (SLR) of 357 articles from the Web of Science Core Collection (2017-2024) identified 44 eligible studies, revealing growing global interest in M-Cs for computer science and interdisciplinary fields. A survey of 100 participants from six East China universities in the AI+X M-Cs program utilized Likert-scale questionnaires adapted from the Course Experience Questionnaire and National College Student Learning Survey. Factor analysis (KMO = 0.859) and multiple linear regression (SPSS v25) demonstrated that teaching resources (β = 0.522, <i>p</i> &lt; 0.001) and learning communities (β = 0.619, <i>p</i> &lt; 0.001) significantly enhanced teaching effectiveness, explaining 70.3% and 64.9% of variance in theoretical–practical outcomes and technical competencies, respectively. While course instruction and assessment showed limited impacts, the findings validate M-Cs’ potential for bridging AI talent gaps through modular, industry-aligned curricula. The study proposes actionable strategies for curriculum integration, university-industry collaboration and competency-based evaluation systems. This research provides evidence-based insights for educators and policymakers to implement scalable M-Cs programs, effectively addressing the global AI talent deficit (e.g., China's 5-million professional gap) and fostering innovation in higher education. By emphasizing resource-rich and community-driven approaches, it supports the development of T-shaped professionals with interdisciplinary competencies, thereby enhancing workforce readiness and economic growth in emerging fields like healthcare AI and intelligent manufacturing.