Frontiers in Educational Research, 2026, 9(5); doi: 10.25236/FER.2026.090523.
Liyun Zhu1, Liying Zhu2, Shi Yin3, Yu Deng1, Weifang Zhong4
1College of Economics and Management, Hebei Agricultural University, Baoding, Hebei, 071000, China
2College of Agronomy, Hebei Agricultural University, Baoding, China
3College of Humanities and Social Sciences, Hebei Agricultural University, Baoding, Hebei, 071000, China
4College of Ocean, Hebei Agricultural University, Qinhuangdao, Hebei, 066003, China
In the era of generative artificial intelligence (GenAI), cultivating university students’ GenAI literacy has become imperative for promoting the digital transformation of higher education. Despite growing recognition of its importance, systematic assessment frameworks and empirical evidence regarding students' GenAI literacy remain underdeveloped. This study develops a comprehensive five-dimensional GenAI literacy framework encompassing Knowledge and Understanding (KU), Usage and Application (UA), Assessment and Creation (AC), Usage Attitudes (AT), and Usage Ethics (UE). Survey data from 482 Chinese university students are analyzed using obstacle factor diagnosis. The findings reveal that knowledge and understand dimension constitutes the primary bottleneck, with its three indicators exhibiting the highest obstacle degrees. Additionally, core obstacles evolve as literacy levels advance. Low-level groups face knowledge and attitude barriers, medium-level groups struggle with knowledge depth, high-level groups require enhanced assessment and creation abilities. Based on these findings, a five-stage improvement pathway is proposed: Knowledge Foundation → Attitude Guidance → Ethics Safeguard → Application Implementation → Creative Elevation, providing evidence-based guidance for curriculum design.
Generative Artificial Intelligence; Literacy; Higher Education; Obstacle Factors
Liyun Zhu, Liying Zhu, Shi Yin, Yu Deng, Weifang Zhong. Obstacle Factors Diagnosis of Generative AI Literacy in Higher Education. Frontiers in Educational Research (2026), Vol. 9, Issue 5: 168-173. https://doi.org/10.25236/FER.2026.090523.
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