Artificial Intelligence-Enabled Evaluation of Vocational Literacy of Students in Vocational Colleges: Realistic Dilemmas and Implementation Paths
Year: 2026
Type: article
Abstract: The scientificity and developmental nature of vocational literacy evaluation is a key link in the high-quality development of vocational education. At present, there are prominent problems in the evaluation of vocational literacy of vocational college students, such as data silos, dominance of static evaluation, and lagged feedback, which make it difficult to meet the needs of developmental evaluation. Against the background of the in-depth integration of artificial intelligence and educational evaluation, and in combination with the characteristics of vocational college students, this paper constructs a four-dimensional framework for students’ vocational literacy, and on this basis, designs an intelligent evaluation model of vocational literacy with “data-driven, human-machine collaboration” as the core. Through multi-modal whole-process data collection, data fusion analysis, real-time feedback and growth intervention, the unity of scientificity, accuracy and educational nature of vocational literacy evaluation is realized. Meanwhile, in response to the possible risks of algorithm bias, data security and educational alienation in the application of artificial intelligence in vocational literacy evaluation, this paper puts forward prevention and response strategies, in order to provide an operable framework and practical guidance for the reform of artificial intelligence-enabled vocational literacy evaluation.
Author: Haixia Liu
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7225735658