Effectiveness of AI-Driven Assessments in Enhancing Learning Evaluation through Predictive Technology in Vocational Secondary School
Year: 2025
Type: article
Study design: Clinical Trial
Abstract: This study evaluates the effectiveness of Artificial Intelligence (AI)-driven assessments in enhancing learning evaluation through predictive technology in vocational secondary schools. Using a quasi-experimental design, the study involved two groups: an experimental group (n = 100) that used AI-driven assessments and a control group (n = 100) with traditional methods. Both groups took pre-tests and post-tests to measure knowledge changes, along with surveys and observations to assess engagement and satisfaction. The experimental group showed significantly higher post-test scores (85.6% vs. 76.4%), indicating improved performance. Additionally, 89.25% of students in the experimental group re-ported greater engagement. 90.20% of the students expressed high satisfaction with the assessment process, rating their experience as very satisfying. Observational data confirmed a more active learning environment in the experimental group. The findings suggest that AI-driven assessments provide more efficient and adaptive evaluations, enhancing both learning outcomes and student engagement, with real-time feedback supporting continuous improvement.
Author: Ridho Dedy Arief Budiman
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
Funder: Universitas Negeri Yogyakarta
OpenAlex ID: W4412412789