An Exploratory Cross-Sectional Correlational Study of Students' Perceptions Following an AI-Supported Gamified Formative-Assessment Intervention
Year: 2026
Type: article
Study design: Observational Study
Abstract: Against the growing integration of artificial intelligence in higher education, this study examines the mechanism through which AI-supported gamification is associated with language learning outcomes in university language courses in the Greater Bay Area. Drawing on self-determination theory and a formative assessment perspective, it develops and tests a conceptual model linking gamification attitudes, learning motivation, perceived AI use, and perceived language learning improvement. A quantitative survey yielded 72 valid responses. The measurement scale showed good internal consistency . In addition, a post-hoc power analysis using G*Power 3.1 indicated that, with , , and a moderate effect size , statistical power reached .90, suggesting that the sample size was adequate for regression-based analysis. The results showed that gamification attitudes were significantly and positively associated with perceived language learning improvement, while learning motivation was moderately and positively associated with improvement. Mediation analysis indicated that learning motivation significantly predicted perceived language learning improvement . After learning motivation was included in the model, the direct effect of gamification attitudes was no longer significant, and bootstrap analysis supported a full mediation pattern. Although perceived AI use did not show a significant direct effect on perceived language learning improvement, AI remained an important supportive element in the intervention. In this study, AI was embedded in a recurring feedback-revision-practice-diagnosis-reinforcement cycle, providing individualized formative feedback and informing subsequent gamified practice and teacher guidance. Overall, the findings suggest that AI-supported gamification may contribute to language learning outcomes primarily through its relationship with students’ learning motivation. The study offers practical implications for the design of motivation-oriented, AI-supported formative assessment in higher education language learning contexts.
DOI: 10.4018/ijgbl.426374
Author: Miao Zhang
Institution: Macau University of Science and Technology
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7204813104