Technology Acceptance Factors in Shaping Students’ Behavioral Intention to Use an AI-Powered English Practice System
Abstract
This study employs the Technology Acceptance Model (TAM) to investigate students’ acceptance of an AI-powered English practice system developed through a “vibe coding” (natural language-based) approach. The research examines the influence of technology acceptance factors, specifically Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) on students’ Behavioral Intention (BI) to adopt the system in a formal educational context. Adopting a quantitative, survey-based design, the study involved 77 Grade 9 students from two lower secondary schools in Vietnam who used the system over a 10-week period. Data were collected through a 30-item questionnaire and analyzed using descriptive statistics, Pearson correlation, and multiple regression analysis. The findings indicate that students held generally positive perceptions across all constructs, with mean scores in the “Agree” range (PEOU = 3.67; PU = 3.65; BI = 3.78). Correlation analysis revealed strong and statistically significant positive relationships among PEOU, PU, and BI (p < .001), particularly between PEOU and PU (r = 0.783). Regression results further show that PEOU and PU jointly explain 54.5% of the variance in BI, with PU (β = 0.407) exerting a stronger influence than PEOU (β = 0.374). These findings suggest that while ease of use facilitates engagement, perceived learning value plays a more decisive role in shaping students’ intention to use the system. Overall, the study provides empirical evidence supporting the effectiveness and acceptability of accessible AI-powered learning systems in lower secondary English education, particularly those developed through low-barrier approaches such as vibe coding.
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