Predictive Power of Curriculum Quality Based on ASEAN University Network Quality Assurance (AUN-QA) Criteria on Innovation and Digital Readiness of the Bachelor of Nursing Science Program, Boromarajonani College of Nursing, Uttaradit

Abstract

This mixed methods study aimed to: 1) examine the quality of the Thai Bachelor of Nursing Science program, based on the ASEAN University Network Quality Assurance (AUN-QA) criteria on innovation and digital readiness; and 2) investigate the predictive power of curriculum quality on innovation and digital readiness. The sample included 80 graduate employers and 52 clinical preceptors. Data were collected using questionnaires and in-depth interviews. Research instruments were validated for content validity by experts (IOC = .80 – 1.00) and demonstrated high reliability (Cronbach’s alpha = 0.92). Data were analyzed using descriptive statistics, multiple regression analysis, and content analysis. The results showed as follows.

1. The overall curriculum quality according to AUN-QA criteria was rated at a good level (M = 5.27, SD = 0.71), with academic staff scoring the highest (M = 5.48, SD = 0.65).

2. The program’s innovation and digital readiness was at a high level (M = 4.01, SD = 0.61), with the highest score in digital technology safety and ethics (M = 4.25, SD = 0.55).

3. Curriculum quality significantly predicted innovation and digital readiness, accounting for 65.9% of the variance (R² = 0.659). The strongest predictors were facilities and infrastructure (b = 0.385), followed by teaching and learning methods (b = 0.342), academic staff (b = 0.288), and student support services (b = 0.245).

The results suggest that nursing education institutions should strengthen digital infrastructure, integrate technology into teaching and learning, enhance faculty digital competencies, and establish effective student support systems to advance the innovation and digital readiness of nursing curricula in the digital era.