AI Assistant Framework on Competency-Based Learning for Digital Competency Development

The accelerating adoption of AI in education highlights the need for an assistant that is explicitly grounded in competency-based learning to develop learners’ digital competencies. This study proposes the AI Assistant Framework on Competency-Based Learning for Digital Competency Development (AICoLED) and evaluates its appropriateness through expert judgment. We synthesized contemporary literature to derive a framework that integrates four inputs (AI technology infrastructure, competency framework, educational content, user interface design), five processes (competency assessment, personalized learning, interactive assistance, competency development, feedback/evaluation), and four outputs (digital competency enhancement, learning achievement, behavioural change, system performance). A structured instrument comprising 44 items across eight domains was rated by eight experts (n = 8) on a 5-point scale. We summarized item- and domain-level means and SDs and mapped means to appropriateness levels. The overall mean across items was 4.69 (SD = 0.49), corresponding to the rating of “Most appropriate.” The section means ranged from 4.63 to 4.75. The highest-rated domain was Innovation and Creativity (Mean = 4.75, SD = 0.44); the lowest was Output Components (Mean = 4.63, SD = 0.62). Top-rated items included content competency alignment (1.1.3), systematic linkage of inputs (1.1.5), accuracy and coverage of competency assessment (1.2.1), framework novelty (4.1), and currency of NLP use (6.1.1) (all Means = 4.88, SD = 0.35). Items with greater dispersion concerned system indicators and competency standards (1.3.2-1.3.4; 6.3.2-6.3.3), with SD up to 0.76. Expert appraisal indicates that AICoLED is conceptually straightforward, pedagogically coherent, and technically feasible; however, the measurement components (output indicators and competency standards) require tighter operationalization before pilot deployment. Future work should pilot the framework in authentic contexts, validate measurement models, and assess effectiveness and scalability.