Explainable Agentic AI for Big Data–Driven Evaluation and Visual Analytics of Digital Literacy in Higher Vocational Teacher Education

Large-scale, diverse data produced by higher vocational teacher colleges' digital transformation challenges traditional methods for evaluating digital literacy. The reliability of current analytics and black-box artificial intelligence (AI) models for educational decision-making is limited by their frequent lack of autonomy and transparency. In order to assess digital literacy at higher vocational teacher colleges using big data and visual analytics, this study suggests an Explainable Agentic AI framework. In order to facilitate adaptive data exploration, competency evaluation, and insight generation across multimodal educational data, such as learning behavior logs, assessment records, and digital engagement indicators, the framework combines autonomous agentic intelligence with explainable AI (XAI). While XAI methods offer clear explanations of literacy aspects, decision rationale, and uncertainty, agentic components dynamically handle data processing, feature reasoning, and model selection. Effective human-AI collaboration is made possible by an interactive visual analytics layer that allows for layered investigation of learner patterns, temporal dynamics, and cohort heterogeneity. When compared with traditional machine learning techniques, experimental results on large-scale datasets from higher vocational teacher colleges show better assessment accuracy, robustness, and interpretability. This work demonstrates the promise of agentic AI for explainable big data exploration and promotes reliable instructional intelligence by combining agentic autonomy, explainability, and visual analytics within a scalable big data paradigm.