The EELA-QA Framework: A Conceptual Model for Quality Assurance of Artificial Intelligence Use in Thai Primary Schools
Keywords:
EELA-QA framework, artificial intelligence in education, quality assurance, primary schools, learning evidenceAbstract
Background and Objective: The increasing use of artificial intelligence (AI) in Thai primary schools raises concerns about child data protection, algorithmic fairness, equitable access, and the validity of learning evidence. These concerns are particularly important for young learners, who have limited capacity to provide informed consent, regulate their own AI use, and critically evaluate AI-generated outputs. This study aimed to develop the EELA-QA framework for the quality assurance of AI use in Thai primary schools.
Methodology: A two-phase conceptual framework development design was employed. First, 47 Thai- and English-language research articles, reviews, and policy documents published mainly between 2018 and 2026 were selected from 226 initial records and synthesized through conceptual synthesis. Second, seven experts reviewed the draft framework using an Index of Item-Objective Congruence (IOC) form, an appropriateness rating form, and open-ended questions.
Results: The framework comprises four dimensions: ethics, equity, learning evidence, and accountability. Item-level IOC values ranged from 0.71 to 1.00, and the arithmetic mean across 31 items was 0.92. The overall appropriateness rating was high (M = 4.38, SD = 0.54). Expert feedback clarified child data protection, multi-source learning evidence, and traceability through SAR and PLC processes.
Conclusion: EELA-QA provides a conceptual foundation for school-level self-review of AI use that is safe, fair, evidence-based, and auditable. It is not intended as a high-stakes assessment tool. Field feasibility studies and further validation are required before the framework is developed into a full assessment instrument.
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