VietTESOL International Convention 2026

Many young learners in Vietnam study within two parallel systems. Daily learning is guided by mainstream coursebooks, while achievement is increasingly measured through Cambridge English Qualifications for Young Learners. Although these systems share common language goals, they often differ in vocabulary coverage, task formats, and assessment expectations. This makes it difficult for teachers to determine whether classroom success truly reflects Cambridge readiness and when learners are well prepared to sit for an examination. The presentation reports two key findings from the development of an AI-supported diagnostic approach for Cambridge Young Learners. The first finding demonstrates that meaningful learning gaps become visible only when both coursebook content and Cambridge assessment objectives are mapped onto a common set of fine-grained learning outcomes. This enables teachers to move beyond overall test scores and identify precisely which language knowledge, skills, and task types each learner has mastered, is developing, or has yet to encounter. The second finding focuses on addressing these gaps through AI-supported personalized learning. Rather than assigning the same practice to an entire class, a personalization engine recommends targeted activities based on each learner's diagnostic profile and unmet learning outcomes. Continuous diagnostic evidence also helps teachers monitor learner progress, make more informed decisions about Cambridge readiness, and provide timely intervention before high-stakes examinations. Participants will leave with practical ideas for integrating AI-assisted diagnostic assessment into their existing teaching practice. The session argues that AI most effectively complements teachers' professional judgement, making assessment more actionable and instruction more personalized.