AI in Quality Assurance

 In the past, when designing learning outcomes for a course or educational program, educators often had to spend significant time researching and memorizing the verbs in Bloom’s taxonomy. Memorizing or manually referencing Bloom’s verb table across various materials was not only time-consuming but also prone to errors. For example, if a teacher wanted to express the competency of "analyzing," they would have to look up suitable verbs in Bloom’s taxonomy and then cross-check their alignment with learning objectives.

Today, with the support of AI models specifically trained in quality assurance, educators no longer need to “commit to memory” every detail of Bloom’s taxonomy. Instead, they can interact directly with AI to quickly identify appropriate verbs and pinpoint areas for improvement. This significantly reduces the effort required to consult resources or verify the coherence of training programs. For instance, if an educator aims to adjust a learning outcome from the level of "understand" to "apply," AI can suggest verbs like "perform," "apply," or "execute," accompanied by a clear explanation of their relevance.

Additionally, AI can provide detailed advice and guidance to help educators better understand accreditation standards such as AUN-QA or the Ministry of Education and Training’s requirements. Rather than navigating through numerous legal documents or accreditation manuals, teachers simply need to ask questions and receive immediate, accurate answers. This makes the process of skill enhancement more flexible and engaging, allowing educators to explore quality assurance knowledge in their own proactive and creative way.

In summary, AI does not replace the role of educators but acts as an intelligent “assistant” that collaborates with them to design and refine educational programs. It helps save time, accelerates workflows, and, most importantly, sustainably enhances the quality of education.

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Le Phuong Truong