AI-Supported Workflow for Online Lecture Production in Higher Education: A Case Study at the Education Technology and Adaptive Learning Institute (ETALI)

Digital transformation in higher education has significantly changed the way universities design curricula, develop learning resources, organize teaching, assess learning outcomes, and manage training activities. In online and distance education, online lectures are no longer supplementary teaching materials; they have become core learning resources that directly influence students’ self-learning, interaction, and achievement of course learning outcomes. However, many higher education institutions still produce online lectures through fragmented processes, relying heavily on individual lecturers’ digital skills and separate tools such as PowerPoint, Canva, Quizizz, Zoom, Google Forms, learning management systems, or generative artificial intelligence. This study proposes an AI-supported and quality-controlled workflow for online lecture production at the Education Technology and Adaptive Learning Institute (ETALI), Thai Nguyen University of Technology. Based on document analysis, process analysis, synthesis of instructional design models, and practical requirements of distance education, the study develops a nine-step workflow: learning material planning, input standardization, document processing, AI-generated outline/script/MCQ drafting, academic approval, learning material production, quality assurance, LMS deployment, and data-driven improvement. The proposed model integrates available digital tools such as PowerPoint, Canva, Quizizz, Moodle LMS, OCR/Mathpix, N8N, Gamma, and large language models while maintaining the human-in-the-loop principle for academic decision-making. The study contributes a practical framework for shifting from tool-based production to process-based governance of online lecture production in higher education.