Designing AI-Enhanced Flipped Classrooms for Remote Applied Sciences: A Framework for Distance Educators - Yeni Erita; Liza Yuliana (RADINKA JAYA UTAMA PUBLISHER, PADANG< INDONESIA, 2026)
The rapid evolution of generative artificial intelligence and large language models has opened new frontiers in contemporary distance education. In the context of remote applied sciences—such as engineering, aquaculture, biochemistry, and agriculture—educators face persistent hurdles due to the spatial separation of experimental laboratories, the abstract nature of physical-chemical phenomena, and low cognitive retention under conventional asynchronous models. This study introduces a comprehensive instructional architecture: the AI-Enhanced Flipped Classroom Framework. The primary objective of this study is to formulate, implement, and empirically evaluate a pedagogical framework that integrates text-to-text LLMs (e.g., ChatGPT, Claude) and text-to-image diffusion models (e.g., DALL-E 3, Midjourney) as cognitive partners to scaffold remote students during the pre-class asynchronous phase. A rigorous multi-phase explanatory sequential mixed-methods research design was executed across four applied science modules at a large distance learning institution over a full academic semester. Quantitative datasets tracked cognitive engagement matrices, conceptual mastery scores, and structural equation modeling (SEM) indices, alongside qualitative semi-structured focus groups exploring the student lived experience. The findings demonstrate that remote students leveraging the AI-FCF framework achieved a 38.5% statistically significant increase in cognitive retention, a marked decline in intrinsic cognitive load during subsequent live virtual practical sessions, and enhanced self-regulated learning capacities. Crucially, the deployment of