Multimodal Generative Architectures for Knowledge Automation: Applications in Educational Engineering and Technical Communication

Generative Artificial Intelligence (GAI) represents a disruptive evolution in intelligent systems, enabling the automated creation of multimodal content across text, image, audio, and structured data. This article explores GAI as a framework for knowledge automation, focusing on its integration into engineering education, scientific visualization, and technical communication. A thematic review of prior research highlights the use of neural inference, optoelectronic sensing, and multimodal data processing in academic and applied contexts. The paper analyzes the architecture of transformer-based models (e.g., GPT-5, Gemini, Claude 3), their capacity for adaptive content generation, and their role in democratizing access to technical knowledge. Ethical and epistemic challenges—such as algorithmic bias, model opacity, and cognitive illusion—are critically examined. Strategic recommendations are proposed for ethical deployment, including participatory model design, open infrastructure, and continuous impact evaluation. The article concludes that GAI, when governed responsibly, can serve as a catalyst for inclusive, automated, and collaborative knowledge production in engineering domains.

Knowledge automation, Generative artificial intelligence, Educational engineering

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