Bio-Eng-LLM AI Assist: A modular chatbot platform for interdisciplinary research and education

This article presents Bio-Eng-LLM AI chatbot Assist, a versatile platform designed to support interactive learning and research across multiple disciplines. Initially developed for biomass research, the system’s capabilities have since expanded to serve broader educational and scientific domains. It integrates large language models ( LLM s) with advanced tools for document analysis, real-time file and web data integration, image understanding, and speech recognition. At the core of the platform lies a Retrieval Augmented Generation ( RAG ) framework, which improves the contextual relevance and factual accuracy of responses by incorporating external information sources. Bio-Eng-LLM also includes image generation via diffusion models and secure web-based search and summarization features. Its user-friendly interface supports multimodal interactions – text, image, and voice – enabling dynamic and personalized assistance in academic environments. By simplifying access to complex information and promoting interdisciplinary collaboration, Bio-Eng-LLM fosters AI literacy and facilitates both knowledge discovery and communication.

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Publication Details

Journal
SoftwareX
Published
2025-07-18
DOI
https://doi.org/10.1016/j.softx.2025.102260
Citations
1
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
2.48
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article

Bio-Eng-LLM AI Assist: A modular chatbot platform for interdisciplinary research and education

1 citations
SoftwareX
AI in Service Interactions
2.48
article

Bio-Eng-LLM AI Assist: A modular chatbot platform for interdisciplinary research and education

article en
1 citations

Abstract

This article presents Bio-Eng-LLM AI chatbot Assist, a versatile platform designed to support interactive learning and research across multiple disciplines. Initially developed for biomass research, the system’s capabilities have since expanded to serve broader educational and scientific domains. It integrates large language models ( LLM s) with advanced tools for document analysis, real-time file and web data integration, image understanding, and speech recognition. At the core of the platform lies a Retrieval Augmented Generation ( RAG ) framework, which improves the contextual relevance and factual accuracy of responses by incorporating external information sources. Bio-Eng-LLM also includes image generation via diffusion models and secure web-based search and summarization features. Its user-friendly interface supports multimodal interactions – text, image, and voice – enabling dynamic and personalized assistance in academic environments. By simplifying access to complex information and promoting interdisciplinary collaboration, Bio-Eng-LLM fosters AI literacy and facilitates both knowledge discovery and communication.

SoftwareXVol. 31
Helmholtz Centre for Environmental Research (DE), Deutsches Biomasseforschungszentrum (DE), Leipzig University (DE)
Quality Education
Openalex Percentile: Top 8%
AI in Service Interactions
2.48
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