LAMB: An open-source software framework to create artificial intelligence assistants deployed and integrated into learning management systems

• A learning assistant is a software tool that combines traditional software engineering with generative AI and retrieval augmented generation (RAG) to deliver a new class of educational applications. • This paper introduces the LAMB framework. LAMB allows creating and deploying learning assistants based on custom educational content, such as textbooks, lecture notes, and video recordings. • LAMB allows for creating and managing knowledge bases for RAG, as well as prompt engineering and plugins to define the behavior of the learning assistants. • The learning assistants created with LAMB run seamlessly within the context of the learning management system of the learning institution via the IMS LTI interoperability protocol. • The learning assistants created with LAMB provide accurate responses based on their knowledge base, providing references, and are less prone to hallucinations than regular LLM-based chatbots. This paper presents LAMB (Learning Assistant Manager and Builder), an innovative open-source software framework designed to create AI-powered Learning Assistants tailored for integration into learning management systems. LAMB addresses critical gaps in existing educational AI solutions by providing a framework specifically designed for the unique requirements of the education sector. It introduces novel features, including a modular architecture for seamless integration of AI assistants into existing LMS platforms and an intuitive interface for educators to create custom AI assistants without coding skills. Unlike existing AI tools in education, LAMB provides a comprehensive framework that addresses privacy concerns, ensures alignment with institutional policies, and promotes using authoritative sources. LAMB leverages the capabilities of large language models and associated generative artificial intelligence technologies to create generative intelligent learning assistants that enhance educational experiences by providing personalized learning support based on clear directions and authoritative fonts of information. Key features of LAMB include its modular architecture, which supports prompt engineering, retrieval-augmented generation, and the creation of extensive knowledge bases from diverse educational content, including video sources. The development and deployment of LAMB were iteratively refined using a minimum viable product approach, exemplified by the learning assistant: “Macroeconomics Study Coach,” which effectively integrated lecture transcriptions and other course materials to support student inquiries. Initial validations in various educational settings demonstrate the potential that learning assistants created with LAMB have to enhance teaching methodologies, increase student engagement, and provide personalized learning experiences. The system's usability, scalability, security, and interoperability with existing LMS platforms make it a robust solution for integrating artificial intelligence into educational environments. LAMB's open-source nature encourages collaboration and innovation among educators, researchers, and developers, fostering a community dedicated to advancing the role of artificial intelligence in education. This paper outlines the system architecture, implementation details, use cases, and the significant benefits and challenges encountered, offering valuable insights for future developments in artificial intelligence assistants for any sector.