This paper proposes a novel approach to use artificial intelligence(Al),particularly large language models(LLMs)and other foundation models(FMs)in an educational environment.It emphasizes the integration of teams of t...This paper proposes a novel approach to use artificial intelligence(Al),particularly large language models(LLMs)and other foundation models(FMs)in an educational environment.It emphasizes the integration of teams of teachable and self-learning LLMs agents that use neuro-symbolic cognitive architecture(NSCA)to provide dynamic personalized support to learners and educators within self-improving adaptive instructional systems(SIAIS).These systems host these agents and support dynamic sessions of engagement workflow.We have developed the never ending open learning adaptive framework(NEOLAF),an LLM-based neuro-symbolic architecture for self-learning AI agents,and the open learning adaptive framework(OLAF),the underlying platform to host the agents,manage agent sessions,and support agent workflows and integration.The NEOLAF and OLAF serve as concrete examples to illustrate the advanced AI implementation approach.We also discuss our proof of concept testing of the NEOLAF agent to develop math problem-solving capabilities and the evaluation test for deployed interactive agent in the learning environment.展开更多
文摘This paper proposes a novel approach to use artificial intelligence(Al),particularly large language models(LLMs)and other foundation models(FMs)in an educational environment.It emphasizes the integration of teams of teachable and self-learning LLMs agents that use neuro-symbolic cognitive architecture(NSCA)to provide dynamic personalized support to learners and educators within self-improving adaptive instructional systems(SIAIS).These systems host these agents and support dynamic sessions of engagement workflow.We have developed the never ending open learning adaptive framework(NEOLAF),an LLM-based neuro-symbolic architecture for self-learning AI agents,and the open learning adaptive framework(OLAF),the underlying platform to host the agents,manage agent sessions,and support agent workflows and integration.The NEOLAF and OLAF serve as concrete examples to illustrate the advanced AI implementation approach.We also discuss our proof of concept testing of the NEOLAF agent to develop math problem-solving capabilities and the evaluation test for deployed interactive agent in the learning environment.
