Future of Education with Neuro-Symbolic AI Agents in Self-Improving Adaptive Instructional Systems  

作　　者：Richard Jiarui Tong Xiangen Hu 

机构地区：[1]Macao University of Science and Technology,Macao 999078,China [2]The Hong Kong Polytechnic University,Hong Kong 100872,China

出　　处：《Frontiers of Digital Education》2024年第2期198-212,共15页数字教育前沿（英文）

摘　　要：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.

关 键 词：large language models(LLMs) neurosymbolic cognitive architecture(NSCA) adaptive instructional systems(AIS) open learning adaptive framework(OLAF) never ending open learning adaptive framework(NEOLAF) artificial intelligence in education(AIED) intelligent tutoring system(ITS) LLM agent 

分 类 号：G434[文化科学—教育学]