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Research on the Application of Large Models in Intelligent Question Answering and Learning Recommendation

Author(s)

Yan Yang*, Rong Li

Affiliation(s)

Computer School, Central China Normal University, Wuhan, China

Abstract

This paper systematically investigates the application of large language models in two core educational scenarios: intelligent question answering and personalized learning recommendations. Addressing challenges such as response latency, high training costs, and insufficient domain knowledge during model deployment, we propose three key technical solutions: lightweight model deployment, efficient training optimization, and domain knowledge enhancement. Model compression accelerates service response, training strategy optimization significantly reduces domain adaptation costs, and the integration of structured knowledge effectively improves answer accuracy and professionalism. Experiments demonstrate that this integrated application strategy substantially improves overall system performance and service efficiency, providing a practical technical pathway and implementation reference for building next-generation intelligent educational support systems.

Keywords

Large Language Models; Intelligent Question Answering; Learning Recommendation

References

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