Xiamen University wanted a practical way to teach future public-governance professionals how to use and evaluate generative AI, but needed a hands-on environment that could connect theory, prompt design, agent building, and model experimentation for non-computer-science students.
Reported impact
Xiamen University created a more practical AI-governance curriculum, giving students real model experience and helping the university train interdisciplinary public-governance talent for AI-era service and policy work.
Xiamen University worked with Zhongshu Information Technology and Baidu AI Cloud to build a digital teaching platform for 'AI + public governance.' The platform combines theory modules with hands-on labs so students can move from AI literacy into real model practice instead of learning concepts only from lectures. Baidu contributed the Qianfan Lite platform and Speed model as the technical base of the practical module, allowing faculty and students to work in an authentic large-model environment where they can train models, design prompts, build AI agents, and perform personalized fine-tuning. The broader teaching architecture is designed for public-governance education rather than computer-science specialization: it focuses on responsible AI use, real governance scenarios, and the interdisciplinary skills needed to apply AI in policy and public-service work. That turns the platform into both a teaching tool and a talent-development pipeline for AI-enabled governance.
Students and faculty move from theory modules into practice modules that use Qianfan Lite and the Speed model for prompt engineering, model training, agent creation, and fine-tuning exercises. The outputs feed back into public-governance teaching scenarios so AI experimentation stays linked to real policy and service problems.
Students and teachers can use a real large-model working environment for personalized fine-tuning practice (original: 教师和学生可以在平台进行模型训练、提示词工程开发、人工智能体搭建和个人定制化大模型微调的实操).
The practical module includes model training, prompt engineering, and AI-agent creation (original: 实践模块由模型训练、提示词工程和智能体助手创建等功能组成).
Zhongshu and Baidu used Qianfan Lite and the Speed model to build an 'AI + public governance' teaching platform for Xiamen University (original: 众数信科联合百度智能云，基于千帆Lite平台和Speed模型，为厦门大学打造"人工智能+公共治理"数字教学平台).
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