大语言模型赋能智能学伴:系统架构与风险防控

Large Language Model Empowering Smart Learning Partners:System Architecture and Risk Control

大语言模型作为新一代人工智能技术的代表,因其在对话流利性、任务处理通用性和逻辑推理性方面的优势而受到教育领域从业者的广泛关注。当前智能学伴已在促进学习互动、提供个性化辅导、增进情感交流、辅助智能测评等方面进行了一定探索,但是仍然面临对话问答能力弱、感知性差和教学适用性低等挑战。大语言模型强大的推理和表达能力为破解这一难题提供了新的思路。大语言模型赋能智能学伴需要重点解决系统架构设计和风险防控两大问题。具体而言,大语言模型支持下的智能学伴系统架构应包括输入层、数据层、技术实现层、应用层和输出层,通过“多平台协作+智能机器人代理”的方式,实现多角色互动、强交互能力、专业教学问答与深度感知等功能。为避免技术整合过程中可能出现的数据偏见、认知误导、知识版权归属分歧、隐私泄露等风险,智能学伴系统需在数据收集、算法设计和应用实践等方面加强监管和规范,以确保数据的客观性、算法的准确性以及用户的隐私权和知识产权。未来要纵深推进智能学伴的发展,还需结合学科专业知识和教学基础理论,提升知识推理的科学性和教学的专业性。

As a representative of the next-generation AI technology,large language models have garnered considerable attention from educators due to their advantages in fluency of dialogue,task processing versatility and logical reasoning.Although smart learning partners have made some progress in facilitating learning interaction,offering personalized tutoring,enhancing emotional exchange and supporting intelligent assessment,they still face challenges such as limited dialogue and questioning capabilities,poor perceptual skills,and low applicability in teaching. Large language models’powerful reasoning and expressive capabilities offer new avenues for addressing these challenges. Empowering smart learning partners with large language models requires focusing on two major issues: system architecture design and risk control. Specifically, the architecture should include input, data, technical implementation, application and output layers. By employing a“multi-platform collaboration + intelligent robotic agents” approach, the system can achieve multi-role interaction, robust interactivity, professional teaching Q&A, and deep perception capabilities. To avoid risks such as data bias, cognitive misguidance, disputes over knowledge copyrights and privacy breaches that may arise during the technical integration process, the system must enhance oversight and regulation in data collection, algorithm design and application practice to ensure the objectivity of data, the accuracy of algorithm and the users’privacy and intellectual property rights. To further advance the development of smart learning partners in the future, it is essential to integrate subject knowledge and foundation theory of teaching to enhance the scientific rigor of knowledge reasoning and the professionalism of teaching.