AI系统能动态识别用户专业水平，提升人机交互精准度。
Dynamic Agentic AI Expert Profiler System Architecture for Multidomain Intelligence Modeling
- 基于LLaMA v3.1构建分层模块架构，实时分析用户语言判断专业等级。
- 动态评估中97%的判断与用户自评一致，静态评估匹配率83%-97%。
- 适合需要个性化反馈的教育、医疗等多领域智能交互场景。
在人工智能主导的时代，现代系统需与不同背景和技能水平的用户沟通。为实现有意义的人机交互，系统必须感知上下文和用户专业程度。本文提出一种代理式AI专家画像系统，将自然语言回答分类为新手、基础、高级和专家四个等级。系统基于LLaMA v3.1（8B）构建模块化分层架构，包含文本预处理、评分、聚合与分类组件。评估分为两个阶段：静态阶段使用82名参与者录制的对话转录本；动态阶段通过代理式AI访谈员开展402场实时访谈，每轮回应后即时评估专业水平。将评估结果与用户自评对比，跨领域匹配率达83%至97%。差异主要源于自我评价偏差、表达不清及语言模型对细微专业性的误判。
原文摘要 · Abstract (English)
In today's artificial intelligence driven world, modern systems communicate with people from diverse backgrounds and skill levels. For human-machine interaction to be meaningful, systems must be aware of context and user expertise. This study proposes an agentic AI profiler that classifies natural language responses into four levels: Novice, Basic, Advanced, and Expert. The system uses a modular layered architecture built on LLaMA v3.1 (8B), with components for text preprocessing, scoring, aggregation, and classification. Evaluation was conducted in two phases: a static phase using pre-recorded transcripts from 82 participants, and a dynamic phase with 402 live interviews conducted by an agentic AI interviewer. In both phases, participant self-ratings were compared with profiler predictions. In the dynamic phase, expertise was assessed after each response rather than at the end of the interview. Across domains, 83% to 97% of profiler evaluations matched participant self-assessments. Remaining differences were due to self-rating bias, unclear responses, and occasional misinterpretation of nuanced expertise by the language model.
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