数据科学教育中的人工智能驱动形成性评价与自适应学习：评估一个基于大语言模型的虚拟教学助手
AI-driven formative assessment and adaptive learning in data-science education: Evaluating an LLM-powered virtual teaching assistant
AI总结：
本文提出VITA平台，集成LLM聊天机器人BotCaptain，通过对话式辅导、xAPI数据管道和自适应路径引擎，实现数据科学教育中的形成性评价与个性化学习，并提供了架构基准和部署蓝图。
摘要：
本文介绍了VITA（虚拟教学助手），一个自适应分布式学习（ADL）平台，该平台嵌入了一个由大语言模型（LLM）驱动的聊天机器人（BotCaptain），以提供对话式支持、可互操作的分析和具有完整性意识的评估，用于数据科学领域的人才培养。该平台将上下文感知的对话式辅导与旨在促进反思性推理的形成性评价模式相结合。本文描述了一个端到端的数据管道，该管道将聊天日志转换为Experience API（xAPI）语句；还介绍了教师仪表板，用于显示异常值以便及时干预；以及一个自适应路径引擎，用于在学习者的进阶、巩固和补救内容之间进行路由。本文还从概念上将VITA与新兴的辅导架构进行了基准比较，包括基于检索增强生成（RAG）的助手和学习工具互操作性（LTI）集成的中心，强调了内容接地、互操作性和部署复杂性之间的权衡。贡献包括一个用于可互操作对话分析的可重用架构、一套用于保持完整性的形成性评价模式目录，以及一个将自适应路径集成到数据科学课程中的实用蓝图。本文最后总结了实施经验和一个路线图（RAG集成、幻觉缓解和LTI 1.3/OpenID Connect），以指导多课程评估和更广泛的采用。鉴于传统教学中日益增长的需求和可扩展性限制，该方法展示了对话式人工智能如何大规模地支持参与度、及时反馈和个性化学习。未来工作将完善该平台的自适应智能，并考察其在各种教育环境中的适用性。
英文摘要：
This paper presents VITA (Virtual Teaching Assistants), an adaptive distributed learning (ADL) platform that embeds a large language model (LLM)-powered chatbot (BotCaptain) to provide dialogic support, interoperable analytics, and integrity-aware assessment for workforce preparation in data science. The platform couples context-aware conversational tutoring with formative-assessment patterns designed to promote reflective reasoning. The paper describes an end-to-end data pipeline that transforms chat logs into Experience API (xAPI) statements, instructor dashboards that surface outliers for just-in-time intervention, and an adaptive pathway engine that routes learners among progression, reinforcement, and remediation content. The paper also benchmarks VITA conceptually against emerging tutoring architectures, including retrieval-augmented generation (RAG)--based assistants and Learning Tools Interoperability (LTI)--integrated hubs, highlighting trade-offs among content grounding, interoperability, and deployment complexity. Contributions include a reusable architecture for interoperable conversational analytics, a catalog of patterns for integrity-preserving formative assessment, and a practical blueprint for integrating adaptive pathways into data-science courses. The paper concludes with implementation lessons and a roadmap (RAG integration, hallucination mitigation, and LTI~1.3 / OpenID Connect) to guide multi-course evaluations and broader adoption. In light of growing demand and scalability constraints in traditional instruction, the approach illustrates how conversational AI can support engagement, timely feedback, and personalized learning at scale. Future work will refine the platform's adaptive intelligence and examine applicability across varied educational settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
