AI-driven formative assessment and adaptive learning in data-science education: Evaluating an LLM-powered virtual teaching assistant

• URL: http://arxiv.org/abs/2509.20369v1
• Date: Wed, 17 Sep 2025 11:27:45 GMT
• Title: AI-driven formative assessment and adaptive learning in data-science education: Evaluating an LLM-powered virtual teaching assistant
• Authors: Fadjimata I Anaroua, Qing Li, Yan Tang, Hong P. Liu,
• Abstract summary: VITA (Virtual Teaching Assistants) is an adaptive distributed learning platform that embeds a large language model (LLM)-powered bot (BotCaptain)<n>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.<n>Future work will refine the platform's adaptive intelligence and examine applicability across varied educational settings.
• Score: 6.874351093155318
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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