BLADE: Better Language Answers through Dialogue and Explanations
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Abstract (arXiv)
Large language model (LLM)-based educational assistants often provide direct answers offering little incentive for students to explore or engage with course materials. We present BLADE (Better Language Answers through Dialogue and Explanations), a retrieval-augmented generation (RAG) based conversational assistant grounded in course-specific content that guides students toward relevant materials through citation-grounded dialogue rather than delivering unsourced solutions. We evaluate BLADE in an advanced undergraduate NLP course with extensive instructional resources, where locating and synthesizing relevant material is a central challenge. During quizzes, students are assigned to one of three conditions: BLADE only, direct course materials only, or both; we measure performance and resource usage. Results show that students consistently select BLADE over direct materials when both are available, and that quiz performance is highest when students use BLADE alone. In contrast, simultaneous use of both resources is associated with significantly lower performance, suggesting that combining modalities may introduce cognitive load without benefit. These findings show the potential of RAG-based assistants as a citation-grounded interface for applying course content under open-book exam-like conditions, while cautioning that adding direct-material access does not necessarily help, and may hinder, in-task performance.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 31 Jan 2026.
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References · 9
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2024Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutorscited by 60
- 2025MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutorscited by 47
- 2023Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preferencecited by 37
- 2024Enhancing Critical Thinking in Education by means of a Socratic Chatbotcited by 21
- 2025Findings of the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutorscited by 16
- 2024Boosting Large Language Models with Socratic Method for Conversational Mathematics Teachingcited by 14
- 2025Discerning minds or generic tutors? Evaluating instructional guidance capabilities in Socratic LLMscited by 5
- 2025Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learningcited by 2
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- 2026To Memorize or to Retrieve: Scaling the Interaction Between Pretraining and Retrieval
- 2025Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study
- 2024Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems