BLADE: Better Language Answers through Dialogue and Explanations
- URL: http://arxiv.org/abs/2604.03236v1
- Date: Sat, 31 Jan 2026 06:13:37 GMT
- Title: BLADE: Better Language Answers through Dialogue and Explanations
- Abstract summary: We present BLADE (Better Language Answers through Dialogue and Explanations), a grounded conversational assistant.<n> BLADE uses a retrieval-augmented generation (RAG) framework over curated course content.<n>Instead of delivering final answers, BLADE prompts direct engagement with source materials to support conceptual understanding.
- Score: 3.5164356851161984
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Large language model (LLM)-based educational assistants often provide direct answers that short-circuit learning by reducing exploration, self-explanation, and engagement with course materials. We present BLADE (Better Language Answers through Dialogue and Explanations), a grounded conversational assistant that guides learners to relevant instructional resources rather than supplying immediate solutions. BLADE uses a retrieval-augmented generation (RAG) framework over curated course content, dynamically surfacing pedagogically relevant excerpts in response to student queries. Instead of delivering final answers, BLADE prompts direct engagement with source materials to support conceptual understanding. We conduct an impact study in an undergraduate computer science course, with different course resource configurations and show that BLADE improves students' navigation of course resources and conceptual performance compared to simply providing the full inventory of course resources. These results demonstrate the potential of grounded conversational AI to reinforce active learning and evidence-based reasoning.
Related papers
- Design and Deployment of a Course-Aware AI Tutor in an Introductory Programming Course [3.8144358095849946]
Large Language Models (LLMs) have become part of how students solve programming tasks, offering immediate explanations and even full solutions.<n>Previous work has highlighted that novice programmers often heavily rely on LLMs, thereby neglecting their own problem-solving skills.<n>We designed a course-specific online Python tutor that provides retrieval-augmented, course-aligned guidance without generating complete solutions.
arXiv Detail & Related papers (2026-04-12T08:17:25Z) - AI Slop or AI-enhancement? Student perceptions of AI-generated media for an English for Academic Purposes course [15.537382882048066]
This paper reports on the development, implementation, and evaluation of teacher-prompted, AI-generated supplemental materials in an English for Academic Purposes course at a Hong Kong Community College.<n>The instructor generated videos, podcasts, infographics, and individualized feedback reports from course materials and student work for 106 English as a Foreign Language learners.<n>Students rated the materials highly for perceived usefulness and ease of use, and preferred assessment-linked content presented in visual and multimodal formats.
arXiv Detail & Related papers (2026-04-08T09:47:35Z) - From Slides to Chatbots: Enhancing Large Language Models with University Course Materials [14.450839675608693]
We investigate how incorporating university course materials can enhance LLM performance in computer science courses.<n>We compare two strategies, Retrieval-Augmented Generation (RAG) and Continual Pre-Training (CPT), to extend LLMs with course-specific knowledge.<n>Our experiments reveal that, given the relatively small size of university course materials, RAG is more effective and efficient than CPT.
arXiv Detail & Related papers (2025-10-25T12:31:26Z) - INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models [15.825663946923289]
Large language models (LLMs) excel at answering questions but remain passive learners-absorbing static data without the ability to question and refine knowledge.<n>This paper explores how LLMs can transition to interactive, question-driven learning through student-teacher dialogues.
arXiv Detail & Related papers (2024-12-16T02:28:53Z) - Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs [49.18567856499736]
We investigate whether large language models (LLMs) can be supportive of open-ended dialogue tutoring.<n>We apply a range of knowledge tracing (KT) methods on the resulting labeled data to track student knowledge levels over an entire dialogue.<n>We conduct experiments on two tutoring dialogue datasets, and show that a novel yet simple LLM-based method, LLMKT, significantly outperforms existing KT methods in predicting student response correctness in dialogues.
arXiv Detail & Related papers (2024-09-24T22:31:39Z) - Integrating A.I. in Higher Education: Protocol for a Pilot Study with 'SAMCares: An Adaptive Learning Hub' [0.6990493129893112]
This research aims to introduce an innovative study buddy we will be calling the 'SAMCares'
The system leverages a Large Language Model (LLM) and Retriever-Augmented Generation (RAG) to offer real-time, context-aware, and adaptive educational support.
arXiv Detail & Related papers (2024-05-01T05:39:07Z) - A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation [51.31429493814664]
We present a benchmark named multi-source Wizard of Wikipedia for evaluating multi-source dialogue knowledge selection and response generation.
We propose a new challenge, dialogue knowledge plug-and-play, which aims to test an already trained dialogue model on using new support knowledge from previously unseen sources.
arXiv Detail & Related papers (2024-03-06T06:54:02Z) - Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems.
KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities.
KERL achieves state-of-the-art results in both recommendation and response generation tasks.
arXiv Detail & Related papers (2023-12-18T06:41:23Z) - DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain
Question Answering over Knowledge Base and Text [73.68051228972024]
Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge.
Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
arXiv Detail & Related papers (2023-10-31T04:37:57Z) - Can We Trust AI-Generated Educational Content? Comparative Analysis of
Human and AI-Generated Learning Resources [4.528957284486784]
Large language models (LLMs) appear to offer a promising solution to the rapid creation of learning materials at scale.
We compare the quality of resources generated by an LLM with those created by students as part of a learnersourcing activity.
Our results show that the quality of AI-generated resources, as perceived by students, is equivalent to the quality of resources generated by their peers.
arXiv Detail & Related papers (2023-06-18T09:49:21Z) - Empowering Language Models with Knowledge Graph Reasoning for Question
Answering [117.79170629640525]
We propose knOwledge REasOning empowered Language Model (OREO-LM)
OREO-LM consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs.
We show significant performance gain, achieving state-of-art results in the Closed-Book setting.
arXiv Detail & Related papers (2022-11-15T18:26:26Z) - Retrieval-Free Knowledge-Grounded Dialogue Response Generation with
Adapters [52.725200145600624]
We propose KnowExpert to bypass the retrieval process by injecting prior knowledge into the pre-trained language models with lightweight adapters.
Experimental results show that KnowExpert performs comparably with the retrieval-based baselines.
arXiv Detail & Related papers (2021-05-13T12:33:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.