BLADE: Better Language Answers through Dialogue and Explanations
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910102478389248 |
|---|---|
| author | Jayaweera, Chathuri Dorr, Bonnie J. |
| author_facet | Jayaweera, Chathuri Dorr, Bonnie J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03236 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BLADE: Better Language Answers through Dialogue and Explanations Jayaweera, Chathuri Dorr, Bonnie J. Human-Computer Interaction Computation and Language 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. |
| title | BLADE: Better Language Answers through Dialogue and Explanations |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2604.03236 |