mlsanityData to 14 Oct 2026

Machine Assistant with Reliable Knowledge: Enhancing Student Learning via RAG-based Retrieval

arXiv 2506.23026 ↗First posted 28 Jun 2025cs.IRcited by 1

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

We present Machine Assistant with Reliable Knowledge (MARK), a retrieval-augmented question-answering system designed to support student learning through accurate and contextually grounded responses. The system is built on a retrieval-augmented generation (RAG) framework, which integrates a curated knowledge base to ensure factual consistency. To enhance retrieval effectiveness across diverse question types, we implement a hybrid search strategy that combines dense vector similarity with sparse keyword-based retrieval. This dual-retrieval mechanism improves robustness for both general and domain-specific queries. The system includes a feedback loop in which students can rate responses and instructors can review and revise them. Instructor corrections are incorporated into the retrieval corpus, enabling adaptive refinement over time. The system was deployed in a classroom setting as a substitute for traditional office hours, where it successfully addressed a broad range of student queries. It was also used to provide technical support by integrating with a customer-specific knowledge base, demonstrating its ability to handle routine, context-sensitive tasks in applied domains. MARK is publicly accessible at https://app.eduquery.ai.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 29 of its 49 references that are in the map. First posted on arXiv 28 Jun 2025.

Builds on

29 of 49 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference4 Oct

Other references · 28

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  6. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  7. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  8. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  9. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  10. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  11. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  12. 2017Billion-scale similarity search with GPUscited by 2,497
16 more
  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  3. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  4. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Modelscited by 283
  5. 2021COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted Listcited by 113
  6. 2022FaithDial: A Faithful Benchmark for Information-Seeking Dialoguecited by 109
  7. 2025Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2cited by 97
  8. 2024Reducing hallucination in structured outputs via Retrieval-Augmented Generationcited by 93
  9. 2024Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrieverscited by 84
  10. 2023A Multi-Task Embedder For Retrieval Augmented LLMscited by 64
  11. 2025LLM Evaluation Based on Aerospace Manufacturing Expertise: Automated Generation and Multi-Model Question Answeringcited by 7
  12. 2024CourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Educationcited by 5
  13. 2024Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learningcited by 4
  14. 2024Integrating AI Tutors in a Programming Coursecited by 3
  15. 2024Faculty Perspectives on the Potential of RAG in Computer Science Higher Educationcited by 3
  16. 2025Large Language Models for Education: ChemTAsk -- An Open-Source Paradigm for Automated Q&A in the Graduate Classroomcited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Automated Generation of Curriculum-Aligned Multiple-Choice Questions for Malaysian Secondary Mathematics Using Generative AIcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study
  2. 2024KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?
  3. 2024Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems
  4. 2024Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU
  5. 2025RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning
  6. 2024Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval
  7. 2025ARK: Answer-Centric Retriever Tuning via KG-augmented Curriculum Learning
  8. 2025Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home