mlsanityData to 14 Oct 2026

Faculty Perspectives on the Potential of RAG in Computer Science Higher Education

arXiv 2408.01462 ↗First posted 28 Jul 2024cs.CY cs.ET cs.LGcited by 3

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

The emergence of Large Language Models (LLMs) has significantly impacted the field of Natural Language Processing and has transformed conversational tasks across various domains because of their widespread integration in applications and public access. The discussion surrounding the application of LLMs in education has raised ethical concerns, particularly concerning plagiarism and policy compliance. Despite the prowess of LLMs in conversational tasks, the limitations of reliability and hallucinations exacerbate the need to guardrail conversations, motivating our investigation of RAG in computer science higher education. We developed Retrieval Augmented Generation (RAG) applications for the two tasks of virtual teaching assistants and teaching aids. In our study, we collected the ratings and opinions of faculty members in undergraduate and graduate computer science university courses at various levels, using our personalized RAG systems for each course. This study is the first to gather faculty feedback on the application of LLM-based RAG in education. The investigation revealed that while faculty members acknowledge the potential of RAG systems as virtual teaching assistants and teaching aids, certain barriers and features are suggested for their full-scale deployment. These findings contribute to the ongoing discussion on the integration of advanced language models in educational settings, highlighting the need for careful consideration of ethical implications and the development of appropriate safeguards to ensure responsible and effective implementation.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 6 of its 20 references that are in the map. First posted on arXiv 28 Jul 2024.

Builds on

6 of 20 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Evaluating Students' Open-ended Written Responses with LLMs: Using the RAG Framework for GPT-3.5, GPT-4, Claude-3, and Mistral-Large8 May

Other references · 5

  1. 2023Detecting LLM-Generated Text in Computing Education: A Comparative Study for ChatGPT Casescited by 32
  2. 2024Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinationscited by 27
  3. 2024Evaluating the Effectiveness of LLMs in Introductory Computer Science Education: A Semester-Long Field Studycited by 26
  4. 2023Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perceptioncited by 19
  5. 2024Analyzing LLM Usage in an Advanced Computing Class in Indiacited by 6

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Surveycited by 30
  2. 2025CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generationcited by 1
  3. 2025Machine Assistant with Reliable Knowledge: Enhancing Student Learning via RAG-based Retrievalcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems
  2. 2025Small Models, Big Support: A Local LLM Framework for Educator-Centric Content Creation and Assessment with RAG and CAG
  3. 2024RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
  4. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
  5. 2025From Textbook to Talkbot: A Case Study of a Greek-Language RAG-Based Chatbot in Higher Education
  6. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
  7. 2024Deploying Large Language Models With Retrieval Augmented Generation
  8. 2026Large Language Models in Teaching and Learning: Reflections on Implementing an AI Chatbot in Higher Education ↗