# Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations

arXiv: https://arxiv.org/abs/2403.03008 · HTML: https://mlsanity.com/paper/2403.03008/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning](https://mlsanity.com/paper/2310.01061/) (2023)
- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling](https://mlsanity.com/paper/1906.07241/) (2019)
- [A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases](https://mlsanity.com/paper/2311.07509/) (2023)
- [A Transparency Index Framework for AI in Education](https://mlsanity.com/paper/2206.03220/) (2022)
- [Building Contextual Knowledge Graphs for Personalized Learning Recommendations using Text Mining and Semantic Graph Completion](https://mlsanity.com/paper/2401.13609/) (2024)

## Built on by (most cited first, 30 of 31)

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [Current state of LLM Risks and AI Guardrails](https://mlsanity.com/paper/2406.12934/) (2024)
- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://mlsanity.com/paper/2501.03151/) (2025)
- [A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications](https://mlsanity.com/paper/2404.14809/) (2024)
- [Can Vision-Language Models Evaluate Handwritten Math?](https://mlsanity.com/paper/2501.07244/) (2025)
- [1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?](https://mlsanity.com/paper/2406.14721/) (2024)
- [A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI](https://mlsanity.com/paper/2412.13942/) (2024)
- [Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications](https://mlsanity.com/paper/2501.11632/) (2025)
- [Position: LLMs Can be Good Tutors in English Education](https://mlsanity.com/paper/2502.05467/) (2025)
- [GoAI: Enhancing AI Students' Learning Paths and Idea Generation via Graph of AI Ideas](https://mlsanity.com/paper/2503.08549/) (2025)
- [Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph](https://mlsanity.com/paper/2412.01837/) (2024)
- [Graph Foundation Models for Recommendation: A Comprehensive Survey](https://mlsanity.com/paper/2502.08346/) (2025)
- [RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.06056/) (2025)
- [Multilingual Generative Retrieval via Cross-lingual Semantic Compression](https://mlsanity.com/paper/2510.07812/) (2025)
- [LLM-Assisted Knowledge Graph Completion for Curriculum and Domain Modelling in Personalized Higher Education Recommendations](https://mlsanity.com/paper/2501.12300/) (2025)
- [AdaptBot: Combining LLM with Knowledge Graphs and Human Input for Generic-to-Specific Task Decomposition and Knowledge Refinement](https://mlsanity.com/paper/2502.02067/) (2025)
- [Synthesizing High-Quality Programming Tasks with LLM-based Expert and Student Agents](https://mlsanity.com/paper/2504.07655/) (2025)
- [Performance of large language models in numerical vs. semantic medical knowledge: Benchmarking on evidence-based Q&As](https://mlsanity.com/paper/2406.03855/) (2024)
- [Personalized Learning Path Planning with Goal-Driven Learner State Modeling](https://mlsanity.com/paper/2510.13215/) (2025)
- [Knowledge Pyramid Construction for Multi-Level Retrieval-Augmented Generation](https://mlsanity.com/paper/2407.21276/) (2024)
- [Synergizing LLMs and Knowledge Graphs: A Novel Approach to Software Repository-Related Question Answering](https://mlsanity.com/paper/2412.03815/) (2024)
- [Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLM](https://mlsanity.com/paper/2503.00309/) (2025)
- [Explainable AI the Latest Advancements and New Trends](https://mlsanity.com/paper/2505.07005/) (2025)
- [Unifying Post-hoc Explanations of Knowledge Graph Completions](https://mlsanity.com/paper/2507.22951/) (2025)
- [KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval](https://mlsanity.com/paper/2508.20417/) (2025)
- [Large Language Models and Knowledge Graphs for Astronomical Entity Disambiguation](https://mlsanity.com/paper/2406.11400/) (2024)
- [Embedding-Based Rankings of Educational Resources based on Learning Outcome Alignment: Benchmarking, Expert Validation, and Learner Performance](https://mlsanity.com/paper/2512.13658/) (2025)
- [Why teaching resists automation in an AI-inundated era: Human judgment, non-modular work, and the limits of delegation](https://mlsanity.com/paper/2604.07285/) (2026)

## Abstract (arXiv)

In the era of personalized education, the provision of comprehensible explanations for learning recommendations is of a great value to enhance the learner's understanding and engagement with the recommended learning content. Large language models (LLMs) and generative AI in general have recently opened new doors for generating human-like explanations, for and along learning recommendations. However, their precision is still far away from acceptable in a sensitive field like education. To harness the abilities of LLMs, while still ensuring a high level of precision towards the intent of the learners, this paper proposes an approach to utilize knowledge graphs (KG) as a source of factual context, for LLM prompts, reducing the risk of model hallucinations, and safeguarding against wrong or imprecise information, while maintaining an application-intended learning context. We utilize the semantic relations in the knowledge graph to offer curated knowledge about learning recommendations. With domain-experts in the loop, we design the explanation as a textual template, which is filled and completed by the LLM. Domain experts were integrated in the prompt engineering phase as part of a study, to ensure that explanations include information that is relevant to the learner. We evaluate our approach quantitatively using Rouge-N and Rouge-L measures, as well as qualitatively with experts and learners. Our results show an enhanced recall and precision of the generated explanations compared to those generated solely by the GPT model, with a greatly reduced risk of generating imprecise information in the final learning explanation.