# AIriskEval-edu Demo: Auditing of Pedagogical Risks in Educational Explanations

arXiv: https://arxiv.org/abs/2607.25634 · HTML: https://mlsanity.com/paper/2607.25634/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 22 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Builds on (most cited first, 18 of 18)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms](https://mlsanity.com/paper/2211.09210/) (2022)
- [Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment](https://mlsanity.com/paper/2506.11880/) (2025)
- [A multimodal dataset for understanding the impact of mobile phones on remote online virtual education](https://mlsanity.com/paper/2412.14195/) (2024)
- [PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)](https://mlsanity.com/paper/2507.02966/) (2025)
- [mEBAL2 Database and Benchmark: Image-based Multispectral Eyeblink Detection](https://mlsanity.com/paper/2309.07880/) (2023)
- [Privacy-Aware Detection of Fake Identity Documents: Methodology, Benchmark, and Improved Algorithms (FakeIDet2)](https://mlsanity.com/paper/2508.11716/) (2025)
- [Is My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2](https://mlsanity.com/paper/2606.14748/) (2026)
- [SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality](https://mlsanity.com/paper/2501.10977/) (2025)
- [Evaluating Social Engineering Risks in AI-based Interaction using Biometrics and a Gaming Setup](https://mlsanity.com/paper/2606.17793/) (2026)
- [EduEVAL-DB: A Role-Based Dataset for Pedagogical Risk Evaluation in Educational Explanations](https://mlsanity.com/paper/2602.15531/) (2026)
- [Auditing Training Data in Domain-adapted LLMs: LoRA-MINT](https://mlsanity.com/paper/2606.06946/) (2026)
- [Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond](https://mlsanity.com/paper/2607.02197/) (2026)
- [AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations](https://mlsanity.com/paper/2607.01934/) (2026)
- [Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering](https://mlsanity.com/paper/2607.07179/) (2026)
- [Unraveling Machine Behavior by Multi-Level Bias Analysis and Detection: Methodology and Application to Computer Vision](https://mlsanity.com/paper/2607.07236/) (2026)

## Abstract (arXiv)

We present AIriskEval-edu Demo, a platform that audits the pedagogical quality of instructional explanations and provides explainable audit results. The platform evaluates an explanation against a rubric covering five dimensions of pedagogical risk: factual accuracy, depth and completeness, focus and relevance, student-level appropriateness, and ideological bias. For each dimension, it returns a binary decision and a confidence score. Detected risks also include a natural-language rationale and, except for Depth and Completeness, a localized evidence span. The platform integrates GPT-5.5 through an external API and a self-hosted Llama 3.1 8B evaluator that runs on consumer-grade GPUs. The local evaluator is fine-tuned on AIriskEval-edu, a dataset of K-12 instructional explanations with risk and explainability annotations. The platform operates in two modes: in AI mode, both evaluators assess stored explanations generated under six simulated teacher profiles, each representing a distinct pedagogical behavior and potential risk; in human mode, the local evaluator audits user-written explanations in real time. The local evaluator outperforms GPT-5.5 on most reported metrics, offering educational institutions a practical way to keep audited content within their own infrastructure.