---
title: "Who cites The AI teacher test: Measuring — 52 citing papers"
description: "52 papers of the Elamur Research library cite «The AI teacher test: Measuring the pedagogical ability of Blender and GPT-3 in educational…"
canonical: "https://research.elamur.ai/free/en/papers/tack-2022-ai-teacher-test-measuring-pedagogical/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/tack-2022-ai-teacher-test-measuring-pedagogical/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [The AI teacher test: Measuring the pedagogical…](https://md.research.elamur.ai/free/en/papers/tack-2022-ai-teacher-test-measuring-pedagogical/) / cited by

# Who cites «The AI teacher test: Measuring the pedagogical ability of Blender and GPT-3 in educational dialogues»

52 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · [← back to «The AI teacher test: Measuring the pedagogical ability of Blender and GPT-3 in educational dialogues»](https://md.research.elamur.ai/free/en/papers/tack-2022-ai-teacher-test-measuring-pedagogical/)

1. [**ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education**Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.*2023 · 7,440 citations*](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/)
2. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
3. **[MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://md.research.elamur.ai/free/en/papers/macina-2023-mathdial-dialogue-tutoring-dataset-rich/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jakub Macina, Nico Daheim, Sankalan Chowdhury, et al.*2023 · 131 citations*
4. **[Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors](https://md.research.elamur.ai/free/en/papers/maurya-2025-unifying-ai-tutor-evaluation-evaluation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kaushal Kumar Maurya, Kv Aditya Srivatsa, Kseniia Petukhova, et al.*2025 · 53 citations*
5. [**Can Language Models Employ the Socratic Method? Experiments with Code Debugging arXiv:2310.03210**Erfan Al-Hossami, Razvan Bunescu, Justin Smith, et al.*2023 · 38 citations*](https://md.research.elamur.ai/free/en/papers/al-hossami-2023-language-models-employ-socratic-method/)
6. **[Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks](https://md.research.elamur.ai/free/en/papers/zhao-2026-evaluating-answer-leakage-robustness-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jin Zhao, Marta Knežević, Tanja Käser*2026 · 1 citation*
7. **[Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://md.research.elamur.ai/free/en/papers/vajjala-2026-opportunities-challenges-llms-education-nlp/) [Workshop on Innovative Use of NLP for Building Educational Applications (BEA)](https://md.research.elamur.ai/free/en/venues/bea/)**Sowmya Vajjala, Bashar Alhafni, Stefano Banno, et al.*2026*

45 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/tack-2022-ai-teacher-test-measuring-pedagogical/) — The AI teacher test: Measuring the pedagogical ability of Blender and GPT-3 in educational dialogues