🔗 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»

1. ChatGPT for Good? On Opportunities and Challenges of Large Language Models for EducationEnkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.2023 · 7,440 citations
2. Evaluating Large Language Models: A Comprehensive SurveyarXiv:2310.19736Zishan Guo, Renren Jin, Chuang Liu, et al.2023 · 182 citations
3. MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning ProblemsFindings of the Association for Computational LinguisticsJakub 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 TutorsConference of the North American Chapter of the Association for Computational Linguistics (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 DebuggingarXiv:2310.03210Erfan Al-Hossami, Razvan Bunescu, Justin Smith, et al.2023 · 38 citations
6. Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student AttacksAnnual Meeting of the Association for Computational Linguistics (ACL)Jin Zhao, Marta Knežević, Tanja Käser2026 · 1 citation
7. Opportunities and Challenges of LLMs in Education: An NLP PerspectiveWorkshop on Innovative Use of NLP for Building Educational Applications (BEA)Sowmya Vajjala, Bashar Alhafni, Stefano Banno, et al.2026

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

This page in Markdown → https://md.research.elamur.ai/free/en/papers/tack-2022-ai-teacher-test-measuring-pedagogical/cited-by/