🧑‍🔬 Who cites Richard Baraniuk

100 citations of their works from 76 papers of our library (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · ← Richard Baraniuk

1. Simulating Classroom Education with LLM-Empowered AgentsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, et al.2025 · 84 citations
2. CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principlesFindings of the Association for Computational Linguistics: EMNLP 2023Shashank Sonkar, Naiming Liu, Debshila Mallick, et al.2023 · 71 citations
3. AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and GuardrailsACM Conference on Learning @ Scale (L@S)Sankalan Pal Chowdhury, Vilém Zouhar, Mrinmaya Sachan2024 · 66 citations
4. Pedagogical Alignment of Large Language ModelsFindings of the Association for Computational Linguistics: EMNLP 2024Shashank Sonkar, Kangqi Ni, Sapana Chaudhary, et al.2024 · 54 citations
5. Training LLM-Based Tutors to Improve Student Learning Outcomes in DialoguesLecture notes in computer scienceAlexander Scarlatos, Naiming Liu, Jaewook Lee, et al.2025 · 38 citations
6. Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model TutorsConference on Empirical Methods in Natural Language Processing (EMNLP)Nico Daheim, Jakub Macina, Manu Kapur, et al.2024 · 35 citations
7. MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM TutorsConference on Empirical Methods in Natural Language Processing (EMNLP)Jakub Macina, Nico Daheim, Ido Hakimi, et al.2025 · 33 citations
8. From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement LearningConference on Empirical Methods in Natural Language Processing (EMNLP)David Dinucu-Jianu, Jakub Macina, Nico Daheim, et al.2025 · 19 citations
9. Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with MisconceptionsarXiv:2310.02439Naiming Liu, Shashank Sonkar, Zichao Wang, et al.2023 · 14 citations
10. Position: LLMs Can be Good Tutors in English EducationConference on Empirical Methods in Natural Language Processing (EMNLP)Jingheng Ye, Shen Wang, Deqing Zou, et al.2025 · 9 citations
11. A Review of Incorporating Psychological Theories in LLMsConference of the European Chapter of the Association for Computational Linguistics (EACL)Zizhou Liu, Ziwei Gong, Lin Ai, et al.2026 · 1 citation
12. 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
13. PATS: Personality-Aware Teaching Strategies with Large Language Model TutorsFindings of the Association for Computational LinguisticsDonya Rooein, Sankalan Pal Chowdhury, Mariia Eremeeva, et al.2026 · 1 citation
14. SEFL: A Framework for Generating Synthetic Educational Assignment Feedback with LLM AgentsLanguage Resources and Evaluation Conference (LREC)Mike Zhang, Amalie Pernille Dilling, Léon Gondelman, et al.2026 · 1 citation
15. Beyond Accuracy: Alignment and Error Detection across Languages in the Bi-GSM8K Math-Teaching BenchmarkFindings of the Association for Computational LinguisticsJieun Park, KyungTae Lim, Joon-ho Lim2026
16. CogBench: Benchmarking Cognitive Alignment of Large Language Models in Educational Question AnsweringFindings of the Association for Computational LinguisticsTong Lu, Zhichun Wang, Yuanhao Sun, et al.2026
17. CogNet-KG: Empowering Tutoring Dialogues with a Cognitively-Structured Knowledge Graph for STEM LearningFindings of the Association for Computational LinguisticsDing Yu, Yu Lu, Tengju Li, et al.2026
18. MMTutorBench: The First Multimodal Benchmark for AI Math TutoringAnnual Meeting of the Association for Computational Linguistics (ACL)Tengchao Yang, Sichen Guo, Mengzhao Jia, et al.2026
19. 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
20. Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical ArgumentationAnnual Meeting of the Association for Computational Linguistics (ACL)Minjing Shi, Junling Wang, Jingwei Ni, et al.2026
21. Towards Pedagogically Aligned LLM Tutors for Math Mistake RemediationWorkshop on Innovative Use of NLP for Building Educational Applications (BEA)Kseniia Petukhova, Tien Dat Nguyen, Ekaterina Kochmar2026

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