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Outcomes – Numeracy
Research synthesis is AI-generated, human reviewed. Updated 05/2026.
Displaying 211 - 240 of 245
Classroom Education Plan Essa Evidence Packet
QoreInsights. (05/2024). LXD Research. https://www.researchgate.net/publication/382150661_QoreInsights_ESSA_Evidence_P…
Evaluating and Optimizing Educational Content with Large Language Model Judgments
Joy He-Yueya, Noah D. Goodman, Emma Brunskill. (05/2024). arXiv. http://arxiv.org/pdf/2403.02795v2
LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought
Zhuoxuan Jiang, Haoyuan Peng, Shanshan Feng, Fan Li, Dongsheng Li. (05/2024). arXiv. http://arxiv.org/pdf/2405.06705v1
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration
Jaewook Lee, Digory Smith, Simon Woodhead, Andrew Lan. (05/2024). arXiv. http://arxiv.org/pdf/2405.00864v1
Improving Teaching at Scale: Can AI Be Incorporated Into Professional Development to Create Interactive, Personalized Learning for Teachers?
Yasemin Copur-Gencturk, Jingxian Li, Sebnem Atabas. (05/2024). American Educational Research Journal. https://journals.sagepub.com/doi/full/10.3102/00028312241248514
Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes
Rose E. Wang, Qingyang Zhang, Carly Robinson, Susanna Loeb, Dorottya Demszky. (04/2024). arXiv. http://arxiv.org/pdf/2310.10648v3
Large Language Models for Education: A Survey and Outlook
Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S. Yu, Qingsong Wen. (04/2024). arXiv. http://arxiv.org/pdf/2403.18105v2
Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges
Qingyao Li, Lingyue Fu, Weiming Zhang, Xianyu Chen, Jingwei Yu, Wei Xia, Weinan Zhang, Ruiming Tang, Yong Yu. (04/2024). arXiv. http://arxiv.org/pdf/2401.08664v3
Improving Student Learning with Hybrid Human-AI Tutoring: A Three-Study Quasi-Experimental Investigation
Danielle R. Thomas, Jionghao Lin, Erin Gatz, Ashish Gurung, Shivang Gupta, Kole Norberg, Stephen E. Fancsali, Vincent Aleven, Lee Branstetter, Emma Brunskill, Kenneth R. Koedinger. (03/2024). Association for Computing Machinery. https://dl.acm.org/doi/pdf/10.1145/3636555.3636896
Does Feedback on Talk Time Increase Student Engagement? Evidence from a Randomized Controlled Trial on a Math Tutoring Platform
Dorottya Demszky, Rose E. Wang, Sean Geraghty, Carol Yu. (03/2024). ACM Digital Library. https://dl.acm.org/doi/10.1145/3636555.3636924
Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts
Zewei Tian, Min Sun, Alex Liu, Shawon Sarkar, Jing Liu. (03/2024). arXiv. http://arxiv.org/pdf/2403.03920v1
Generative AI for Education (GAIED): Advances, Opportunities, and Challenges
Paul Denny, Sumit Gulwani, Neil T. Heffernan, Tanja KŠser, Steven Moore, Anna N. Rafferty, Adish Singla. (02/2024). arXiv. https://arxiv.org/pdf/2402.01580
The Impact of Artificial Intelligence on Students' Learning Experience
Abill Robert, Kaledio Potter, Louis Frank. (02/2024). SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4716747
Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative Storytelling
Chao Zhang, Xuechen Liu, Katherine Ziska, Soobin Jeon, Chi-Lin Yu, Ying Xu. (02/2024). arXiv. http://arxiv.org/pdf/2402.01927v2
Edu-ConvoKit An Open-Source Library for Education Conversation Data
Rose E. Wang, Dorottya Demszky. (02/2024). arXiv. http://arxiv.org/pdf/2402.05111v1
Are Lesson Plans Created by ChatGPT More Effective? An Experimental Study
Muhammet Remzi Karaman, Idris G¬öksu. (02/2024). International Journal of Technology in Education. https://www.researchgate.net/publication/377964050_Are_Lesson_Plans_Created_by_…
Generative AI and Its Educational Implications
Kacper Lodzikowski, Peter W. Foltz, John T. Behrens. (01/2024). arXiv. https://arxiv.org/pdf/2401.08659
Exploring User Perspectives on ChatGPT: Applications, Perceptions, and Implications for AI-Integrated Education
Reza Hadi Mogavi, Chao Deng, Justin Juho Kim, Pengyuan Zhou, Young D. Kwon, Ahmed Hosny Saleh Metwally, Ahmed Tlili, Simone Bassanelli, Antonio Bucchiarone, Sujit Gujar, Lennart E. Nacke, Pan Hui. (11/2023). arXiv. https://arxiv.org/pdf/2305.13114
Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference
Zachary Levonian, Chenglu Li, Wangda Zhu, Anoushka Gade, Owen Henkel, Millie-Ellen Postle, Wanli Xing. (11/2023). arXiv. http://arxiv.org/pdf/2310.03184v2
Math Education With Large Language Models: Peril or Promise?
Harsh Kumar, David M. Rothschild, Daniel G. Goldstein, Jake M. Hofman. (11/2023). SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4641653
The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts
Dorottya Demszky, Heather Hill. (10/2023). arXiv. https://arxiv.org/pdf/2211.11772
"Mistakes Help Us Grow”: Facilitating and Evaluating Growth Mindset Supportive Language in Classrooms
Kunal Handa, Margaret Clapper, Jessica Boyle, Rose E Wang, Diyi Yang, David S Yeager, Dorottya Demszky. (10/2023). arXiv. https://arxiv.org/pdf/2310.10637
Data-Driven Artificial Intelligence in Education: A Comprehensive Review
Kashif Ahmad, Waleed Iqbal, Ammar El-Hassan, Junaid Qadir, Driss Benhaddou, Moussa Ayyash, Ala Al-Fuqaha. (09/2023). IEEE. https://ieeexplore.ieee.org/document/10247566
Let's Have a Chat! A Conversation with ChatGPT: Technology, Applications, and Limitations
Sakib Shahriar, Kadhim Hayawi. (08/2023). arXiv. http://arxiv.org/pdf/2302.13817v4
Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction
Rose Wang, Dorottya Demszky. (06/2023). arXiv. http://arxiv.org/pdf/2306.03090v1
Towards Applying Powerful Large AI Models in Classroom Teaching: Opportunities, Challenges and Prospects
Kehui Tan, Tianqi Pang, Chenyou Fan, Song Yu. (06/2023). arXiv. http://arxiv.org/pdf/2305.03433v2
Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home
Eda Okur, Saurav Sahay, Roddy Fuentes Alba, Lama Nachman. (06/2023). arXiv. http://arxiv.org/pdf/2306.00482v1
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning
Mengxue Zhang, Zichao Wang, Zhichao Yang, Weiqi Feng, Andrew Lan. (06/2023). arXiv. http://arxiv.org/pdf/2306.00784v1
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation
Zhenwen Liang, Wenhao Yu, Tanmay Rajpurohit, Peter Clark, Xiangliang Zhang, Ashwin Kaylan. (05/2023). arXiv. http://arxiv.org/pdf/2305.14386v1
Solving math word problems with process- and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving and Irina Higgins. (11/2022). arXiv. http://arxiv.org/pdf/2211.14275v1
