Research Study Repository
A comprehensive collection of academic research on generative AI in preK12 education organized into three categories:
- Descriptive - Research that describes how generative AI is being used in classrooms, schools, or districts or how products are designed and built.
- Impact (includes RCT + Quasi-Experimental) - Studies that test how well something works including but not limited to randomly dividing people into groups and comparing the results.
- Review - Studies that combine and summarize all the research on a specific genAI topic to find patterns and answers.
We aim to include all research in the above categories on generative AI in preK12 education in the US. As research diverges from genAI for preK12 in the US - such as machine learning, education systems beyond preK12, or studies conducted outside the US - inclusion in the repository is based on relevance to our target audiences:
- Superintendents, state, and federal K12 leaders
- Education support organizations (unions, parent groups, etc.)
- Leadership and product teams at technology companies
- Academic researchers
- Global education leaders
The Research Repository includes pre-published works but does not include journalism on AI for education.
Research synthesis is AI-generated, human reviewed. Updated 05/2025.
Showing 61 - 90 of 580 results
Human Empathy As Encoder: Ai-Assisted Depression Assessment In Special Education
Boning Zhao. (05/2025). arXiv.
What is the application? Learning – Student Support, Analyzing
Who is the user? Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12)
Why use AI? Efficiency, Outcomes – Social Emotional
Study design: Descriptive – Product Development, Impact – Quasi–experimentalLearn Like Feynman: Developing And Testing An Ai-Driven Feynman Bot
Akshaya Rajesh, Sumbul Khan. (05/2025). arXiv.
What is the application? Learning – Student Support
Who is the user? Student
Which age? Post-Secondary, Adult
Why use AI? Outcomes – Other Academic, Outcomes – Durable Skills
Study design: Impact – Quasi–experimentalFrom Coders To Critics: Empowering Students Through Peer Assessment In The Age Of Ai Copilots
Santiago Berrezueta-Guzman, Stephan Krusche, Stefan Wagner. (05/2025). arXiv.
What is the application? Teaching – Assessment and Feedback, Learning – Student Support
Who is the user? Student, Educator
Which age? Post-Secondary
Why use AI? Outcomes – Durable Skills
Study design: Descriptive – Implementation and Use, Impact – Quasi–experimentalA Human-Centric Approach To Explainable Al For Personalized Education
Vinitra Swamy. (05/2025). arXiv.
What is the application? Teaching – Instructional Materials, Teaching – Assessment and Feedback, Learning – Student Support
Who is the user? Student, Educator
Which age? Post-Secondary
Why use AI? Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Implementation and Use, Descriptive – Product DevelopmentDistinguishing Fact From Fiction: Student Traits, Attitudes, And Ai Hallucination Detection In Business School Assessment
Dr Canh Thien Dang, Dr An Nguyen. (05/2025). arXiv.
What is the application? Teaching – Assessment and Feedback, Learning – Student Support
Who is the user? Student, Educator
Which age? Post-Secondary
Why use AI? Outcomes – Other Academic, Outcomes – Durable Skills
Study design: Descriptive – Implementation and UseParticle Builder - Learn About The Standard Model While Playing Against An Ai*
Mohammad Attar, Andrew Carse, Yeming Chen, Thomas Green, Jeong-Yeon Ha, Yanbai Jin, Amy McWilliams, Theirry Panggabean, Zhengyu Peng, Lujin Sun, Jing Ru, Jiacheng She, Jialin Wang, Zilun Wei, Jiayuan Zhu, Lachlan McGinness. (05/2025). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support
Who is the user? Student, Educator
Which age? High School (9-12)
Why use AI? Outcomes – Other Academic, Outcomes – Durable Skills
Study design: Descriptive – Implementation and UseRatas: A Generative Ai Framework For Explainable And Scalable Automated Answer Grading
Masoud Safilian, Amin Beheshti, and Stephen Elbourn. (05/2025). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Educator
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation
Study design: Descriptive – Product Development, Impact – Quasi–experimentalFrom Eduvisbench To Eduvisagent: A Benchmark And Multi-Agent Framework For Reasoning-Driven Pedagogical Visualization
Haonian Ji, Shi Qiu, Siyang Xin, Siwei Han, Zhaorun Chen, Dake Zhang, Hongyi Wang, Huaxiu Yao. (05/2025). arXiv.
What is the application? Teaching – Instructional Materials
Who is the user? Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12)
Why use AI? Outcomes – Literacy, Outcomes – Numeracy, Outcomes – Other Academic, Outcomes – Differentiation
Study design: Descriptive – Product DevelopmentA Structured Unplugged Approach For Foundational Ai Literacy In Primary Education
Maria Cristina Carrisi, Mirko Marras, and Sara Vergallo. (05/2025). arXiv.
What is the application? Teaching – Instructional Materials
Who is the user? Educator
Which age? Elementary (PK5)
Why use AI? Outcomes – Other Academic, Outcomes – Durable Skills
Study design: Descriptive – Implementation and Use, Impact – Quasi–experimentalEvaluating Llm Adaptation To Sociodemographic Factors: User Profile Vs. Dialogue History
Qishuai Zhong, Zongmin Li, Siqi Fan, Aixin Sun. (05/2025). arXiv.
What is the application? Analyzing
Who is the user? Others
Which age? Post-Secondary
Why use AI? Outcomes – Differentiation
Study design: Descriptive – Implementation and UseLmcd: Language Models Are Zeroshot Cognitive Diagnosis Learners
Yu He, Zihan Yao, Chentao Song, Tianyu Qi, Jun Liu, Ming Li, Qing Huang. (05/2025). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12)
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation
Study design: Descriptive – Product Development, Impact – Quasi–experimentalCoderagent: Simulating Student Behavior For Personalized Programming Learning With Large Language Models
Yi Zhan, Qi Liu, Weibo Gao, Zheng Zhang, Tianfu Wang, Shuanghong Shen, Junyu Lu and Zhenya Huang. (05/2025). arXiv.
What is the application? Teaching – Assessment and Feedback, Learning – Student Support
Who is the user? Educator
Which age? Post-Secondary
Why use AI? Outcomes – Differentiation
Study design: Descriptive – Product DevelopmentDissecting Physics Reasoning In Small Language Models: A Multi-Dimensional Analysis From An Educational Perspective
Nicy Scaria, Silvester John Joseph Kennedy, Diksha Seth, Deepak Subramani. (05/2025). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support
Who is the user? Educator
Which age? High School (9-12)
Why use AI? Outcomes – Other Academic
Study design: Descriptive – Implementation and UseEvaluating Software Plagiarism Detection In The Age Of Ai
Timur Sağlam, Larissa Schmid. (05/2025). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Educator
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Other Academic
Study design: Descriptive – Implementation and UseAutomated Evaluation Of Children'S Speech Fluency For Low-Resource Languages
Bowen Zhang, Nur Afiqah Abdul Latiff, Justin Kan, Rong Tong, Donny Soh, Xiaoxiao Miao, Ian McLoughlin. (05/2025). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Educator
Which age? Elementary (PK5)
Why use AI? Efficiency, Outcomes – Literacy, Outcomes – Differentiation
Study design: Descriptive – Implementation and UseIntegrating Emotional Intelligence, Memory Architecture, And Gestures To Achieve Empathetic Humanoid Robot Interaction In An Educational Setting
Fuze Sun, Lingyu Li, Shixiangyue Meng, Xiaoming Teng, Terry Payne, Paul Craig. (05/2025). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support
Who is the user? Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12), Post-Secondary
Why use AI? Outcomes – Literacy, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Social Emotional, Outcomes – Durable Skills
Study design: Impact – Quasi–experimentalInvestigating Pedagogical Teacher And Student Llm Agents: Genetic Adaptation Meets Retrieval-Augmented Generation Across Learning Styles
Debdeep Sanyal, Agniva Maiti, Umakanta Maharana, Dhruv Kumar, Ankur Mali, C. Lee Giles, Murari Mandal. (05/2025). arXiv.
What is the application? Teaching – Instructional Materials, Teaching – Assessment and Feedback, Learning – Student Support
Who is the user? Student, Educator
Which age? High School (9-12), Post-Secondary
Why use AI? Outcomes – Literacy, Outcomes – Numeracy, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills, Reimagined Schooling
Study design: Descriptive – Implementation and UseLLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance
Yu Wang, Cailing Cai, Zhihua Xiao, Peifung E. Lam. (05/2025). arXiv.
What is the application? Analyzing
Who is the user? Others
Which age?
Why use AI? Efficiency
Study design: Descriptive – Product DevelopmentAre LLMs Ready for English Standardized Tests? A Benchmarking and Elicitation Perspective
Luoxi Tang, Tharunya Sundar, Shuai Yang, Ankita Patra, Manohar Chippada, Giqi Zhao, Yi Li, Riteng Zhang, Tunan Zhao, Ting Yang, Yuqiao Meng, Weicheng Ma, Zhaohan Xi. (05/2025). arXiv.
What is the application? Learning – Student Support
Who is the user? Student
Which age? High School (9-12), Post-Secondary, Adult
Why use AI? Outcomes – Literacy, Outcomes – Durable Skills
Study design: Descriptive – Implementation and Use, Descriptive – Product DevelopmentPersonalizing Student-Agent Interactions Using Log-Contextualized Retrieval Augmented Generation (RAG)
Clayton Cohn, Surya Rayala, Caitlin Snyder, Joyce Horn Fonteles, Shruti Jain, Naveeduddin Mohammed, Umesh Timalsina, Sarah K. Burriss, Ashwin T, Namrata Srivastava, Menton Deweese, Angela Eeds, Gautam Biswas. (05/2025). arXiv.
What is the application? Learning – Student Support, Teaching – Instructional Materials
Who is the user? Student, Educator
Which age? High School (9-12)
Why use AI? Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Implementation and UseEnhancing Mathematics Learning for Hard-of-Hearing Students Through Real-Time Palestinian Sign Language Recognition: A New Dataset
Fidaa khandaqji, Huthaifa I. Ashqar, Abdelrahem Atawnih. (05/2025). arXiv.
What is the application? Learning – Student Support, Teaching – Instructional Materials
Who is the user? Student, Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12), Post-Secondary
Why use AI? Outcomes – Literacy, Outcomes – Numeracy, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Implementation and UsePedagogy-R1: Pedagogically-Aligned Reasoning Model with Balanced Educational Benchmark
Unggi Lee, Jaeyong Lee, Jiyeong Bae, Yeil Jeong, Junbo Koh, Gyeonggeon Lee, Gunho Lee, Taekyung Ahn, Hyeoncheol Kim. (05/2025). arXiv.
What is the application? Teaching – Instructional Materials, Teaching – Assessment and Feedback
Who is the user? Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12)
Why use AI? Outcomes – Other Academic, Outcomes – Differentiation
Study design: Descriptive – Product DevelopmentSupporting Preschool Emotional Development with Al-Powered Robots
Santiago Berrezueta-Guzman, María Dolón-Poza, Stefan Wagner. (05/2025). arXiv.
What is the application? Learning – Student Support, Teaching – Instructional Materials
Who is the user? Educator
Which age? 0-3 years
Why use AI? Outcomes – Differentiation, Outcomes – Social Emotional
Study design: Impact – Quasi–experimentalTowards Robust Evaluation of STEM Education: Leveraging MLLMs in Project-Based Learning
Yanhao Jia, Xinyi Wu, Qinglin Zhang, Yiran Qin, Luwei Xiao, Shuai Zhao. (05/2025). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Educator
Which age? High School (9-12), Post-Secondary
Why use AI? Efficiency, Outcomes – Other Academic
Study design: Descriptive – Implementation and UseFrom EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Pedagogical Visualization
Haonian Ji, Shi Qiu, Siyang Xin, Siwei Han, Zhaorun Chen, Dake Zhang, Hongyi Wang, Huaxiu Yao. (05/2025). arXiv.
What is the application? Learning – Student Support, Teaching – Instructional Materials
Who is the user? Student
Which age? Elementary (PK5), Middle School (6-8), High School (9-12)
Why use AI? Outcomes – Literacy, Outcomes – Numeracy, Outcomes – Other Academic, Outcomes – Differentiation
Study design: Descriptive – Product DevelopmentA Participatory Strategy for AI Ethics in Education and Rehabilitation grounded in the Capability Approach
Valeria Cesaroni, Eleonora Pasqua, Piercosma Bisconti, Martina Galletti. (05/2025). arXiv.
What is the application? Learning – Student Support
Who is the user? Educator, Others
Which age? Elementary (PK5), Middle School (6-8), High School (9-12)
Why use AI? Outcomes – Differentiation, Outcomes – Social Emotional
Study design: Descriptive – Product DevelopmentCAFES: A Collaborative Multi-Agent Framework for Multi-Granular Multimodal Essay Scoring
Jiamin Su, Yibo Yan, Zhuoran Gao, Han Zhang, Xiang Liu, Xuming Hu. (05/2025). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Others
Which age? Post-Secondary
Why use AI? Outcomes – Other Academic, Outcomes – Differentiation
Study design: Descriptive – Product DevelopmentFrom Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models
Yongan Yu, Alexandre Krantz, Nikki G. Lobczowski. (05/2025). arXiv.
What is the application? Learning – Student Support, Teaching – Instructional Materials
Who is the user? Educator
Which age? High School (9-12), Post-Secondary
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Differentiation
Study design: Descriptive – Product DevelopmentBeyond Retrieval: Joint Supervision and Multimodal Document Ranking for Textbook Question Answering
Hessa Alawwad, Usman Naseem, Areej Alhothali, Ali Alkhathlan, Amani Jamal. (05/2025). arXiv.
What is the application? Learning – Student Support, Teaching – Instructional Materials
Who is the user? Educator
Which age? Middle School (6-8), High School (9-12)
Why use AI? Outcomes – Other Academic
Study design: Descriptive – Product DevelopmentAutomated Bias Assessment in AI-Generated Educational Content Using CEAT Framework
Jingyang Peng, Wenyuan Shen, Jiarui Rao, and Jionghao Lin. (05/2025). arXiv.
What is the application? Analyzing, Teaching – Instructional Materials
Who is the user? Educator
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Differentiation
Study design: Descriptive – Product Development
