Research Study Repository

Striving to be the comprehensive collection of academic research on generative AI in preK-12 education.

Research Categories

We organize studies into three distinct categories to help you find exactly what you need today:

Where is the field right now?

Research that describes how generative AI is being used in classrooms, schools, or districts, or how products are designed and built.

Descriptive – Implementation and Use

Descriptive – Product Development

When Does AI Support Thinking, And When Does It Replace It? Learners' Conceptualisations Of AI As A Dynamic Cognitive Partner: A Typology. Cecilia Ka Yuk Chan. (04/2026). arXiv.

What is proven to be effective?

Research that tests how well something works using Randomized Controlled Trials (RCT) and Quasi-Experimental designs (QED) that randomly divide people into groups and compare the results.

Impact – Randomized Controlled Trial

Impact – Quasi-experimental

Assessing The Impact And Underlying Pathways Of Sequenced AI Feedback On Student Learning. Jie Cao, Chloe Qianhui Zhao, Christian Schunn, Elizabeth A. McLaughlin, Jionghao Lin, Kenneth R. Koedinger. (04/2026). arXiv.

What are the overarching trends?

Research that combines and summarizes all the research on a specific genAI topic to find overarching patterns and answers.

Systematic Review

The Landscape Of AI In Science Education: What Is Changing And How To Respond. Xiaoming Zhai, Kent Crippen. (03/2026). arXiv.

Scope & Relevance

To maintain a high standard of academic rigor and practical utility, we filter all inclusions based on the following guardrails:

We Include

Generative AI in US preK-12 education
Vetted, peer-reviewed studies
Pre-published academic works

We Exclude

Broad machine learning / non-genAI topics
Higher education or international systems
Journalism and media opinion pieces

This Repository is For You

This platform is designed to enable collaboration and insight across two primary sectors:

K-12 Leadership & Policy

Superintendents, state, and federal K12 leaders
Education support organizations (unions, parent groups, etc.)
Global education leaders

Research & Development

Academic researchers
Education support organizations (unions, parent groups, etc.)
Leadership and product teams at technology companies

Research synthesis is AI-generated, human reviewed. Updated 05/2026.

Showing 811 - 840 of 1372 results

Detecting LLM-Generated Short Answers and Effects on Learner Performance

Shambhavi Bhushan, Danielle R. Thomas, Conrad Borchers, Isha Raghuvanshi, Ralph Abboud, Erin Gatz, Shivang Gupta, Kenneth R. Koedinger. (06/2025). arXiv.

What is the application? Teaching – Assessment and Feedback, Analyzing
Who is the user? Student
Which age? Post-Secondary
Why use AI? Outcomes – Other Academic, Other
Study design: Technical – Computational, Quantitative – Others
A Large-Scale Real-World Evaluation of an LLM-Based Virtual Teaching Assistant

Sunjun Kweon, Sooyohn Nam, Hyunseung Lim, Hwajung Hong, Edward Choi. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user? Student
Which age? Post-Secondary
Why use AI? Outcomes – Differentiation, Other
Study design: Quantitative – Others
From Generation to Adaptation: Comparing AI-Assisted Strategies in High School Programming Education

Tong Hua, Songzan Wang. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user? Student
Which age? High School (9-12)
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Durable Skills
Study design: Descriptive – Implementation and Use, Quantitative – Others
Personalizing 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 S, Namrata Srivastava, Menton Deweese, Angela Eeds, Gautam Biswas. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user? Student
Which age? High School (9-12)
Why use AI? Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Implementation and Use, Technical – Computational
Students' Reliance on AI in Higher Education: Identifying Contributing Factors

Griffin Pitts, Neha Rani, Weedguet Mildort, Eva-Marie Cook. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user? Student
Which age? Post-Secondary
Why use AI? Outcomes – Other Academic, Outcomes – Durable Skills
Study design: Quantitative – Others
Feeling Machines: Ethics, Culture, and the Rise of Emotional AI

Vivek Chavan, Arsen Cenaj, Shuyuan Shen, Ariane Bar, Srishti Binwani, Tommaso Del Becaro, Marius Funk, Lynn Greschner, Roberto Hung, Stina Klein, Romina Kleiner, Stefanie Krause, Sylwia Olbrych, Vishvapalsinhji Parmar, Jaleh Sarafraz, Daria Soroko, Daksitha Withanage Don, Chang Zhou, Hoang Thuy Duong Vu, Parastoo Semnani, Daniel Weinhardt, Elisabeth Andre, Jorg Kruger, Xavier Fresquet. (06/2025). arXiv.

What is the application? Learning – Student Support, Communicating / Social Tools, Analyzing
Who is the user? Student, Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12), Adult
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Social Emotional
Study design:
Sense and Sensibility: What makes a social robot convincing to high-school students?

Pablo Gonz¬ález-Oliveras, Olov Engwall, Ali Reza Majlesi. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user? Student
Which age? High School (9-12)
Why use AI? Outcomes – Other Academic, Outcomes – Durable Skills
Study design: Impact – Quasi–experimental
Rethinking Citation of AI Sources in Student-AI Collaboration within HCI Design Education

Prakash Shukla, Suchismita Naik, Ike Obi, Jessica Backus, Nancy Rasche, Paul Parsons. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user? Student
Which age? Post-Secondary
Why use AI? Outcomes – Durable Skills
Study design: Descriptive – Implementation and Use
Designing Effective LLM-Assisted Interfaces for Curriculum Development

Abdolali Faraji, Mohammadreza Tavakoli, Mohammad Moein, Mohammadreza Molavi, Gabor Kismihok. (06/2025). arXiv.

What is the application? Teaching – Instructional Materials
Who is the user? Educator
Which age? Post-Secondary, Adult
Why use AI? Efficiency, Outcomes – Differentiation
Study design: Descriptive – Product Development, Quantitative – Others
Graduated Realism: A Pedagogical Framework for AI-Powered Avatars in Virtual Reality Teacher Training

Judson Leroy Dean Haynes IV. (06/2025). arXiv.

What is the application? Teaching – Professional Learning
Who is the user? Educator
Which age? Post-Secondary, Adult
Why use AI? Efficiency, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Systematic Review
"Check My Work?" Measuring Sycophancy in a Simulated Educational Context

Chuck Arvin. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user? Student
Which age? High School (9-12), Post-Secondary
Why use AI? Outcomes – Numeracy, Outcomes – Other Academic, Outcomes – Differentiation
Study design: Technical – Computational
LLM-Driven Personalized Answer Generation and Evaluation

Mohammadreza Molavi, Mohammadreza Tavakoli, Mohammad Moein, Abdolali Faraji, Gabor Kismihok. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user? Student
Which age? Post-Secondary, Adult
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation
Study design: Technical – Computational
RETUYT-INCO at BEA 2025 Shared Task: How Far Can Lightweight Models Go in AI-powered Tutor Evaluation?

Santiago G¬óngora, Ignacio Sastre, Santiago Robaina, Ignacio Remersaro, Luis Chiruzzo, Aiala Ros¬á. (06/2025). arXiv.

What is the application? Analyzing
Who is the user? Others
Which age?
Why use AI? Efficiency, Outcomes – Numeracy
Study design: Technical – Computational
Combining Log Data and Collaborative Dialogue Features to Predict Project Quality in Middle School AI Education

Conrad Borchers, Xiaoyi Tian, Kristy Elizabeth Boyer, Maya Israel. (06/2025). arXiv.

What is the application? Teaching – Assessment and Feedback, Analyzing
Who is the user? Student
Which age? Middle School (6-8)
Why use AI? Outcomes – Other Academic, Outcomes – Durable Skills
Study design: Technical – Computational, Quantitative – Others
COGENT: A Curriculum-oriented Framework for Generating Grade-appropriate Educational Content

Zhengyuan Liu, Stella Xin Yin, Dion Hoe-Lian Goh, Nancy F. Chen. (06/2025). arXiv.

What is the application? Teaching – Instructional Materials
Who is the user?
Which age? Elementary (PK5)
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation
Study design: Descriptive – Product Development, Technical – Computational
SRLAgent: Enhancing Self-Regulated Learning Skills through Gamification and LLM Assistance

Wentao Ge, Yuqing Sun, Ziyan Wang, Haoyue Zheng, Weiyang He, PiaoHong Wang, Qianyu Zhu, Benyou Wang. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user? Student
Which age? Post-Secondary
Why use AI? Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills, Other
Study design: Descriptive – Product Development, Impact – Randomized Controlled Trial
Educators' Perceptions of Large Language Models as Tutors: Comparing Human and AI Tutors in a Blind Text-only Setting

Sankalan Pal Chowdhury, Terry Jingchen Zhang, Donya Rooein, Dirk Hovy, Tanja Kasser, Mrinmaya Sachan. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user? Educator
Which age? Elementary (PK5)
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Differentiation
Study design: Quantitative – Others
SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research

Ahmed Adel Attia, Jing Liu, Carol Espy-Wilson. (06/2025). arXiv.

What is the application?
Who is the user?
Which age? Elementary (PK5), Middle School (6-8)
Why use AI? Outcomes – Numeracy, Outcomes – Other Academic
Study design: Technical – Computational
AI Tutors vs. Tenacious Myths: Evidence from Personalised Dialogue Interventions in Education

Brooklyn J. Corbett, Jason M. Tangen. (06/2025). 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 – Differentiation, Outcomes – Durable Skills
Study design: Impact – Randomized Controlled Trial
Intent Matters: Enhancing AI Tutoring with Fine-Grained Pedagogical Intent Annotation

Kseniia Petukhova, Ekaterina Kochmar. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user? Student
Which age? Middle School (6-8), High School (9-12)
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation
Study design: Technical – Computational
Automatic Generation of Inference Making Questions for Reading Comprehension Assessments

Wanjing Anya Ma, Michael Flor, Zuowei Wang. (06/2025). 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? Efficiency, Outcomes – Literacy
Study design: Technical – Computational
Exploring Effective Strategies for Building a Customised GPT Agent for Coding Classroom Dialogues

Luwei Bai, Dongkeun Han, Sara Hennessy. (06/2025). arXiv.

What is the application? Analyzing
Who is the user? Educator, Others
Which age? Elementary (PK5)
Why use AI? Efficiency
Study design: Descriptive – Product Development, Quantitative – Others
Adultification Bias in LLMs and Text-to-Image Models

Jane Castleman, Aleksandra Korolova. (06/2025). arXiv.

What is the application?
Who is the user?
Which age? Elementary (PK5), Middle School (6-8), High School (9-12), Post-Secondary
Why use AI? Other
Study design: Technical – Computational, Quantitative – Others
Small Models, Big Support: A Local LLM Framework For Teacher-Centric Content Creation And Assessment Using RAG and CAG

Zarreen Reza, Alexander Mazur, Michael T. Dugdale, Robin Ray-Chaudhuri. (06/2025). arXiv.

What is the application? Teaching – Instructional Materials, 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, Technical – Computational, Quantitative – Others
Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests

Stefan Dascalescu, Dumitran Adrian Marius, Mihai Alexandru Vasiluta. (06/2025). 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), Post-Secondary
Why use AI? Efficiency, Outcomes – Durable Skills
Study design: Descriptive – Product Development, Technical – Computational, Quantitative – Others
T-TEXTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation

Nirmal Gelal, Chloe Snow, Ambyr Rios, Hande Ku¬çuk McGinty. (06/2025). arXiv.

What is the application? Teaching – Instructional Materials
Who is the user? Educator
Which age? High School (9-12)
Why use AI? Efficiency, Outcomes – Literacy, Outcomes – Differentiation
Study design: Technical – Computational
Evaluating Vision - Language and Large Language Models for Automated Student Assessment in Indonesian Classrooms

Nurul Aisyah, Muhammad Dehan Al Kautsar, Arif Hidayat, Raqib Chowdhury, Fajri Koto. (06/2025). arXiv.

What is the application? Teaching – Assessment and Feedback
Who is the user? Educator
Which age? Elementary (PK5)
Why use AI? Outcomes – Literacy, Outcomes – Numeracy, Outcomes – Differentiation
Study design: Technical – Computational
Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights

Giorgio Biancini, Alessio Ferrato, Carla Limongelli. (06/2025). 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 – Product Development, Quantitative – Others
Simulating LLM-to-LLM Tutoring for Multilingual Math Feedback

Junior Cedric Tonga, KV Aditya Srivatsa, Kaushal Kumar Maurya, Fajri Koto, Ekaterina Kochmar. (06/2025). arXiv.

What is the application? Learning – Student Support
Who is the user?
Which age? Elementary (PK5), Middle School (6-8), High School (9-12)
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation
Study design: Technical – Computational
Sentiment Analysis in Learning Management Systems Understanding Student Feedback at Scale

Mohammed Almatauri. (06/2025). arXiv.

What is the application? Analyzing
Who is the user? Educator
Which age? Post-Secondary
Why use AI? Outcomes – Other Academic
Study design: Technical – Computational

Research Study Repository

Research Categories

Scope & Relevance

This Repository is For You

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Submit a research study

Detecting LLM-Generated Short Answers and Effects on Learner Performance

A Large-Scale Real-World Evaluation of an LLM-Based Virtual Teaching Assistant

From Generation to Adaptation: Comparing AI-Assisted Strategies in High School Programming Education

Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)

Students' Reliance on AI in Higher Education: Identifying Contributing Factors

Feeling Machines: Ethics, Culture, and the Rise of Emotional AI

Sense and Sensibility: What makes a social robot convincing to high-school students?

Rethinking Citation of AI Sources in Student-AI Collaboration within HCI Design Education

Designing Effective LLM-Assisted Interfaces for Curriculum Development

Graduated Realism: A Pedagogical Framework for AI-Powered Avatars in Virtual Reality Teacher Training

"Check My Work?" Measuring Sycophancy in a Simulated Educational Context

LLM-Driven Personalized Answer Generation and Evaluation

RETUYT-INCO at BEA 2025 Shared Task: How Far Can Lightweight Models Go in AI-powered Tutor Evaluation?

Combining Log Data and Collaborative Dialogue Features to Predict Project Quality in Middle School AI Education

COGENT: A Curriculum-oriented Framework for Generating Grade-appropriate Educational Content

SRLAgent: Enhancing Self-Regulated Learning Skills through Gamification and LLM Assistance

Educators' Perceptions of Large Language Models as Tutors: Comparing Human and AI Tutors in a Blind Text-only Setting

SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research

AI Tutors vs. Tenacious Myths: Evidence from Personalised Dialogue Interventions in Education

Intent Matters: Enhancing AI Tutoring with Fine-Grained Pedagogical Intent Annotation

Automatic Generation of Inference Making Questions for Reading Comprehension Assessments

Exploring Effective Strategies for Building a Customised GPT Agent for Coding Classroom Dialogues

Adultification Bias in LLMs and Text-to-Image Models

Small Models, Big Support: A Local LLM Framework For Teacher-Centric Content Creation And Assessment Using RAG and CAG

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests

T-TEXTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation

Evaluating Vision - Language and Large Language Models for Automated Student Assessment in Indonesian Classrooms

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights

Simulating LLM-to-LLM Tutoring for Multilingual Math Feedback

Sentiment Analysis in Learning Management Systems Understanding Student Feedback at Scale