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Research Study Repository
Research synthesis is AI-generated, human reviewed. Updated 08/2026.
Showing 1231 - 1260 of 1627 results
Event Segmentation Applications In Large Language Model Enabled Automated Recall Assessments
Ryan A. Panela, Alexander J. Barnett, Morgan D. Barense, Bjornn Herrmann. (02/2025). arXiv.
What is the application? Teaching – Assessment and Feedback, Analyzing
Who is the user? Others
Which age? Adult
Why use AI? Efficiency
Study design: Technical – ComputationalEssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models
Jiamin Su, Yibo Yan, Fangteng Fu, Han Zhang, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu. (02/2025). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user?
Which age? Middle School (6-8), High School (9-12)
Why use AI? Efficiency, Outcomes – Literacy
Study design: Technical – ComputationalEdgeAIGuard: Agentic LLMs for Minor Protection in Digital Spaces
Ghulam Mujtaba, Sunder Ali Khowaja, Kapal Dev. (02/2025). arXiv.
What is the application? Analyzing
Who is the user?
Which age? Middle School (6-8), High School (9-12)
Why use AI? Efficiency, Outcomes – Social Emotional, Other
Study design: Technical – ComputationalCo-designing Large Language Model Tools for Project-Based Learning with K-12 Educators
Prerna Ravi, John Masla, Gisella Kakoti, Grace C. Lin, Emma Anderson, Matt Taylor, Anastasia K. Ostrowski, Cynthia Breazeal, Eric Klopfer, Hal Abelson. (02/2025). arXiv.
What is the application? Teaching – Instructional Materials, Teaching – Assessment and Feedback, Organizing
Who is the user? Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12)
Why use AI? Efficiency, Outcomes – Differentiation, Outcomes – Durable Skills, Reimagined Schooling
Study design: Descriptive – Implementation and Use, Descriptive – Product DevelopmentAutograding Mathematical Induction Proofs with Natural Language Processing
Chenyan Zhao, Mariana Silva, Seth Poulsen. (02/2025). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Student
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Numeracy
Study design: Impact – Quasi–experimental, Technical – ComputationalUnifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors
Kaushal Kumar Maurya, KV Aditya Srivatsa, Kseniia Petukhova and Ekaterina Kochmar. (02/2025). arXiv.
… large language models (LLMs) are effective as AI tutors and whether they demonstrate pedagogical abilities necessary for good AI tutoring in educational dialogues. Previous efforts towards … designed to assess the pedagogical value of LLM-powered AI tutor responses grounded in student mistakes or confusion in …What is the application? Teaching – Assessment and Feedback, Learning – Student Support
Who is the user? Student
Which age? Elementary (PK5), Middle School (6-8)
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation
Study design: Descriptive – Product Development, Technical – ComputationalTutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation
Zhaoxing LI, Vahid Yazdanpanah, Jindi Wang, Wen Gu, Lei Shi, Alexandra I. Cristea, Sarah Kiden, Sebastian Stein. (01/2025). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support
Who is the user? Student
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation
Study design: Descriptive – Product Development, Impact – Randomized Controlled Trial, Technical – ComputationalPersonalized Parsons Puzzles as Scaffolding Enhance Practice Engagement Over Just Showing LLM-Powered Solutions
Xinying Hou, Zihan Wu, Xu Wang, Barbara J. Ericson. (01/2025). 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
Study design: Impact – Randomized Controlled TrialIntelliChain: An Integrated Framework for Enhanced Socratic Method Dialogue with LLMs and Knowledge Graphs
Changyong Qi, Linzhao Jia, Yuang Wei, Yuan-Hao Jiang, Xiaoqing Gu. (01/2025). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support
Who is the user? Student, Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12), Post-Secondary
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Product Development, Technical – Computational, Quantitative – OthersiLLuMinaTE: An LLM-XAI Framework Leveraging Social Science Explanation Theories Towards Actionable Student Performance Feedback
Vinitra Swamy, Davide Romano, Bhargav Srinivasa Desikan, Oana-Maria Camburu, Tanja Kaser. (01/2025). arXiv.
What is the application? Teaching – Assessment and Feedback, Learning – Student Support
Who is the user? Student
Which age? Post-Secondary
Why use AI? Outcomes – Other Academic, Outcomes – Differentiation
Study design: Descriptive – Product Development, Technical – Computational, Quantitative – OthersGenerative AI in Education: From Foundational Insights to the Socratic Playground for Learning
Xiangen Hu, Sheng Xu, Richard Tong, & Art Graesser. (01/2025). arXiv.
What is the application? Teaching – Instructional Materials, Teaching – Assessment and Feedback, Learning – Student Support, Communicating / Social Tools
Who is the user? Student
Which age? Middle School (6-8), High School (9-12), Post-Secondary
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Product Development, Technical – ComputationalGenerating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
Yooseop Lee, Suin Kim, Yohan Jo. (01/2025). arXiv.
What is the application? Teaching – Instructional Materials
Who is the user? Educator
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Other Academic
Study design: Technical – ComputationalFine-tuning ChatGPT for Automatic Scoring of Written Scientific Explanations in Chinese
Jie Yang, Ehsan Latif, Yuze He, Xiaoming Zhai. (01/2025). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Educator
Which age? Middle School (6-8), High School (9-12)
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation
Study design: Technical – ComputationalExploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
Qiming Bao, Juho Leinonen, Alex Yuxuan Peng, Wanjun Zhong, Ga‘l Gendron, Tim Pistotti, Alice Huang, Paul Denny, Michael Witbrock, Jiamou Liu. (01/2025). arXiv.
What is the application? Teaching – Instructional Materials, Teaching – Assessment and Feedback
Who is the user? Student
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Product Development, Technical – ComputationalEvaluating GenAI for Simplifying Texts for Education: Improving Accuracy and Consistency for Enhanced Readability
Stephanie L. Day, Jacapo Cirica, Steven R. Clapp, Veronika Penkova, Amy E. Giroux, Abbey Banta, Catherine Bordeau, Poojitha Mutteneni, Ben D. Sawyer. (01/2025). arXiv.
What is the application? Teaching – Instructional Materials
Who is the user? Others
Which age? Elementary (PK5), Middle School (6-8), High School (9-12)
Why use AI? Efficiency, Outcomes – Literacy, Outcomes – Other Academic, Outcomes – Differentiation
Study design: Technical – ComputationalEnhancing textual textbook question answering with large language models and retrieval augmented generation
Hessa A. Alawwad, Areej Alhothali, Usman Naseem, Ali Alkhathlan, Amani Jamal. (01/2025). arXiv.
What is the application? Learning – Student Support
Who is the user?
Which age? Middle School (6-8)
Why use AI? Outcomes – Other Academic
Study design: Technical – ComputationalDebugging Without Error Messages: How LLM Prompting Strategy Affects Programming Error Explanation Effectiveness
Audrey Salmon, Katie Hammer, Eddie Antonio Santos, Brett A. Becker. (01/2025). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Student
Which age? High School (9-12), Post-Secondary
Why use AI? Outcomes – Other Academic
Study design: Technical – ComputationalBridging the Early Science Gap with Artificial Intelligence Evaluating Large Language Models as Tools for Early Childhood Science Education
Annika Bush, Amin Alibakhshi. (01/2025). arXiv.
What is the application? Teaching – Instructional Materials
Who is the user? Educator
Which age? Elementary (PK5)
Why use AI? Outcomes – Other Academic
Study design: Quantitative – OthersAuto-Evaluation: A Critical Measure in Driving Improvements in Quality and Safety of AI-Generated Lesson Resources
Hannah-Beth Clark, Margaux Dowland, Laura Benton, Reka Budai, Ibrahim Kaan Keskin, Emma Searle, Matthew Gregory, Mark Hodierne, William Gayne, John Roberts. (01/2025). 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? Efficiency, Outcomes – Other Academic
Study design: Descriptive – Product Development, Quantitative – OthersA Zero-Shot LLM Framework for Automatic Assignment Grading in Higher Education
Calvin Yeung, Jeff Yu, King Chau Cheung, Tat Wing Wong, Chun Man Chan, Kin Chi Wong, Keisuke Fujii. (01/2025). 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, Outcomes – Durable Skills
Study design: Impact – Quasi–experimentalA Study on Educational Data Analysis and Personalized Feedback Report Generation Based on Tags and ChatGPT*
Yizhou Zhou, Mengqiao Zhang, Yuan-Hao Jiang, Xinyu Gao, Naijie Liu, Bo Jiang. (01/2025). arXiv.
What is the application? Teaching – Assessment and Feedback, Analyzing
Who is the user? Educator
Which age? Elementary (PK5)
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Differentiation
Study design: Descriptive – Product Development, Technical – Computational, Quantitative – OthersA Review of Artificial Intelligence Interventions for Students with Autism Spectrum Disorder
Sofia Kotsi, Spyridoula Handrinou, Georgia Iatraki, Spyridon-Georgios Soulis. (01/2025). MDPI.
What is the application? Learning – Student Support
Who is the user? Student
Which age? Elementary (PK5), Middle School (6-8), High School (9-12), Post-Secondary
Why use AI? Outcomes – Differentiation, Outcomes – Social Emotional, Outcomes – Durable Skills
Study design: Systematic ReviewA Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education
Ziqing Li, Mutlu Cukurova, Sahan Bulathwela. (01/2025). 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), Post-Secondary
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation
Study design: Technical – ComputationalEducation in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning
David Baidoo-Anu, Leticia Owusu Ansah. (01/2025). SSRN.
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? Efficiency, Outcomes – Literacy, Outcomes – Other Academic, Outcomes – Differentiation, Reimagined Schooling
Study design: Systematic ReviewPredicting Long-Term Student Outcomes from Short-Term EdTech Log Data
Ge Gao, Amelia Leon, Andrea Jetten, Jasmine Turner, Husni Almoubayyed, Stephen Fancsali, Emma Brunskill. (01/2025). arXiv.
What is the application? Analyzing
Who is the user? Student
Which age? Elementary (PK5), Middle School (6-8)
Why use AI? Outcomes – Literacy, Outcomes – Numeracy
Study design: Quantitative – OthersThe effectiveness of ChatGPT in assisting high school students in programming learning: evidence from a quasi-experimental research
Tzu-Chi Yang, Yi-Chuan Hsu, Jiun-Yu Wu. (01/2025). Interactive Learning Environments.
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–experimentalYou're (Not) My Type - Can LLMs Generate Feedback of Specific Types for Introductory Programming Tasks?
Dominic Lohr, Hieke Keuning, Natalie Kiesler. (12/2024). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Student
Which age? Post-Secondary
Why use AI? Outcomes – Other Academic, Outcomes – Differentiation
Study design: Descriptive – Product Development, Technical – ComputationalLeveraging AI for Rapid Generation of Physics Simulations in Education: Building Your Own Virtual Lab
Yossi Ben-Zion, Roi Einhorn Zarzecki, Joshua Glazer, Noah D. Finkelstein. (12/2024). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support
Who is the user? Student, Educator
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Product DevelopmentImproving the Validity of Automatically Generated Feedback via Reinforcement Learning
Alexander Scarlatos, Digory Smith, Simon Woodhead, Andrew Lan. (12/2024). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user?
Which age? Middle School (6-8)
Why use AI? Outcomes – Numeracy
Study design: Technical – ComputationalHarnessing AI in Secondary Education to Enhance Writing Competence
Eyvind Elstad, Harald Eriksen. (12/2024). arXiv.
What is the application? Teaching – Instructional Materials, Teaching – Assessment and Feedback, Learning – Student Support, Organizing
Who is the user? Student, Educator
Which age?
Why use AI? Efficiency, Outcomes – Literacy, Outcomes – Differentiation
Study design:
