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Research Study Repository
Research synthesis is AI-generated, human reviewed. Updated 11/2026.
Showing 181 - 210 of 259 results
The Advancement of Personalized Learning Potentially Accelerated by Generative AI
Yuang Wei, Yuan-Hao Jiang, Jiayi Liu, Changyong Qi, Linzhao Jia, Rui Jia. (02/2025). arXiv.
What is the application? Teaching – Instructional Materials, Teaching – Assessment and Feedback, Teaching – Professional Learning, 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), Post-Secondary
Why use AI? Efficiency, Outcomes – Literacy, Outcomes – Numeracy, Outcomes – Differentiation, Outcomes – Durable Skills, Reimagined Schooling
Study design: Descriptive – Product Development, Systematic ReviewSET-PAIRED: Designing for Parental Involvement in Learning with an AI-Assisted Educational Robot
Hui-Ru Ho, Nitigya Kargeti, Ziqi Liu, Bilge Mutlu. (02/2025). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support
Who is the user? Parent/Caregiver
Which age? Elementary (PK5)
Why use AI? Efficiency, Outcomes – Literacy, Outcomes – Numeracy, Outcomes – Differentiation, Outcomes – Social Emotional, Other
Study design: Descriptive – Product Development, Quantitative – OthersScaffolding Middle-School Mathematics Curricula With Large Language Models
Rizwaan Malik, Dorna Abdi, Rose Wang, Dorottya Demszky. (02/2025). British Journal of Educational Technology.
What is the application? Teaching – Instructional Materials
Who is the user? Educator
Which age? Middle School (6-8)
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Differentiation
Study design: Descriptive – Product Development, Technical – Computational, Quantitative – OthersMindCraft: Revolutionizing Education through AI-Powered Personalized Learning and Mentorship for Rural India
Arihant Bardia, Aayush Agrawal. (02/2025). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support
Who is the user? Student, Educator, Others
Which age? Middle School (6-8), High School (9-12), Post-Secondary
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Social Emotional, Outcomes – Durable Skills, Reimagined Schooling
Study design: Descriptive – Product DevelopmentOne Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
Ben Liu, Jihai Zhang, Fangquan Lin, Xu Jia, Min Peng. (02/2025). arXiv.
What is the application? Learning – Student Support
Who is the user? Student
Which age? Elementary (PK5)
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Technical – ComputationalFrom Correctness to Comprehension: AI Agents for Personalized Error Diagnosis in Education
Yi-Fan Zhang, Hang Li, Dingjie Song, Lichao Sun, Tianlong Xu, Qingsong Wen. (02/2025). arXiv.
What is the application? Teaching – Assessment and Feedback, Analyzing
Who is the user? Educator
Which age? Elementary (PK5)
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation
Study design: Technical – ComputationalAutograding 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.
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 – ComputationalIntelliChain: 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 – OthersA 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 – OthersPredicting 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 – OthersImproving 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 – ComputationalA Benchmark for Math Misconceptions: Bridging Gaps in Middle School Algebra with AI-Supported Instruction
Nancy Otero, Stefania Druga and 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, Outcomes – Differentiation
Study design: Technical – ComputationalScaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education
Karen D. Wang, Zhangyang Wu, L'Nard Tufts II, Carl Wieman, Shima Salehi, Nick Haber. (12/2024). 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? Efficiency, Outcomes – Numeracy, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Implementation and UseMNIST-Fraction: Enhancing Math Education with AI-Driven Fraction Detection and Analysis
Pegah Ahadian, Yunhe Feng, Karl Kosko, Richard Ferdig, Qiang Guan. (12/2024). arXiv.
What is the application? Teaching – Assessment and Feedback, Learning – Student Support, Analyzing
Who is the user? Student, Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12), Post-Secondary
Why use AI? Efficiency, Outcomes – Numeracy
Study design: Technical – ComputationalBuilding Bridges - AI Custom Chatbots as Mediators between Mathematics and Physics
Julia Lademann, Jannik Henze, Sebastian Becker-Genschow. (12/2024). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support
Who is the user? Student
Which age? Middle School (6-8)
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Impact – Randomized Controlled TrialAI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrained Models for Autonomous Error Analysis and Correction
Tianlong Xu, Yi-Fan Zhang, Zhendong Chu, Shen Wang, Qingsong Wen. (12/2024). arXiv.
What is the application? Teaching – Assessment and Feedback, Learning – Student Support
Who is the user? Student
Which age? Elementary (PK5)
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Differentiation, Reimagined Schooling, Other
Study design: Descriptive – Implementation and Use, Descriptive – Product Development, Technical – Computational, Quantitative – OthersVISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM
Jeongwoo Lee, Kwangsuk Park, Jihyeon Park. (11/2024). arXiv.
What is the application? Teaching – Instructional Materials, Teaching – Assessment and Feedback
Who is the user? Educator
Which age? High School (9-12)
Why use AI? Efficiency, Outcomes – Numeracy
Study design: Descriptive – Product Development, Technical – ComputationalSBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation
Prakhar Dixit, Tim Oates. (11/2024). arXiv.
What is the application? Learning – Student Support
Who is the user? Student
Which age? Middle School (6-8)
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation
Study design: Descriptive – Product Development, Technical – ComputationalAutomatic Generation of Question Hints for Mathematics Problems using Large Language Models in Educational Technology
Junior Cedric Tonga, Benjamin Clement, Pierre-Yves Oudeyer. (11/2024). arXiv.
What is the application? Learning – Student Support
Who is the user?
Which age? High School (9-12)
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation
Study design: Technical – ComputationalThe Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models
Subhankar Maity, Aniket Deroy. (10/2024). arXiv.
What is the application? Teaching – Instructional Materials, Teaching – Assessment and Feedback
Who is the user? Student, Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12), Post-Secondary, Adult
Why use AI? Efficiency, Outcomes – Literacy, Outcomes – Numeracy, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Implementation and Use, Descriptive – Product DevelopmentPromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation
Mohi Reza, Ioannis Anastasopoulos, Shreya Bhandari, Zachary A. Pardos. (10/2024). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support
Who is the user? Educator
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Numeracy
Study design: Descriptive – Product Development, Impact – Randomized Controlled TrialMathFish: Evaluating Language Model Math Reasoning via Grounding in Educational Curricula
Li Lucy, Tal August, Rose E. Wang, Luca Soldaini, Courtney Allison, Kyle Lo. (10/2024). 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 – Numeracy
Study design: Technical – ComputationalLLM-based Cognitive Models of Students with Misconceptions
Shashank Sonkar, Xinghe Chen, Naiming Liu, Richard G. Baraniuk, Mrinmaya Sachan. (10/2024). arXiv.
What is the application? Teaching – Assessment and Feedback, Learning – Student Support
Who is the user?
Which age? Middle School (6-8), High School (9-12)
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation
Study design: Technical – ComputationalAutomated Feedback in Math Education: A Comparative Analysis of LLMs for Open-Ended Responses
Sami Baral, Eamon Worden, Wen-Chiang Lim, Zhuang Luo, Christopher Santorelli, Ashish Gurung, Neil Heffernan. (10/2024). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Student, Educator
Which age? Middle School (6-8)
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Differentiation
Study design: Technical – Computational, Quantitative – OthersMalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in Education
Naiming Liu, MyCo Le, Shashank Sonkar, Richard G. Baraniuk. (10/2024). arXiv.
What is the application? Teaching – Assessment and Feedback, Learning – Student Support
Who is the user? Student, Educator
Which age? Elementary (PK5), Middle School (6-8), High School (9-12)
Why use AI? Outcomes – Numeracy, Outcomes – Other Academic, Outcomes – Differentiation
Study design: Technical – ComputationalLearning to Love Edge Cases in Formative Math Assessment: Using the AMMORE Dataset and Chain-of-Thought Prompting to Improve Grading Accuracy
Owen Henkel, Hannah Horne-Robinson, Maria Dyshel, Nabil Ch, Baptiste Moreau-Pernet, Ralph Abood. (09/2024). 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 – Numeracy, Outcomes – Differentiation
Study design: Technical – ComputationalA Comprehensive Review on Generative AI for Education
Uday Mittal, Siva Sai, Vinay Chamola, Devika Sangwan. (09/2024). IEEE.
What is the application? Teaching – Instructional Materials, Teaching – Assessment and Feedback, Teaching – Professional Learning, Learning – Student Support, Communicating / Social Tools, Organizing, Analyzing
Who is the user? Student, Parent/Caregiver, Educator
Which age? 0-3 years, Elementary (PK5), Middle School (6-8), High School (9-12), Post-Secondary, Adult
Why use AI? Efficiency, Outcomes – Literacy, Outcomes – Numeracy, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills, Reimagined Schooling
Study design: Systematic ReviewStudents' Perceived Roles, Opportunities, and Challenges of a Generative AI-powered Teachable Agent: A Case of Middle School Math Class
Yukyeong Song, Jinhee Kim, Zifeng Liu, Chenglu Li, Wanli Xing. (08/2024). arXiv.
What is the application? Learning – Student Support, Communicating / Social Tools
Who is the user? Student
Which age? Middle School (6-8)
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation, Outcomes – Social Emotional, Outcomes – Durable Skills, Reimagined Schooling
Study design: Descriptive – Implementation and Use, Descriptive – Product DevelopmentImpact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception
Harsh Kumar, Ilya Musabirov, Mohi Reza, Jiakai Shi, Xinyuan Wang, Joseph Jay Williams, Anastasia Kuzminykh, Michael Liut. (08/2024). arXiv.
What is the application? Learning – Student Support
Who is the user? Student, Others
Which age? Post-Secondary, Adult
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Other Academic, Outcomes – Durable Skills
Study design: Impact – Randomized Controlled Trial
