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Research Study Repository
Research synthesis is AI-generated, human reviewed. Updated 08/2026.
Showing 61 - 90 of 91 results
Evaluating the Effectiveness of Large Language Models in Solving Simple Programming Tasks: A User-Centered Study
Kai Deng. (07/2025). 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: Impact – Quasi–experimentalCan theory-driven learning analytics dashboard enhance human-AI collaboration in writing learning? Insights from an empirical experiment
Angxuan Chen, Jingjing Lian, Xinran Kuang, Jiyou Jia. (06/2025). 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 – Literacy, Outcomes – Social Emotional, Outcomes – Durable Skills
Study design: Impact – Quasi–experimentalExploring the Usage of Generative AI for Group Project-Based Offline Art Courses in Elementary Schools
Zhiqing Wang, Haoxiang Fan, Shiwei Wu, Qiaoyi Chen, Yongqi Liang, Zhenhui Peng. (06/2025). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support, Communicating / Social Tools
Who is the user? Student, Educator
Which age? Elementary (PK5)
Why use AI? Efficiency, Outcomes – Differentiation, Outcomes – Durable Skills, Reimagined Schooling
Study design: Descriptive – Implementation and Use, Descriptive – Product Development, Impact – Quasi–experimentalSense and Sensibility: What makes a social robot convincing to high-school students?
Pablo Gonz‡lez-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–experimentalTestAgent: An Adaptive and Intelligent Expert for Human Assessment
Junhao Yu, Yan Zhuang, YuXuan Sun, Weibo Gao, Qi Liu, Mingyue Cheng, Zhenya Huang, Enhong Chen. (06/2025). arXiv.
What is the application? Teaching – Assessment and Feedback
Who is the user? Student, Others
Which age? Elementary (PK5), Middle School (6-8), High School (9-12), Post-Secondary
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Differentiation
Study design: Descriptive – Product Development, Impact – Quasi–experimental, Technical – ComputationalLearn 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? Adult
Why use AI? Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Product Development, Impact – Quasi–experimentalFrom Chalkboards to Chatbots: Evaluating the Impact of Generative AI on Learning Outcomes in Nigeria
Mart’n De Simone, Federico Tiberti, Maria Barron Rodriguez, Federico Manolio, Wuraola Mosuro, Eliot Jolomi Dikoru. (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? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation, Reimagined Schooling
Study design: Impact – Randomized Controlled Trial, Impact – Quasi–experimentalAI Meets the Classroom: When Do Large Language Models Harm Learning?
Matthias Lehmann, Philipp B. Cornelius, Fabian J. Sting. (03/2025). arXiv.
What is the application? Learning – Student Support
Who is the user? Student
Which age? Post-Secondary
Why use AI? Outcomes – Other Academic
Study design: Impact – Randomized Controlled Trial, Impact – Quasi–experimentalAutograding 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 – ComputationalA 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–experimentalThe 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–experimentalModifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality
Kaixun Yang, Mladen Rakovi_, Zhiping Liang, Lixiang Yan, Zijie Zeng, Yizhou Fan, Dragan Ga_evi_, Guanliang Chen. (12/2024). arXiv.
What is the application? Learning – Student Support
Who is the user? Student
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Literacy, Outcomes – Durable Skills
Study design: Impact – Quasi–experimentalMindScratch: A Visual Programming Support Tool for Classroom Learning Based on Multimodal Generative AI
Yunnong Chen, Shuhong Xiao, Yaxuan Song, Zejian Li, Lingyun Sun, Liuqing Chen. (12/2024). arXiv.
What is the application? Teaching – Instructional Materials, Learning – Student Support
Who is the user? Student
Which age? Elementary (PK5)
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills, Reimagined Schooling
Study design: Descriptive – Product Development, Impact – Quasi–experimental, Technical – ComputationalLearning-By-Teaching With ChatGPT: The Effect Of Teachable ChatGPT Agent On Programming Education
Angxuan Chen, Yuang Wei, Huixiao Le, Yan Zhang. (12/2024). arXiv.
What is the application? Learning – Student Support, Communicating / Social Tools
Who is the user? Student
Which age? Post-Secondary
Why use AI? Outcomes – Other Academic, Outcomes – Social Emotional, Outcomes – Durable Skills
Study design: Impact – Quasi–experimentalThe Influence of Artificial Intelligence Tools on Student Performance in e-Learning Environments: Case Study
Mohd Elmagzoub Eltahir, Frdose Mohd Elmagzoub Babiker. (11/2024). Electronic Journal of e-Learning.
What is the application? Teaching – Assessment and Feedback, Learning – Student Support
Who is the user? Student, Educator
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Social Emotional, Outcomes – Durable Skills
Study design: Impact – Quasi–experimental, Quantitative – OthersGenerative AI Usage and Exam Performance
Janik Ole Wecks, Johannes Voshaar, Benedikt J. Plate, Jochen Zimmermann. (11/2024). arXiv.
What is the application? Learning – Student Support
Who is the user? Student
Which age? Post-Secondary
Why use AI? Outcomes – Literacy, Outcomes – Other Academic, Outcomes – Durable Skills
Study design: Impact – Quasi–experimentalThe Neglected 15%: Positive Effects Of Hybrid Human-Ai Tutoring Among Students With Disabilities
Danielle R. Thomas, Erin Gatz, Shivang Gupta, Vincent Aleven, Kenneth R. Koedinger. (07/2024). Artificial Intelligence in Education: 25th International Conference, AIED 2024.
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, Outcomes – Social Emotional
Study design: Impact – Quasi–experimentalBeyond Answers: Large Language Model-Powered Tutoring System in Physics Education for Deep Learning and Precise Understanding
Zhoumingju Jiang, Mengjun Jiang. (06/2024). 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 – Differentiation, Outcomes – Durable Skills
Study design: Impact – Quasi–experimentalIntelligent Tutor: Leveraging ChatGPT and Microsoft Copilot Studio to Deliver a Generative AI Student Support and Feedback System within Teams
Wei-Yu Chen. (05/2024). 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? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Durable Skills
Study design: Descriptive – Implementation and Use, Descriptive – Product Development, Impact – Quasi–experimental, Quantitative – OthersApp Planner: Utilizing Generative AI in K-12 Mobile App Development Education
David Y.J. Kim, Prerna Ravi, Randi Williams, Daeun Yoo. (04/2024). arXiv.
What is the application? Learning – Student Support
Who is the user? Student
Which age? High School (9-12)
Why use AI? Efficiency, Outcomes – Social Emotional, Outcomes – Durable Skills
Study design: Descriptive – Product Development, Impact – Quasi–experimentalImproving 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.
What is the application? Learning – Student Support
Who is the user? Student, Educator
Which age? Middle School (6-8)
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Differentiation, Reimagined Schooling
Study design: Impact – Quasi–experimentalAre Lesson Plans Created by ChatGPT More Effective? An Experimental Study
Muhammet Remzi Karaman, Idris G¬öksu. (02/2024). International Journal of Technology in Education.
What is the application? Teaching – Instructional Materials
Who is the user? Educator
Which age? Elementary (PK5)
Why use AI? Efficiency, Outcomes – Numeracy
Study design: Impact – Quasi–experimentalSupporting Teachers' Professional Development With Generative AI: The Effects on Higher Order Thinking and Self-Efficacy
Jijian Lu, Ruxin Zheng, Zikun Gong, Huifen Xu. (02/2024). IEEE.
What is the application? Teaching – Instructional Materials, Teaching – Professional Learning
Who is the user? Educator
Which age? Post-Secondary
Why use AI? Efficiency, Outcomes – Durable Skills
Study design: Impact – Quasi–experimentalDesign and implementation of an Al-enabled visual report tool as formative assessment to promote learning achievement and self-regulated learning: An experimental study
Xiaofang Liao, Xuedi Zhang, Zhifeng Wang, Heng Luo. (01/2024). BJET.
What is the application? Teaching – Assessment and Feedback, Learning – Student Support, Analyzing
Who is the user? Student
Which age? High School (9-12)
Why use AI? Efficiency, Outcomes – Other Academic, Outcomes – Differentiation, Outcomes – Social Emotional
Study design: Impact – Quasi–experimental"Mistakes Help Us Grow‚Äù: 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.
What is the application? Teaching – Professional Learning
Who is the user? Educator
Which age? Elementary (PK5), Middle School (6-8)
Why use AI? Efficiency, Outcomes – Numeracy, Outcomes – Differentiation, Outcomes – Social Emotional
Study design: Impact – Quasi–experimental, Technical – ComputationalImplementing Learning Principles with a Personal AI Tutor: A Case Study
Ambroise Baillifard, Maxime Gabella, Pamela Banta Lavenex, Corinna S. Martarelli. (09/2023). arXiv.
… constraints. Here we explore the integration of AI tutors to complement learning programs in accordance with … study was conducted at UniDistance Suisse, where an AI tutor app was provided to psychology students taking a … from existing course materials using GPT-3, the AI tutor developed a dynamic neural-network model of each …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 – Quasi–experimentalChatGPT: Revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in Emirates schools
Saif Alneyadi, Yousef Wardat. (06/2023). CedTech.
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 – Differentiation
Study design: Impact – Quasi–experimentalCan Artificial Intelligence Improve Gender Equality? Evidence from a Natural Experiment
Leo Bao, Difang Huang, Chen Lin. (08/2022). SSRN.
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 – Other Academic, Other
Study design: Impact – Quasi–experimentalA New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions
Francois St-Hilaire, Dung Do Vu, Antoine Frau, Nathan Burns, Farid Faraji, Joseph Potochny, Stephane Robert, Arnaud Roussel, Selene Zheng, Taylor Glazier, Junfel Vincent Romano, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Tommy Delarosbil, Seulmin Ahn, Simon Eden-Walker, Kritika Sony, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Victor Chen, Hossein Sahraei, Robert Larson, Nadia Markova, Andrew Barkett, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, Ekaterina Kochmar. (03/2022). arXiv.
What is the application? Teaching – Assessment and Feedback, Learning – Student Support
Who is the user? Student
Which age? Adult
Why use AI? Outcomes – Other Academic, Outcomes – Differentiation, Reimagined Schooling
Study design: Impact – Quasi–experimentalHome-Tutoring Services Assisted with Technology: Investigating the Role of Artificial Intelligence Using a Randomized Field Experiment
Jun Hyung Kim, Minki Kim, Do Won Kwak, Sol Lee. (09/2021). Sage.
What is the application? Teaching – Assessment and Feedback
Who is the user? Educator
Which age? Elementary (PK5), Middle School (6-8)
Why use AI? Outcomes – Numeracy, Outcomes – Differentiation
Study design: Impact – Randomized Controlled Trial, Impact – Quasi–experimental
