Thursday, November 20, 2025
Exit Slip (2025.11.20)
Thursday, November 13, 2025
Exit Slip (2025.11.13)
We have been working on the inquiry project in this week's session. For the most part, we looked at some of our papers in depth and have chosen a few more informal sources that focused on the limitations of AI. We have also been carefully examined our papers and their relevance and importance to our topic. A few adjustments would be made to our annotated bibliography.
Friday, November 7, 2025
Inquiry Project - Annotated Bibliography
EDUC 450 Inquiry Project
Research Topic: How can AI improve student learning and academic performance?
Kai Jordan Li
Helin Carter Wang
Annotated Bibliography
1. Alarbi, K., Halaweh, M., Tairab, H., Alsalhi, N. R., Annamalai, N., & Aldarmaki, F. (2024).
Making a revolution in physics learning in high schools with ChatGPT: A case study in UAE.
Eurasia Journal of Mathematics, Science and Technology Education, 20(9), em2499. https://doi.org/10.29333/ejmste/14983
This case study on ChatGPT in high school physics shows concrete classroom uses (explanations, problem help, feedback) and reports measurable learning gains, helping me see how conversational AI could support conceptual understanding in physics instead of only procedural help.
2. AI in education: Enhancing learning experiences and student outcomes.
Applied and Computational Engineering.
https://doi.org/10.54254/2755-2721/51/20241187
Its practical examples of AI-supported learning environments are very good; it reinforces the idea that AI impact depends on thoughtful integration aligned with clear learning goals.
3. Exploring the role of AI in education.
London Journal of Social Sciences.
https://doi.org/10.31039/ljss.2023.6.108
This paper gives a broad overview of AI functions (tutoring, analytics, personalization) and limitations, helping me map the landscape and situate my more math/physics-specific sources within larger ethical and pedagogical debates.
4. Lee, I., & Perret, B. (2022).
Preparing high school teachers to integrate AI methods into STEM classrooms.
Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12783–12791. https://ojs.aaai.org/index.php/AAAI/article/view/21557
It centers teacher preparation for using AI in STEM; it underscores that student gains in math/physics depend on whether teachers are equipped to choose, adapt, and critique AI tools.
5. Levis, M. (2024, January 16).
Understanding the limitations of AI (Artificial Intelligence).
Medium. https://medium.com/@marklevisebook/understanding-the-limitations-of-ai-artificial-intelligence-a264c1e0b8ab
6. Leo R. De Velez, Christian M. Alis, Christopher P. Monterola et al.
Impact of Artificial Intelligence Generated Feedback on Math Quiz Scores, 21 July 2025, PREPRINT (Version 1) available at Research Square
https://doi.org/10.21203/rs.3.rs-7110559/v1
It isolates AI-generated feedback on math quizzes; its findings that targeted AI feedback improves scores inform my focus on AI as a formative assessment tool to strengthen problem-solving.
7. Mahligawati, F., Allanas, E., Butarbutar, M. H., & Nordin, N. A. N. (2023).
Artificial intelligence in physics education: A comprehensive literature review.
Journal of Physics: Conference Series, 2596(1), 12080. https://doi.org/10.1088/1742-6596/2596/1/012080
This physics-specific literature review sees how AI has been used across topics like mechanics and EM; it confirms that AI can support simulations, intelligent tutoring, and diagnosis of misconceptions in physics learning.
8. Masilamony, I. (2025).
Exploring the applications of generative AI in high school STEM education.
https://doi.org/10.48550/arxiv.2510.21718
This piece surveys generative AI applications in high school STEM, expanding my sense of possibilities (e.g., code generation, visualization, problem variation) and pushing us to think beyond only Q&A-style uses.
9. Patero, J. L. (2023).
Revolutionizing math education: Harnessing ChatGPT for student success.
International Journal of Advanced Research in Science Communication and Technology, 807–813. https://doi.org/10.48175/IJARSCT-12375
This article shows that ChatGPT improves math learning by offering personalized support and interactive problem-solving. Grade 9 to 12 students gained higher test and quiz scores, stronger problem-solving skills, and increased interest and confidence. Teachers also benefited by having more time for deeper discussions and creative instruction.
10. Tiwari, R. (2023).
The integration of AI and machine learning in education and its potential to personalize and improve student learning experiences.
International Journal of Scientific Research in Engineering and Management, 7(2). https://doi.org/10.55041/IJSREM17645
This article outlines how AI/ML enable personalization and adaptive pathways; it supports my argument that individualized pacing and targeted practice are key mechanisms behind improved academic performance.
11. Villasenor, J. (2025, February 20).
How CHATGPT can improve education, not threaten it.
Scientific American. https://www.scientificamerican.com/article/how-chatgpt-can-improve-education-not-threaten-it/
This non-academic article frames ChatGPT as an opportunity rather than a threat and emphasizes human–AI partnership, helping me articulate a balanced position on using AI to enhance learning while maintaining teacher judgment and academic integrity.
12. Vitomir Kovanovic, & Rebecca Marrone. (2025, October 31).
MIT researchers say using CHATGPT can rot your brain. The truth is a little more complicated. The Conversation
This article critiques sensationalized claims about ChatGPT harming cognition and instead explains that impacts depend on how AI is used. It helps me frame AI as a tool that can either support or hinder learning depending on instructional design and student habits.
13. Wang, T., Lund, B. D., Marengo, A., Pagano, A., Mannuru, N. R., Teel, Z. A., & Pange, J. (2023).
Exploring the potential impact of artificial intelligence (AI) on international students in higher education: Generative AI, chatbots, analytics, and international student success.
Applied Sciences, 13(11), 6716. https://doi.org/10.3390/app13116716
Although focused on international students in higher ed, this article highlights how AI chatbots and analytics can scaffold language and content learning, reminding me that AI support is especially powerful for students facing additional barriers in STEM.
14. Yi, L., Liu, D., Jiang, T., & Xian, Y. (2025).
The effectiveness of AI on K-12 students' mathematics learning: A systematic review and meta-analysis.
International Journal of Science and Mathematics Education, 23(4), 1105–1126. https://doi.org/10.1007/s10763-024-10499-7
It directly examines K–12 math learning with AI tools; its overall positive effect sizes give me empirical backing that well-designed AI systems can boost math performance rather than just sounding promising in theory.
Thursday, November 6, 2025
Exit Slip (2025.11.6)
In this week's session, I focused on my inquiry question: How can AI improve student learning and academic performance? I went down a Google Scholar rabbit hole and was honestly surprised by how much has already been published—systematic reviews, case studies, chatbot interventions, adaptive systems, etc. Most of what I skimmed backed up my initial assumption: when used intentionally (feedback, personalization, tutoring, analytics), AI tools would boost performance and engagement.