Inquiry Project Reflection + Link to slides (2025.12.4)

Helin and I had our presentation today, and time flew by faster than we expected. Here’s my personal reflection on Inquiry 1. Preparing and delivering our presentation on “How AI improves student learning and academic performance” pushed me to move beyond abstract enthusiasm into evidence-anchored practice. Going in, I expected a familiar story—AI helps because it’s “smart”—but the reading and our rehearsal cycles clarified that the gains come from three very specific mechanisms: personalization (adaptive paths that meet students where they are), immediate feedback (formative, targeted), and teacher analytics (dashboards that surface patterns and outliers). Designing the mini activity where classmates used an LLM to critique their own thinking made this concrete: the strongest moments usually weren’t generic explanations, but the model diagnosing that person’s misconception and proposing a next step. Presenting under time pressure also taught me to prioritize claims tied to data (e.g., K–12 math meta-analyses and physics case studies) and to name limitations up front along with guardrails (process logs, prompt scaffolds, and assessment designs that reward reasoning). For Inquiry 2, I want to carry forward two threads: (1) a small, classroom-level study in math/physics that compares an AI-supported formative workflow (diagnostic prompt → targeted practice → reflection) against a conventional one, using pre/post performance and a simple engagement/progress tracker; and (2) a practical “AI use agreement” for students that aligns with academic integrity while still encouraging metacognitive help (e.g., “explain my error,” “generate isomorphic practice,” “ask me probing questions”). This first phase has opened unexpected directions around equity: AI appears especially helpful for multilingual learners and students who benefit from private, low-stakes rehearsal before speaking. My curiosity now points to designing prompts that teach habits, not just give answers, and to building lightweight teacher dashboards that convert model feedback into actionable small-group plans. In short, Inquiry 1 provided me an oppurtunity to expand from “AI as content generator only” to “AI as formative infrastructure too”. 

Below is the link to our google slides:

https://docs.google.com/presentation/d/1Dhpj_U8SHdDNISd-rr8wr9umChSctiC5T_-dau6eDhU/edit?usp=sharing

Exit Slip (2025.11.20)

Our presentation activity would be a mini learning session, following closely to our topic on how we can use AI to learn. We will give a prompt for which the audience will search on ChatGPT and learn something from it. Then we'll have a few people to share with the class on what they have just learned with ChatGPT, and how it connects to what we talk about in our presentation.

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. 

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.

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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.

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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.

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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.

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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

http://theconversation.com/mit-researchers-say-using-chatgpt-can-rot-your-brain-the-truth-is-a-little-more-complicated-259450

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.

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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.

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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.

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. 

Exit Slip (2025.10.16)

In this week's session, we opened with PH4 “mathematical poetry.” The constraint was interesting: once you fix the first line, the swaps basically pre-wire the later lines. I tried a six-word set and a weave pattern (AB CDEF → BA DCFE → BDA FCE…) and started wondering about variations—using word clusters, homophones, or anagrams per slot so the combinatorics stay alive without going random. It felt more like tinkering with permutations than “writing,” which I kind of appreciate.

Entrance Slip (2025.10.16)

Search Topic:

How does artificial intelligence contribute to stronger academic performances? 

I am looking more on the use of GenAI, LLM's like ChatGPT, Google Gemini, Deepseek, etc. I personally have been benefited a lot by the use of ChatGPT through part of my studies. The proper use of it, of course, is essential as there are disadvantages to it, but in general, AI is the future. It is inevitable that we will be seeing more AI coming into our lives, so embrace it rather than rejecting it. 

Exit Slip (2025.10.9)

This week's session was quite refreshing. The Fibonacci poem warm-up felt like follwing a random rule to write a poem, which is not usually how I write. Still, the constraint did something useful: it pushed me to edit, pick sharper words, and notice a certain rhythm. It made me think that in class, the right constraint can be a ladder rather than a cage. Apple-cider time was a wholesome chaos in a good way—people swapping stations without fuss, knives and hand-cranks going, press at the end like a boss fight. It was one of those rare moments where group work actually felt like a real production line and not a forced activity. 

Entrance Slip (2025.10.9)

The readings prompted me to look at three threads together: space, language, and assessment. On space, I get the appeal of learning gardens even if I’m not exactly an outdoors person—there is something about fresh air that quiets the noise. If I ever use a garden, I’d keep it lean and purposeful: a portable blackboard, tight notes, a short task that only makes sense because we’re outside, then back to regular tools. Calling ecosystems “co-teachers” is an interesting nudge; at minimum it reminds me to treat the place with respect instead of as decor.

On language, Kimmerer’s “grammar of animacy” stuck. I rely on precise math/physics terms because they prevent us from talking past each other, but I can see how a nouns-only register flattens things. A small shift in sequence might help: let students notice a phenomenon first—growth, pull, drift—then name it, and keep a parallel, verb-ish gloss so the idea feels alive instead of museum-labeled. It’s also a quiet way to signal that more than one knowledge system can sit at the table.

On assessment, the case for refocusing from grades to learning lands for me, with caveats. Grades still translate for parents and universities, but in class we can lower the temperature: more low-stakes completion credit where it makes sense, a retake or alternate demo of understanding, co-making a rubric so students see the target and help define “excellent.” I like the “firm goals, flexible means” framing. It doesn’t throw out standards; it just gives students room to show what they can do without everything riding on one number.

Exit Slip (2025.10.2)

This week’s conics warm-up worked better than I expected. Starting with “where do circles/parabolas actually show up?” and letting us sketch and name our way in made the unit feel like a casual list of shapes. The hands-on locus demo was a nice one as it clicked that why “a set of points” is a useful way to think. Also, seeing the relationships among the conics helped: ellipses with eccentricity less than 1, the parabola as the boundary case at 1—that “family resemblance” made the equations feel motivated. The quick detour to our group's Gabriel’s Horn scratched the pure-math itch too: finite volume, infinite surface area is the kind of paradox that reminds people definitions matter.

Ed Doolittle’s Star Blanket patterns are basically sequences and symmetries people can hold, and scrabble was a smart reminder that games are rule systems we can tune to the language, not the other way around. Between that and the represent-in-many-ways exercise (draw, name), the through-line for me was pretty simple: pick representations that actually carry the structure—whether that’s a string tracing a locus, a blanket encoding a sequence, or a board game redesigned for morphology—and then give the formal math its spine once people can feel what’s going on, for the lower levels of math of course.

Entrance Slip (2025.10.2）

Kimmerer’s chapter nudged me to look at the language we teach with, not just the ideas. In math and physics I rely on technical terms because precision matters; they keep us from talking past each other. I can also see how a nouns-only register can flatten experience—everything becomes a labeled thing on a list. The “grammar of animacy” reframes that: verbs and relational words point our attention to process, change, and responsibility. It also raised the harder question of language loss; when a community’s words for the living world go quiet, the ways of noticing they encode fade too. That feels relevant in school, where the words we privilege quietly train students on what counts.

Practically, I’m not throwing away terminology, but I can change the sequence and the tone. Start with an encounter, then name: watch condensate bead and slide before we say “surface tension,” feel the “tug” in a stretched line before “force,” track a plant’s height over days before “rate.” When we introduce the formal terms, keep a parallel, verb-rich gloss on the board so both registers stay visible. I may also try small translation moves, like rewriting a definition in process language, or telling a proof as a story of what one quantity does to another—so students hear that concepts are relationships, not just objects. I do want to emphasize that none of this replaces the precision we need for problem solving, but it might keep the ideas from feeling mechanical and make room for easier understandings.

Entrance + Exit Slips (2025.9.25)

For the reading, I mostly heard a call to “backsourcing”—making a few of our own everyday things—not as nostalgia, but as a way to rebuild attention, pride, and small-scale community. That landed for me in a measured way. I think the idea that knowing how something is made changes how you value it is interesting, and I felt that with the grass rope makings and other small craftings. At the same time, I’m mindful that global supply chains also keep people connected to culture. I’m leaning toward a hybrid: keep the reach of global goods, but deliberately fold in some local making so students get the feel of process, not just the product.

This week’s session made the ideas concrete. The color-matching task flipped “I see green” into “which green, exactly?”—active noticing. Twisting corn husks into rope was better than I expected: thickness drifted, tension exposed weak spots, and the material itself set the pace. I found myself iterating without being told—sense the strand, pick the next husk, adjust, which is basically a hands-on feedback loop. I can imagine using short, focused slices of this as a warm-up or side quest that makes formal ideas a bit more fun and novel. 

Entrance + Exit slips (2025.9.18)

 Going into the reading, the idea of the garden as a “co-teacher” made a certain kind of sense, at least as a way to notice how abstractions often start from contact with the world. Framed that way, the design question shifts a bit—from simply moving the same lesson outside to asking what could plausibly happen because we’re in that space. I can imagine some light-lift connections that don’t fight the content too much: timing shadow lengths before talking rates of change, noticing standing waves on a branch before formalizing boundary conditions, or using a magnifier and sunlight to nudge a quick chat about focal length. If a space like this is going to last, looping it into assessment, research, and scheduling would probably help it feel less like an add-on.

The class’s hours in the garden gave that theory a bit of texture. The sound-drawing task, which I thought would be too abstract, ended up being a simple perceive-and-represent loop that felt close to modeling. Apple picking and just paying attention to small details worked as a reset after a busy week. I can see why shorter, routine “micro-labs” might lower the barrier for some students and open a few more sensory entry points into ideas we usually meet through notes and formulas. That said, I still suspect most of the heavy lifting with math and physics happens when we’re back at a desk with time, tools, and a board.

If I try this again, I’d keep it practical and site-dependent so it doesn’t turn into a garden-shaped classroom. A quick sit-spot map using a chosen sense, a few days of vine-length measurements before a slope discussion, a brief wave demo on a line or branch—small things that are easy to pivot if the weather turns. Paperwork and buy-in are real, but standing permission, a short safety routine, and a rain plan would smooth the edges. Overall, I’m open to using the space when it actually carries some of the load, and then letting the more formal work do what it does best once we’re back inside.

Entrance Slip (2025.9.11)

From the reading, one idea that stayed with me is the emphasis on being a “reflective teacher.” The three qualities of open-mindedness, responsibility, and wholeheartedness feel like strong guiding principles. I had already thought about open-mindedness and responsibility, but wholeheartedness stood out as a reminder that our commitment is not only to subject matter, but to every student we encounter. Teaching is not just about transmitting facts; it is also about investing ourselves in our students’ growth. Another point that made me pause was the reminder to distinguish between beliefs that rest on tested evidence and those that do not. As a teacher, our words carry weight, and I realize that even casual statements can influence students in ways we don’t always see. That means I need to be intentional, not only about what I know, but also about admitting what I do not know. In this way, reflection helps me clarify my own assumptions before passing them on. In conclusion, I found the idea of reflection as an ongoing process helpful. It does not always require a formal moment of meditation; it can happen as we walk home, recap a class, or even engage in dialogue with students. Teaching seems to be less about fixed routines and more about evolving alongside our students. Reflection, then, is a habit that shapes us continually, reminding us that the only constant in education is change.

Exit Slip 2 (2025.9.11)

Today’s class in the garden reminded me that learning does not always have to be confined to classroom walls. Although the activities seemed simple (drawing, listening, talking), the experience reinforced the idea that connecting with nature can create new ways for students to engage with knowledge. When learners step outside, they encounter patterns, systems, and relationships that textbooks often struggle to capture. For example, a math lesson could draw on the spacing of plants to discuss geometry, or a physics class could examine forces through the way branches bend in the wind. Even though these outdoor lessons do not replace traditional teaching, they can complement it by sparking curiosity and giving abstract concepts a more tangible context. I realized that the value of such sessions is less about memorizing facts and more about shifting perspectives—helping students see the interconnectedness of knowledge and the world around them. As future educators, we may not always have access to perfect outdoor environments, but we can still consider how to incorporate elements of real-life observation into our teaching if we think it may serve better than a regular class. Ultimately, learning in nature, when used appropriately, may broaden students’ sense of curiosity and make their academic journey more dynamic.

Exit slip 1 (2025.9.4)

The video’s core message is that good teaching starts by learning from students and intentionally seeing through their eyes. For foreign teachers especially, that means treating cultural difference not as a hurdle but as essential context—taking time to understand local norms, communication styles, and what “respect” or “participation” looks like in that setting. The first step is helping students open up: build trust, invite their stories, ask open-ended questions, and listen without rushing to correct. As students feel safe to share who they are and where they come from, teachers gain the perspective needed to adapt examples, expectations, and feedback so learning is truly relevant. Basically, when students’ voices are heard, instruction becomes culturally responsive, relationships deepen, and real understanding follows.

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