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I did not start thinking seriously about AI from one role. I started seeing it differently as I moved between being a computer science student, teaching STEM, working as an LA/DL, doing educational research, and working at a robotics startup. Each experience showed me a different part of the same problem: how can we use technology to make people better at learning and solving problems without taking the problem-solving away from them?
As an Instructional Student Assistant at San Francisco State University, I work with students on programming logic and software engineering while developing Python automation for academic workflows. As an E-GAISE research assistant, I have analyzed submissions from 34 student teams and contributed to research involving AI-integrated grading systems. Before that, I worked as an Engineering Operator at a robotics startup, where I built Python tools for data ingestion, labeling, and validation.
These experiences led me to my main thesis: AI is most valuable in STEM education when it provides context-aware assistance, creates opportunities for practice and feedback, and keeps humans responsible for reasoning and verification. A student can create a diagram before asking AI for design feedback. An instructor can provide course objectives and common mistakes before using AI to create a Kahoot, Blooket, or debugging game. A developer can provide an existing codebase before asking AI to make a change.
AI Needs Context
One of the most useful ways I see AI being used in a classroom is by giving it context rather than starting with an empty prompt.
For example, imagine that a student is working on a software design assignment. Instead of immediately asking an AI, "How should I build this?", the student could first create a class diagram, flowchart, wireframe, pseudocode outline, or system design.
The student could then give that design to the AI and ask:
- Does this design satisfy the requirements?
- What relationships am I missing?
- Is there unnecessary complexity?
- What edge cases should I consider?
- What could I improve?
- What tradeoffs am I making?
This creates a different learning experience.
The AI now has context about what the student is thinking, and the student has already performed part of the intellectual work. The AI becomes something that can critique and question the student's thinking instead of generating the entire solution.
I think this is especially important in computer science because design is part of the learning process. If a student creates a diagram, writes pseudocode, or explains an algorithm before using AI, there is something concrete to evaluate.
The same principle applies to professional software development. A developer can provide an existing codebase, architecture diagram, API specification, test case, or error log before asking AI for help. The more useful question is not simply, "Can AI write this?" but "What context does AI need to understand what I am trying to accomplish?"
AI and Gamified Learning
Another classroom application I have become interested in is using AI to create short, bite-sized learning activities.
During my LA/DL sessions, I use activities similar to Kahoot and Blooket to review programming concepts and assignments. I have found that these activities can make a 50-minute session more interactive because students are not simply listening to a lecture.
AI could help instructors create variations of these activities.
For example, I could provide AI with the assignment objectives, course concepts, common student mistakes, and examples of the code being taught. AI could then help generate questions for a Kahoot, Blooket, or another classroom game.
The instructor would still need to review the material before using it, but AI could make it much faster to create several versions of an activity.
The activity could also go beyond a traditional multiple-choice quiz. A programming class could have a Code Detective game where students receive clues about a programming bug and identify the problematic line. Another activity could ask students to predict what a program will output before running it. Students could work in teams to determine which algorithm would be most appropriate for a particular problem.
The goal is not to use AI simply because it is available. The goal is to use it to create more opportunities for students to practice.
This connects to my previous experience teaching STEM, where I used project-based activities involving coding, game design, and robotics. I learned that students can understand technical concepts differently depending on how the material is presented. A short game, demonstration, or hands-on activity can sometimes create a better entry point than another explanation.
AI gives educators another way to experiment with those activities.
AI as a Learning Assistant
AI can also be useful when I am the student.
When I encounter a difficult programming problem, I do not necessarily want an AI system to give me the finished solution. Doing that can remove one of the most important parts of computer science education: learning how to reason through a problem.
Instead, I can ask questions such as:
- What concept am I misunderstanding?
- What edge case am I missing?
- Can you give me a hint without giving me the solution?
- Why does my approach fail?
- What should I test next?
- Can you explain this error differently?
I can also give AI my own work first.
For example, I might write pseudocode, create a class diagram, implement a first version, or explain my reasoning before asking for feedback. This gives the AI something concrete to evaluate.
It also forces me to confront my own understanding before asking for help.
That is an important distinction between using AI to learn and using AI to avoid learning.
What My LA/DL Experience Taught Me
My experience as an LA/DL has made this distinction even clearer.
A student does not always need more information. Sometimes they need the information presented differently. Sometimes they need someone to ask a question that helps them identify their own mistake.
For example, if a student is confused about tracing code, I might change the activity and have them diagram the execution of the program. If several students make the same mistake, I can turn that mistake into a group debugging activity.
I have also learned that engagement matters. A 50-minute session is relatively short, so I try to structure the time around an icebreaker, a game or review activity, assignment content, demonstrations, and a workshop where students can ask questions and begin working.
AI could help me create more variations of these activities, but it does not know my students in the same way that I do.
I still have to decide:
- What should students learn?
- What is appropriate for their current level?
- What misconceptions are they experiencing?
- How much content can realistically fit into 50 minutes?
- Which activity will encourage participation?
- Is the material appropriate for this particular class?
This is where the human role remains important. AI can assist with preparation, but the instructor still has to understand the students and make the educational decisions.
AI in Educational Research
My research experience has given me another perspective on AI in education.
At San Francisco State University, I analyzed submissions from 34 student teams across two semesters using Python and Pandas. The work involved extracting chat logs and reflective writing and analyzing performance across multiple rubric dimensions. I also contributed to research involving AI-integrated grading systems and educational technology.
I have also worked on an E-GAISE agentic grading pipeline that uses Python, LangGraph, Pandas, and Pydantic to evaluate submissions against a rubric. The system uses evidence and validation mechanisms rather than simply accepting an AI-generated evaluation as correct.
This experience has made me more interested in how AI is integrated into a workflow, rather than simply whether AI is being used.
For example, an AI-assisted grading system needs a clear rubric, evidence from the student's work, and a way to evaluate whether its conclusions are consistent. The important question is not whether AI can produce a grade, but whether the overall process produces useful and defensible feedback.
That perspective connects closely to my experience in software engineering.
Human-in-the-Loop: What I Learned From Robotics
My experience at a robotics startup helped me understand why this distinction matters.
My work involved data ingestion, labeling, validation, and internal tooling. The technical problem was not simply getting software to run. The larger problem was making sure the data moving through the workflow could actually be trusted.
I learned that automation is valuable because it can reduce repetitive work and make systems faster. But automation can also make mistakes faster.
That lesson carries directly into AI.
An AI system can generate ten explanations in seconds, but if those explanations are incorrect, it has simply produced ten incorrect explanations faster. The solution is not necessarily to avoid automation. Instead, the workflow needs enough context, testing, and human judgment to determine whether the result is useful.
This gives me a model for how I want to use AI:
Context → AI assistance → Human review → Feedback → Iteration
The same workflow can apply to a classroom activity, an AI-generated explanation, an automated grading system, or a software development task.
The human does not have to perform every step manually. Instead, AI can handle parts of the process while the human remains responsible for understanding the goal and deciding whether the result is acceptable.
The Risk of Overusing AI
The biggest concern I have with AI in education is that convenience can become a substitute for learning.
If a student asks an AI to solve every programming assignment, they may complete the assignment without developing the ability to solve a similar problem independently.
There is a major difference between:
"Give me the answer."
and:
"Here is my design. Can you help me find weaknesses in it?"
The second approach preserves the student's role in the learning process.
The student has made decisions, created an artifact, and thought about the problem. AI becomes a source of feedback instead of a replacement for reasoning.
This is why I think educators should focus less on simply asking whether students are using AI and more on teaching students how to use it responsibly.
Students should know how to question AI output, test generated code, verify information, recognize incorrect assumptions, and explain their own work.
What AI Has Taught Me About Learning
My experiences have changed my definition of effective AI use.
I used to think about AI primarily as something that could make tasks faster. Now I think about it more as something that can create additional opportunities for experimentation.
As a student, I can use AI to challenge my reasoning.
As an educator, I can use it to brainstorm activities, create practice problems, and develop different explanations.
As a researcher, I can investigate how AI changes educational workflows and how those systems can be evaluated.
As a software developer, I can use AI to investigate problems while still being responsible for the resulting system.
In all of these situations, the most valuable workflow is not:
Human → AI → Answer
It is closer to:
Human → Context/Design → AI → Feedback → Human Decision → Iteration
That distinction is important.
Conclusion
AI will continue to change STEM education and software engineering, but I do not think the future should be about replacing the human parts of learning.
My experiences in education and software engineering have taught me that context, iteration, feedback, and human judgment matter.
In the classroom, that could mean creating a design before asking AI for feedback. It could mean using AI to turn common programming mistakes into a short debugging game. It could mean generating a Kahoot or Blooket from course material and then adapting it to the needs of the class.
For a student, it could mean creating pseudocode before asking AI for implementation feedback. For a developer, it could mean giving AI enough information about an existing system before asking it to make a change. For an educational researcher, it could mean designing an AI-assisted grading workflow around evidence and clear evaluation criteria.
These approaches have something in common: AI is helping with the process, but humans remain responsible for the thinking.
That is the lesson I am carrying forward from my work as a student, educator, researcher, and engineer.
AI should not make us think less.
It should give us better ways to think, practice, test, create, and learn.
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