You built the app, ran the experiment, learned a programming language, and explored a field in every way you could think of. You know you love it. Then, a college application asks why you want to study your intended major, and somehow you have nothing to say.
You probably have plenty of material. The difficulty is deciding which part belongs in the essay. A technical project can fill an entire response with what it does, leaving very little space for what you thought about while doing it.
The five complete essays below take different approaches to that problem, with examples from computer science, chemical engineering, computational linguistics, and environmental science. Some begin with research; one begins with a deeply disappointing crayon. Each gives us something specific to understand about the student’s attraction to a field.
The prompts and college references come from the original samples and may have changed. Read these as writing examples, and check your own application’s instructions before drafting. For help building your response, start with How to Write a Why Major Essay That Shows How You Think.
Example 1: Computer science and economics at Yale
Prompt from the sample: Why do these areas appeal to you?
Full essay
MiniDiscs, Segways, Google Glass. Just a few innovations languishing in our ever-growing technology graveyard. But why?
I’m drawn to the intersection of computer science and economics to better understand why some technologies are readily adopted while others are ignored. This interest comes from my own cybersecurity business ventures. While researching brain biometrics (using one’s brainwaves as a password), I found that previous implementations, despite nearly perfect accuracy, weren’t widely adopted—they used wet electrodes, which proved impractical from a consumer perspective.
Through CS and economics, I aim to identify the obstacles that might impact other machine-learning-based innovations. I’m particularly interested in behavioral economics, which could offer insight into the limits of human rationality and strategies for adopting cutting-edge technology in the future.
Why this worked
The opening hands us three recognizable products and asks a question that makes them newly interesting. You do not need to know anything about cybersecurity to wonder why a technically ambitious invention ends up gathering dust. That accessibility matters: the reader can enter the student's curiosity before encountering the specialized research.
Then comes the detail that earns the combination of majors: wet electrodes. The student has noticed a gap between an impressive technical result and something a person would willingly use. That observation gives economics a necessary role in the essay. We understand why someone working in computing would want to study adoption and consumer behavior because the project has exposed a question technical accuracy cannot settle on its own.
The student also resists spending this short response promoting the business. We get just enough context to locate the observation, then move into what it made the writer want to understand. That choice keeps the intellectual interest at the center. A paragraph about launching a venture could establish initiative; this paragraph establishes a particular reason to keep learning.
Finally, behavioral economics narrows the next step. The ending tells us where the student wants to look for an explanation. Across a remarkably small amount of space, the essay moves from a public puzzle to a personal investigation to an academic direction. The reader can follow the connection without being handed a miniature résumé.
Try this in your draft: If you want to combine fields, identify the problem that made the second field useful. Explain what you could understand better by bringing them together.
Example 2: Computer science at Georgia Tech
Prompt from the sample: Why do you want to study your chosen major, and why do you want to study that major at Georgia Tech?
Full essay
In the spring of 2020, hospital overcrowding and differing stances on quarantine made me curious about the extent to which the pandemic was impacting my community; I designed a program that charted the spread of COVID-19 in Northern Virginia using Python. There were far more local cases than I initially realized, and showing the results to my parents increased their vigilance in adhering to CDC guidelines.
Using computer science to make a tangible—albeit small—impact on the real world felt exciting. To chase that feeling, I learned TensorFlow and image processing to develop my Lyme Disease Rash-Identifier and Scoliosis-Identifier. When they reported 89% and 75% accuracy, I looked into the viability of using them to mitigate hospital overcrowding. There was much I’d failed to consider: I’d accounted for racial diversity in the samples of rash images I’d trained the model on, but what if the results were still biased?
Engineers have a responsibility to develop tools that serve the real world. By studying Computer Science at Georgia Tech, I can narrow my focus enough to fully understand the problems I plan to solve. I’m especially interested in the Intelligence and People threads, where I could refine my machine learning skills and push forward cutting-edge technology geared towards working with and helping millions of people. Outside of class, I’ll join Big Data Big Impact to gather data on healthcare resources, patient demographics, and medical outcomes to identify gaps in care and propose data-driven solutions, such as optimized clinic locations or mobile health services.
Machine-learning developments have boundless potential to simplify complex processes. If implemented correctly in places that require maximum efficiency, like hospitals, they could save lives. At Georgia Tech, I want to study Computer Science because it sits at the forefront of technological innovation and building a more compassionate, equitable society.
Why this worked
The first project's impact is small enough to believe and concrete enough to matter. The student's parents become more vigilant after seeing local COVID data. We know who used the information and what they did with it, which gives the writer's excitement a visible cause. “I made an impact” would ask us to supply all of that meaning ourselves.
The admission of uncertainty is the essay's strongest move. After reporting the models' accuracy figures, the student asks whether the results could still be biased. It is tempting to end a project description at the impressive number and wait for applause. This writer continues into a harder question: how should a developer judge whether a tool is ready to help people?
That turn reveals an increasingly demanding standard for the student's own work. It also creates a convincing reason for college. Further study becomes a way to investigate limitations the student has actually encountered, giving the academic plan more substance than a general wish to become a better programmer.
The Georgia Tech details follow that development. The student connects technical learning to human consequences, then explains a proposed use for healthcare data. Because we have already seen the questions behind those choices, the school paragraph feels like a continuation of the essay. The student can imagine work to do there.
The projects are ambitious, but the self-questioning makes the writer interesting. We meet someone who finds computing exciting enough to keep using it and consequential enough to keep examining their decisions.
Try this in your draft: Read the sentence after your project's result. Use it to explain what the result made you reconsider, investigate, or want to learn.
Example 3: Chemical engineering through experiments
Prompt from the sample: Most students choose their intended major or area of study based on a passion or inspiration that's developed over time – what passion or inspiration led you to choose this area of study? (300 word maximum)
Full essay
As a fourth grader, I lived for PBS, hoping to catch a glimpse of animals on NOVA. So I launched an expedition into a small gorge just beyond my backyard in the Austin suburbs. Though it was the middle of summer in Texas, I tucked my pants into my socks to protect against deadly scorpion stings. In the gorge, I investigated actual animal tracks and droppings and engineered unsuccessful squirrel traps out of sticks, rope, and crushed-up fruit.
That same year in class, I made an important discovery about the liquid phase of matter. Our science class created new crayons by shaving and melting old ones. I made two: one from blue crayons (the “control group”) and one made of purple, blue, green, and pink shavings. I was devastated when my crayons came back. The blue one turned out as expected. But, the jazzy, party-colored crayon I’d imagined had become a wan brown chunk. I’d thought that as the crayons melted, the different colors would stay in the same relative positions in space, but instead, they melded together, introducing me to the idea that fluids with the same composition form homogeneous mixtures.
I’ve always felt the need to see how what I learned worked in real life. In middle school, I moved on to researching and creating my own pocket-sized paper-clip artillery after being inspired by Robert Goddard, the inventor of the liquid-fueled rocket. In high school, I delved into bioprinting, working with Dr. Juyi Li to study angiogenesis—tissue engineering has always been an interest of mine, and I even wrote a hypothetical TED Talk on tissue and stem cell engineering for my Spanish class. It’s this passion for practical applications that drives me to study chemical engineering: being able to see theory come to life, successful or not.
Why this worked
The squirrel traps are unsuccessful. The multicolored crayon is a disaster. The student's curiosity survives both. That makes the childhood scenes useful: they establish a recognizable way of approaching the world, complete with the enthusiasm and questionable engineering judgment of an actual child.
The crayon paragraph gives us something especially valuable: the student's prediction. We know what they expected to happen before we learn what happened instead, so we can follow the surprise. A project description that includes only the final result loses that opportunity. Here, the mismatch lets us see the writer learning through an experiment.
“Jazzy, party-colored crayon” and “wan brown chunk” also do real work. The disappointment becomes funny because the desired object and the actual object are so vividly different. The humor reveals how invested the student was. There is a person inside the science essay, with aesthetic ambitions that melted rather badly.
Later projects make the early scenes relevant to the present. Paper-clip artillery and bioprinting continue the desire to put ideas into physical form, while the Spanish-class TED Talk suggests an interest persistent enough to wander into another classroom. The experiences vary in sophistication, but the same appetite for application connects them.
The ending names that pattern: seeing theory come to life, including when it fails. By then, the essay has given us enough evidence to recognize the pattern for ourselves. That is what allows the childhood material to earn its space.
Try this in your draft: When describing an experiment, include what you expected. When using an early memory, show how the habit it reveals appears in your more recent work.
Example 4: Computer science and linguistics at William and Mary
Prompt from the sample: Share more about a personal academic interest or career goal. (300 words)
Full essay
Junior year IB Chinese was a huge step from Chinese 4. I tried using ChatGPT and Google Translate to compare my writing with their corrections, but oftentimes, these applications would completely restructure my writing, using grammar and vocabulary that I’d never learned before.
So, I developed Huayu, an AI-powered writing assistant. Huayu takes a learner’s previous work and textbooks as context to apply current knowledge to current writing assignments, ensuring that users won’t be overwhelmed with unfamiliar vocabulary while receiving writing feedback. Additionally, the assistant offers alternative sentence structures that broaden learners’ vocabulary and grammar usage.
Since intonation is important in Chinese, I plan to develop this further by including an interactive AI chat feature, which would occasionally sprinkle in new vocabulary, encouraging learners to decipher meaning through context—the same way we pick up language through conversation. This summer, I worked with Dr. Rebecca Hwa, a computational linguistics expert, toward this goal. Although my project proved too ambitious to complete in one summer, under her mentorship, I learned how to fine-tune AI models and gather meaningful training data.
At William & Mary, I plan to dual-major in Computer Science and Linguistics. I’ve already begun studying the scientific structure of language—syntax, semantics, pragmatics—by writing responses to linguistic philosophy texts from de Beauvoir, Chomsky, Jung, Kripke, and more on my blog, Gigi Jean’s. These readings changed the way I think about what it means for a machine to “understand” language. Engaging with language’s deeper structures through philosophy and linguistics has made me a stronger software developer. I want to study how language reflects the human mind and the societies that shape it in order to better understand how language can be modeled computationally. In doing so, I hope to one day build technologies that advance communication and respect the complexities of human thought.
Why this worked
The opening identifies a precise frustration: the feedback changes the student's Chinese into language they have not learned. That gives us a clear way to understand the project that follows. We can judge why a tool tailored to a learner's existing knowledge would be appealing because the writer has already shown us the experience it is meant to improve.
That sequence makes the app more informative than its feature list alone would be. The design choices express an idea about learning: useful feedback has to meet the learner somewhere they can begin. The student is thinking about the person receiving the output, which gives the computing work a distinctive purpose.
Admitting that the next stage was too ambitious for one summer strengthens the account. We can distinguish the tool the student developed, the extension they hope to build, and the skills they acquired while working toward it. That precision makes the remaining ambitions easier to trust. An unfinished project can reveal a serious learner when the writer explains what the attempt taught them.
The final paragraph also changes the scale of the inquiry. Building a writing assistant leads the student toward the question of what it means for a machine to “understand” language. Philosophy and linguistics have a role in that question because the project has already exposed how complicated useful language support can be.
The most effective connection here is between a practical frustration and an intellectual problem. The major choice grows out of both: the student wants to build something better and understand more deeply what they are trying to model.
Try this in your draft: Explain why you made one design choice. What did it reveal about the users, system, or subject you needed to understand?
Example 5: Quantitative sciences and environmental science at Emory
Prompt from the sample: What academic areas are you interested in exploring at Emory University and why? (200 words)
Full essay
I first noticed fonio in a Senegalese village kitchen during a French-immersion trip. The grain’s traditional processing is water-intensive, but inventor Sanoussi Diakité’s automatic-threshing machine drastically reduced that demand and gained acceptance in local markets, demonstrating how cultural knowledge and innovation can maximize sustainability. Years later, at Governor's School, I performed experiments demonstrating how auxin changes seedling root growth, affecting how efficiently crops absorb nutrients. Both experiences showed me how targeted interventions can yield outsized ecological impacts.
I want to amplify these efforts by combining statistical analysis with sustainable agriculture through Emory’s Quantitative Sciences + Environmental Science track. Studying climate change, whether analyzing DNA supercoiling or modeling wildfires in the San Ynez Mountains, requires advanced statistical techniques. Courses like Modern and Ancient Tropical Environments align with my agricultural interests in regions like southeastern Senegal. I’m especially interested in data projects in Emory’s WaterHub, a model for sustainable resource use. Having used predictive modeling and corpus creation at UCSB’s SRA, I hope to sharpen those computational skills in courses like Environmental Data Science.
My French immersion experience taught me to treat culture and science as inseparable. At Emory, I’ll continue using data and research to both understand the world and sustain it.
Why this worked
A grain in a Senegalese kitchen gives this interest a specific point of entry. The student quickly explains what caught their attention about its processing, so the opening does more than supply a memorable location. It introduces the relationship they want to investigate: how a targeted change can affect the use of resources.
The move to seedling research is effective because the writer identifies what connects the experiences. Travel and lab work could easily become two unrelated credentials. Here, the student groups them around an observation about interventions and ecological effects, showing us how they organize what they have learned.
That connection gives quantitative study a purpose. The writer wants tools for investigating environmental problems they have already encountered. Statistical analysis enters the essay as something they hope to use, and the later reference to predictive modeling supplies a basis for that ambition.
The college paragraph is dense, but its clearest connections point back to the opening. Agricultural interests inform the course choice; resource use informs the interest in WaterHub; previous computational work informs the desire for further data training. Those links give the reader a way to understand why these opportunities attracted this student.
The final return to French immersion is particularly useful. It lets an experience outside the apparent subject of the essay remain part of the student's scientific identity. We can see how a language-learning experience influenced the problems they noticed and the approach they hope to bring to environmental study. Their academic path feels individual because its influences are allowed to be individual.
Try this in your draft: If two experiences seem unrelated, ask whether they taught you to notice the same kind of problem. Explain that relationship before adding another activity.
What to borrow from these STEM essays
Pay attention to the point where each writer moves beyond reporting an experience. One questions whether technical accuracy will persuade consumers. Another questions whether a working model is enough. A failed crayon experiment reveals a habit of testing expectations; a language tool raises questions about understanding.
Find that point in your own draft. What did the work make you think about that you would not have understood as clearly beforehand? Give that thought enough space for the reader to follow it.
You can keep the technical detail that makes your reasoning understandable. You can keep the funny detail, too. A STEM essay is allowed to sound as though someone enjoyed writing it.
For more approaches, read 5 Humanities and Social Science Why Major Essay Examples and Why They Worked. The film student has something useful to teach the programmer about explaining a choice.
At Principia, we help students turn their projects and questions into essays that reveal the person doing the work. Our college consulting team can help you identify which details deserve space and develop the reflection that makes them matter.
