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Can someone who struggles with math still apply to computer science or AI? Start by identifying the specific math gaps, reviewing course requirements and real work activities, and using RIASEC interests to sort majors into three working lists: keep, verify, or set aside.
By: Fermat Institute
Published: Aug 3, 2026
Updated: Aug 3, 2026
15 min read
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Place the option in "Pending Verification" first rather than moving it directly to "Retain." Weakness in mathematics does not automatically rule these majors out, but current popularity is not a reason to force computer science or AI onto the shortlist. What needs to be done tonight is to break a vague question down into four pieces of evidence: what kind of math difficulty you're facing, exactly what your intended major studies, whether you can endure daily tasks, and whether you still want to continue after a 7-day verification period.
| Don’t ask first tonight | Write the evidence first |
|---|
If you want to turn reading into self-measurement, continue into an assessment.
| Does weak mathematics rule out applying? | Is it because of gaps in foundational knowledge, weak abstract reasoning, or test anxiety | Type of mathematics-related risk |
| Are computing and AI popular? | Core courses in target school training programs | Curriculum reality |
| Does liking computers mean the field suits me? | Are you willing to write code, fix bugs, read documents, and work on projects | Work activity preferences |
| Could the gap be addressed over time? | Are you willing to continue trying during the 7-day verification | Evidence that the gap can be addressed |
| Should the option be excluded now? | Reasons for keeping an option / pending verification / temporary exclusion | Application-list status |
Many families hesitate not over the specific major, but over two fears that appear before the same application choices form: students fear being overwhelmed by math and coding throughout their four years of college, while parents fear missing out on popular fields like computer science or AI. Don't try to convince each other—first, write down the five items listed below.
This article does not predict admissions outcomes, guarantee employment, salary, or career success, nor does it replace the provincial examination authority, university admission regulations, subject requirements, curriculum guidelines, or the official college application system. FermatMind is not an official college application platform; RIASEC and MBTI can only serve as exploratory tools and should not be used to determine your major.
After the application choices table is opened, computers and AI rarely emerge as major options—they often become a point of contention within the family.
| Dinner table arguments | Real questions | What to check next |
|---|---|---|
| If your mathematics is weak, you definitely cannot study computing | Which aspect of mathematics is weak? Does the core curriculum focus on triggering weaknesses? | Curriculum plan, proportion of mathematics courses, algorithm/data course requirements |
| AI is the future, so you have to apply | Does the target AI major focus on models, data, algorithms, or application development? | Course schedule, practical projects, graduation requirements |
| I like using computers, so a computing major must suit me | Do you like using computers, or are you willing to understand the system and write code? | Small code tasks, technical documents, project assignments |
| Weak mathematics can always be made up later | Are you willing to make up for it? Is it supplementing the foundation, or is it a long-term strong rejection? | Seven-day learning and verification record |
| Apply first and decide later | If the core courses are not suitable for you, is it easy to change majors? | Admissions regulations, major transfer policy, major group risks |
Students may focus on college life: Will I fail calculus? What if I cannot get my code to run? Will I understand algorithms? Is AI just a sea of formulas? Meanwhile, parents focus on opportunity cost: computer science remains popular, AI sounds promising for the future, not applying might mean missing out, and the student's current fear may simply stem from lack of experience.
Both sides are protecting the future, yet the debates often reduce to just two sentences:
Both statements are overly simplistic. The correct approach isn't first deciding whether or not to apply, but instead breaking down the issue into four layers: eligibility level (whether you qualify to apply), course level (can you actually learn it), task level (are you willing to engage with it), and real-world level (can it be validated in practice).
This article is solely designed to help you complete this judgment matrix. It will not tell you which major is "best suited" for you, nor will it present any test as a major-matching tool.
Before discussing math and interests, go through the eligibility requirements first. Many families skip this step and immediately argue over "whether to pursue computer science," only to discover that the target university's program group, subject requirements, health restrictions, single-subject requirements, or admission policies themselves need to be verified upfront.
| Eligibility requirements | What do you want to check | What will happen if you don’t find out |
|---|---|---|
| Subject-combination requirements | Requirements for physics/chemistry and other subjects in the target province, target university, and target major group | A preferred option may still be ineligible |
| Enrollment plan | Whether the target major will enroll students in the province/subject category, the planned number, and which majors are included in the major group | Mistakenly thinking that you can apply for a certain major, the actual major group risk is more complicated |
| Single subject requirements | Are there any special instructions for mathematics, foreign languages or other subjects | Ignore the hard limit and subsequent discussions will be invalid |
| Physical examination/color vision/vision restrictions | Are there any physical examination or color vision restrictions for specific majors | May be restricted in admission or affected in subsequent studies |
| Tuition fees/city/accommodation | Family budget, city costs, accommodation conditions | Majors can be studied, but the actual cost is unacceptable |
| Major change policy | Is it allowed to change, what is the threshold, and is the success rate public | "Enroll first and transfer later" may be only an assumption |
Only when there are no obvious conflicts in the eligibility requirements does the next level of inquiry begin: Is this direction worth further validation?
If your decision also involves the risk of reassignment within a university program group, first use the Program-Group Unacceptable-Major Checklist. Computer science and artificial intelligence are not one uniform major. Compare the target program’s actual curriculum.
| Field | Common learning focus | Possible sources of mathematical or abstract demands | More questions to ask |
|---|---|---|---|
| Computer science | Programming, data structures, algorithms, system foundations, networks, databases | Discrete mathematics, algorithm complexity, system abstraction | Am I willing to understand the underlying rules instead of just using software? |
| Software Engineering | Requirements, development, testing, collaboration, engineering process, project delivery | Algorithm foundations, system design, project complexity | Can I accept long-term debugging, requirement changes, and collaborative delivery? |
| Artificial intelligence | Machine learning, data, models, probability statistics, linear algebra, optimization methods | Linear algebra, probability statistics, calculus, model derivation | Am I willing to face formulas, models and uncertain results? |
| Data Science / Big Data | Data processing, statistical analysis, databases, visualization, business problems | Probability and statistics, data modeling, mathematical interpretation | Am I willing to clean data, find patterns, and explain conclusions? |
| Network security | Network protocols, system security, attack and defense experiments, vulnerability analysis, rule awareness | Discrete mathematics, cryptography basics, system details | Do I like tracking anomalies, checking rules, and doing experiments? |
| Digital media/interaction/product-related direction | Front-end, design, user experience, product logic, content and technical tools | Code basics, interaction logic, some graphics/technical courses | Do I prefer a mixed task of "technology + expression + user problems"? |
This is an evaluation map, not an official classification or a recommendation list.
The same overall level of difficulty with math can reflect very different risks. A person who makes calculation errors but is willing to correct them should not be placed in the same evaluation category as someone who has long since developed aversion to abstract symbols.
| Type of mathematics difficulty | Common manifestations | University content that may affect | How to verify within 7 days | Conclusions that should not be drawn directly |
|---|---|---|---|---|
| Slow in calculation/more careless | Can do it but easy to make mistakes, steps are unstable | Programming details, debugging, algorithm implementation | Do a small code task to see if you can patiently check for errors | Cannot directly judge that it is not suitable for computers |
| Difficulty remembering formulas | Struggling to memorize and panic when changing questions | Linear algebra, probability, optimization courses | Watch a concept-focused introduction and check whether the visual and intuitive explanations make sense | You cannot judge college performance based on high school memory alone |
| Weak abstract reasoning | Difficult to understand proofs, recursions, and symbolic relationships | Discrete mathematics, algorithms, machine learning | Watch a visualization of recursion, graph theory, or an algorithm and explain it back in your own words | AI/algorithm direction requires key verification |
| Unfamiliar with probability and statistics | Insensitive to distribution, expectation, correlation, error | Data science, machine learning, experimental analysis | Read an introduction to probability and statistics and do 5 basic questions | Data/AI direction cannot be kept on the shortlist based on popularity alone |
| Difficulty understanding functions/limits/rates of change | Weak sense of changing trends, continuity, and gradients | Advanced mathematics, optimization, model training | Look at intuitive explanations of derivatives/gradients to see if you can keep up | AI/optimization related courses should be verified carefully |
| Foundational gaps, with willingness to address them | I haven’t learned solidly in some aspects, but I don’t exclude making up for them | First-year calculus, linear algebra, programming | Study the missing foundations for 30 minutes a day for 7 consecutive days and record stuck points | It can be verified, don’t deny it immediately |
| Test anxiety | Usually I can understand, but my performance on the test is unstable | Final exam, timed computer-based work, and defense pressure | Take a time-limited quiz to record emotions and mistakes | The problem may be stress management, not just ability |
| Strong aversion to mathematics | Not just inability, but constant boredom | Mathematics classes, model classes, algorithm classes, AI basic classes | Look at the course catalog and 1 introductory class to record the degree of resistance | Be very cautious in the direction of AI/data/theory |
The key isn't "how high your current math score is"—it's whether you're willing to engage in long-term mathematical refinement, abstract understanding, and error feedback. If you are completely unwilling to engage with these demands, do not keep AI or computer science on the shortlist solely because those fields are popular.
There's also a more practical assessment: if you have a gap in just one foundational area but are willing to spend 30 minutes daily on remediation and document the reasons for your mistakes, this constitutes "verifiable risk." But if you consistently resist formulas, code, error messages, and abstract concepts—and refuse to engage with them—this is termed "core task rejection." The former can continue being evaluated through course review; the latter cannot be overridden by merely choosing a popular major name.
What you're looking for isn't a general online "introduction to computer science" article—it's the specific curriculum of your target school. Focus on three key elements: course names, credit/hours and required course ratios, and prerequisites. Pay special attention to which courses are core requirements and which are merely elective.
| Course or module | What it tests | What it means to be "bad at math" | Pre-check moves |
|---|---|---|---|
| Calculus | Functions, limits, rates of change, abstract calculations | Foundational gaps will become visible quickly | Watch an introductory course on advanced mathematics and write down the first point you don’t understand |
| Linear algebra | Vectors, matrices, spaces, transformations | Common underlying tools in the AI/data direction | See intuitive explanations of matrices and vectors |
| Probability and statistics | Uncertainty, distribution, sampling, error | Central to data science and machine learning | Do 5 basic probability questions to determine whether you are willing to continue |
| Discrete mathematics | Logic, sets, graphs, recursion | Common foundation for algorithms, networks, security, and theory | Look at graph theory/recursion introduction and try to repeat it |
| Programming | Syntax, logic, debugging, and problem decomposition | Not just mathematics, but also a test of patience and error correction | Do a 1-hour small task |
| Data structures and algorithms | Abstract structure, efficiency, complexity | Weak abstract reasoning makes this especially important to verify | Look at stack, queue, recursive or search algorithms |
| Basics of machine learning | Data, model, error, optimization | One of the core pressures in the direction of AI | Watch an introductory lesson on "How to train models" |
| Operating system/network/database | Rules, system mechanisms, engineering details | Emphasizes understanding systems and tolerating detail | See if you like to understand how the system runs |
| Project practice | Collaboration, documentation, debugging, delivery | This often reveals what studying the major is actually like | Find a course project description or homework sample |
After reviewing the curriculum, don’t just say “there are many courses.” Instead, categorize them into three groups:
If resistance is concentrated in core required courses, the risk is significant.
A simple approach is to print out the target curriculum and use three colors to mark it: green for courses you're willing to take, yellow for those that can be accepted with additional preparation, and red for long-term avoidance. If red flags appear only in a few peripheral courses, further investigation may still be warranted; if they cluster around key foundational courses like calculus, linear algebra, probability, algorithms, and machine learning, these cannot simply be dismissed with the phrase “I’ll just work harder.”
Computer Science/AI is not just about math, nor is it merely about sitting in front of a computer. It involves a set of repetitive work activities over the long term. Whether you can accept these activities holds more significance than simply "whether I like computers" or not.
| Work Activities | Real Meaning | Possible Corresponding RIASEC Interest Clues | Self-Assessment Questions |
|---|---|---|---|
| Programming implementation | Turn ideas into runnable code | Realistic / Investigative / Conventional | Am I willing to check for errors line by line? |
| Debugging and troubleshooting | Read error reports, reproduce problems, and locate causes | Investigative / Conventional | If the problem is not solved immediately, will I keep investigating? |
| Algorithms and modeling | Abstract problems, comparison solutions, optimization efficiency | Investigative | Do I enjoy "why is this faster/more stable"? |
| Data analysis | Clean data, find patterns, interpret results | Investigative / Conventional / Enterprising | Can I accept dirty data and uncertain conclusions? |
| System maintenance | Understand rules, operate stably, handle exceptions | Realistic / Conventional | Do I like to make complex systems controllable? |
| Project collaboration | Requirements communication, version management, delivery iteration | Enterprising / Social / Conventional | Can I accept changing requirements, schedule pressure, and revisions to my code? |
| Product and user understanding | Putting technology into real usage scenarios | Social / Enterprising / Artistic | Do I care why users need this thing? |
| Reading English-language documentation | Look up information, read framework documentation, and read APIs | Investigative / Conventional | Am I willing to spend extended periods reading technical material that I do not initially understand? |
The RIASEC assessment does not determine whether "you are suited for a computer-related career." Instead, it transforms vague likes and dislikes into specific questions about work activities. If you'd like to first convert your interests into testable questions, you can take the RIASEC Career Interest Assessment. The results should be viewed only as exploratory clues, not as a decision-making engine for major selection.
The following three cases are neither major recommendations nor judgment templates. They merely demonstrate one point: even if someone has "poor math skills," their verification pathways may ultimately diverge completely.
Xiao Lin is not strong in math and often scores around average on exams. However, he is willing to work on small tools. When a website report fails, he checks the documentation; when code doesn't run, he first thinks "where's the error" before fleeing.
| Verification point | Better signal | Risk signal |
|---|---|---|
| Small code tasks | Able to complete and willing to fix errors | Errors immediately trigger strong frustration |
| Introduction to Algorithms | Can understand the basic ideas | Give up as soon as an abstract structure appears |
| Curriculum plan | Core courses are difficult but acceptable | Core courses are widely resisted |
| Interview feedback | After hearing about the real pressures, you still want to learn more | After hearing it, you just want to escape |
Such students should not be automatically disqualified from computer science simply because they're average in math. A more stable approach is to include software engineering, computer science, and cybersecurity tracks in the "under review" list, then monitor the results for seven days.
At this stage, continue evaluating computer science and software engineering tracks while verifying the course requirements at each target school.
Xiao Zhou's main issue is not a broad lack of foundational ability, but a long-standing aversion to abstract mathematics and model derivations. He feels excited by the popularity of AI, but shows clear resistance when encountering linear algebra, probability and statistics, optimization, or machine learning.
At this point, the claim "AI is the future" fails to address relevant risks. Fields related to AI often require greater tolerance for mathematics, statistics, modeling, and algorithms. Course offerings at target schools still need individual verification; however, if core courses in a curriculum trigger prolonged resistance, such programs should at least be temporarily excluded.
At this stage, place AI in "under review" or "temporarily excluded"; meanwhile, validate pathways toward engineering-oriented, applied, product, digital media, interaction design, or information management fields with technological intersections. This is an evaluation pathway, not a major recommendation.
Chen enjoys building websites, editing videos, researching tools, writing product documentation, and thinking deeply about why users can't understand a feature. However, he has limited interest in low-level systems, algorithmic competitions, and abstract mathematics.
Such students don’t necessarily have to choose between "pure computer science" and "no technical involvement." A more reasonable approach is to break down the goals: fields such as pure computer science, software engineering, digital media technology, interaction design, information management, educational technology, and data visualization all require varying degrees of technical skills, though their courses and daily work activities differ significantly.
At this stage, treat pure AI and theoretical computing tracks with caution. Application-oriented, product, interaction, and content technology paths still require validation against each school's actual majors and curriculum.
The week may not produce a final answer. It should remove options supported only by popularity and identify directions worth deeper verification.
| Day | Action | What to record | How to evaluate it |
|---|---|---|---|
| Day 1 | Find the curriculum plan of a target school | The ratio of core courses, mathematics courses, and practical courses | Does the curriculum match what I imagined? |
| Day 2 | Watch an introductory lesson on linear algebra, algorithms, or programming | What made sense, where I got stuck, and how resistant I felt | Is this a foundational gap or a strong aversion? |
| Day 3 | Do a small code task | Can I diagnose the error and keep trying? | Debugging tolerance |
| Day 4 | Find a real course assignment or project description | Task format, delivery method, difficulty | Does my picture of the major match its actual coursework? |
| Day 5 | Interview a current student or practitioner | The most painful and rewarding part | Check assumptions formed from short videos or family slogans |
| Day 6 | Write a three-column list | Reasons for keeping an option, to be verified, and for temporary exclusion | Turn emotions into evidence |
| Day 7 | Review with parents | Evidence behind each conclusion | Decide whether to continue checking the university’s major group |
| Status | Conditions for placement | Typical examples | Next step |
|---|---|---|---|
| Keep | Eligibility requirements met; most core courses are acceptable; at least one low-cost verification is completed; the work activities are not strongly aversive | Moderate mathematics performance but willing to debug code and able to complete basic tasks | Continue to check the school level, major groups, and training programs |
| To be verified | Interested but insufficient evidence; the course is risky but uncertain; parents and students have different judgments | Want to apply for AI, but have not tried introductory linear algebra, probability, or machine learning | Do the 7-day verification, do not decide immediately |
| Temporarily excluded | Eligibility requirements are not met; core courses have been resisted for a long time; the work activities are clearly unappealing; only supported by popular narratives | Strongly reject mathematics and code, want to apply only because "AI is popular" | Switch to other directions or technical intersections |
Debates around computers and AI easily devolve into mutual accusations. Parents say students fear hardship, while students claim parents only care about money. What truly advances decision-making is evidence, not attitude.
| Not recommended | Why it doesn’t work | Better way to ask |
|---|---|---|
| "Computer science is popular and must be applied for." | Popularity cannot replace checking the curriculum and your capacity to sustain it | What are the core courses of this major? Which ones would you like to learn? |
| "If you're not good at math, don't think about it." | Mix different math problems into one category | What kind of math are you bad at? Can you verify this? |
| "AI is the future; skipping it would be a loss." | Trends cannot replace the fit or the realities of learning | What exactly do you learn in the AI major? Do you accept models and statistics? |
| "You just don't work hard." | Turn the discussion into a personal attack | Which tasks are you willing to practice for a long time, and which ones are you constantly resisting? |
| "Apply first and decide later about it." | Ignoring major groups, changing majors and course risks | If you can't change majors, can you still accept it? |
Students also need to refine their expression. Instead of simply saying "I don't like math" or "I just prefer computers," more useful statements are:
I first review the curriculum for this major, then complete a small task. If I can accept both courses and tasks, I'll keep it on the shortlist. If evidence is insufficient, I'll hold it for further verification. If I clearly resist core courses and work activities, I'll temporarily exclude it.
If parental disagreement is already strong, refer to Gaokao college application choices: A communication framework for when parents and students have conflicting opinions. This article focuses solely on mathematics, course content, and computer science/AI-related work activities validation.
This article breaks down the question "Do I have poor math skills and am I eligible to apply to Computer Science/AI?" into verifiable subquestions:
This article does not:
Testing can help organize your questions but cannot make decisions for you.
If you currently only feel "computing is popular" or "AI has potential," don't rush to include it in your final application choices. First complete two steps:
If you haven't yet established an overall major selection process, start by reading Gaokao College Application Choices: Selecting a Major – How to Use RIASEC, MBTI, and Career Interest Tests; if you're finding yourself with too many majors after scoring, use the Major Shortlist Checklist to narrow your options.
Finally, summarize your conclusion in one sentence: I didn't give up because I'm bad at math, nor did I choose computer science or AI simply because those fields are popular; instead, I evaluated this direction based on course content, task requirements, and verification results, placing it in the categories of retained, under review, or temporarily excluded.
It can remain under review, but you cannot conclude immediately that admission is certain or impossible. You must first identify which specific area of mathematics is weak, then examine the target school's curriculum and core courses. If you're willing to strengthen your foundational skills and can accept programming and debugging tasks, some computer-related directions still merit further evaluation; if you have a long-standing aversion to mathematics, code, and abstract problems, proceed with caution.
AI typically relies more on mathematics, statistics, algorithms, and model understanding. There can be significant differences in course offerings across institutions; however, if a curriculum places a high proportion of courses such as linear algebra, probability and statistics, optimization, and machine learning, and you have long resisted these topics, the AI direction should at least be placed in "under review" or "temporarily excluded."
Common topics include advanced calculus, linear algebra, probability and statistics, discrete mathematics, and abstract thinking in algorithms, data structures, and machine learning. Specific courses are subject to the curriculum plan of the target school. Do not rely solely on the statement "computing requires math"—verify the specific courses and credit structure.
Possibly, possibly not. The level of difficulty depends on the type of mathematical weakness, major direction, school course rigor, whether you're willing to review foundational knowledge, and whether you enjoy programming, debugging, systems, and project tasks. A seven-day trial period is more reliable than simply imagining four years of hardship.
Enjoying computers does not mean you're suited for learning computer science. You need to assess whether you're willing to write code, read documentation, debug, break down problems, and understand system rules. If you prefer expression, design, users, content, or tool application, you may instead evaluate areas at the intersection of digital media, interaction, product, or information management technologies.
Many AI curricula place greater emphasis on mathematics, statistics, algorithms, and modeling, but there can be significant differences across institutions. Do not judge solely based on major names. Instead, examine the curriculum first, then assess the proportion of courses in machine learning, linear algebra, probability and statistics, and optimization.
No. The RIASEC can only help you observe your preferred work activities and environments, such as analysis, operation, organization, expression, service, or influence. It cannot predict admission, course grades, employment, salary, or career success.
Do not conclude outright with "popular" or "uncertain." Recommend adding computer science/AI to the pending verification list: review the curriculum, observe a foundational course, complete a small coding task, interview a student, and then decide whether to retain or temporarily exclude it.