ICSP / AI / 01
AI and machine learning
Models, tested honestly.
Open the syllabusUnited States · Grades 9 to 12 · Projects for Regeneron ISEF 2027
A computer science project that impresses doctoral-level judges is rarely the one with the most code. It is the one with a clear question, a fair test and an honest account of what the student did. This guide reads Society for Science’s ISEF pages and the 2026 to 2027 International Rules for what matters to a CS or math student: which category to enter, what a mentor may and may not do, how paid help must be disclosed, and how to design an experiment whose result a judge can trust.
Live teaching since 2020 · 10,000+ students · we teach method; the project is the student’s
In short
Regeneron ISEF, run by Society for Science, is the pre-college science fair that students in grades 9 to 12 reach by winning at an affiliated fair. Computer science projects usually enter Software Design, Robotics and Intelligent Machines or Mathematics. Modern Age Coders teaches the Python, AI and research method live online, in a batch or 1-on-1; the student does the project.
Learn with a teacher
Skills first: machine learning, algorithms and the Python to test an idea properly.

ICSP / 01
Training, testing and judging models honestly, from Python to real models.
Open the syllabus →
ICSP / 02
The core of an Algorithms subcategory project.
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ICSP / 03
Cleaning data, summarising it and showing results clearly.
Open the syllabus →Regeneron ISEF 2027
Society for Science calls Regeneron ISEF the world’s largest international pre-college science competition. Students cannot enter directly: they compete at a Society-affiliated local, regional or state fair and are selected there. The 2027 fair is in Los Angeles in May 2027.
| Rule | What the International Rules say |
|---|---|
| Grade | Grades 9 to 12 or equivalent |
| Age | Must not have reached age 20 on or before May 1 preceding ISEF |
| Projects | One project per student |
| Research window | No more than 12 months of continuous research, and none performed before January 2026 |
| Teams | No more than three members, the same ones who competed at the affiliated fair |
| Unsuitable projects | Demonstrations, literature reviews, library research and explanation models are not recommended or appropriate |
| Language | English, for project boards and abstracts |
| Abstract | A one-page abstract of at most 250 words, written after experimentation |
Society for Science says that each year more than 1,600 high school students from nearly every US state and more than 60 countries, regions and territories compete at its affiliated fairs for a place at ISEF, where projects are judged by doctoral-level scientists.
Its ISEF pages also host a searchable database of abstracts from 2014 to 2026, a useful way to see how strong projects frame a question in 250 words.
Every path runs through an affiliated fair, and each fair sets its own deadlines, forms and review dates. Society for Science’s find-a-fair tool lists them. Ask your school’s science teacher which fair your school feeds.
Projects that need review, such as any study with human participants, must be approved before experimentation starts, so the fair’s calendar shapes the whole year.
Sources: Regeneron ISEF and the International Rules for 2026 to 2027, Society for Science, read 31 October 2026. Regeneron ISEF is a program of Society for Science. We are not connected with Society for Science, Regeneron or any affiliated fair.
Categories
Most coding projects belong in Software Design, Robotics and Intelligent Machines, or Mathematics, with Embedded Systems and Computational Biology and Bioinformatics as neighbours. The finalist chooses, and Society for Science advises picking the category whose judges are most qualified to assess the project.
| Category | How Society for Science defines it | Some subcategories |
|---|---|---|
| Software Design (SFTD) | The study or development of software, information processes or methodologies to demonstrate, analyze or control a process or solution | Algorithms, Cybersecurity, Databases, Human/Machine Interface, Languages and Operating Systems, Mobile Apps, Online Learning |
| Robotics and Intelligent Machines (ROBO) | Studies in which the use of machine intelligence is paramount to reducing the reliance on human intervention | Biomechanics, Cognitive Systems, Control Theory, Machine Learning, Robot Kinematics |
| Mathematics (MATH) | The study of the measurement, properties and relationships of quantities and sets, using numbers and symbols | Algebra, Analysis, Combinatorics, Graph Theory and Game Theory, Geometry and Topology, Number Theory, Probability and Statistics |
| Technology Enhances the Arts (TECA) | Named by the Software Design and Robotics pages as the home for studies designed for an artistic purpose | See the category page |
| Embedded Systems (EBED); Computational Biology and Bioinformatics (CBIO) | Separate categories for hardware-software systems and for computing applied to biology | See each category page |
The Machine Learning subcategory is meant for projects about how machine learning works. Society for Science says projects that use machine learning as a tool to interpret data should be placed in the category of the data or results instead.
So a model that improves how neural networks train is a ROBO project; a model that classifies plant leaves is probably a plant sciences project that happens to use code. That distinction decides which judges read the board.
Judges at ISEF are assigned by subcategory expertise, so Society for Science advises choosing the primary subcategory rather than Other. Number Theory, for example, lists cryptography among its topics, which makes it a natural home for a cipher project.
Local and state fairs may use different categories or none, and a student does not have to enter the same category at ISEF as at their earlier fair.
Sources: ISEF Categories and Subcategories, with the Software Design, Mathematics and Robotics and Intelligent Machines pages, read 31 October 2026.
Mentor rules
A mentor may teach and advise, but the research must be the student’s. The International Rules say a research plan with a mentor should spell out which parts the student does and which the mentor does, and every project files a Student Support Disclosure Form, 2A, that asks whether the family paid for any support.
| Role | Who it can be | What the rules ask of them |
|---|---|---|
| Student researcher | The student or a team of up to three | Responsible for all aspects of the project, from approvals and forms to experimentation, engineering and data analysis |
| Adult Sponsor | A teacher, parent, professor or other professional scientist | Works with the student on risks, reviews the Student Checklist and research plan, and checks eligibility |
| Qualified Scientist | A doctoral or professional degree holder, or someone with extensive expertise in the area | Approves the plan before experimentation and supervises, directly or through a Direct Supervisor; required for some study types |
| Direct Supervisor | An adult familiar with the project, no advanced degree needed | Provides onsite supervision when the Qualified Scientist is not local |
| Other supporting adults | Graduate students, relatives, external experts and others | Listed on Form 2A with their contribution and how long they helped |
Form 2A asks directly: "Did you or your parents pay for any support for this project?" It asks the student to explain fees, tuition or payment for anything that supported the project, naming mentorship and documentation preparation as examples.
If a family pays us for lessons that a student then uses on an ISEF project, the honest course is to list that on Form 2A. We tell families this before the first lesson, and we describe our role in writing so the student can disclose it accurately.
The rules require Regulated Research Institution Form 2C when work was conducted or mentored, virtually or on site, at a regulated research institution, industrial setting or any work site other than home, school or field.
Whether online lessons from a company like ours count is a question for the student’s fair and its review committee. We advise families to ask early, in writing, rather than guess.
Sources: International Rules 2026 to 2027, including the Roles and Responsibilities section and Forms 2A and 2C, read 31 October 2026.
AI rules
Only as a cited resource. The 2026 to 2027 International Rules allow AI as a project resource if it is cited and acknowledged, but say a student may not use generative AI to write the research plan, abstract or poster, or to create citations, because it is known to hallucinate references.
Society for Science lists this AI rule among its final rule changes for 2026 to 2027. The ethics statement adds that inappropriate use of AI, like fraudulent data or plagiarism, is grounds for a project to fail to qualify.
Its generative AI guidance table, dated October 2025, calls using AI to initially write the research plan, abstract, paper or poster never acceptable, and asks students to keep a logbook of prompts when AI helps develop ideas. Form 2A also has an AI usage disclosure.
Our lessons do not use chatbots to write anything for a project. Students learn to read papers themselves, write their own code and explain their own results, which is what judges probe in an interview.
A machine learning model the student builds and trains is a different thing from generative AI writing their work. The first can be the research; the second is not allowed to be.
Sources: International Rules 2026 to 2027, the Final Rule Modifications for 2026 to 2027 and the Generative AI Use Table, read 31 October 2026. Affiliated fairs may adopt stricter AI policies.
Research plan
The 2026 to 2027 rules list seven parts: rationale, research question or engineering goal with expected outcomes, materials, procedures, risk and safety, data analysis and bibliography. Below, each is filled in for a made-up project of ours, to show the level of precision a plan needs.
| Section (from the rules) | Our example |
|---|---|
| Rationale | Route-finding programs search many cells they never use. A distance hint may cut wasted work on grid maps. |
| Question and expected outcome | On random 30 by 30 grids, does adding a distance hint reduce cells expanded without lengthening the path? Expected: fewer cells, equal path length. |
| Materials | A laptop, Python, a random maze generator with fixed seeds, no human participants. |
| Procedures | Generate 40 mazes from seeds 0 to 39. Run both searches on each. Record cells expanded and path length. Skip mazes with no path. |
| Risk and safety | No physical risk; no personal data collected. |
| Data analysis | For each maze, the saving is 1 minus hinted over plain. Report the median and range of savings across mazes. |
| Bibliography | Textbook and peer-reviewed sources on graph search, read and cited by the student. |
Our example tests a well-known idea, which makes it a good lesson and a weak project. The rules call demonstrations not appropriate for ISEF; a real project needs a question whose answer is not already in a textbook.
A student could push it further: new kinds of maps, a new hint, or a measurement nobody has published. That step, finding the open question, is the student’s.
The rules exempt from review a computer application tested only by the student researcher, with no health or safety hazard. Once other people try the app, review and approval are needed before testing.
Testing a student-designed invention or prototype also requires a Risk Assessment Form 3. A software student who plans a user study should raise it with their Adult Sponsor before writing a line of survey.
Worked experiment
Run both on the same inputs, many times, and compare them input by input. Report the typical difference and its spread, not one lucky run. Our seeded experiment below does exactly that for the made-up project above.
# Our example experiment: does a distance hint (A*) expand fewer cells than plain search (Dijkstra)?
import heapq, random, statistics
def make_grid(seed, size=30, wall_chance=0.25):
rng = random.Random(seed)
grid = [[rng.random() < wall_chance for _ in range(size)] for _ in range(size)]
grid[0][0] = grid[size - 1][size - 1] = False
return grid
def search(grid, use_hint):
n = len(grid); goal = (n - 1, n - 1)
hint = (lambda r, c: (goal[0] - r) + (goal[1] - c)) if use_hint else (lambda r, c: 0)
frontier = [(hint(0, 0), 0, (0, 0))]
cheapest = {(0, 0): 0}; expanded = 0
while frontier:
_, cost, (r, c) = heapq.heappop(frontier)
if cost > cheapest[(r, c)]:
continue
expanded += 1
if (r, c) == goal:
return expanded, cost
for dr, dc in ((1, 0), (-1, 0), (0, 1), (0, -1)):
nr, nc = r + dr, c + dc
if 0 <= nr < n and 0 <= nc < n and not grid[nr][nc]:
if cost + 1 < cheapest.get((nr, nc), 10**9):
cheapest[(nr, nc)] = cost + 1
heapq.heappush(frontier, (cost + 1 + hint(nr, nc), cost + 1, (nr, nc)))
return expanded, None # no path in this maze
savings, skipped = [], 0
for seed in range(40):
g = make_grid(seed)
plain, length1 = search(g, use_hint=False)
hinted, length2 = search(g, use_hint=True)
if length1 is None:
skipped += 1
continue
assert length1 == length2 # both must find equally short paths
savings.append(1 - hinted / plain)
print(len(savings), "solvable mazes,", skipped, "skipped")
print("median saving", round(100 * statistics.median(savings), 1), "%")
print("range", round(100 * min(savings), 1), "to", round(100 * max(savings), 1), "%")Our own teaching program, standard Python with no libraries to install. Fixed seeds mean anyone can rerun it and get the same mazes, which is the point.
| Measure | Result |
|---|---|
| Mazes generated | 40 (seeds 0 to 39) |
| Mazes with a path | 30; 10 skipped because no path existed |
| Path length | Identical for both searches in all 30 mazes |
| Median saving in cells expanded | 41.3 percent |
| Range of savings | 23.7 percent to 66.1 percent |
Each maze is used by both searches, so a hard maze cannot favour one side. The program also checks both find equally short paths, so the saving is not bought with worse answers.
Counting cells expanded, rather than seconds, removes noise from the laptop. Time can be measured too, but it should never be the only number.
A sentence a judge can trust: "Across 30 solvable seeded mazes, the distance hint expanded a median 41.3 percent fewer cells, ranging from 23.7 to 66.1 percent, with equal path lengths." It says what, how many, how much and how varied.
The 10 skipped mazes also belong in the report. Leaving out inconvenient trials without saying so is how honest projects go wrong.
Practice set
Five original problems, written by us, of the kind we use in class. Each answer explains the reasoning a judge would expect a student to give.
Judges ask how a student knows their result is real. A plain project with a careful comparison usually answers that better than a polished app with no measurement.
Teaching that habit is where we spend most of our time with research students.
The same habits suit summer research programs and competitions. Our AI research programs guide and machine learning competitions guide cover those routes.
Students still choosing a topic can browse our AI science fair project ideas as a starting point, then make the question their own.
A year plan
Our suggested rhythm for a student aiming at fairs in early 2027 and ISEF in May 2027. Fair deadlines vary, so the student sets real dates with their Adult Sponsor.
| When | Student’s project work | Skills we can teach alongside |
|---|---|---|
| Months 1 and 2 | Read widely; narrow to one question | Reading a paper; Python basics |
| Month 3 | Write the research plan; get approvals if needed | Turning a question into a measurable claim |
| Months 4 to 6 | Build the software and the test harness | Algorithms, data structures, testing |
| Months 7 and 8 | Run the experiments, log everything | Seeds, repeated trials, avoiding leaks |
| Month 9 | Analyse results, including failures | Medians, ranges, simple charts |
| Months 10 and 11 | Write the abstract and board in own words | Explaining method aloud, without scripts |
| Month 12 | Affiliated fair, then ISEF if selected | Practice interviews on our example projects |
We do not choose the question, write code for the project, run its experiments, analyse its data or write or edit its abstract, plan or board. The rules expect independent research in the student’s own words, and we hold to that.
We teach on our own examples, like the maze experiment above, and the student applies the skill to their own project.
Ask your child to explain, in two minutes, what they measured and why the comparison is fair. If they can, the project is theirs and is in good shape.
Ask the Adult Sponsor early which forms the project needs. Society for Science’s rules wizard lists them from a few questions.
Readiness
Strong coders are not automatically ready for research. These are the steps we see between writing programs and testing ideas.
| Step | What the student can do |
|---|---|
| 1. Builder | Writes working programs from a specification |
| 2. Tester | Checks programs against many inputs, not one |
| 3. Experimenter | Compares two methods fairly and reports median and spread |
| 4. Researcher | Asks a question whose answer is unknown and designs the test for it |
The most common weakness in student CS projects is a missing comparison. Ask "compared with what?" about any result.
Keep a record of any paid help as it happens. Filling in Form 2A accurately is far easier with dates and descriptions written down.
Write your question so that a number answers it. "Is my app better?" cannot be tested; "Does it cut steps per task?" can.
Keep a dated research notebook from the first day. Judges and review committees both trust a project whose history they can see.
The catalogue
Online, live and with a single regular teacher, as a class or private lessons.
ICSP / AI / 01
Models, tested honestly.
Open the syllabusICSP / AI / 02
From raw data to results.
Open the syllabusICSP / AI / 03
The research workhorse.
Open the syllabusICSP / AL / 01
Structures and complexity.
Open the syllabusICSP / AL / 02
Fast, correct algorithms.
Open the syllabusICSP / AL / 03
Defensive security basics.
Open the syllabusICSP / MA / 01
Explore conjectures in code.
Open the syllabusICSP / MA / 02
Structured, typed programs.
Open the syllabusICSP / MA / 03
Speed for heavy experiments.
Open the syllabusHow lessons run
Weekdays or weekends, flexible: choose your batch, or for a very personalised plan choose 1-on-1 at your convenience, weekend mornings or weekdays.
Weekdays
After school, a steady weekly slot.
Weekends
Longer sessions for method and analysis.
1-on-1
Skills matched to the student’s chosen field.
We never touch the project itself.
A note of our role for Form 2A.
Students write every word.
Seeds, repeats, median and spread.
Live online, any state.
Fifteen to twenty per batch, or one student.
Student work
Apps, games and AI projects made by our learners in class. More in the student labs.

AI and ML
An AI nutrition coach that reads what you eat and works you toward a target.

AI and ML
A chatbot that answers mathematics and programming questions, built and deployed by a student.

AI and ML
An assistant that helps a young person recognise unsafe situations online.

Web app
A weather forecasting site with live conditions for any location.
Fees
Fair entry, travel and any lab costs are separate and paid to the fair or institution; our fee is for skills lessons only. We bill monthly, and if a student uses what they learn on an ISEF project, the family should list our lessons on Form 2A.
Free first class
USD 0
no card required
Group batch
USD 100
a month, billed in US dollars
One to one
USD 150
a month, billed in US dollars
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Questions from families and teachers
By competing at a Society-affiliated local, regional or state science fair and being selected there. Society for Science says competing at ISEF starts at an affiliated fair, and each fair sends the number of projects in its agreement.
Society for Science says the 2027 Regeneron ISEF will be in Los Angeles, California, in May 2027. Check its site for exact dates as they are published.
Usually Software Design for software and algorithms, Robotics and Intelligent Machines for work on machine intelligence, or Mathematics for proofs and number theory. Projects that only use machine learning as a tool go in the category of the data they study.
Students in grades 9 to 12 or equivalent who have not reached age 20 on or before May 1 preceding ISEF, and who are selected by an affiliated fair. Each student may enter one project, and teams may have up to three members.
Yes, within limits. The International Rules say a research plan with a mentor should set out which parts the student does and which the mentor does, and all support must be listed on Form 2A. The research must be the student's own.
Yes. Form 2A asks whether the student or their parents paid for any support for the project, and to explain it, naming mentorship and documentation preparation as examples. We give families a written description of our role.
Only as a cited resource. The 2026-2027 rules say a student may not use generative AI to write the research plan, abstract or poster, or to create citations, and any AI use must be cited and disclosed on Form 2A.
Not if the student researcher is the only person testing the program and there is no health or safety hazard. If other people test it, IRB review and approval are needed before testing begins.
No more than 12 months of continuous research, and for this cycle no research performed before January 2026. Continuation projects need extra documentation showing the new work is different.
Weekdays or weekends, flexible: choose your batch, or for a very personalised plan choose 1-on-1 at your convenience, weekend mornings or weekdays.
No. Modern Age Coders is an independent coding school with no connection to Society for Science, Regeneron or any affiliated fair. We teach skills and method; we do not do projects.
Elsewhere on this site
Programs, contests and ideas around research.
Summer and school-year research options for teens.
Contests where a model is scored on hidden data.
Starting points to make your own.
Where to build skills before a project year.
For students who want contests as well as research.
Dates for this school year.
Part of the guide: US math and coding competitions guide
More guides: US state math tests by state · US gifted and selective school tests · US math placement and college readiness tests · Homeschool and school math curricula guide
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Tell us your child’s grade, their field of interest and how far the project has got. The teacher runs a short Python and method task and suggests which skills to build first.
To understand our approach, read how we teach, or look at our full course list.
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