// teaching_module

Teaching

Seven courses, four years, and roughly 5,000 graded assignments — from differential equations to graduate AI.

01 philosophy

Three levels of teaching

Effective teaching operates at distinct levels of sophistication, and after watching AI transform how students interact with mathematics, I'm convinced we need to fundamentally rethink what we're trying to accomplish.

LEVEL 01

Understand the procedure

Getting students to grasp procedures and follow algorithmic steps without getting lost. This sounds basic, but most instructors never reach this baseline. And when students can pull up ChatGPT for step-by-step solutions, pure procedural instruction becomes pointless — it prepares them for a world that disappeared last year.

LEVEL 02

See beneath the formulas

Where math stops being about following rules and starts being about making sense. Students develop intuition — a gut feeling about which approach might work. As machines handle calculations, human value shifts toward pattern recognition and strategic thinking, and AI can serve as the infinitely patient tutor that lets students explore without time pressure.

LEVEL 03

Bridge intuition and rigor

Where I'm still figuring things out: moving fluidly between understanding what's happening and executing technical details. When students can outsource calculations, the remaining human value lies in knowing which calculations to perform and why — making the bridge between intuition and formalism absolutely critical.

The reality is that AI makes incomplete teaching genuinely dangerous. Students who only get Level 1 become obsolete when AI performs those procedures. Students getting intuition without technical grounding can't verify ideas rigorously. My current challenge is sequencing explanations so formal definitions feel like natural extensions of intuitive ideas — and showing students how to use AI as a learning amplifier rather than a shortcut. Reaching Level 3 consistently isn't just a pedagogical goal anymore; it's essential preparation for a world where humans and AI collaborate on mathematical problems.

02 service_record

Teaching timeline

2026 Spring

TA · Grad AI  ·  Lead TA · Differential Equations

2025 Fall

TA · Markov Chains  ·  TA · Algorithm Design & Analysis

2025 Summer

TA · AwesomeMath Summer Program

2025 Spring

TA · Intro to Math Finance  ·  TA · Intro to ML (SCS Majors)

2024 Fall

TA · Differential Equations  ·  TA · Intro to ML (SCS Majors)

2024 Summer

TA · AwesomeMath Summer Program

2024 Spring

TA · Continuous Time Finance

2023 Fall

Peer Tutor · Great Ideas in CS  ·  TA · Differential Equations

2023 Spring

Peer Tutor · Math Concepts & Functional Programming  ·  TA · Differential Equations

2022 Fall

TA · Eureka  ·  Peer Tutor · Math Concepts & Functional Programming

2022 Summer

Co-Instructor · Blissful Coding Club

03 artifacts

Featured teaching materials

21-270 · SPRING 2025

Intro to Math Finance: Complete Problem Session Guide

A comprehensive 56-page document covering everything you need to know about intro mathematical finance. Created for the first-ever problem sessions — 1.5-hour review sessions every Wednesday night covering content and practice problems.

56 pagesfirst-time material
Download PDF
10-315 · FALL 2024

Machine Learning Homework & Mini-Project

A brand-new homework assignment built from scratch — all problems, code implementation, and a custom autograder. Introduced a creative Kaggle mini-project component for students to apply ML concepts hands-on.

custom autograderkaggle
Download PDF
21-260 · SPRING 2023

Differential Equations: Practice Final with Solutions

When no practice exam was provided, I created this comprehensive practice final by selecting key problems from the textbook and writing complete solutions — helping students prepare effectively for their final exam.

full solutionsexam prep
Download PDF
04 transmissions

Sample teaching videos

These showcase my teaching style and approach to explaining complex concepts.

05 feedback_stream

Student testimonials

"Jerick was the best TA I have ever had. He is the only reason why I learned anything from this class. Our recitations were at 8am and I never once missed one because he was so good — it says something about how good of a TA you are when you have people consistently show up to 8ams including people who aren't even supposed to be in your recitation.

Additionally, Jerick went above and beyond outside of recitations. He answered the majority of Piazza posts (about 70%) throughout the semester even though they were supposed to be split equally amongst TAs. He also wrote an unofficial practice exam for the final when the professor did not. Finally, for homework 10 we had only done 2 practice problems in class both with major mistakes in them, and everyone was very confused, so Jerick took it upon himself to create a document that step-by-step shows us how to solve PDEs in order to help everyone finish the homework and understand the material for the final.

Jerick deserves so much recognition for his work as a TA in differential equations. I know a lot of people feel similarly because immediately after the exam someone posted a Piazza post thanking him which many other students responded to also thanking him."

Helped me improve from D average to an A. Went over and beyond to help. Deserves Nobel prize.
Best TA I have ever had, went out of his way over and over to help the students succeed and taught more than what was expected.
Jerick's recitations were helpful as an overview of lecture and he was helpful in giving additional study resources and preparing for exams.
This TA was always going above and beyond to not only help us clear up the necessary concepts for homeworks but to also prepare for the exam and its concepts! I really benefited from all their recitations.
I liked his style of re-explaining the week's concepts in a much simpler way.
Offered office hours often and quickly responded to Piazza and email with good insights.