TAI: You Learning Agent
TAI: You Learning Agent
TAI: You Learning Agent
03
TAI
UX Design
UX Design
AI Product
AI Product
Intro
I worked with the Teaching Assistant Intelligence (TAI) team, a cross-functional research and product group at UC Berkeley’s Vive Center, focused on creating AI-powered tools to transform how students learn in large STEM classes. On this project, I collaborated with two other UI/UX designers, two product managers, and a team of five to ten engineers. I was responsible for the redefining the new user flow for the up-coming version, refine key features such as note, knowledge base, and file/video chat functions.
I worked with the Teaching Assistant Intelligence (TAI) team, a cross-functional research and product group at UC Berkeley’s Vive Center, focused on creating AI-powered tools to transform how students learn in large STEM classes. On this project, I collaborated with two other UI/UX designers, two product managers, and a team of five to ten engineers. I was responsible for the redefining the new user flow for the up-coming version, refine key features such as note, knowledge base, and file/video chat functions.
I worked with the Teaching Assistant Intelligence (TAI) team, a cross-functional research and product group at UC Berkeley’s Vive Center, focused on creating AI-powered tools to transform how students learn in large STEM classes. On this project, I collaborated with two other UI/UX designers, two product managers, and a team of five to ten engineers. I was responsible for the redefining the new user flow for the up-coming version, refine key features such as note, knowledge base, and file/video chat functions.
The Problem
Large STEM classes at Berkeley face three major pain points:
❌ Limited personalized attention: Office hours are overcrowded and intimidating.
🧩 Fragmented learning materials: Lecture slides, past exams, labs, and readings are scattered across different platforms, making it hard to study systematically.
🤖 Generic AI isn’t enough: Tools like ChatGPT can’t provide course-specific, reliable answers — leading to frustration or misinformation.
👉 This leads to students relying on guesswork, overloading TAs with repetitive questions, and missing deep conceptual understanding
❌ Limited personalized attention: Office hours are overcrowded and intimidating.
🧩 Fragmented learning materials: Lecture slides, past exams, labs, and readings are scattered across different platforms, making it hard to study systematically.
🤖 Generic AI isn’t enough: Tools like ChatGPT can’t provide course-specific, reliable answers — leading to frustration or misinformation.
👉 This leads to students relying on guesswork, overloading TAs with repetitive questions, and missing deep conceptual understanding
❌ Limited personalized attention: Office hours are overcrowded and intimidating.
🧩 Fragmented learning materials: Lecture slides, past exams, labs, and readings are scattered across different platforms, making it hard to study systematically.
🤖 Generic AI isn’t enough: Tools like ChatGPT can’t provide course-specific, reliable answers — leading to frustration or misinformation.
👉 This leads to students relying on guesswork, overloading TAs with repetitive questions, and missing deep conceptual understanding
MVP
Personalized, course-specific teaching assistant that supports students throughout their learning process
Personalized, course-specific teaching assistant that supports students throughout their learning process
Personalized, course-specific teaching assistant that supports students throughout their learning process
Try the MVP: tai.berkeley.edu
Try the MVP: tai.berkeley.edu
Try the MVP: tai.berkeley.edu
Feature 1
From local "ChatGPT" to End-to-end Intelligent Learning Agent
From local "ChatGPT" to End-to-end Intelligent Learning Agent
From local "ChatGPT" to End-to-end Intelligent Learning Agent
Students can start directly from the course landing page to ask questions in natural language. Instead of relying on generic LLM responses, the assistant anchors every answer to instructor-provided materials, ensuring accuracy and academic integrity. When students ask a question, the system retrieves relevant sections from the course’s slides, notes, or problem sets, and shows linked references, allowing students to trace exactly where the information came from. This creates trust and transparency, especially in technical courses where precision matters.
Students can start directly from the course landing page to ask questions in natural language. Instead of relying on generic LLM responses, the assistant anchors every answer to instructor-provided materials, ensuring accuracy and academic integrity. When students ask a question, the system retrieves relevant sections from the course’s slides, notes, or problem sets, and shows linked references, allowing students to trace exactly where the information came from. This creates trust and transparency, especially in technical courses where precision matters.
Students can start directly from the course landing page to ask questions in natural language. Instead of relying on generic LLM responses, the assistant anchors every answer to instructor-provided materials, ensuring accuracy and academic integrity. When students ask a question, the system retrieves relevant sections from the course’s slides, notes, or problem sets, and shows linked references, allowing students to trace exactly where the information came from. This creates trust and transparency, especially in technical courses where precision matters.
Features 3
From local "ChatGPT" to End-to-end Intelligent Learning Agent
From local "ChatGPT" to End-to-end Intelligent Learning Agent
From local "ChatGPT" to End-to-end Intelligent Learning Agent
When a course PDF is uploaded, the assistant automatically parses its structure—detecting sections, headings, and key concepts. These are presented as a sidebar table of contents and overview bullets inside the chat, giving students multiple ways to navigate. Students can jump directly to relevant sections or ask file-based questions, and the model responds with precise, section-grounded answers.
When a course PDF is uploaded, the assistant automatically parses its structure—detecting sections, headings, and key concepts. These are presented as a sidebar table of contents and overview bullets inside the chat, giving students multiple ways to navigate. Students can jump directly to relevant sections or ask file-based questions, and the model responds with precise, section-grounded answers.
When a course PDF is uploaded, the assistant automatically parses its structure—detecting sections, headings, and key concepts. These are presented as a sidebar table of contents and overview bullets inside the chat, giving students multiple ways to navigate. Students can jump directly to relevant sections or ask file-based questions, and the model responds with precise, section-grounded answers.
Features 3
From local "ChatGPT" to End-to-end Intelligent Learning Agent
From local "ChatGPT" to End-to-end Intelligent Learning Agent
From local "ChatGPT" to End-to-end Intelligent Learning Agent
Lecture recordings are automatically broken down into meaningful segments, each paired with its transcript. Students can skim through the chaptered timeline or click on transcript sections to jump to specific moments, making long videos searchable and digestible.
Lecture recordings are automatically broken down into meaningful segments, each paired with its transcript. Students can skim through the chaptered timeline or click on transcript sections to jump to specific moments, making long videos searchable and digestible.
Lecture recordings are automatically broken down into meaningful segments, each paired with its transcript. Students can skim through the chaptered timeline or click on transcript sections to jump to specific moments, making long videos searchable and digestible.
Next steps
From local "ChatGPT" to End-to-end Intelligent Learning Agent
From local "ChatGPT" to End-to-end Intelligent Learning Agent
From local "ChatGPT" to End-to-end Intelligent Learning Agent
While the current MVP focuses primarily on answering students’ course-related questions with high accuracy, our vision goes beyond that. We aim to transform TAI from a local “ChatGPT” clone into an end-to-end intelligent learning agent, one that not only provides answers but also helps students formulate structured notes, plan their study paths, and evaluate their learning progress over time. This semester, we’re focusing on designing and prototyping these expanded capabilities, laying the foundation for a more adaptive, personalized, and longitudinal learning experience.
Stay tuned!
While the current MVP focuses primarily on answering students’ course-related questions with high accuracy, our vision goes beyond that. We aim to transform TAI from a local “ChatGPT” clone into an end-to-end intelligent learning agent, one that not only provides answers but also helps students formulate structured notes, plan their study paths, and evaluate their learning progress over time. This semester, we’re focusing on designing and prototyping these expanded capabilities, laying the foundation for a more adaptive, personalized, and longitudinal learning experience.
Stay tuned!
While the current MVP focuses primarily on answering students’ course-related questions with high accuracy, our vision goes beyond that. We aim to transform TAI from a local “ChatGPT” clone into an end-to-end intelligent learning agent, one that not only provides answers but also helps students formulate structured notes, plan their study paths, and evaluate their learning progress over time. This semester, we’re focusing on designing and prototyping these expanded capabilities, laying the foundation for a more adaptive, personalized, and longitudinal learning experience.
Stay tuned!