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    Scientific Teaching For University Englishversion

    1

    An educational psychology-based tutor that teaches university slides step-by-step using learner aspect.

    Free

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    See it in action

    You say

    Use scientific-teaching-for-university-englishversion in hard mode to teach me this Linear Algebra course. Here is the first slide on Eigenvector decomposition.

    Your agent does

    Before we look at the math, let's understand why we are here. In a huge system of data, we want to find the 'skeleton' that stays still while everything else moves.

    Slide 1: Definition of Eigenvectors

    1. 'Ax = λx' — This formula is trying to say: If you transform space (A), most vectors (x) will turn and stretch. But an Eigenvector is special; it only stretches (λ), it never turns.
    2. Imagine 2D space like a piece of dough. If you pull it horizontally, any point on the horizontal line stays on that line. That's your Eigenvector.

    Why is this here? The professor is setting up the 'basis' for PCA and vibration analysis. Next, we will break down the 'λ' symbol step-by-step. Ready for the next slide?

    About this skill

    The problem

    University lecture notes are often written for experts, leaving students overwhelmed by technical jargon and abstract formulas. Standard AI chat responses often provide a high-level summary that skips the step-by-step intuition needed to actually learn the material.

    What it does

    • Explains lecture slides page-by-page using four distinct difficulty modes based on educational psychology.
    • Breaks down complex mathematical formulas and computer science algorithms into intuitive mental models before introducing formal definitions.
    • Deconstructs the instructor's intent to explain why specific concepts are placed at certain points in the curriculum.
    • Provides contextual evaluations and concrete examples for every technical term to ensure comprehension over memorization.
    • Supports image-to-explanation workflows for slide screenshots and handwritten notes.

    Why this beats prompting it yourself

    A simple prompt usually results in an expert-to-expert summary. This skill uses a tiered pedagogical framework that prevents the AI from skipping basic steps. It manages the learning state, ensuring it waits for your input between slides rather than dumping the entire deck at once.

    Use cases

    • Mastering hardcore STEM subjects like Machine Learning or Advanced Physics slide-by-slide.
    • Understanding complex English lectures when your primary language or background knowledge is different.
    • Deep-diving into specific abstract concepts when a standard textbook explanation fails to click.
    • Building a global knowledge framework before a final exam by reviewing an entire slide deck's logic.

    Known limitations

    Requires the user to paste slides or text sequentially for the best results. It is designed for teaching, not for completing homework assignments or writing essays.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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