PrepSup

Automatically ingests PDF files (textbooks, lecture slides, study guides) and extracts structured educational content through OCR and layout analysis. The system identifies text blocks, preserves hierarchical structure (chapters, sections, subsections), and segments content into logical learning units. This extracted content serves as the source material for downstream flashcard generation and tutoring contexts.

Transforms extracted PDF content or user-provided text into question-answer flashcard pairs using a large language model (likely GPT-3.5/4 or similar). The system applies prompt engineering to generate flashcards at configurable difficulty levels, enforces answer length constraints, and optionally includes mnemonics or memory aids. Generated flashcards are stored in a database with metadata (source document, difficulty, topic tags) for retrieval and spaced repetition scheduling.

Provides conversational tutoring interface where students ask subject-specific questions and receive AI-generated explanations tailored to their apparent knowledge level. The system maintains a lightweight learner profile (topics studied, past question history, self-reported difficulty areas) and uses this context to adjust explanation depth, terminology complexity, and example selection. Tutoring operates in a multi-turn conversation loop where the AI can ask clarifying questions, probe for misconceptions, and suggest follow-up topics based on student responses.

Implements a scheduling algorithm (likely SM-2 or similar variant) that determines when each flashcard should be reviewed based on user performance history. The system tracks correct/incorrect responses, time since last review, and difficulty rating to calculate optimal review intervals. Students are presented with a daily review queue prioritizing cards due for review, with adaptive scheduling that increases intervals for well-learned material and shortens intervals for struggling cards. Review statistics (retention rate, cards learned, study streak) are tracked and displayed to motivate continued practice.

Provides a hierarchical organization system for flashcards sourced from multiple PDFs, user inputs, and AI generation. Students can create decks, organize by course/subject/topic, tag flashcards with custom metadata, and merge or split collections. The system maintains source attribution (which PDF or input generated each flashcard) and allows bulk operations (edit, delete, export) across collections. Collections can be shared with classmates or made public, with optional access controls and version tracking.

Applies domain-aware heuristics to estimate appropriate difficulty levels for AI-generated flashcards based on subject area, question type, and content complexity. The system recognizes patterns (e.g., definition questions are typically easier than application questions) and adjusts difficulty ratings accordingly. Difficulty levels influence both the initial spaced repetition schedule and the adaptive tutoring explanation depth. Users can manually override difficulty ratings, and the system learns from these corrections to improve future calibration.

Aggregates user study data (review frequency, accuracy, time spent, topics covered) and generates visualizations and summary statistics to track learning progress. The system calculates metrics like retention rate (percentage of cards answered correctly), cards mastered (cards reaching spaced repetition completion), study streak (consecutive days of study), and estimated time-to-mastery for remaining cards. Progress is displayed via dashboards with charts (retention over time, cards by topic, study frequency) and exportable reports. Analytics inform recommendations for study focus areas and pacing adjustments.

Implements a freemium pricing tier system where core flashcard functionality (creation, basic review, spaced repetition) is available free, while premium features (advanced AI tutoring, PDF analysis, analytics, collection sharing) require paid subscription. The system enforces usage limits on free tier (e.g., max 100 flashcards, 1 PDF upload per month, limited tutoring queries) and displays upgrade prompts at feature boundaries. Subscription management (billing, plan selection, cancellation) is handled through a payment processor (Stripe, etc.) with account-level feature flags controlling access.

Analyzes flashcard content across multiple subjects and identifies conceptual relationships, prerequisites, and knowledge dependencies. When a student studies a topic, the system suggests related flashcards from other subjects that build on or relate to the current concept. For example, studying 'photosynthesis' in biology might suggest related chemistry flashcards on 'electron transfer' or physics flashcards on 'light energy'. The system maintains a knowledge graph of concept relationships (likely built from flashcard content analysis and user study patterns) and uses this to recommend prerequisite topics or advanced extensions.