Doctrina AI

Accepts unstructured text, PDFs, or document uploads and generates hierarchical summaries at configurable abstraction levels (key points, paragraph summaries, full-text condensation). Uses extractive and abstractive summarization patterns, likely leveraging transformer-based models to identify salient information and rewrite at target length. Processes input through tokenization, semantic segmentation, and neural abstractive generation to produce human-readable study notes without requiring manual highlighting or annotation.

Parses input content (text, documents, or URLs) and automatically generates multiple-choice, true/false, or short-answer quiz questions with answer keys. Uses question-generation models that identify factual claims, definitions, and conceptual relationships in source material, then templates them into quiz formats. Likely employs named entity recognition and semantic parsing to extract testable facts, then generates plausible distractors for multiple-choice options using language models. No visible customization of difficulty level, question type distribution, or learning objective alignment.

Accepts essay prompts or topic descriptions and generates essay outlines, thesis statements, paragraph structures, and full essay drafts. Uses prompt-to-structure models that decompose writing tasks into logical sections (introduction, body paragraphs, conclusion), then generates content for each section using conditional text generation. Likely employs essay-specific templates and rhetorical pattern matching to ensure coherent argumentation. No visible plagiarism detection, citation management, or academic integrity safeguards mentioned.

Generates full-length practice exams or test simulations from course material, combining quiz generation with timed delivery and scoring. Likely uses question pooling, randomization, and test-assembly algorithms to create varied exam variants from a question bank. Provides automated scoring, answer review, and performance analytics to identify weak areas. No visible adaptive difficulty adjustment, spaced repetition scheduling, or integration with learning management systems for progress tracking.

Exports generated summaries, quizzes, essays, and exams in multiple formats (PDF, Word, plain text, potentially LMS-compatible formats) for offline use, printing, or sharing with peers. Implements format conversion pipelines that preserve structure and formatting across output types. No visible collaboration features, version control, or institutional sharing workflows mentioned. Export likely uses standard document generation libraries without advanced layout customization.

Accepts multiple input formats (PDF, Word documents, plain text, potentially images with OCR) and parses them into machine-readable text for downstream processing by summarization, quiz, and essay modules. Uses document parsing libraries (likely PyPDF2, python-docx, or similar) to extract text while preserving structure, and may employ OCR (Tesseract or cloud-based vision APIs) for scanned documents or images. No visible handling of complex layouts (multi-column, tables, embedded images) or metadata extraction.

Provides full access to all core capabilities (summarization, quiz generation, essay assistance, exam simulation) without requiring payment, credit card entry, or premium tier upsells. Implements a freemium model with no visible token limits, usage quotas, or feature gating. Authentication likely uses email-based signup with minimal friction. No visible rate limiting, usage tracking, or conversion funnels to paid tiers mentioned in available information.