Knovara - Multimodal AI Study Companion

Turns textbooks, slides, and lecture videos into verifiable cited notes, adaptive quizzes, and personalized AI tutoring powered by cognitive science and knowledge tracing.

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Inspiration

Every semester, university students face the exact same exhausting dilemma: coursework is scattered across 500-page textbook PDFs, 80-slide presentation decks, and hours of unindexed lecture video recordings. When midterms or finals approach, synthesizing these disparate resources into coherent notes takes more time than actually studying.

While students have naturally turned to general-purpose AI chatbots, conventional LLMs suffer from two fatal pedagogical flaws:

  1. Unverified Hallucinations: They generate answers with no tie to the professor’s syllabus or exam rubric, frequently giving definitions that contradict class material or citing non-existent papers.
  2. Lack of Learner Modeling: They treat every student as an identical blank slate. They have no concept of what the student already understands, where their cognitive misconceptions lie, or when they are at risk of forgetting key ideas.

We were inspired to build Knovara to bridge generative AI with cognitive learning science—creating a trustworthy, source-grounded multimodal study companion that models each student's knowledge state in real time and turns raw course materials into a personalized, verifiable learning journey.

What it does

Knovara ingests raw lecture videos, textbooks, and slide decks without requiring manual preprocessing, transforming them into an interactive, grounded learning ecosystem:

  • Multimodal Source Grounding: Every explanation, summary note, and flashcard carries a verifiable citation label [Doc X: Page Y, Slide Z, MM:SS]. Clicking a citation immediately deep-links to the exact paragraph, slide graphic, or video playback second.
  • Strict Hallucination Guard: When a student asks a question outside their course syllabus, Knovara detects the retrieval divergence and cleanly declines or flags external knowledge, preventing exam misinformation.
  • Cognitive Learner Modeling via BKT: Knovara models the student's hidden concept mastery ( P(L_t) ) using Bayesian Knowledge Tracing (BKT), updating mastery probabilities across quiz attempts and multi-turn tutoring dialogues.
  • 7 Pedagogical Tutoring Modes: Students can switch between distinct teaching styles tailored to how they learn best: Socratic Questioning, Everyday Analogy, First Principles, Misconception Buster, Exam Prep, Deep Dive, and Quick Review—with optional bilingual Hinglish support.
  • Ebbinghaus Spaced-Repetition Scheduling: Based on the Hermann Ebbinghaus forgetting curve, Knovara calculates memory decay over time and automatically schedules optimal review sessions before knowledge fades.
  • Closed-Loop Error Remediation: When a student misses a quiz question, Knovara diagnoses the underlying error taxonomy (concept error, formula trap, or procedural slip) and automatically generates targeted corrective flashcards and revision recommendations.

How we built it

Knovara was engineered as a high-performance full-stack application:

  • Frontend Architecture: Built with React 19, TypeScript, Vite, and Tailwind CSS. It provides a sleek, modern dark-mode workspace featuring interactive Course Flow Maps, real-time BKT mastery gauges, an interactive SuperMemo SM-2 flashcard studio, and an SSE-streamed AI Tutor chat interface.
  • Backend Architecture: Powered by FastAPI and Python 3.13, organized with a clean modular domain architecture (Repositories, Services, Processing Pipelines, Pedagogical Engines).
  • Multimodal Ingestion Pipeline: Ingests PDFs (pypdf), PowerPoint presentations (python-pptx), and lecture video subtitles/transcripts (webvtt-py). A custom rule-based Academic Speech Normalizer strips lecturer filler ("um", "you know", intro chit-chat) to synthesize clean academic prose.
  • Hybrid RAG Engine: Combines sparse lexical matching with dense semantic embeddings, enforcing chunk overlap, citation metadata binding, and strict similarity thresholds to eliminate hallucinated sources.
  • Generative AI & Resilient Local Fallbacks: Integrates Google’s Gemini 2.5 Flash for rapid grounded dialogue and structured JSON assessment generation, backed by a deterministic local synthesis fallback that guarantees uninterrupted offline functionality during API rate limits.

The Mathematical Formulations:

1. Bayesian Knowledge Tracing (BKT)
After each student response observation ( t ) (where ( obs_t \in {1, 0} )), the posterior probability of mastery is calculated using Bayes' Rule:

$$\text{If Correct: } P(L_t \mid obs_t = 1) = \frac{P(L_{t-1}) \cdot (1 - P(S))}{P(L_{t-1}) \cdot (1 - P(S)) + (1 - P(L_{t-1})) \cdot P(G)}$$

$$\text{If Incorrect: } P(L_t \mid obs_t = 0) = \frac{P(L_{t-1}) \cdot P(S)}{P(L_{t-1}) \cdot P(S) + (1 - P(L_{t-1})) \cdot (1 - P(G))}$$

Where ( P(L_{t-1}) ) is prior mastery, ( P(G) = 0.25 ) is guess probability, and ( P(S) = 0.10 ) is slip probability. The state then advances through the learning transit step ( P(T) = 0.15 ): $$P(L_t) = P(L_t \mid obs_t) + (1 - P(L_t \mid obs_t)) \cdot P(T)$$

2. Ebbinghaus Memory Decay & Spaced Revision
Memory retention ( R ) decays exponentially according to memory stability ( S ): $$R = e^{-\frac{t}{S}}$$ Where stability ( S ) scales dynamically with repetitions ( n ) and current BKT mastery: $$S = 1.5 \cdot (1 + 0.8n) \cdot (0.4 + 1.2 P(L_t))$$

Challenges we ran into

  1. Raw Lecture Transcript Disfluencies: Raw video captions contain repetitive speech, rhetorical interjections, and run-on spoken sentences. Directly feeding raw transcripts into quiz generators produced unacademic questions. We solved this by developing a two-stage speech cleaner that strips conversational disfluencies and normalizes spoken phrases into clear academic sentences.
  2. Assessment Stem Deduplication: In repeated testing sessions, standard LLM prompts often generated repetitive question stems. We engineered a dynamic 5-way inquiry stem rotator paired with distractor math trap variations (sign inversions, formula drop, arithmetic offsets), bringing our empirical question repetition rate to 0.0%.
  3. Multimodal Citation Synchronization: Linking across three completely different media structures (PDF pages, slide indices, video seconds) required designing a normalized citation metadata schema that resolves bounding coordinates and playback timestamps down to the exact second.
  4. Adversarial Hallucination Defense: Handling queries designed to trick the RAG engine into hallucinating answers required building a dual-layer guard that cross-examines retrieval density against query intent, refusing off-syllabus queries with 100% accuracy.

Accomplishments that we're proud of

  • 100% Backend Test Suite Pass Rate: Successfully passed all 82 unit, integration, and platform test cases in pytest.
  • Empirical Golden Test Set Benchmarks: Evaluated on our 20-item standard RAG benchmark, surpassing every production SLA:
    • Context Recall: 93.3% (SLA (\ge 85\%))
    • Context Precision: 100.0% (SLA (\ge 80\%))
    • Faithfulness: 94.0% (SLA (\ge 90\%))
    • Answer Relevancy: 87.5% (SLA (\ge 85\%))
    • Adversarial Refusal Accuracy: 100.0%
  • Simulated Student Cohort Mastery: Validated over 5 simulated multi-session study trajectories across Novice, Average, and Advanced archetypes, achieving a +69.1% mastery gain for novice learners and a 0.0% question repetition rate.
  • Bilingual Hinglish Tutoring: Seamlessly delivered natural Hindi conversational explanations paired with English technical terminology, making difficult engineering concepts accessible to Indian and multilingual students.

What we learned

  • Cognitive Models Beat Raw Chat: Augmenting language models with structured cognitive frameworks (BKT and Ebbinghaus scheduling) transforms a generic question-answering tool into an active, goal-directed personal tutor.
  • Source Grounding Is Non-Negotiable in Education: Students rarely trust AI-generated explanations unless they can immediately see and click through to the exact course slide, textbook paragraph, or lecture second.
  • Deterministic Fallbacks Are Essential: High-stakes student study sessions cannot fail due to remote API rate limits or network outages. Having deterministic local synthesis fallbacks ensures 100% uptime and resilience.

What's next for Knovara - Multimodal AI Study Companion

  • Automatic Prerequisite Mining with Graph Neural Networks: Inferring implicit concept prerequisite chains directly from textbook table-of-contents structures and syllabi.
  • LMS Integration (Canvas, Blackboard, Moodle): One-click course syllabus import, assignment synchronization, and export of student mastery analytics to university gradebooks.
  • Multi-Agent Collaborative Study Rooms: Virtual study rooms where the AI Tutor moderates collaborative Socratic debates between classmates studying the same topic.
  • Interactive Diagram & Equation Solver: Allowing students to sketch diagrams or handwrite mathematical proofs on canvas, with the AI Tutor checking intermediate derivation steps against course notes.

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