How it works

From course setup
to department-wide
insight.

LectureIQ operates across the full teaching lifecycle — before, during, and after every lecture — building a continuously improving picture of comprehension for every stakeholder.

Before lecture
Course setup + pre-loading
During lecture
Live capture + mid-class questions
After lecture
Full quiz + comprehension engine
Insight layers
Student · Lecturer · Department
End-to-end pipeline

The full lifecycle,
connected.

Click any stage to explore what happens at each step.

Phase 1 — Course foundation
Course upload
Syllabus, exams, docs
Context engine
Extracts topics, objectives, and exam patterns into a persistent knowledge base for the course
each lecture
Phase 2 — Pre-lecture
Slide upload
Session slides
Pre-processing engine
Session topics extracted from slides and cross-referenced with the course knowledge base. Key concept checkpoints identified for mid-class triggering.
class begins
Phase 3 — During lecture
Lecture audio
Live or uploaded
Transcription + topic engine
Continuous speech-to-text with live topic mapping. Cross-references transcript against today's slide map as the lecture progresses.
Mid-class questions
At key moments
Pilot
end of class
Phase 4 — End-of-lecture quiz
Full quiz
8–10 questions
Question generation engine
Generates 8–10 questions from transcript + course materials. Distributed across every topic covered. Three cognitive levels enforced. Students join via room code.
all responses
Phase 5 — Comprehension engine
Comprehension engine
All signals unified
Last 3–5 minutes of class

End-of-lecture quiz

At the end of class, a full 8–10 question quiz is generated and launched. Questions are distributed across every topic covered today, at three cognitive levels, with distractors drawn from related course material. Students join via room code — no app needed.

Questions are generated from the combined transcript + pre-loaded course context. Distractor quality improves over the semester as the knowledge base grows.
What happens here
Full quiz generation
8–10 questions across all topics covered in the lecture
Cognitive distribution
Recall, application, and synthesis questions in each session
Student join
Room code on any phone browser — no install required
Insight layers

One engine.
Three perspectives.

The comprehension engine feeds three distinct insight layers — each tailored to the decisions that stakeholder actually needs to make.

Student insight

Know where you stand, right after class.

Every student sees their own comprehension breakdown immediately after submitting the quiz. Which topics you got, which you didn't, how the class performed overall, and — crucially — which gaps the professor will address next session. No waiting for a grade.

Personal accuracy by topic
Class-wide comparison
Flags for topics to review before next class
Persistent across the semester
Your session summary
7/10
correct
Top topic
Entropy
Needs work
Laws
Entropy90%
PV Work75%
Cycles65%
Laws42%
Laws of Thermodynamics below threshold. Your professor will revisit next class.
Lecturer insight

What landed. What to revisit.

The instructor sees a full comprehension map within minutes of class ending — topic-by-topic scores, cognitive level breakdowns, and trends across sessions. Topics that consistently drop signal a structural teaching gap, not a one-off bad session.

Live dashboard as students respond
Topic-level and subtopic-level breakdowns
Longitudinal trend per topic over the semester
Session comparison and regression alerts
PHYS 201 — Session overview
47
students
3 min
avg time
2
gaps
72%
class avg
Entropy82%
Heat transfer55%
Laws40%
PV Work70%
Cycles65%
Longitudinal trend — Laws of Thermodynamics
W1
W2
W3
W4
W5
Department insight

Structural signals, not individual snapshots.

Aggregated across courses and instructors, LectureIQ surfaces the patterns that no single professor can see alone. Which concepts struggle department-wide? Which courses are improving? Where is intervention needed before it shows up in exam failure rates?

Cross-course concept gap tracking
Per-instructor and per-cohort views
Semester-over-semester trend data
Intervention flagging before grade drops
Physics dept — Fall 2025
PHYS 201
Prof. Sharma
72%
PHYS 202
Prof. Lee
81%
PHYS 301
Prof. Nakamura
59%
PHYS 401
Prof. Okonkwo
68%
PHYS 301 trending down 3 consecutive sessions. Intervention recommended.
Cross-course concept gap
Thermodynamics laws3/4 courses struggling
Entropy concepts2/4 courses struggling
Quantum superposition1/4 courses struggling
Question quality

Not generic quiz questions.
Yours.

Every question is grounded in what was actually said in your lecture, framed using your course materials, with distractors drawn from related content your students have seen. The difference in quality is immediately apparent.

Lecture-anchored
Mapped to what was actually said, not just the topic
Distributed
Spread across all concepts covered — no clustering
Plausible distractors
Wrong answers drawn from related course content
Cognitive spread
Recall, application, and synthesis in every session
Example question
ApplicationThermodynamics · Entropy

A gas expands irreversibly into a vacuum. The internal energy remains unchanged. Which statement about entropy is correct?

AEntropy decreases because no work is done
BEntropy increases because the process is irreversible
CEntropy stays the same since internal energy is unchanged
DEntropy decreases toward minimum at equilibrium
Distractor C targets a common conflation between internal energy and entropy — drawn from a student misconception identified in past exam data for this course.
Deployment

Your data. Your infrastructure.

Cloud

Managed deployment

Get up and running in a day. We host, maintain, and scale the infrastructure. FERPA-compatible data handling with SOC 2 compliance in progress.

Audio processed via our secure pipeline
Student responses stored on our infrastructure
SOC 2 compliance (in progress)
FERPA-compatible data handling
On-premise

Self-hosted

Deploy on your institution's servers. Student data never leaves your network. Bring your own AI credentials, or run a local model.

No student data leaves your network
Bring your own AI credentials or local model
Self-contained database — no external dependencies
Docker image available on request

Ready to try it?

One setup session. Works with slides you already have. No new tools for students.

Request a demoBack to overview