AI Usage Analytics

Understand exactly how your students engage with AI tools

Gain comprehensive insights into student AI interactions across your courses. Monitor engagement patterns, session frequency, and learning trajectories to distinguish between constructive learning partnerships and surface-level task completion.

Track authentic learning behaviors, identify students who need additional support, and recognize those effectively using AI to deepen their understanding.

Course Analytics

Computer Science II

Live Dashboard
Total Interactions
2,847
Active Students
156
Avg Session Time
12m
Risk Alerts
3

Student Usage Patterns

Alice ChenLow
Gradual Learner
Bob MartinezHigh
Last-Minute
Charlie KimMedium
Balanced
Diana PatelHigh
One-Shot

Course-Aware AI Assistant

AI that understands your curriculum, materials, and learning objectives

Course Assistant
CSSE2002
Can you help me understand the quicksort algorithm from @CSSE2002/lecture7?
Lecture 7: Sorting
I can see you're referencing Lecture 7 from your Computer Science course. Let me explain quicksort using the examples from your lecture materials...
Course Syllabus
Textbook Ch. 4
Try typing @CSSE2002/assignment1...

Provide students with AI assistance that seamlessly integrates with your course materials. Our @reference system enables direct citations of lecture videos, assignment rubrics, readings, and course resources, ensuring responses align with your specific curriculum.

Transform generic AI interactions into contextually relevant guidance that reinforces your teaching objectives and course-specific concepts.

The Best Models for All

Ensure equal access to the most advanced AI capabilities for every student

Eliminate educational inequality by providing all students access to premium AI models through institutional accounts. Choose from Claude, GPT-4, Gemini, and other leading models based on the specific requirements of each assignment or learning objective.

Maintain full administrative oversight while ensuring no student is disadvantaged by subscription limitations or access restrictions.

AI Model Selection

Choose the best model for your task

Claude 4 Sonnet
Reasoning & Analysis
Anthropic
GPT-4o
Multimodal
OpenAI
Gemini 2.5 Flash
Code Generation
Google
Claude 4 Opus
Research & Citations
Anthropic
GPT-4.1
Creative Writing
OpenAI
5+
Models Available
100%
Student Access
24/7
Availability

Learning Gap Intelligence

Identify and address course content that consistently challenges students

Learning Gap Analysis

CSSE2002 - Week 8

Updated 2h ago

Topics Requiring Attention

Recursive Functions
143
Database Normalization
98
Memory Management
84
Big O Notation
67

Frequent Questions

1
How do I trace through a recursive call?
2
What's the difference between 2NF and 3NF?
3
When should I use malloc vs calloc?
4
Why is this algorithm O(n²)?
Suggested Actions
• Schedule additional recursion workshop • Create video walkthrough for normalization • Update assignment instructions for clarity

Analyze student AI interactions to identify recurring conceptual difficulties and knowledge gaps across your curriculum. Our system highlights topics that generate frequent help requests and pinpoints areas requiring instructional enhancement.

Transform aggregate student challenges into data-driven improvements for course design, lecture content, and assignment clarity.

Attach a Chat to Assessment

Make the learning process visible through shareable AI conversations

Enable students to document and share their AI-assisted learning journey alongside assignment submissions. Transform private AI interactions into transparent learning evidence that demonstrates critical thinking and iterative problem-solving.

Foster academic integrity through transparency while recognizing students who effectively use AI as a learning catalyst rather than a completion tool.

Assignment Submission

Showcase your learning process

Database_Assignment.pdf
2.3 MB • Submitted 2 hours ago
Submitted successfully
Learning Chat History
45 minute conversation • 23 exchanges
Referenced: Lecture 12, Assignment rubric
Asked clarifying questions about normalization
Iterated through multiple solution approaches
Chat Preview
Student: "I'm confused about the difference between 2NF and 3NF in @CSSE2002/assignment3"
AI: "Let me help you understand using the example from your assignment..."
View full conversation →
Learning Process Visible
Instructors can see authentic engagement and iterative problem-solving approach

Beyond Plagiarism Detection

Move beyond detection to understanding authentic learning patterns

Deep Intelligence Analysis

Kurnell

Traditional Detection

AI Generated Text
73%
False Positives
67%

Kurnell Insights

Learning Conversations
12 sessions
Authentic Engagement
Evidence-based
Learning Pattern: Alice Chen
Week 1
Week 2

Replace unreliable AI detection tools with comprehensive usage pattern analysis. Distinguish between students using AI for ideation and exploration versus direct content generation, providing evidence-based insights into learning authenticity.

Make assessment decisions based on demonstrated learning behaviors rather than algorithmic guesswork, supporting academic integrity while embracing AI as an educational tool.

Ready to Transform AI Education at Your Institution?

Join leading universities in creating transparent, accountable, and effective AI-assisted learning environments.