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Computer Science II
How to Perform a Binary Search?
Kurnell
Lecture 3
Notes.pdf
CanvasQuiz 1

AI conversations are enriched with course content, providing contextual responses based on lectures, assignments, and materials.

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Deep Analysis of AI Usage

Instructor
Student: s4536325
Computer Science II
How to Perform a Binary Search?
Insights:
Interaction Type
Learning
Confidence Score
0%

Real-time analytics help instructors understand student interactions and identify learning patterns for better course outcomes.

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82% of students use
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on a regular basis

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AI Usage Analytics

Computer Science II • Live Insights

Kurnell
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

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"Having an AI that could reference the assignment brief and explain the requirements saved me hours. I didn't have to bounce between documents — it was all in one place."

Undergraduate student, Faculty of Business

"For a technical subject in my degree, it helped me break down the steps rather than just giving me the solution. It even flagged parts where I could lose marks — that feedback made a real difference."

Bachelor of IT student

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3rd year Computer Science

"It referenced my lecture notes and showed me where my argument was weak. Game changer."

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Academic staff

"It's not about detecting AI anymore. It's about understanding it. This platform gets that."

Course coordinator

"I can stop playing detective and focus on teaching again."

Senior lecturer

"Having an AI that could reference the assignment brief and explain the requirements saved me hours. I didn't have to bounce between documents — it was all in one place."

Undergraduate student, Faculty of Business

"For a technical subject in my degree, it helped me break down the steps rather than just giving me the solution. It even flagged parts where I could lose marks — that feedback made a real difference."

Bachelor of IT student

"I honestly didn't expect much at first, but this was the first AI tool that felt tailored to my course. It referenced actual lecture content and helped me understand the underlying concepts."

Dual-degree student (Engineering & Business)

"I didn't have to copy-paste between rubrics and ChatGPT anymore."

3rd year Computer Science

"It referenced my lecture notes and showed me where my argument was weak. Game changer."

Business major

"When we reviewed the usage data during our finance assessments, it was clear: students using the tool for structured assistance made fewer common errors and produced more coherent arguments."

Teaching staff, undergraduate business program

"We trialled it in a computer systems subject. This gave us visibility into student learning behaviour — not just plagiarism. We could see who was using AI constructively and who was relying on it to replace learning."

Course coordinator, cross-faculty undergraduate unit

"Kurnell showed us which students were learning — and which were shortcutting."

Academic staff

"It's not about detecting AI anymore. It's about understanding it. This platform gets that."

Course coordinator

"I can stop playing detective and focus on teaching again."

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Referencing Lecture Content

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Computer Science II
Referencing Lecture Content
Kurnell
In Lecture 7 you mentioned that quicksort can be O(n²) in worst case, but I'm still confused about when this happens vs the O(n log n) case?
Student
Lecture 7: Sorting Algorithms
AI Assistant
Great question! You're referring to slide 15 where we covered pivot selection strategies. The time complexity depends entirely on how the pivot divides the array:
AI Assistant
• **Best/Average case: O(n log n)** - When pivot divides array roughly in half • **Worst case: O(n²)** - When pivot is always the smallest/largest element As I showed in the demo on slide 18, this happens with already sorted arrays using first element as pivot.
Ah yes! That example with the sorted array made it click. So randomized pivot selection helps avoid this?
Student
AI Assistant
Exactly! That's the key insight from slide 23 - randomized quicksort gives expected O(n log n) performance by avoiding predictable worst-case inputs.

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