Student Interview Protocol

CS 205 Data Structures — Spring 2026
Semi-structured interviews for qualitative data collection

Overview

Purpose: Understand why and how students experienced the AI-assisted teaching materials. Complements quantitative data (pre/post test scores, survey Likert ratings) with rich qualitative insights.

Participants: 6–10 students from Section A (Prof. Weihao's section)

Duration: 15–20 minutes per interview

When: Last week of class or finals week (after post-test and post-survey)

Where: Your office, quiet classroom, or Zoom (if in-person isn't possible)

Incentive (optional): Small gift card ($5–10 coffee/dining) or extra credit point

Participant Selection

Select for maximum variation — you want diverse perspectives, not just happy students:

2–3 high gainers — students whose post-test score improved significantly over pre-test
2–3 low/no gainers — students who didn't improve much (their perspective is just as valuable)
2–3 varied AI hint users — some who used hints heavily, some who rarely used them
Mix of demographics — different majors, prior experience levels

You can identify candidates after grading the post-test by matching anonymous IDs to pre-test scores.

Tip: Send a class-wide email inviting volunteers, then purposefully select from the volunteer pool to get the variation above. Don't reveal why you picked specific students.

How to Record Audio

Recording lets you quote students directly in your paper — this is what makes qualitative data powerful. You don't need fancy equipment.

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Option 1: iPhone Voice Memos Recommended

Simplest setup. Built into every iPhone, records high-quality audio, and files are easy to export.

Open the Voice Memos app on your iPhone
Place the phone on the table between you and the student, screen up
Tap the red record button to start
Say: "This is interview [number], [date]" at the beginning
Tap stop when done
Rename the recording (e.g., "Interview-01-2026-04-28")
Share via AirDrop, email, or Files to back up

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Option 2: Zoom Recording Good for remote

Works for remote interviews or if you want automatic transcription.

Start a Zoom meeting (even in-person — just have both sit at your desk)
Click Record → Record to this Computer
Zoom saves an audio file (.m4a) automatically when you end the meeting
Enable Audio Transcript in Zoom settings for free auto-transcription

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Option 3: Otter.ai Best for transcription

Free app (300 min/month) that records AND transcribes in real time. Huge time saver.

Download Otter.ai (free) on your phone
Sign up with your university email
Tap Record — it transcribes as you speak
After the interview, you'll have both audio and a searchable transcript
Export transcript as .txt or .docx

My recommendation: Use Otter.ai if you have time to set it up — it saves hours of manual transcription. Otherwise, Voice Memos is perfectly fine. You can always use Whisper (OpenAI's free speech-to-text) later to transcribe Voice Memo files: whisper recording.m4a --model medium --language en

Recording Consent

Interview Questions

How to use this guide: Ask the main question, then use the probes only if the student gives a short answer or doesn't touch on the topic naturally. Let students talk — don't rush to the next question. Silence is okay.

Warm-Up Opening (2 min)

Q1 — WARM-UP

How are you feeling about this semester overall? How did Data Structures go for you?

Probes if needed: Was it what you expected? Was it harder/easier than you thought?

Section A General Learning Experience (5 min)

Q2 — LEARNING

Think back over the semester — was there a specific moment or topic where something really clicked for you?

Probes: What helped it click? Was it something in class, a resource, studying on your own?

Q3 — CHALLENGES

What was the most challenging part of the course for you?

Probes: How did you try to overcome it? What would have helped?

Q4 — MATERIALS

What did you think about the course materials and resources this semester? Anything that stood out — good or bad?

Probes: Slides, examples, online materials? Anything different from other courses?

Section B AI-Assisted Content (5–7 min)

Q5 — AI AWARENESS

During the semester, did you notice anything different about how some of the course content was created or delivered?

Note: Don't reveal AI involvement yet — see if they noticed on their own. If they mention AI, follow up. If not, move to Q6.

Q6 — AI REVEAL & REACTION

Some of the teaching materials in this course were generated or enhanced with AI tools. How do you feel about that?

Probes: Were you surprised? Does knowing that change how you feel about the materials? Would you have wanted to know earlier?

Q7 — AI HINTS

How did you use the AI-generated hints when they were available? Walk me through what that looked like.

Probes: Did you use them every time? Did you try the problem first? Were they helpful or not? Can you give me a specific example?

Q8 — AI IMPACT

Do you think the AI-generated content helped you learn, didn't make a difference, or maybe even got in the way?

Probes: Why do you think that? Can you think of a specific time it helped or hurt? How would the course have been different without it?

Section C Confidence & Self-Reflection (3–4 min)

Q9 — CONFIDENCE

Compared to the start of the semester, how confident do you feel about data structures now?

Probes: What gave you that confidence (or what held it back)? Could you explain a BST or hash table to a friend?

Q10 — OVER-RELIANCE

Some students worry about relying too much on AI tools. Did that ever cross your mind?

Probes: Did you ever feel like you understood something because of the hint but then couldn't do it on your own? How did you balance using hints vs. figuring things out yourself?

Closing Wrap-Up (2 min)

Q11 — IMPROVEMENT

If you could change one thing about how this course was taught, what would it be?

Probes: More of something? Less of something? Something completely different?

Q12 — FUTURE

Would you want AI-assisted materials in your other courses? Why or why not?

Probes: All courses or specific types? What would make it work well vs. poorly?

Q13 — OPEN

Is there anything else about your experience this semester that you'd like to share — anything I didn't ask about?

Note: This often produces the best quotes. Let them talk.

After Each Interview

Post-Interview Checklist

Save and back up the audio file immediately

Write 3–5 bullet points of your impressions while they're fresh (1 min)

Note anything surprising or that contradicts your expectations

Label the file: Interview-[##]-[date]-[pseudonym] (e.g., Interview-03-2026-04-30-StudentC)

Transcribe within 48 hours (while context is fresh)

How to Analyze & Use in Your Paper

Step 1: Transcribe

Use Otter.ai, Zoom auto-transcription, or Whisper to convert audio to text. Clean up obvious errors but keep the student's words — don't polish their grammar.

Step 2: Code Themes

Read through all transcripts and highlight recurring patterns. Common themes might be:

Perceived helpfulness — "the hints helped me break down problems"
Self-regulation — "I tried on my own first before looking at hints"
Over-reliance concern — "I worried I wasn't really learning"
Engagement — "it made studying more interesting"
Trust in AI — "I wasn't sure if the hints were always correct"
Comparison to other courses — "I wish my other classes had this"

Step 3: Write It Up

In the Results section of your paper, weave quotes into your quantitative findings:

"Students who showed high learning gains (pre-post delta > 5) attributed their improvement to the interactive hints. As Student C explained: 'When I was stuck on heaps, the hint didn't just give me the answer — it asked me to think about what the parent-child relationship means. That's when it clicked for me.'"

Aim for 2–3 representative quotes per theme. Include both positive and critical perspectives.

For your paper: The standard approach is to report "semi-structured interviews were conducted with N=X students from the treatment section, selected for maximum variation in learning gains and AI hint usage. Interviews were audio-recorded, transcribed, and analyzed using thematic analysis (Braun & Clarke, 2006)."

Suggested Timeline

Week 15 (now): Send volunteer recruitment email to Section A
Week 16: Conduct 6–10 interviews (2–3 per day over 3–4 days)
Week 16–17: Transcribe all interviews
Summer: Code themes and integrate into paper