Here are some of the best questions for a student survey about part-time employment support, plus practical tips for getting deeper insights. With Specific, you can build your own conversational survey in seconds—no technical skills needed.
Best open-ended questions for a student survey about part-time employment support
Open-ended questions are powerful—they unlock detailed, honest stories you’d never get from simple yes/no questions. When you want rich context, unexpected perspectives, or to truly understand why students feel a certain way, these questions shine. They also reduce bias and make respondents feel valued.[1][2][3][4][5]
What challenges have you faced in finding part-time employment while studying?
How has working part-time impacted your academic performance or workload?
What kind of support do you wish the university offered to help students find jobs?
Can you describe a positive (or negative) experience you’ve had with on-campus employment support?
How do you usually search for part-time work opportunities?
What skills or qualifications have you gained from part-time work that help your studies or future career?
Are there particular industries or job types you’d like more support or information about?
What obstacles prevent you from working part-time (e.g., scheduling, transportation, lack of opportunities)?
What suggestions do you have to improve the support services for students seeking part-time jobs?
How do you balance your part-time work with academic, social, and personal commitments?
The benefit? Students can provide context, highlight challenges, and share actionable suggestions—resulting in more effective support programs.[1][3]
Best single-select multiple-choice questions for a student survey about part-time employment support
Single-select multiple-choice questions are your tool for quick, structured answers—perfect for quantifying data or when you want to get a conversation started. These questions are especially helpful when students may find it daunting to articulate complex thoughts from scratch. They can easily select an answer and, if needed, you can dig deeper with follow-ups.
Question: What is your main reason for seeking part-time employment?
To earn extra income
To gain work experience
To develop new skills
To meet people/socialize
Other
Question: Which type of part-time job are you most interested in?
On-campus positions
Off-campus positions
Remote/online jobs
No preference
Other
Question: Have you ever used the school’s part-time employment support services?
Yes, and it was helpful
Yes, but it was not helpful
No, I was not aware of these services
No, I did not need them
Other
When to follow up with "why?" It’s gold to ask “why?” when a student picks a specific option, as it reveals their actual motivations and barriers. For example: If a student selects “To gain work experience,” a follow-up like, “Why is gaining work experience important to you right now?” can open up deeper insight about their goals.
When and why to add the "Other" choice? Adding “Other” invites students to write in personalized answers you might not have anticipated. Following up on these responses can uncover needs or opinions you didn’t plan for and can be a rich source of insight.
NPS question for student surveys about part-time employment support
NPS (Net Promoter Score) is a simple, scalable way to measure student satisfaction and loyalty toward your support services. By asking, “How likely are you to recommend our part-time employment support to another student?” you get a clear, comparable metric of effectiveness. It also opens the door for valuable follow-ups: Why did they give that score? With NPS, you can track changes over time and segment responses for targeted improvements. Generate an NPS survey for student part-time employment support for an easy start.
The power of follow-up questions
Follow-up questions are where conversational surveys shine. According to recent research, open-ended responses are packed with insights, but they can be incomplete or unclear without probing further.[1][2][3] Specific’s automated follow-ups use AI to ask context-aware questions in real time—just like an expert would. This saves you the back-and-forth (no more tracking down respondents via email), making the survey feel like a smooth, natural chat. Learn more about automated follow-ups and unlock richer context.
Student: “I had trouble finding relevant part-time jobs.”
AI follow-up: “What made it difficult for you to find jobs that matched your skills or schedule?”
How many follow-ups to ask? Usually, 2–3 follow-ups per key question are plenty. Having the option to stop once the student has explained enough is crucial—Specific enables this, so you avoid survey fatigue and collect just the right level of context.
This makes it a conversational survey: instead of a static form, it feels dynamic and two-way, increasing engagement and honesty from students.
AI analysis, unstructured text: Even with lots of free-text responses, it’s easy to analyze the data using AI. See how AI makes student survey analysis effortless with summaries, themes, and deep dives—no spreadsheets needed!
Automated follow-up questions are still a new concept—try generating a survey to see just how much more actionable your feedback can be.
How to prompt ChatGPT to generate questions for a student survey about part-time employment support
If you like brainstorming with AI, clear prompts make a big difference. Try this simple initial instruction:
Suggest 10 open-ended questions for student survey about part-time employment support.
But you'll get better results with more detail: include your specific goals, audience, and desired insights. For example:
We’re planning a survey for university students to improve our part-time job support services. Our goal is to uncover challenges, unmet needs, and ways to make these services more effective. Please suggest 10 open-ended, actionable questions.
After generating an initial list, refine the process:
Look at the questions and categorize them. Output categories with the questions under them.
Pick the most relevant categories and dive deeper:
Generate 10 questions about 'Challenges in finding jobs', 'Balancing work and study', and 'Desired improvements in support services'.
What is a conversational survey?
A conversational survey is designed to feel like a real dialogue, not a rigid questionnaire. Thanks to AI (and especially GPT-powered platforms like Specific), surveys can now ask relevant, on-the-fly follow-ups, clarify unclear points, and adapt the tone to the respondent—just like a good interviewer would.
This stands in stark contrast to traditional survey creation, which is often manual, repetitive, and lacks contextual follow-up. With Specific’s AI survey generator, you simply describe what you want, and the platform structures questions, logic, and tone in seconds. It’s an enormous timesaver (especially for longer, nuanced surveys).
Manual Survey | AI-Generated Survey |
|---|---|
Create all questions one by one, adjust logic manually | AI creates a full, tailored survey with conversation flow from a short prompt |
No auto follow-up, stuck with static questions | Dynamic, real-time follow-ups for richer context |
Harder to analyze lots of text | AI groups, summarizes, and helps you chat about results instantly |
Why use AI for student surveys? AI-driven surveys reach students where they are—on their phones, in chat. The experience is less formal and more engaging, which means more honest, detailed feedback. AI survey examples show higher completion rates and richer data simply because the conversation feels less like a “test.”
Specific is known for its conversational survey user experience—it makes responding easy and the feedback process enjoyable whether you are the creator or student respondent. If you want a deeper dive into how to launch your own AI survey, check the detailed guide on creating student surveys about part-time employment support.
See this part-time employment support survey example now
Experience how smart surveys can reveal actionable insights from students. See how conversational AI, real-time follow-ups, and effortless analysis set your student feedback apart.