Here are some of the best questions for an online course student survey about assessment fairness, plus tips on how to craft them effectively. You can generate a fully tailored survey in seconds with Specific—no headaches, just deep insights every time.
Best open-ended questions for course assessment fairness feedback
We use open-ended questions to collect rich, nuanced insights that closed questions often miss. They’re great for surfacing opinions and issues we haven’t anticipated—a necessity when exploring students’ perceptions of assessment fairness. Open-ended responses can reveal context behind scores and help us identify hidden barriers or flaws. The value of this approach is backed up by research: a cross-industry study revealed that 81% of respondents mention issues not listed in rating grids, proving the unique insights open answers can provide. [3]
How would you describe the fairness of the grading criteria in this course?
Can you share an example of a time when you felt an assessment was unfair? What could have made it better?
What changes would help make assessments feel more fair?
Do you think the instructor explains expectations clearly before assessments? Please elaborate.
Were there any parts of the assessment process that were unclear or confusing?
How did feedback on your assessments affect your perception of fairness?
To what extent do you feel assessments match what is taught in the course?
If you could change one thing about how you’re assessed, what would it be and why?
Are there any circumstances that made completing assessments difficult for you?
What advice would you give to improve the fairness of this course's assessments for future students?
Open-ended questions let students get specific—and they love sharing details. In fact, a study of over 75,000 hospital patients found 76% left at least one open-text comment, showing just how ready people are to give feedback in their own words. [1] Still, open-ended answers can suffer from higher item nonresponse rates (as much as 18%, compared to 1-2% for closed questions) [2], so use a mix for balanced data.
Best single-select multiple-choice questions for student assessment fairness surveys
Single-select multiple-choice questions are perfect for quantifying key touchpoints and quickly surfacing common patterns. When we want to kick-start a conversation or lower the effort for students to respond (especially on mobile), this format gets us actionable data fast—while making it easy for students to engage and start thinking before you probe deeper with follow-ups.
Question: Overall, do you feel the assessment methods in this course were fair?
Yes, always
Most of the time
Sometimes
Rarely
Never
Question: How clear were the grading criteria before each assessment?
Very clear
Somewhat clear
Not very clear
Not at all clear
Other
Question: Did you feel you had equal opportunity to succeed on assessments compared to your classmates?
Yes
No
Not sure
When to follow up with "why?" Use a "why" follow-up whenever a student chooses a critical or ambiguous answer—something like “Not at all clear” or “No”—to dig into the underlying cause. This adds priceless context to your quantitative data, explaining the “what” with a powerful “why”. For example, if a student answers “Not very clear” to grading criteria, follow up by asking, “Why were the criteria unclear to you?”
When and why to add the "Other" choice? Sometimes you can’t predict every possible answer—real life is messy! Adding “Other” gives students a voice if their experience doesn’t fit your options. The magic happens when you follow up with, “Please describe,” allowing you to uncover solutions or problems you didn’t even know existed.
NPS for assessment fairness: does it make sense?
Net Promoter Score (NPS) is typically used to measure overall satisfaction and loyalty, but it can be adapted to assess trust and advocacy around assessment fairness. By asking, “How likely are you to recommend this course to a friend specifically because of its fair assessments?” on a 0–10 scale, you’re able to benchmark and track fairness perceptions over time. With NPS follow-ups, you’ll also catch hidden concerns or promoters you might otherwise overlook. Try instantly creating an NPS survey for online course students about assessment fairness to see how this works in practice.
The power of follow-up questions
Specific’s automatic AI follow-up questions turn your survey into a truly conversational experience. By asking smart, context-aware follow-ups, we avoid half-answers, clarify needs, and uncover the story behind each response. If you want to see how this works, check out our feature breakdown on automatic AI follow-up questions.
Automated follow-ups save hours that would normally go into manually clarifying with students via email. Instead, students get a prompt, expert-like conversation in real time—feels more natural and more likely to generate complete, insightful responses. This is where Specific shines: the AI adapts to the situation and extracts meaningful context.
Student: "Sometimes I felt the grading wasn’t fair."
AI follow-up: "Can you describe a specific assignment where you felt this way? What made it seem unfair?"
We’ve seen that without a follow-up like this, teams are left guessing at root causes—making analysis nearly impossible.
How many follow-ups to ask? In our experience, 2–3 targeted follow-ups usually yield enough context to see the bigger picture. With tools like Specific, you can fine-tune how persistent the AI should be, or let students skip when they’ve said enough. It’s about balance—deeper insight, not survey fatigue.
This makes it a conversational survey: Respondents quickly get used to the natural flow, making the exchange feel more like a chat and less like a test. It’s conversational—and it works.
AI analysis of survey responses: You’re probably wondering what to do with all this unstructured data. With AI-powered analytics, analyzing lots of open-ended responses becomes simple. AI clusters themes, summarizes opinions, and cuts through the noise, so you’re not buried in text.
This follow-up mechanism is a new way of collecting data. Try generating a survey and experiment with conversational feedback—you’ll immediately see the difference.
How to compose prompts for AI-generated assessment fairness questions
Feeling stuck or want to go beyond templates? ChatGPT and similar AIs are fantastic survey helpers when you ask the right way. Start simple, then add details for context.
Ask for a set of questions:
Suggest 10 open-ended questions for Online Course Student survey about Assessment Fairness.
But you’ll get even better results by telling the AI more—explain your goals or describe your students and course format. For example:
Our online course includes live lectures and project-based assignments. Suggest 10 open-ended questions to ask students about their perception of assessment fairness, so we can improve clarity and reduce bias.
Once you have a question list, organize and refine:
Look at the questions and categorize them. Output categories with the questions under them.
Then, focus your next prompt on the areas that matter most:
Generate 10 questions for categories [Clarity of Assessment Criteria] and [Improving Fairness].
This workflow gives you a systematic way to tailor surveys—for every course, every audience, every issue.
What is a conversational survey?
A conversational survey feels like a back-and-forth chat, not a cold list of boxes to tick. Instead of collecting short, incomplete responses, it listens and probes further, encouraging respondents to open up. Unlike traditional forms, which often end with confusion and follow-up emails, conversational surveys clarify on the spot. Thanks to platforms like Specific and its AI survey builder, creating these rich experiences is fast, flexible, and friendly for both creators and students.
Manual Surveys | AI-Generated Surveys |
|---|---|
Static forms, same for everyone | Dynamic, adapts to each answer |
Hard to iterate or personalize | Easy to update via AI survey editor |
Time-consuming to analyze feedback | Instant insights with AI analysis |
Low completion rates, limited detail | Feels natural, increases engagement |
Why use AI for online course student surveys? Simple: AI-powered surveys save you time, collect better data, and help you deeply understand issues like assessment fairness—even when students’ answers are complex or surprising. Plus, with conversational AI, we get more honest responses and uncover actionable recommendations that would otherwise stay hidden.
Want to see how to easily build your own? Here’s a guide on creating course assessment fairness surveys in minutes—no hassle, just results. Specific delivers best-in-class conversational survey experience, making feedback smoother for everyone involved.
See this assessment fairness survey example now
Try a new approach to assessment fairness feedback—see how real conversational surveys, powered by Specific, can turn passive feedback into action in days, not weeks. Create your own now and make fairness a reality, not just a talking point.