Q01
Message
All templates
Campus Climate, Belonging, and Inclusion Survey
Measures students' sense of belonging, perceived fairness and safety, and firsthand experiences with exclusionary or discriminatory treatment on campus. Built for institutional research and DEI offices, with an AI follow-up interview that reconstructs the specific incident or context behind a student's belonging score instead of leaving it as an abstract number.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
14 questions · ~7 min
Q02
Opinion ScaleRequired
Overall, how strongly do you feel you belong at (Replace with institution name)?
Scale: 1 – 7
Min:Not at allMax:Extremely strongly
Q03
MatrixRequired
How much do you agree or disagree with each statement about campus climate?
5 rows × 5 columns
- Students of different backgrounds are treated with respect here
- Faculty and staff create an inclusive learning environment in my classes
- I feel physically safe on campus, including at night
- Campus policies and discipline are applied fairly regardless of a student's background
- I would recommend this campus's environment to a prospective student like me
Columns: Strongly disagree · Disagree · Neutral · Agree · Strongly agree
Q04
Multiple ChoiceRequired
In the last 12 months, how often have you personally experienced exclusionary, disrespectful, or discriminatory treatment on campus?
- Never
- Once
- A few times
- Regularly
Q05
Multiple Choice
In the last 12 months, how often have you witnessed someone else experience this kind of treatment on campus?
- Never
- Once
- A few times
- Regularly
- Not sure
Q06
Opinion ScaleRequired
If you experienced or witnessed a climate concern, how comfortable would you be reporting it to the university?
Scale: 1 – 5
Min:Very uncomfortableMax:Very comfortable
Q07
Best–Worst Trade-off (MaxDiff)Required
Which of these should the university prioritize improving first? Pick the one that matters most and the one that matters least each round.
- More diverse faculty and staff
- A clearer, easier process for reporting incidents
- Mandatory bystander/allyship training
- More inclusive course content and assignments
- Better lighting and security across campus
- More funding for identity-based student organizations
- Faster, more visible follow-up after a concern is reported
Pick best & worst per setBest:Improve firstWorst:Improve last
Q08
Multiple Choice
Which of these campus resources have you used in the past year?
- Counseling or mental health services
- Diversity & inclusion office
- Student advocacy or ombuds office
- Bias incident reporting system
- Peer support or affinity group
Q09
AI Interview
Ground the respondent's belonging score and reporting-comfort answer in a real, specific situation: what happened, who was involved, and what shaped how included or excluded they felt. If they reported experiencing or witnessing exclusionary treatment, gently probe what happened, whether they reported it, and why or why not. If their belonging score was high, probe what specifically makes campus feel that way so it can be replicated elsewhere.
Q10
Message
Almost done — just a few quick background questions to help us see patterns across student groups. Feel free to skip any of these.
Q11
Multiple Choice
What is your current academic level?
- First-year undergraduate
- Sophomore
- Junior
- Senior
- Graduate or professional student
- Other
- Prefer not to say
Q12
Multiple Choice
How do you describe your gender identity?
- Woman
- Man
- Non-binary
- Prefer to self-describe
- Prefer not to say
Q13
Multiple Choice
How do you describe your race or ethnicity? Select all that apply.
- American Indian or Alaska Native
- Asian
- Black or African American
- Hispanic or Latino/a/x
- Middle Eastern or North African
- Native Hawaiian or Pacific Islander
- White
- Prefer not to say
Q14
Message
Thank you for your honesty. Your responses are combined with others across campus to inform belonging, safety, and inclusion initiatives — no individual answers are shared with instructors or advisors.
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- Goes beyond a belonging score with an AI follow-up interview that asks respondents to reconstruct the actual incident or context behind a low belonging or reporting-comfort answer, giving DEI offices a real narrative instead of a number
- Pairs quantitative measures (opinion-scale belonging score, climate agreement matrix, frequency-of-experience and witnessing questions, MaxDiff prioritization, resource-usage checklist) with qualitative depth in one flow
- Uses transparent, reviewable AI prompts and automated per-response quality scoring, so IR and DEI teams can audit exactly what was asked and how responses were graded
- Auto-generates a report from combined structured and interview data, with a free tier available and a $50/mo Business plan for full institutional use — no separate academic tier is offered
Jotform
Campus Climate Survey Form TemplateA ready-to-field, drag-and-drop form covering standard campus climate questions. It's built on Jotform's general form platform, so customization and distribution are easy, but the questionnaire itself is static and generic rather than purpose-built for DEI/IR incident analysis.
What it does well
- Quick to deploy and customize via a familiar drag-and-drop form builder
- Benefits from Jotform's broad integration and distribution ecosystem
Where it falls short
- Static question set with no adaptive follow-up to probe individual responses further
- No mechanism to reconstruct the specific incident behind a low score — respondents choose from fixed options only
QuestionPro
Campus Climate Survey Questions + Sample Questionnaire TemplateThis page reads more as a question bank and sample questionnaire guide than a single fielding-ready survey, offering example items researchers can copy into their own instrument. It's a useful reference for question design but leaves assembly and logic-building to the user.
What it does well
- Broad library of sample question wording researchers can borrow from
- Backed by an established enterprise survey platform with analytics tools
Where it falls short
- Presented as a sample/guide rather than a ready-to-send template, requiring manual assembly
- No adaptive or voice AI interview component to follow up on individual open-ended answers
SurveyMonkey
Belonging And Inclusion Survey TemplateA polished, ready-to-send template focused specifically on belonging and inclusion, backed by a well-known survey platform with strong distribution and reporting tools. Like most SurveyMonkey templates, it relies on fixed-choice and rating items without any conversational follow-up.
What it does well
- Purpose-built belonging/inclusion focus with clean, tested question flow
- Reliable distribution, panel, and basic analytics tools from an established platform
Where it falls short
- No adaptive AI or voice interview to dig into the context behind a belonging score
- No published methodology for how underlying question logic or scoring works
SurveySparrow
Free School Climate Survey Template | For StudentsA free, conversational-style template covering general school climate themes; it's framed around students broadly and school settings rather than specifically higher-education campus climate or DEI reporting workflows. Useful as a lightweight starting point, but not tailored to institutional research needs.
What it does well
- Chat-style, student-friendly survey format that may improve completion rates
- Free to use as a starting template
Where it falls short
- Oriented toward general school climate rather than campus-specific belonging, fairness, and discrimination reporting
- No adaptive AI follow-up to reconstruct incidents behind low scores, and no automated quality scoring of responses
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.