Faculty Perspectives on AI in Grading & Assessment
An academic research survey exploring faculty attitudes, concerns, and readiness regarding AI-assisted grading and assessment tools. Covers current practices, openness to adoption, concerns about bias/accuracy/privacy, training needs, and willingness to participate in controlled experiments.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
Which of the following best describes your current primary role?
- Full-time faculty (tenured or tenure-track)
- Full-time faculty (non-tenure-track or contract)
- Part-time or adjunct faculty
- Graduate teaching assistant
- Academic administrator with teaching duties
- Research-only position (no teaching)
- Other
Which of the following types of student assessment do you currently use in your courses? (Select all that apply)
- Essays or written assignments
- Multiple-choice or short-answer exams
- Research papers or term projects
- Lab reports or technical assignments
- Presentations or oral exams
- Peer assessments
- Portfolios
- Discussion or participation-based assessment
- Other (please specify)
In the next section, we will ask about your views on AI-assisted grading and assessment. By "AI-assisted grading," we mean the use of artificial intelligence tools that can help evaluate student work — for example, automated scoring of written assignments, AI-generated feedback suggestions, or AI-flagged inconsistencies in grading. These tools are designed to assist faculty, not replace their judgment. Please keep this description in mind as you answer the following questions.
How open would you be to using AI-assisted tools as part of your grading and assessment process?
How concerned are you that AI-assisted grading tools could introduce or perpetuate biases in student assessment?
How concerned are you about student data privacy when student work is processed by AI-assisted grading systems?
How prepared do you currently feel to use AI-assisted grading tools effectively?
Researchers are exploring the possibility of conducting controlled experiments to evaluate AI-assisted grading tools. These experiments would involve faculty volunteers grading a set of student assignments both with and without AI assistance, then comparing outcomes such as grading consistency, time spent, and student satisfaction. Participation would typically require 3–5 hours over a semester and would be compensated. All data would be anonymized.
We'd like to understand your thinking about how AI-assisted grading might affect assessment integrity — the fairness, validity, and trustworthiness of how student learning is evaluated. In your view, what are the most important considerations when thinking about whether AI tools can uphold assessment integrity?
Based on your responses throughout this survey, please share any additional thoughts or feelings about the role of AI in grading and assessment that we may not have covered.
What is your primary academic discipline or field?
- Arts & Humanities
- Biological & Life Sciences
- Business & Management
- Computer Science & Information Technology
- Education
- Engineering
- Health Sciences & Medicine
- Law
- Mathematics & Statistics
- Physical Sciences
- Social Sciences
- Other (please specify)
Thank you for completing this survey. Your perspectives are valuable to understanding how AI tools may shape the future of academic assessment. Your responses have been recorded and will be kept confidential. If you have any questions about this research, please contact the research team at the email provided in your invitation. You may now close this window.
Which of the following activities are part of your current responsibilities? (Select all that apply)
- Designing course curricula or syllabi
- Delivering lectures or leading seminars
- Grading or assessing student work
- Advising or mentoring students
- Conducting research
- Administrative or committee duties
- Supervising teaching assistants
During a typical teaching week, approximately how many hours do you spend grading and providing feedback on student work?
- Fewer than 2 hours
- 2–5 hours
- 6–10 hours
- 11–15 hours
- 16–20 hours
- More than 20 hours
How likely are you to adopt an AI-assisted grading tool within the next two years, assuming one were available and supported by your institution?
How concerned are you about the accuracy of grades or feedback produced by AI-assisted tools?
Please rank the following data protection measures in order of importance to you if AI-assisted grading tools were used at your institution. (Drag to rank, most important first)
- Student work is processed on-campus servers only (no cloud processing)
- AI tool providers cannot retain or use student data for model training
- Students are informed and consent before their work is processed by AI
- Faculty retain full control over whether and how AI tools are used
- Regular third-party audits of AI tool data practices
- Compliance with institutional data governance policies (e.g., FERPA)
If training on AI-assisted grading tools were offered, which formats would you prefer? (Select all that apply)
- In-person workshop (half-day or full-day)
- Online self-paced modules
- Live webinar or virtual workshop
- One-on-one consultation with an instructional technologist
- Peer mentoring from a colleague already using the tool
- Written documentation or user guides
- I would not be interested in training
- Other (please specify)
Based on the description above, how willing would you be to participate in a controlled experiment evaluating AI-assisted grading tools?
How many years of college or university teaching experience do you have?
- Fewer than 3 years
- 3–7 years
- 8–15 years
- 16–25 years
- More than 25 years
How often do you use standardized rubrics when grading student work?
For which of the following assessment types would you consider using AI-assisted grading? (Select all that apply)
- Multiple-choice or short-answer exams
- Essays or written assignments
- Research papers or term projects
- Lab reports or technical assignments
- Presentations or oral exams
- Discussion or participation-based assessment
- Peer assessment moderation
- I would not consider using AI-assisted grading for any of these
- Other (please specify)
How concerned are you that AI-assisted grading could reduce the personal connection between faculty and students in the feedback process?
How many hours of training would you be willing to invest to learn to use an AI-assisted grading tool effectively?
- None — I would not invest time in training
- 1–2 hours
- 3–5 hours
- 6–10 hours
- More than 10 hours
What would be your primary reason for participating or not participating in such an experiment?
- Curiosity about how AI tools perform in grading
- Desire to contribute to evidence-based research on teaching
- Interest in improving my own grading efficiency
- Concern about the time commitment required
- Skepticism about AI's role in assessment
- Discomfort with being evaluated or observed
- Privacy or ethical concerns about the research design
- Other (please specify)
Which best describes your institution?
- Research university (R1/R2 or equivalent)
- Comprehensive or master's-granting university
- Liberal arts college
- Community or two-year college
- Professional or specialized institution
- Other (please specify)
Overall, how satisfied are you with your current grading and assessment process?
What is the typical enrollment size of the courses you teach?
- Fewer than 20 students
- 20–50 students
- 51–100 students
- 101–200 students
- More than 200 students
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
- Includes a dedicated adaptive AI follow-up interview that probes faculty's own reasoning about how AI-assisted grading might affect their teaching relationships, going beyond fixed-choice questions
- Separates concerns into distinct scaled items for bias, accuracy, loss of personal connection, and student data privacy, rather than one generic 'AI concerns' question
- Captures actionable readiness signals: preferred training formats, hours willing to invest, and willingness to join a controlled grading experiment, plus a ranking of data protection priorities
- Closes with an open-text reflection and discipline/experience/institution-size demographics, enabling segmented reporting on faculty AI grading attitudes
SurveyMonkey
AI Readiness Assessment TemplateThis is a general-purpose AI readiness template aimed at organizations assessing AI adoption broadly, not one built for academic faculty or grading/assessment contexts specifically. It's a fielding-ready static form on an established survey platform, useful as a generic AI-attitudes starting point but not tailored to higher-ed grading concerns. Researchers would need to heavily customize it to cover rubric use, bias-in-grading, or controlled experiment participation.
What it does well
- Backed by a widely-used, established survey platform with broad distribution and analytics tools
- Provides a ready-made framework for gauging general AI adoption attitudes
- Likely offers standard reporting/benchmarking features typical of SurveyMonkey templates
Where it falls short
- Static question set with no adaptive AI follow-up probing into individual faculty reasoning
- Generic organizational AI-readiness focus, not tailored to academic grading, rubrics, or bias-in-assessment concerns
- No voice AI interview option, per-response quality scoring, or published prompt-level methodology
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.
Related templates
More studies from the same category.
AI Ethics Awareness Survey for Students
An academic instrument measuring student awareness of, exposure to, and attitudes toward artificial intelligence ethics. Covers concept familiarity, training exposure, ethical dilemma responses, regulation views, and willingness to prioritize ethics over convenience. Estimated completion: 8–12 minutes.
View templateStudent Attitudes Toward AI Writing Tools
An academic research survey examining how college and university students perceive and use AI writing tools such as ChatGPT, Copilot, and similar technologies. The survey covers usage patterns, perceived benefits and dependency concerns, academic integrity perspectives, quality comparisons, and instructor communication about AI policies.
View templatePost-Training Learning Transfer Assessment
Measures how effectively employees apply recent training to their jobs, identifying barriers, enablers, and perceived outcomes to improve learning transfer and training ROI.
View templateTeacher Workload & Automation Readiness Assessment
A structured instrument for school and district leaders to quantify educator administrative burden, identify top time sinks, and assess readiness for workflow automation. Designed for K–12 teaching staff and instructional support roles.
View templateCourse Engagement & Learning Outcomes Pulse Survey
A bi-weekly pulse survey measuring student engagement behaviors, support resource usage, and perceived learning progress. Designed for course instructors and instructional designers seeking actionable insights to improve eLearning and hybrid course experiences.
View templateRemote Exam Fairness & Privacy Perceptions Survey
Measures student perceptions of fairness, privacy, and acceptability of proctoring practices in remote exams. Designed for higher-education institutions seeking to evaluate and improve remote assessment policies.
View template