The short answer
How should faculty use AI responsibly?
Start with the learning outcome. Decide what students must practice themselves, where AI may support learning, and what evidence will show their reasoning. Use only approved tools and appropriate data, disclose meaningful faculty uses, verify outputs, provide an accessible path, and retain responsibility for teaching and evaluation.
This voluntary planning resource is not legal advice or a substitute for institutional policy, academic-freedom commitments, accreditation requirements, accessibility processes, research oversight, or disciplinary procedures.
Put the playbook to work
Download the Faculty AI Implementation Kit
Use the editable worksheets and copy-ready language to set course and assignment rules, redesign an activity, review a faculty AI workflow, and document a fair evidence review when misuse is suspected.
Match control to consequence
Faculty AI decision map
Classify the activity–not the tool alone. The same product may present different risks depending on the data, learning objective, audience, automation, and consequence of error.
Lower risk: explore
- Brainstorm examples with no student data
- Generate practice questions for faculty review
- Rewrite faculty-authored instructions in plain language
- Develop multiple lesson-plan options
Controlled: verify and document
- Feedback or rubric support
- Student-facing tutoring tools
- Research, coding, or data-analysis assistance
- AI-supported grading preparation
Do not delegate
- Final grades or misconduct findings
- Accommodation or disability decisions
- High-stakes advising without qualified review
- Research compliance or safety approval
Make expectations usable
Set rules by activity, not one vague sentence
A course-wide statement is helpful, but students also need assignment-level directions. Explain the learning reason for each rule and what evidence of process is expected.
AI required
Students must use a named, accessible tool or approved alternative to meet a defined learning objective. Teach the skill and the limits before assessing it.
AI permitted
Students may use AI for stated purposes, such as brainstorming or feedback, with disclosure, verification, and any required citation.
AI limited
AI is allowed for specific steps but not others. Name the boundary–for example, idea generation is allowed, but the submitted analysis must be the student’s.
AI prohibited
Students must complete the activity without AI because independent performance is the learning outcome. Define what counts as AI and provide a clear reason.
Design for visible learning
Assessment that remains meaningful with AI
There is no single "AI-proof" format. Use a balanced set of evidence that helps students demonstrate knowledge, process, judgment, and improvement.
Show the process
Use proposals, annotated sources, decisions, drafts, revision notes, or version history when they serve the learning outcome.
Use authentic context
Connect work to local cases, course discussions, labs, field observations, unique datasets, or decisions with real constraints.
Require explanation
Add brief oral defense, demonstration, peer dialogue, or reflection so students explain choices and answer questions.
Assess verification
Ask students to test claims, inspect sources, identify uncertainty, correct errors, and explain what they accepted or rejected.
Detection is not a substitute for fair process. Current evidence does not support treating a detector score as proof of authorship, authorization, or misconduct. Performance varies by product, model, dataset, genre, language, and revision method; both false positives and false negatives matter. A score may inform an initial review, but it should not be the standalone or sole basis for an adverse decision. Clarify the rule, review process evidence, speak with the student, and follow the institution’s established procedure.
Protect people and work
Student data, access, copyright, and research
Before using an AI tool, confirm that the institution has approved the product and the specific data use. A consumer account is not automatically suitable for education records or confidential work.
Data boundary
- Do not upload identifiable education records without authorization.
- Remove names and unnecessary identifiers.
- Check retention, model-training, sharing, deletion, and contract terms.
- Use the minimum information needed.
Equitable access
- Provide an accessible, no-penalty alternative when appropriate.
- Do not require personal payment or data disclosure without institutional approval.
- Test keyboard, screen-reader, caption, contrast, and mobile usability.
- Coordinate through established accommodation processes.
Copyright and attribution
- Use licensed or authorized inputs.
- Do not assume generated material is accurate, original, or free to use.
- Preserve meaningful human authorship and editorial judgment.
- Follow publisher, discipline, and institutional rules.
Research and scholarly work
- Follow IRB, sponsor, journal, data-use, and research-integrity rules.
- Do not expose unpublished, proprietary, export-controlled, or participant information.
- Document material AI assistance when required.
- Keep researchers accountable for methods and claims.
Five bounded workflows
Faculty AI workflows with human review
Use only approved tools and appropriate information. Treat every output as a draft to test against your course, discipline, sources, students, and professional judgment.
1. Lesson-plan options
Prompt pattern: "Using these faculty-authored outcomes, suggest three active-learning structures. State assumptions and accessibility considerations."
Verify: alignment, timing, inclusiveness, prerequisites, and factual accuracy.
2. Assignment stress test
Prompt pattern: "Review this assignment for unclear AI rules, hidden prerequisites, ambiguous grading criteria, and likely accessibility barriers. Do not rewrite it yet."
Verify: policy fit, disciplinary norms, student workload, and rubric alignment.
3. Practice questions
Prompt pattern: "Draft practice–not graded–questions at three levels using only the supplied material. Include answers with source locations."
Verify: every key, source, difficulty, bias, and distractor.
4. Feedback preparation
Prompt pattern: "Using an anonymized sample and this rubric, identify places where feedback may be needed. Do not assign a grade or infer the student’s intent."
Verify: privacy, fairness, context, tone, rubric fit, and the final faculty feedback.
5. Plain-language revision
Prompt pattern: "Offer a plain-language version of these faculty-authored instructions without removing requirements, dates, or technical terms. List any meaning that may have changed."
Verify: meaning, accessibility, terminology, and consistency across LMS materials.
Connected or agentic tools
An AI agent that can read email, change files, message students, or act in an LMS creates additional risk. Start read-only, minimize permissions, require confirmation for consequential actions, log activity, and maintain a tested way to stop or reverse it.
Use before the semester and before release
Two faculty AI checklists
Course readiness
- Each important activity has a clear AI rule and learning reason.
- Allowed tools and data are institution-approved.
- Disclosure, citation, and process evidence are explained.
- An equitable, accessible path is available.
- Rubrics assess the intended human learning.
- Misconduct concerns use established fair procedures.
AI-assisted material review
- Facts, calculations, quotations, citations, and links are checked.
- No student or confidential data was exposed.
- Bias, tone, accessibility, and harmful assumptions were reviewed.
- Copyright, licenses, and attribution were considered.
- A qualified person approved the final material.
- Material use was documented or disclosed when required.
On smaller screens, scroll horizontally to review the complete table.
| Record | What to capture |
|---|---|
| Purpose | Learning outcome or faculty task; why AI adds value. |
| Boundary | Approved tool, permitted data, users, and prohibited actions. |
| Review | Named human reviewer, verification method, and release decision. |
| Evidence | Errors, revisions, student feedback, accessibility issues, and whether use should continue. |
Continue with MAIN
Related Mississippi AI resources
Higher education policy template
Adapt roles, acceptable uses, data rules, oversight, and review cycles to your institution.
Institutional guidance directory
Review publicly available AI guidance from Mississippi colleges and universities.
Educator and learning resources
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Student AI Playbook
Give students a companion guide for learning, research, coding, privacy, verification, disclosure, and accountable submission.
AI + Dual Enrollment Guide
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Faculty AI questions
Frequently asked questions
How should faculty set AI rules for a course?
State whether AI is required, permitted, limited, or prohibited for each meaningful activity. Explain the learning reason, allowed tools and data, disclosure or citation expectations, and how students can ask questions or request an accessible alternative.
Should faculty use AI detectors to prove misconduct?
No automated detector should be the sole basis for an academic-integrity finding. Follow institutional procedures and consider the full evidence, including the assignment design, drafts, sources, process records, and a fair conversation with the student.
May faculty enter student work into an AI tool?
Only use an institution-approved tool and workflow appropriate for the information involved. Do not upload identifiable student records, grades, accommodation information, unpublished work, or other protected or confidential material unless the institution has authorized that specific use.
Can AI grade student work?
AI may assist an approved, evaluated workflow, but faculty remain responsible for fair and accurate evaluation. Consequential grading decisions require meaningful human review, attention to bias and accessibility, and compliance with institutional policy.
How can faculty design assignments for the AI era?
Align the AI rule to the learning outcome and ask students to show meaningful process, evidence, judgment, reflection, revision, or oral explanation. Design for authentic learning rather than relying on surveillance or detection alone.
Authoritative foundation
Sources and standards
Last source review: August 2026. External resources open in a new tab.
- U.S. Department of Education, Artificial Intelligence and the Future of Teaching and Learning (May 2023)
- U.S. Department of Education, Guidance on Artificial Intelligence Use in Schools (July 2025), including responsible adoption, privacy, stakeholder engagement, and AI literacy principles
- U.S. Department of Education Student Privacy Policy Office, FERPA
- U.S. Department of Justice, accessibility of web content and mobile apps under ADA Title II
- U.S. Copyright Office, Copyright and Artificial Intelligence
- NIST AI Risk Management Framework and Generative AI Profile
- Student Guide to AI, a higher-education resource developed by Elon University and AAC&U
- Van Vlasselaer, Van Droogenbroeck & Spruyt (2026), Who wrote this?, a controlled-dataset study showing materially different detector performance and recommending initial flags rather than sole evidence
- Elkhatat et al. (2026), Evaluating the accuracy and reliability of AI content detectors, documenting reliability and fairness limits in authentic EFL writing
- Turnitin, Using the AI Writing Report, vendor documentation requiring further scrutiny and human judgment rather than sole-basis action
Turn guidance into a teachable practice
Use this playbook with faculty, students, academic leadership, accessibility, IT, privacy, security, libraries, and support teams–then revise it as tools, evidence, and institutional policy change.