Connect academic integrity, research ethics, student rights, accessibility, procurement, and accountable institutional decisions.
Mississippi communities do not have identical needs, infrastructure, histories, or access. Use the evidence and tools as a starting point, then let local people shape the purpose, examples, pace, safeguards, and measures of success.
What disciplinary knowledge must a learner demonstrate?
What AI assistance is allowed, and how should it be disclosed?
Could unpublished research or protected student data be exposed?
What accessible alternative exists for someone who cannot use the tool?
Who reviews consequential advising, admissions, or academic decisions?
The evidence connects human-centered teaching with concrete privacy obligations. That is why this playbook treats course design, research practice, access, and data protection as one institutional responsibility.
UNESCO
UNESCO links meaningful educational use to human agency, ethical validation, pedagogical fit, inclusion, and data privacy.
We use it to connect assignment design and research practice with access, academic judgment, and institutional responsibility.
U.S. Department of Education
The Department administers FERPA and PPRA and provides guidance for postsecondary officials, students, vendors, and researchers.
We use it to keep protected student records, vendor review, complaint routes, and privacy responsibilities visible in campus decisions.
Sources and requirements can change. Check the publication date, read the original material, and confirm the rules that apply locally before making a consequential decision.
Use these four areas to connect academic values with the choices faculty, students, researchers, and campus leaders make.
Move beyond one campus-wide sentence. Expectations should be discipline-specific and visible at both course and assignment level, including attribution, process evidence, and consequences.
Verify generated citations, summaries, interpretations, and claims against the original scholarship. Do not expose unpublished research, human-subject data, or confidential partner information.
Offer an accessible route for students who cannot or do not wish to use a commercial AI system, and a human process for questions, exceptions, and appeals.
Procurement and deployment should examine privacy, accessibility, data rights, vendor terms, academic freedom, and accountability in advising, admissions, and other consequential functions.
✓ Generating study questions from instructor-approved material
✓ Critiquing a draft after the learner develops an argument
✓ Comparing AI synthesis with peer-reviewed scholarship
× Fabricating citations or research findings
× Uploading unpublished, human-subject, or protected institutional data
× Treating an AI-detection score as proof of misconduct
Start with the tool that matches your role, then review it with the people responsible for access, privacy, academic quality, and student rights.
Define expectations before students begin the work.
Create a transparent record of permitted assistance.
Review the whole institutional relationship—not only features.
Compare an AI interpretation of a disciplinary question with peer-reviewed scholarship.
Choose a question that requires disciplinary judgment—not simple recall.
Read a generated interpretation and one or more peer-reviewed sources.
Trace which claims have evidence and which cannot be located.
Discuss authorship, intellectual labor, disciplinary method, and missing perspectives.
Revise the interpretation and identify what makes the revision an original argument.
How is producing knowledge different from producing language that resembles scholarship?
Share the plan with the people responsible for the work and the people who may be affected. Confirm local rules, protect private information, and name the person who can answer questions or correct a problem.