Faculty AI Use
Yes. AI can be a valuable tool for supporting teaching, learning, course development, and academic administration when used appropriately.
Examples of generally appropriate uses include creating course outlines and lesson plans, drafting assignment prompts, developing rubrics, creating study guides and review materials, generating discussion questions, brainstorming classroom activities, and assisting with non-sensitive research and scholarship.
Faculty remain responsible for reviewing all AI-generated content for accuracy, quality, bias, and alignment with course objectives. AI should be viewed as a tool that supports instructional work rather than a substitute for professional judgment.
Yes. AI may be used to help create instructional materials such as syllabi, assignments, discussion prompts, reading guides, practice exercises, study resources, assessment rubrics, and classroom activities.
Faculty should ensure that materials comply with copyright requirements and accurately reflect course expectations and academic standards.
It depends on the AI system, the information contained in the assignment, and the intended use.
The most important consideration is whether the assignment contains information that could identify a student or reveal information protected by FERPA.
Before submitting student work to an AI system, faculty should evaluate whether identifying information has been removed, whether students have been informed of the use, whether the AI system has been reviewed and approved for the intended purpose, and whether the tool's privacy and security protections are appropriate.
As a general rule, faculty should exercise caution when submitting student work into public AI systems.
Yes.
When faculty plan to submit student work into an AI system on behalf of students, transparency is important.
Students should understand why AI is being used, how their work will be used, what information is being submitted, and whether identifying information has been removed.
Being transparent helps maintain trust and supports the University's commitment to protecting student privacy.
Faculty and staff should not enter student educational records or other FERPA-protected information into public AI systems unless the specific use case has been reviewed and approved.
Examples include student ID numbers, grades, gradebook information, academic standing, advising records, class schedules, transcripts, disciplinary records, accommodation records, financial aid information, and any educational record that identifies a student.
FERPA protections continue to apply even when information is submitted to an AI tool. Protecting student privacy remains the responsibility of the University employee using the system.
Not all student information receives the same level of protection under FERPA.
Directory information may generally be disclosed unless a student has requested that it be withheld under FERPA.
Examples of directory information include a student name, university email address, major field of study, participation in officially recognized activities and sports, and other information designated by the University's FERPA policy.
FERPA-protected information includes educational records and other information that should not be disclosed without an appropriate educational purpose or authorization.
Examples of FERPA-protected information include student ID numbers, grades, assignments and evaluations, academic performance, advising notes, class schedules, educational records, disciplinary records, and financial aid information.
A common misconception is that all student information is FERPA protected. For example, a university email address and student major are generally considered directory information, while a student ID number, student grade, and transcript are FERPA-protected information.
When using AI tools, faculty should carefully distinguish between directory information and educational records protected by FERPA.
In some cases, yes.
Assignments that have been appropriately redacted may present less privacy risk than assignments containing identifying information.
Redaction may include removing student names, student ID numbers, email addresses, and references that could identify an individual student.
However, redaction alone does not automatically make a use case acceptable. Faculty should also consider whether the content itself could identify a student, whether personal narratives are involved, whether students have been informed, and whether the AI tool has appropriate privacy protections.
Each use case should be evaluated based on the specific circumstances.
Before uploading student work into any AI system, faculty should verify that no FERPA-protected information is included, remove identifying information whenever possible, consider whether the content could identify a student even without a name attached, review the privacy and security practices of the tool, be transparent with students regarding the use of AI, ensure the use aligns with university policies, and contact ITS if there are questions about an AI tool or use case.
Faculty should remember that information entered into some public AI services may be retained by the provider and could be used in future model development depending on the vendor's terms and settings.
Faculty should use particular caution with personal narratives, reflection papers, journals, autobiographical assignments, and other materials that may contain highly identifiable information.
Even when names are removed, personal experiences may contain details that reveal a student's identity or private circumstances.
When working with assignments that contain sensitive personal information, faculty should carefully evaluate whether AI use is appropriate and whether the educational benefits outweigh the privacy risks. In some situations, avoiding AI analysis altogether may be the best course of action.
Examples of previously reviewed academic uses include AI-assisted evaluation of student programming assignments, analysis of de-identified student writing for instructional improvement, analysis of anatomical images that do not contain identifying information, creation of simulated medical records using synthetic data, teaching students how AI systems summarize classroom discussions, and development of course materials and instructional content.
Approval of one academic use case does not automatically approve all uses of the same tool. Each use must be evaluated based on the data involved, privacy implications, and security requirements.
Faculty use of AI is governed by the same policies that apply to any university technology resource.
These include the Appropriate Use of Information Technology Resources Policy, Data Security Policy, Data Policy, Record Retention and Data Destruction Policy, Cybersecurity Awareness and Training Policy, Risk Management Policy, FERPA requirements, and student privacy obligations.
To review specific policys, faculty and staff can go to Policy Central.
Faculty are responsible for ensuring that AI use supports educational goals while protecting student privacy, maintaining academic integrity, and safeguarding institutional information.