Teaching & Learning
- AI use in coursework is governed by instructor-established rules rather than a single uniform allowance
- Faculty must state in the syllabus when and how AI is permitted, and courses may use no-use, limited-use, or freely permitted models
- When AI is used on an assignment, students may be required to document that use, and AI may not be used to misrepresent authorship, plagiarize, or violate academic honesty rules
c) Assessment authority: Faculty retain full authority to determine where AI is permitted, limited, or excluded within their courses and to design verification processes for authorship and mastery.
a) Instructor rule-setting: Aside from the required AI discipline-specific touchpoints, courses may adopt No Use, Limited Use, or Freely Permitted models. Instructors must state explicitly in the syllabus when and how AI tools are permitted, including assignment-by-assignment expectations, so that students are not confused about the steps required in their work.
● Permissible Uses: Students may use AI tools for clearly defined tasks such as formatting, brainstorming, or exploring alternative approaches to problem-solving.
● Documentation Requirement: When AI is used for an assignment, students must provide an Appendix that includes:
○ The prompts they submitted.
○ The AI-generated outputs they received.
○ A written evaluation of the AI’s accuracy, limitations, and potential biases.
● Prohibited Uses: AI may not be used to misrepresent authorship, plagiarize, or circumvent academic honesty policies.
● Faculty Oversight: Faculty may establish assignment-specific AI guidelines, including when AI use is encouraged, optional, or prohibited.
- Faculty may also verify authorship through interviews, oral defenses, logs, and similar methods
- AI use in exams and assessments is controlled by faculty and may be prohibited for unaided mastery checks
- The university defines unaided assessments as demonstrations of individual capability without prohibited tools, and use of an AI language generator where prohibited counts as academic dishonesty
* Unaided mastery check: Any assessment that verifies individual capability without AI or other prohibited tools, such as proctored exams, oral defenses, in-class performances, live labs, or supervised critiques.
Clear examples of permitted uses (e.g., idea generation, coding assistance) and prohibited uses (e.g., replacing unaided mastery tasks) should be included to prevent ambiguity.
* Unaided mastery checks: Proctored or supervised demonstrations that verify individual capability without tools prohibited for that assignment.
1. Cheating is giving or receiving unauthorized aid on an examination, assignment, or other graded work. Regardless of where the aid comes from — e.g., cell phone, crib sheet, other student, or AI language generator (where prohibited) — it qualifies as academic dishonesty.
c) Verification options: Oral defenses, version histories, notebooks or logs, keystroke or commit traces, build artifacts, lab notebooks, and process portfolios may be used to verify authorship and learning outcomes.
- However, any actual permission to use AI in specific class contexts still depends on course rules
- The university encourages students to learn responsible, discipline-relevant AI use and to use AI critically as part of their development
- Students are expected to evaluate AI outputs for accuracy, bias, compliance, and ethics, and AI is framed as a tool to inform thinking rather than replace it
a) Student learning: Every graduate will encounter structured opportunities to learn responsible, discipline-relevant use of AI or technology-of-field and to practice verification, ethics, and workflow integration.
Students are expected to reflect critically on how AI tools may reinforce biases, omit context, or misrepresent data, incorporating this reflection into their submitted work where appropriate.
Students learn to treat AI as a tool that informs their thinking and creative practice, never as a substitute for it.
● Critical Evaluation: Students will learn to assess AI outputs for accuracy, bias, compliance, and ethical considerations.
- AI coding assistance is permitted only when allowed by the instructor or program
- Programs are also expected to identify secure discipline-specific tools for coursework, clinics, or labs
- The policy explicitly recognizes code assistants and coding assistance as possible tools, but faculty decide where they are permitted, limited, or excluded, and students may need to document and verify such use
Programs in any college may meet requirements through generative AI, machine learning environments, statistical software, code assistants, computational design tools, domain-specific automation, or comparable technology-of-field instruments, selected for disciplinary relevance and ethical fitness.
Clear examples of permitted uses (e.g., idea generation, coding assistance) and prohibited uses (e.g., replacing unaided mastery tasks) should be included to prevent ambiguity.
* AI-enabled workflows: Tool use for brainstorming, outlining, drafting, debugging, analysis, simulation, critique, or revision, accompanied by verification and reflection.
c) Assessment authority: Faculty retain full authority to determine where AI is permitted, limited, or excluded within their courses and to design verification processes for authorship and mastery.
* Program responsibility: Programs will identify discipline-specific secure tools and configurations required for coursework, clinics, or labs.
Research
- Researchers may use artificial intelligence tools to analyze research data only under stated privacy and application requirements
- Proposed AI use must be described in the IRB application, and researchers remain responsible for privacy, security, accuracy, and compliance
- They must not enter identifiable, confidential, sensitive, proprietary, FERPA-protected, HIPAA-regulated, or otherwise restricted research data into a public or unapproved AI platform
#### May I use artificial intelligence tools to analyze research data?
Do not enter identifiable, confidential, sensitive, proprietary, FERPA-protected, HIPAA-regulated, or otherwise restricted research data into a public or unapproved artificial intelligence platform.
Any proposed AI use should be described in the application, including:
* The tool and version;
* What information will be entered;
* Whether the provider retains or uses submitted information;
* Where the information is processed or stored;
* Whether identifiers will be removed;
* How outputs will be verified; and
* Whether institutional approval or a contract is required.
Researchers remain responsible for participant privacy, data security, accuracy, and compliance regardless of the tool used.
- Researchers remain responsible for participant privacy, data security, accuracy, and compliance
- No separate university-wide rule on AI use in manuscripts, grant proposals, or ethics declarations beyond this IRB guidance is explicitly defined in the provided sources
- For IRB-governed research, any proposed AI use must be disclosed in the application with details about the tool, data entered, retention, storage, identifier removal, output verification, and any needed institutional approval or contract
Any proposed AI use should be described in the application, including:
* The tool and version;
* What information will be entered;
* Whether the provider retains or uses submitted information;
* Where the information is processed or stored;
* Whether identifiers will be removed;
* How outputs will be verified; and
* Whether institutional approval or a contract is required.
Researchers remain responsible for participant privacy, data security, accuracy, and compliance regardless of the tool used.
Academic Integrity
- Failure to disclose AI use when required is treated as unethical use
- Disclosure of AI use is required when instructors or assignments require it
- The university defines an AI Awareness Note for student disclosure, and when AI is used for an assignment students must provide an appendix containing prompts, outputs, and an evaluation of accuracy, limitations, and biases
* AI Awareness Note: Concise disclosure by a student that lists tool name, purpose of use, prompt or configuration summary, and verification steps.
d) Documentation requirement: When AI is allowed, instructors may require students to include an Appendix with:
* The prompts or instructions submitted to the AI tool;
* The AI-generated outputs received;
* A written evaluation of the AI’s accuracy, limitations, and potential biases.
● Documentation Requirement: When AI is used for an assignment, students must provide an Appendix that includes:
○ The prompts they submitted.
○ The AI-generated outputs they received.
○ A written evaluation of the AI’s accuracy, limitations, and potential biases.
Unethical use includes:
Students
* Using AI where prohibited by an assignment.
* Not disclosing AI use when assignment requires disclosure.
- Faculty may verify authorship through interviews, oral defenses, logs, version histories, and related process evidence
- The university does not define a specific AI-detection software policy in the provided sources, but it does provide verification and enforcement mechanisms for suspected misuse
- Misuse of AI is treated as an academic integrity violation, with sanctions ranging from assignment penalties to course failure, required tutorial completion, and suspension or expulsion depending on offense count and student level
c) Verification options: Oral defenses, version histories, notebooks or logs, keystroke or commit traces, build artifacts, lab notebooks, and process portfolios may be used to verify authorship and learning outcomes.
For all assignments completed entirely or in part out of class, the instructor reserves the right to interview the student about the work to verify authorship. A student who is unable to demonstrate a basic understanding of the submitted work will be reported for academic dishonesty, and an appropriate penalty will be applied.
Misuse of AI in violation of this policy constitutes an academic integrity violation.
Violations fall under the University’s Academic Integrity Policy and may result in assignment penalties, disciplinary review, or other sanctions as outlined in the University’s Code of Conduct.
The penalty for the first reported offense of academic dishonesty will be determined by the instructor and may result in a reduced or failing grade on the work/test, failure in the course, or other appropriate penalty.
Upon a first report of dishonesty, the student is also required to complete an online Academic Integrity Tutorial. A charge for the tutorial will be applied to the student’s Student Accounts. For undergraduate students, a second offense will result in failure of the class, and a third offense will lead to expulsion from the University. Graduate students will be expelled after a second offense is reported.
Institutional & Administrative
- Faculty may use AI for instructional design, assessment development, course materials, and student engagement, and they should disclose when and how AI supports instruction
- Faculty and staff are encouraged to explore and adopt AI responsibly, but their use must comply with privacy, security, intellectual property, confidentiality, data protection, and academic integrity rules
- Employees are responsible for ensuring their AI use is appropriate and outputs are accurate, and AI may be used to evaluate student work when personal identifying information and sensitive student data are excluded
e) Staff learning: All university staff are encouraged to explore and adopt AI and emerging technologies responsibly in ways that improve operational efficiency, communication, and service to students, consistent with data security and ethical guidelines.
The use of generative AI models within Lindenwood must strictly align with existing employment and student policies on confidentiality, data protection, and academic integrity. Generative AI tools should be deployed with caution, as the responsibility to ensure that their use is appropriate and that outputs are accurate rests entirely with each employee.
● Pedagogical Integration: Faculty may use AI tools to support instructional design, assessment development, course material creation, and student engagement.
● Transparency: Faculty should disclose when and how AI tools are being used to support instruction, ensuring clarity for students.
● Academic Integrity: Faculty remain responsible for ensuring that assignments, assessments, and grading practices uphold the University’s standards of academic honesty.
* The use of AI (closed or open models) to evaluate student work without personal identifying information or sensitive personal student data is permitted. This does not constitute a breach of student privacy or academic standards.
- The university prohibits entering confidential, personal, protected, or otherwise restricted data into open or unapproved AI systems
- The university also classifies data by sensitivity and states that confidentiality, privacy, and data protection requirements apply to AI use
- Non-public or regulated data must be confined to approved institutionally procured systems or compliant configurations, while open systems may be used only for coursework with non-sensitive or synthetic data
d) Data protection: Use of tools must comply with institutional privacy, security, and intellectual property policies; non-public data requires approved environments.
* Public-data training models: Faculty and students may use tools that train on public data, provided that training features are turned off or that the system is configured not to store or reuse entered data. Turning training off resolves the concern for most widely used AI tools.
* Prohibited uses: Uploading confidential, personal, or protected data into open models is not allowed. This includes information covered by FERPA (student identifiers, grades, advising records), HIPAA (protected health information), or export-controlled data.
* Non-public or regulated data: Must be confined to approved, institutionally procured systems or configurations that guarantee compliance.
* Open systems: May be used for coursework when input data is non-sensitive or synthetic.
Students and faculty may not input personally identifiable information (PII), protected health information (PHI), or confidential University records into AI systems. Doing so may violate FERPA, HIPAA, or other applicable laws. All AI use must comply with institutional data governance, legal standards, and accreditation requirements.
Sensitivity labels are used to classify and protect data based on its sensitivity and importance.
Confidential data is information that is either confidential business or personal information that requires higher levels of protection. This includes student information that is not designated directory information, and personally identifiable information (PII), such as name, date of birth, and address. This information could lead to identity theft. This Label is encrypted.
- Lindenwood has an institution-wide AI integration strategy centered on embedding responsible, discipline-specific AI literacy across every program and operational area
- The university also states a timeline toward full program-level AI integration and provides oversight through approved tool lists, secure configurations, and faculty development support
- Governance is assigned to Academic Affairs in consultation with the AI Strategy and Governance Committee and Faculty Council, with annual program mapping, syllabus sampling, periodic artifact review, and biennial policy review
Prepare graduates, faculty, and staff to excel in an AI-driven future by integrating responsible, discipline-specific applications of artificial intelligence and emerging technologies into every program and operational area.
Each degree program will file and maintain an AI and Technology Map updated annually during the standard assessment cycle along with their annual assessment program reports guided by program leadership in consultation with the AI Strategy and Governance Committee.
* Ownership: Academic Affairs holds policy ownership with consultation through the AI Strategy and Governance Committee and Faculty Council.
* Monitoring: Monitoring will include annual AI and Emerging Technology Map submissions for each academic program, sampling review of approximately ten percent of syllabi per cycle selected by deans, and periodic analysis of anonymized assessment artifacts for alignment with program plans. These activities will be conducted in coordination with each college’s assessment unit and the AI Strategy and Governance Committee.
* Review cadence: Biennial policy review to incorporate technological change, accreditation feedback, and assessment findings.
By fall 2027, every academic program will have achieved full AI integration through a deliberate, three-touchpoint framework, ensuring every graduate leaves prepared for an AI-enabled workforce.
* Academic Affairs and IT: Maintain approved tool lists and secure configurations; support procurement; operate training and knowledge bases.
DocuMark: Responsible AI Use for Academic Integrity
Knowing your institution's AI policy is step one. DocuMark helps enforce it fairly by empowering universities to manage AI-generated content, prevent cheating, and support student writing through responsible AI use.
Common Questions About Lindenwood University's AI Policies
Lindenwood University has defined AI policies in 11 of 12 categories, with an overall coverage score of 92%.
Disclosure of AI use is required when instructors or assignments require it. The university defines an AI Awareness Note for student disclosure, and when AI is used for an assignment students must provide an appendix containing prompts, outputs, and an evaluation of accuracy, limitations, and biases. Failure to disclose AI use when required is treated as unethical use.
The university does not define a specific AI-detection software policy in the provided sources, but it does provide verification and enforcement mechanisms for suspected misuse. Faculty may verify authorship through interviews, oral defenses, logs, version histories, and related process evidence. Misuse of AI is treated as an academic integrity violation, with sanctions ranging from assignment penalties to course failure, required tutorial completion, and suspension or expulsion depending on offense count and student level.
The university prohibits entering confidential, personal, protected, or otherwise restricted data into open or unapproved AI systems. Non-public or regulated data must be confined to approved institutionally procured systems or compliant configurations, while open systems may be used only for coursework with non-sensitive or synthetic data. The university also classifies data by sensitivity and states that confidentiality, privacy, and data protection requirements apply to AI use.