This page includes information about: 

1. Identify the Expectation
2. Gather Factual Information 
3. Notify the Student and Report Through the URI Process
4. Know the Possible Academic Outcomes 
5. Beyond Chatbots: Potential Impacts of AI Agents

1. Identify the expectation.

Learning about a concern is a reason to examine the work and follow the appropriate process. However, evidence is needed to make a determination of misconduct.

First, locate the written guidance that applied to the work:

• syllabus;
• assignment instructions;
• Brightspace materials;
• rubric;
• examination directions; or
• other course communication.

As the instructor ask yourself what course or assignment expectation(s) may has the student violated?

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2. Gather factual information.

Preserve materials directly relevant to the suspected concern.

This may include:

• the student’s original submission;
• assignment instructions;
• relevant syllabus language;
• course records;
• source materials;
• relevant correspondence;
• drafts or process documentation already available through the course; and
• a concise explanation of what raised the concern.

Focus on observable evidence, not assumptions about how a student “normally” writes.

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3. Notify the student and report it through the URI process.

URI’s Academic Honesty Procedures require faculty and instructors to:

• Notify the student of the academic-honesty allegation.
• AND provide formal written notice to the Instructor’s College Dean, the Student’s College Dean and the Dean of Student’s Office/Office of Community Standards.

▶️ Use URI’s Academic Misconduct reporting process for faculty. The Office of Community Standards then manages the institutional process.

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4. Know the possible academic outcomes.

When a student has been found responsible, URI identifies three instructor options:

1. failing the student for the assignment;
2. failing the student for the course, with authorization from the instructor’s College Dean; and/or
3. requesting formal Student Conduct action, either instead of or in addition to a grade adjustment.

First offenses for which formal conduct action is not requested receive an official warning letter through the conduct system. Repeat infractions require conduct action.

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About AI Detectors

An AI-detector score is not proof of misconduct. AI-detection systems estimate whether text may have been AI-generated. Their results should not be treated as definitive evidence of authorship.

Instead, focus on:
✅the applicable course expectation,
✅the student’s submitted work,
✅directly observable inconsistencies or evidence, relevant course records, and
✅URI’s established academic-honesty process.

What should I include in a report? 

Suggested faculty documentation:

• Student’s name
• Instructor’s name
• Course and section
• Assignment
• Description of the concern
• Relevant syllabus/assignment expectation
• Student’s submitted work
• Supporting factual evidence
• Relevant communication with the student
• Instructor’s finding (if there was academic dishonesty or not) and the subsequent grade sanction (0 on the assignment, 50% credit on the assignment, etc.) 
• Student’s response to the allegation (admitted it, honest mistake, confusion about the correct way to cite AI use, etc.) 
• Other documentation requested by the Office of Community Standards

▶️ Learn More by downloading our guide on “What to do When You Suspect an Academic Integrity Violation.” 

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5. Beyond Chatbots: Potential Impacts of AI Agents

Generative AI is evolving beyond tools that respond to individual prompts. AI agents can complete a series of actions on a user’s behalf, potentially including navigating websites, gathering information, completing tasks, and interacting with online platforms.

For instructors, this creates a new academic integrity challenge. 

• The question may no longer be only “Did a student use AI to create this assignment?” 
• Increasingly, instructors may also need to consider “What evidence do I have that the student meaningfully participated in the learning process?”

What does this mean for assessment of student learning in my course(s)?

Rather than relying primarily on detecting unauthorized AI use, consider designing assessments that provide multiple opportunities to see students’ thinking and learning develop over time.

Consider strategies such as:

• Capture the process, not only the product. Ask students to submit drafts, notes, research decisions, revisions, or other evidence of how their work developed.
• Build in opportunities for explanation. Ask students to briefly explain, discuss, present, or defend important decisions they made in completing an assignment.
• Connect assignments to the course experience. Incorporate class discussions, course-specific examples, datasets, activities, or experiences that students need to engage with to complete the work successfully.
• Use smaller checkpoints. Break larger assignments into stages so instructors can observe student thinking and provide feedback throughout the process.
• Include reflection and metacognition. Ask students what they learned, what challenged them, what they changed, and how they know their final work meets the assignment’s goals.
• Be explicit about AI expectations. Clearly communicate when AI tools may or may not be used and what students should disclose about their use.

Choose one important assessment in your course and ask:

• If an AI agent could complete this entire assignment for a student, what evidence would I still have that the student achieved the learning outcomes?

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Bottom Line: Designing assessments that make student thinking and learning visible can help address both questions while keeping the focus on learning rather than surveillance.