Teaching & Learning
- The university states that AI use is subject to existing academic integrity and other university policies, and that students should consult their instructors before using AI in coursework
- Teaching guidance emphasizes that instructors should set course-specific expectations (including via syllabus statements) regarding whether and how AI may be used for activities and assignments, and it recommends that students be transparent and appropriately attribute AI-generated content when used
AI use is subject to existing JHU and JHM policies, including codes of conduct, information technology resource policies, and academic integrity policies.
Using AI does not allow an exception to existing requirements and limitations.
Students should consult their instructors before utilizing AI within their coursework.
Faculty members and instructors are responsible for specifying at the beginning of each semester or term the basic rules and procedures for any and all coursework, examinations, and other academic exercises.
Syllabus statement should reflect the unique uses or concerns for a course along with the AI tools that might be available to students or assigned by the instructor. The following section provides examples of statements instructors included in their syllabus to explain appropriate and inappropriate use of AI.
Respect intellectual property rights by acknowledging the sources of AI-generated content used in your work.
- The university’s undergraduate academic ethics policy requires instructors to specify rules for examinations and other graded work at the start of the term
- JHU’s AI guidance does not set a single university-wide rule for AI use in exams/assessments; instead, it frames AI use as subject to existing academic integrity policies and indicates students should consult instructors regarding use in coursework, which includes assessments as defined by course rules
Faculty members and instructors are responsible for specifying at the beginning of each semester or term the basic rules and procedures for any and all coursework, examinations, and other academic exercises.
AI use is subject to existing JHU and JHM policies, including codes of conduct, information technology resource policies, and academic integrity policies.
Using AI does not allow an exception to existing requirements and limitations.
Students should consult their instructors before utilizing AI within their coursework.
- JHU’s broader responsible-use guidance also stresses that AI-generated content may be inaccurate or fabricated and should be reviewed and cross-referenced
- Teaching resources provide example guidance that encourages students to use generative AI tools for learning purposes, while emphasizing verification of outputs and communication with instructors when uncertain
Students are encouraged to explore and experiment with generative AI tools for learning purposes, but any use in assessments must be clearly indicated and appropriately attributed.
Validation: Always verify and cross-reference AI-generated content with credible sources. Maintain a critical approach to information and be mindful of the possibility of manipulated or misleading content.
Communication: Communicate with and seek guidance from your professors or instructors when you are uncertain about the authenticity of AI-generated materials.
AI-generated content can be inaccurate, misleading, or entirely fabricated and may contain copyrighted material.
Review your AI-generated content before sharing it with others.
- JHU does not state a single university-wide rule specifically addressing AI code generation for programming coursework in the provided sources
- The university indicates AI use is subject to existing policies and that students should consult instructors for coursework use, while divisional guidance provides examples suggesting that course syllabi should define whether and how AI tools may be used in assignments (which can include coding work)
AI use is subject to existing JHU and JHM policies, including codes of conduct, information technology resource policies, and academic integrity policies.
Using AI does not allow an exception to existing requirements and limitations.
Students should consult their instructors before utilizing AI within their coursework.
To address these issues, provide explicit guidelines within course syllabi that clearly outline whether and how AI tools can be incorporated into activities or assignments.
Research
- The provided sources do not define research-manuscript-specific rules (e.g., for theses/dissertations or publication submission) beyond these general requirements
- JHU’s responsible AI guidance applies broadly and states that users are responsible for AI-generated material they produce or publish and should review AI-generated content due to risks of inaccuracies, fabrication, and copyrighted material
You are responsible for any content that you produce or publish that includes AI-generated material.
AI-generated content can be inaccurate, misleading, or entirely fabricated and may contain copyrighted material.
Review your AI-generated content before sharing it with others.
- JHU’s guidance emphasizes protecting confidential/nonpublic proprietary data and restricting entry of such information into third-party AI tools that are not JHU-approved
- JHU also states that HopGPT is approved for sensitive data (including PHI and PII), but requires responsible stewardship and notes that IRB approval is required for non-clinical research cases and SIP approval is required for clinical research cases or uses involving PHI or PII
Do not enter nonpublic proprietary Hopkins data—including clinical data, financial information, and business records—into third-party AI tools not approved by Johns Hopkins, in accordance with the University Policy on Acceptable Use and Security of Johns Hopkins Information Technology Resources and the University Code of Conduct.
HopGPT is approved for sensitive data, including PHI and PII. In accordance with Johns Hopkins University and Johns Hopkins Medicine policies, you must act in a manner consistent with appropriate use of PHI/PII and be a responsible steward of the data you enter into and collect from HopGPT.
Note: IRB approval is required for non-clinical research cases and SIP approval required for any clinical research cases or uses involving PHI or PII data.
- The responsible-use AI guidance also states AI use is subject to existing policies and does not create exceptions to requirements and limitations; however, the provided sources do not include a specific AI-focused research integrity rule beyond these existing misconduct frameworks
- JHU’s academic ethics and graduate academic misconduct policies define research misconduct as fabrication, falsification, or plagiarism in proposing, performing, reviewing, or reporting research, and they refer to the university Research Integrity Policy for a complete definition and handling process
Research misconduct is defined as fabrication, falsification, or plagiarism in proposing, performing, reviewing or reporting research. For a complete definition, refer to PDF Document: The Johns Hopkins University Research Integrity Policy. The Policy applies to all University faculty, staff, trainees and students engaged in the proposing, performing, reviewing or reporting of research, regardless of funding source.
Research misconduct is defined as fabrication, falsification, or plagiarism in proposing, performing, or reviewing research, or in reporting research results. For a complete definition, refer to The Johns Hopkins University Research Integrity Policy
The Johns Hopkins University Research Integrity Policy applies to all members of the Johns Hopkins community, including students. Allegations regarding a student that may fall within the definition of research misconduct must be referred to the Research Integrity Officer for assessment under that Policy.
AI use is subject to existing JHU and JHM policies, including codes of conduct, information technology resource policies, and academic integrity policies.
Using AI does not allow an exception to existing requirements and limitations.
Academic Integrity
- JHU teaching guidance recommends transparency and acknowledging sources of AI-generated content used in student work
- Divisional teaching guidance provides example syllabus language indicating that AI use in assessments must be clearly indicated and appropriately attributed and suggests that students should be informed about how to acknowledge AI tool use in submitted work; however, the provided sources do not establish a single university-wide mandatory disclosure standard for all courses
Maintaining transparency about the use of AI-generated content and clearly delineating between automated and human-generated materials is essential to uphold academic integrity.
Transparency: Respect intellectual property rights by acknowledging the sources of AI-generated content used in your work.
If applicable, students should also be informed about the appropriate way to acknowledge the use of such tools in their submitted work.
Students are encouraged to explore and experiment with generative AI tools for learning purposes, but any use in assessments must be clearly indicated and appropriately attributed.
- JHU teaching guidance encourages alternatives to AI detection tools, including using syllabus statements that ask students to self-report AI use
- For enforcement, JHU’s undergraduate academic ethics policy prohibits academic misconduct and outlines procedures requiring faculty review with the student and escalation to the Office of Student Conduct depending on factors such as severity or repeat offenses; however, the provided sources do not define AI-specific penalties or a university-wide requirement to use AI detection systems
Include a syllabus statement with clear expectations of how AI is to be used in the course (if at all). Ask students to self-report how they used generative AI for the assignment.
Academic misconduct is prohibited by this policy.
If a student is suspected of a possible violation of academic ethics, the faculty member in charge of the course must review the facts of the case promptly with the student(s).
If the faculty member attempts to resolve the case directly but cannot reach an agreement with the student (e.g., the student denies violating policy or the student does not agree with the proposed sanction, etc.); if the offense is a second or subsequent offense; or if in the case of a first offense, the faculty member believes that the sanction warranted is a failure in the course or more severe, the faculty member must promptly notify the Student Conduct Office in writing of the alleged violations, information, including potential witnesses, and other pertinent details of the case.
Institutional & Administrative
- The university also provides general responsible-use guidance that users are responsible for AI-generated content they produce or publish and should review AI outputs before sharing
- JHU guidance indicates instructors should consider FERPA before submitting student work to generative AI tools (e.g., to generate draft feedback) and stresses de-identification requirements
- JHU GenAI role-based pages list example staff uses (e.g., composing emails, automating workflows), but do not establish binding requirements beyond the broader responsible-use and data-protection guidance in the provided sources
Instructors should consider FERPA guidelines before submitting student work to generative AI tools like chatbots (e.g., generating draft feedback on student work) or using tools like Zoom’s AI Companion. Proper de-identification under FERPA requires removal of all personally identifiable information, as well as a reasonable determination made by the institution that a student’s identity is not personally identifiable.
You are responsible for any content that you produce or publish that includes AI-generated material.
Review your AI-generated content before sharing it with others.
Real-world examples of HopGPT uses.
To automate certain administrative workflows
To help compose emails
- JHU identifies approved/enterprise tools and specifies access/login requirements for certain tools (e.g., Copilot for M365 with JHED)
- JHU also states HopGPT is approved for sensitive data (including PHI and PII) with required responsible stewardship, and notes IRB and SIP approval requirements for certain research cases involving PHI/PII
- JHU directs users to start with approved AI tools and prohibits entering nonpublic proprietary Hopkins data into third-party AI tools that are not JHU-approved, warning that default settings on unapproved tools are not private
### Start with Our Approved AI Tools
Do not enter nonpublic proprietary Hopkins data—including clinical data, financial information, and business records—into third-party AI tools not approved by Johns Hopkins, in accordance with the University Policy on Acceptable Use and Security of Johns Hopkins Information Technology Resources and the University Code of Conduct.
Information shared with AI tools not approved by Johns Hopkins using default settings is not private and could expose proprietary or sensitive information to unauthorized parties.
Johns Hopkins users must log into Copilot for M365 with their JHED ID in order to properly protect all data and information.
HopGPT is approved for sensitive data, including PHI and PII.
Note: IRB approval is required for non-clinical research cases and SIP approval required for any clinical research cases or uses involving PHI or PII data.
- The provided sources do not define a single centralized governance body (e.g., committee/board) with authority over AI across the entire university
- JHU states it is committed to responsible AI use across Johns Hopkins University and Johns Hopkins Medicine, emphasizing safe, ethical use in compliance with applicable policies
- IT@JH communications indicate that leadership released guidelines for responsible use of generative AI and that IT@JH planned to deploy new tools and resources, including a JHU AI platform (Hopkins AI Lab, later referenced as HopGPT)
At Johns Hopkins University (JHU) and Johns Hopkins Medicine (JHM), we are committed to harnessing the power of AI responsibly, to ensure that these technologies are used safely, ethically, and in compliance with all applicable policies.
JHU and IT@JH leadership recently released a set of guidelines for the responsible use of GenAI.
Over the next few months IT@JH will be deploying new tools and resources — including our own AI platform, the “Hopkins AI Lab.”
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 Johns Hopkins University's AI Policies
Johns Hopkins University has defined AI policies in 12 of 12 categories, with an overall coverage score of 100%.
JHU teaching guidance recommends transparency and acknowledging sources of AI-generated content used in student work. Divisional teaching guidance provides example syllabus language indicating that AI use in assessments must be clearly indicated and appropriately attributed and suggests that students should be informed about how to acknowledge AI tool use in submitted work; however, the provided sources do not establish a single university-wide mandatory disclosure standard for all courses.
JHU teaching guidance encourages alternatives to AI detection tools, including using syllabus statements that ask students to self-report AI use. For enforcement, JHU’s undergraduate academic ethics policy prohibits academic misconduct and outlines procedures requiring faculty review with the student and escalation to the Office of Student Conduct depending on factors such as severity or repeat offenses; however, the provided sources do not define AI-specific penalties or a university-wide requirement to use AI detection systems.
JHU directs users to start with approved AI tools and prohibits entering nonpublic proprietary Hopkins data into third-party AI tools that are not JHU-approved, warning that default settings on unapproved tools are not private. JHU identifies approved/enterprise tools and specifies access/login requirements for certain tools (e.g., Copilot for M365 with JHED). JHU also states HopGPT is approved for sensitive data (including PHI and PII) with required responsible stewardship, and notes IRB and SIP approval requirements for certain research cases involving PHI/PII.