Johns Hopkins University

Engineering for Professionals (EP) student guidance requires students to adhere to instructor standards regarding the disclosure of generative AI use. Disclosure may encompass full chat transcripts, citations of specific models or websites, short descriptions of use, single citations per assignment, or visual highlighting of AI-generated content. Students remain fully responsible for verifying accuracy, identifying bias, and ensuring the quality of final submissions. Students must save GenAI outputs or interaction records if disclosure or verification is requested by the course or instructor, and failure to disclose GenAI use when required may constitute academic misconduct.

Engineering for Professionals (EP) implements a course-level color classification system for generative AI use: Red (GenAI use is not permitted in any form, students must complete all work independently); Yellow (Moderate GenAI use is permitted with restrictions to support learning, and all use must be disclosed according to course expectations); and Green (Liberal GenAI use is permitted throughout the workflow, with expectations for transparency and accountability). Every EP course must have a course-level color designation; Yellow courses must also include assignment-level labels for all assessments and activities, while Red and Green courses need not apply categories at the assignment level. This policy applies strictly to EP courses and does not establish a blanket university-wide permission or exam authorization.

Hopkins Press holds authors accountable for their work, does not permit AI or LLMs to be listed as authors, and will not accept work substantially written by an AI or LLM tool. Its policy requires both a general AI-use statement at the beginning of an article, in book front matter or another prominent place, and specific citations for AI use in text, images, graphs, tables or other parts of the work; it points to MLA, Chicago and APA for citation styles. Authors remain responsible for originality, accuracy, avoiding plagiarism and citing sources, including AI-generated content; they accept liability for AI use.

Johns Hopkins Information Technology (IT@JH) enforces university-wide restrictions on data shared with artificial intelligence tools. Users are strictly prohibited from entering non-public proprietary Hopkins data—including clinical data, financial records, and business records—into third-party AI tools not approved by the university, as default settings expose sensitive information to unauthorized parties. The internal HopGPT environment is approved for sensitive data, including PHI and PII, subject to institutional stewardship. Research use of HopGPT is governed by branch rules: IRB approval is required for non-clinical research cases, while SIP approval is required for clinical research cases or any uses involving PHI or PII data.

The Johns Hopkins Center for Teaching Excellence and Innovation (CTEI) does not recommend incorporating AI detection software into student evaluations because the technology is unreliable, often reports a vague probability of AI use, lacks clear evidence of copying, and generates false positives. This advice serves as instructional guidance rather than a statutory university-wide ban; individual courses (such as Engineering for Professionals Course 605.203.81, Fall 2026 syllabus) state that all submitted work must represent the student's own reasoning and that available technologies—including AI detection software, stylometric analysis, mathematical consistency checks, version-history analysis, and oral follow-ups—may be used to evaluate authenticity of student work. Instructor suspicion or reporting under course syllabi is distinct from an official university finding of misconduct.

The captured Graduate Academic Misconduct Policy applies to graduate students in KSAS and WSE, including EP and AAP, with effective dates11 October2017 for KSAS/WSE Homewood and WSE EP, and29 August2018 for KSAS AAP. After consultation with the AIO, a first offense may be resolved informally by the student and faculty member, program chair or graduate-study director only if the sanction is no greater than course failure and both parties agree; neither party is obliged to agree, and an agreed resolution must be reported to the AIO using the prescribed form. Hearing Panels use the preponderance-of-the-evidence standard, meaning more likely than not. Except for informal first-offense resolutions, a student may appeal a panel finding or sanction within ten university business days of the date of the outcome notice, in writing to the AIO or designee for the Cognizant Dean: grounds are materially consequential procedural error, previously unavailable information that could reasonably have affected the finding or sanction, or excessive sanctions. The appeal is a file review rather than another hearing; after a final determination without panel remand or rehearing, no further appeals are permitted under this policy. Separately, the captured EP faculty guidance says suspected violations, including GenAI misuse, should be reported to the EP AIO and faculty should not independently resolve potential misconduct.