Policy presence
Johns Hopkins University has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
Baltimore, United States
Johns Hopkins University
Record status
Review: Agent reviewed
Policy profile
Review: Machine candidate
AI disclosure
No source-backed public claim about AI disclosure or acknowledgement is present in this profile.
The current public tracker record does not contain claim evidence about disclosing, acknowledging, citing, or declaring AI use.
Coursework
Johns Hopkins University has 3 source-backed public claims for coursework; deterministic analysis status: recommended.
Exams
Johns Hopkins University has 3 source-backed public claims for exams; deterministic analysis status: recommended.
Privacy and data entry
Johns Hopkins University has 2 source-backed public claims for privacy and data entry; deterministic analysis status: recommended.
Academic integrity
No source-backed public claim about academic-integrity treatment of AI use is present in this profile.
The current public tracker record does not contain claim evidence about AI use under academic integrity, misconduct, dishonesty, plagiarism, or cheating rules.
Approved tools
No source-backed public claim identifying approved or licensed AI tools is present in this profile.
The current public tracker record does not contain claim evidence that identifies institutionally approved, licensed, procured, or enterprise AI tools.
Named AI services
Johns Hopkins University has 1 source-backed public claim for named ai services; deterministic analysis status: recommended.
Teaching guidance
Johns Hopkins University has 5 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
Johns Hopkins University has 1 source-backed public claim for research guidance; deterministic analysis status: recommended.
Security and procurement
No source-backed public claim about AI security review or procurement is present in this profile.
The current public tracker record does not contain claim evidence about security review, procurement, vendor approval, risk assessment, authentication, SSO, or enterprise licensing.
AI tools
No tool-level evidence is published for this record yet. Broad AI tool mentions are not expanded into named tool conclusions.
Evidence-backed claims
6 reviewed evidence-backed public claim
Source Status
Johns Hopkins maintains a Teaching @ JHU generative AI guidance hub described as guidelines and best practices for generative AI tools and teaching.
Review: Agent reviewed
Normalized value: central_teaching_guidance_hub
Original evidence
Evidence 1The following pages are guidelines and best practices concerning generative AI tools and teaching. They will continue to evolve over time based on changes in the technology and use cases in the Johns Hopkins community.
Teaching
Johns Hopkins guidance tells faculty to consult local divisional guidelines for discipline-specific generative AI information when such guidance is published.
Review: Agent reviewed
Normalized value: consult_divisional_guidelines
Original evidence
Evidence 1Faculty should also consult local divisional guidelines for discipline-specific information if published.
Teaching
The Johns Hopkins generative AI guidance page says it was developed by JHU centers for teaching and learning to guide teaching strategies related to generative AI.
Review: Agent reviewed
Normalized value: centers_for_teaching_guidance
Original evidence
Evidence 1This page was developed collectively by the Johns Hopkins centers for teaching and learning to provide guidance on teaching strategies as they relate to or are impacted by generative artificial intelligence (AI).
Teaching
Johns Hopkins guidance describes potential instructional uses of generative AI tools, including course-material generation and adaptive personalized feedback.
Review: Agent reviewed
Normalized value: genai_for_course_materials_feedback
Original evidence
Evidence 1One prominent use case is in content creation and course development. These tools can assist instructional designers and educators in generating engaging and interactive course materials, ranging from automated quizzes and assessments to case studies and customized learning modules.
Other
Johns Hopkins guidance says generative AI implementation in higher education should be approached carefully, including attention to bias detection, mitigation, fairness, inclusivity, and human intervention.
Review: Agent reviewed
Normalized value: ethical_bias_human_intervention_caveat
Original evidence
Evidence 1Although the benefits of generative AI tools in higher education are promising, approaching their implementation with care is necessary. Ethical considerations, such as bias detection and mitigation, should be addressed to ensure fairness and inclusivity.
Privacy
The Johns Hopkins Teaching @ JHU generative AI guidance hub includes dedicated topics for FERPA guidelines, HIPAA guidelines, ownership of data, and ethical considerations.
Review: Agent reviewed
Normalized value: ferpa_hipaa_ownership_topics_present
Original evidence
Evidence 1Guidelines FERPA Guidelines HIPAA Guidelines Ownership of Data Ethical Considerations Guidelines for Students Example Syllabi Statements Detection Tools: Limitations and Alternatives
Candidate claims
0 machine or needs-review claim
Official sources
8 source attribution
Academic Integrity – Johns Hopkins Engineering CTLD
engineering.jhu.edu
Faculty Facing Guidance on Student Generative AI-Use
ep.jhu.edu
Generative AI / CoPilot / ChatGPT Information - WSEIT
wseit.engineering.jhu.edu
Generative AI Tool Guidance | Teaching @ JHU
teaching.jhu.edu
Generative AI Tool Guidance | Teaching @ JHU
teaching.jhu.edu
Guidelines - Teaching @ JHU
teaching.jhu.edu
Student Use of Generative AI - Johns Hopkins Engineering CTLD
engineering.jhu.edu
Syllabus Statements | Teaching @ JHU
teaching.jhu.edu