Policy presence
Illinois Institute of Technology has 4 source-backed public claims for policy presence; deterministic analysis status: unclear.
Chicago, United States
Illinois Institute of Technology
Illinois Institute of Technology has 5 source-backed AI policy claims from 3 official source attributions. Review state: agent reviewed; 5 reviewed claims. Last checked May 17, 2026.
Illinois Institute of Technology AI policy short answer
v1 public contract
Illinois Institute of Technology has 5 source-backed AI policy claims from 3 official source attributions, including 5 reviewed claims. The record review state is agent reviewed; original-language evidence snippets, source URLs, confidence, and public JSON are preserved for citation. Last checked May 17, 2026. Discovery context: Illinois Institute of Technology is listed as QS 2026 rank =591.
Citation-ready summary
As of this public record, University AI Policy Tracker lists Illinois Institute of Technology as an agent-reviewed AI policy record last checked on May 17, 2026 and last changed on May 17, 2026. The record contains 5 source-backed claims, including 5 reviewed claims, from 3 official source attributions. Original-language evidence snippets and source URLs remain canonical, with public JSON available at https://eduaipolicy.org/api/public/v1/universities/illinois-institute-of-technology.json. The entity-level confidence is 93%. This tracker is not legal advice, not academic integrity advice, and not an official university statement unless the linked source is the university's own official page.
Policy signals in this record
- Evidence includes Source status claims.
- Evidence includes Academic integrity claims.
- Evidence includes Teaching claims.
- No specific AI service name is highlighted by the current public claim text.
- Teaching, assessment, coursework, or syllabus-related language appears in the public claim text.
Review: Agent reviewed
This reference record summarizes visible public data only. Official sources and original-language evidence remain canonical; confidence is separate from review state.
This page is not legal advice, not academic integrity advice, and not an official university statement unless a linked source is the university's own official page.
Policy profile
Deterministic source-backed dimensions derived from this record's public claims.
Review: Machine candidate
Policy profile rows are machine-candidate derived metadata. They are not final policy conclusions; inspect the linked claim evidence before reuse.
Analysis page-quality metadata is available at /api/public/v1/analysis/page-quality.json.
AI disclosure
Illinois Institute of Technology has 1 source-backed public claim for ai disclosure; deterministic analysis status: recommended.
Coursework
Illinois Institute of Technology has 4 source-backed public claims for coursework; deterministic analysis status: conditionally_allowed.
Exams
Illinois Institute of Technology has 4 source-backed public claims for exams; deterministic analysis status: conditionally_allowed.
Privacy and data entry
Illinois Institute of Technology has 2 source-backed public claims for privacy and data entry; deterministic analysis status: conditionally_allowed.
Academic integrity
Illinois Institute of Technology has 2 source-backed public claims for academic integrity; deterministic analysis status: conditionally_allowed.
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
No source-backed public claim naming a specific AI service is present in this profile.
The current public tracker record does not contain claim evidence naming a specific AI service.
Teaching guidance
Illinois Institute of Technology has 4 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
Illinois Institute of Technology 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.
Coverage score measures breadth of public, source-backed coverage only. It is not a policy quality, strictness, legal adequacy, safety, or compliance score.
Evidence-backed claims
5 reviewed evidence-backed public claim
Source Status
Illinois Tech publishes official Academic Affairs guidance on academic honesty and generative AI, and related Center for Learning Innovation resources for teaching and learning with AI.
Review: Agent reviewed
Normalized value: official_ai_guidance_found
Original evidence
Evidence 1This guideline on academic honesty and generative AI is intended to provide guidance for both students and faculty on engaging with generative AI productively, while still maintaining a rigorous and honest academic environment.
Academic Integrity
Illinois Tech's Academic Affairs guidance ties permitted use of generative AI or other outside resources to each course's learning objectives and syllabus expectations.
Review: Agent reviewed
Normalized value: ai_use_course_objective_dependent
Original evidence
Evidence 1This means that each syllabus needs to clearly define what the learning objectives are and be clear as to what is and is not allowed in terms of "outside" resources to demonstrate proficiency with that learning objective.
Academic Integrity
Illinois Tech guidance says misrepresenting proficiency by submitting someone else's product, directly or indirectly, is academic dishonesty.
Review: Agent reviewed
Normalized value: misrepresentation_dishonesty
Original evidence
Evidence 1Therefore, misrepresentation of proficiency by (getting either directly or indirectly someone else’s) product is dishonesty. It cheats the student of their needed personal development, and it is academic dishonesty, which results in disciplinary action.
Teaching
Illinois Tech's Center for Learning Innovation asks faculty to provide clear syllabus expectations for AI use and provides suggested syllabus language for no, some, and significant AI use.
Review: Agent reviewed
Normalized value: syllabus_ai_expectations_guidance
Original evidence
Evidence 1In the interest of transparency and academic integrity, faculty are asked to ensure their syllabus provides students with clear expectations regarding the use of AI in their courses. Below are recommended statements dependent on instructor expectations.
Teaching
Illinois Tech's suggested AI and assessment syllabus language says AI-assisted grading support should be explicitly stated in assignment instructions when such tools are used, with final grading oversight retained by the instructor.
Review: Agent reviewed
Normalized value: ai_assisted_grading_disclosure_sample_language
Original evidence
Evidence 1"While I may use AI-assisted tools to support the grading process and provide formative feedback, this will be explicitly stated in the assignment instructions whenever such tools are used. Regardless of the tools involved, please be assured that I maintain final oversight, review, and ultimate responsibility for all assigned grades."
Candidate claims
0 machine or needs-review claim
Candidate claims are not final policy conclusions. They preserve source URL, source snapshot hash, evidence, confidence, and review state so the record can be audited before review.
Official sources
3 source attribution
Academic Honesty Guidelines
iit.edu
AI for Teaching and Learning
iit.edu
Code of Academic Honesty
iit.edu
Change log
Source-check timeline and diff-style claim/evidence preview.
View the public change record for this university, including source snapshot hashes, claim review states, and a diff-style preview of current source-backed evidence.
Corrections and missing evidence
Corrections create review tasks and do not directly change this public record.
If an official source is missing, stale, moved, blocked, or incorrectly summarized, submit a source URL, policy change report, or institution correction for review. Corrections must preserve source URLs, source language, original evidence, review state, and audit history.