ai_tool_treatment
USJ's AI guiding principles advise users to systematically verify data and information sources provided by generative AI.
Open, evidence-backed AI policy records for public reuse.
Change log
Source-check timeline, source snapshot hashes, claim review state, and a diff-style preview of current source-backed claim evidence.
Current public record freshness and review state.
Saint Joseph University of Beirut (USJ) currently has 6 source-backed claim records and 4 official source attributions. Latest tracked changed date: May 17, 2026.
This tracker 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.
Diff-style preview built from current public claim/evidence records. Full old/new source diffs require paired historical snapshots.
Inserted lines represent current public claim and evidence records in the source-backed dataset.
6 claim records
USJ's AI guiding principles advise users to systematically verify data and information sources provided by generative AI.
USJ's ChatGPT-3 guidance says using ChatGPT is not necessarily an academic-integrity violation, but unauthorized use to create content submitted as one's own is considered plagiarism and a violation of academic-integrity policy.
USJ's CINIA page presents guiding principles intended to support effective, responsible, and ethical use of artificial intelligence in higher education teaching and learning, and says the principles were approved and validated by USJ's Committee for Digital and Artificial Intelligence Strategic Orientation.
USJ's AI guiding principles frame academic integrity as appropriately incorporating AI-altered or manipulated digital content and citing it accordingly.
USJ's CINIA guidance warns that free AI tools may carry privacy-related risks and should be used with awareness of secure and responsible-use considerations.
USJ's CINIA training catalog includes student training on proper use of AI tools, with ethical questions such as data confidentiality, AI-error responsibility, and possible discrimination from biased training data.
4 source attributions
official_guidance checked May 17, 2026
official_guidance checked May 17, 2026
official_guidance checked May 17, 2026
official_guidance checked May 17, 2026