Change log

University of Johannesburg

Source-backed change history with no release-to-release policy diff rows recorded yet; current claims, official sources, review state, and freshness remain visible across 0 public release records.

Change summary

Current public record freshness and review state.

University of Johannesburg currently has 3 source-backed claim records and 3 official source attributions. Latest tracked changed date: May 16, 2026. No tracker diff rows are recorded in the latest public release.

This page combines all public release diffs for University of Johannesburg. Individual release snapshots remain available from their release-specific URLs.

No release-to-release policy diff rows are recorded for this university yet. The page still tracks current source-backed claims, official source attributions, review state, source freshness, and public JSON for discovery and citation.

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.

Newly extracted claims are tracker additions and are not necessarily newly published by the university. Source snapshot changes show hash changes for the same source URL and are not by themselves policy changes.

Diff categories

Semantic classification for this release diff.

Policy text0Newly extracted0Evidence0Source snapshots0Source text0Source added0Source removed0

Combined release diff

Unified tracker diff generated from all public release snapshots for this university.

University of Johannesburg combined release diff

Initial tracked release. Lines represent public claim/evidence records entering the release snapshot.

+6-0
11 # University of Johannesburg AI policy record
2+academic_integrity: UJ's practice note states that presenting the work of a generative AI tool, in whole or in part, as one's own is academic dishonesty, and says AI use should be acknowledged where used.
3+Evidence (en, 7b2880ce8629): To present the work of someone else or of a generative AI tool, in whole or in part, as one’s own, is academic dishonesty. To mitigate the risks of academic misconduct, in the context of generative AI, it is recommended that: students and researchers be transparent and sign a declaration that the work is their own.
4+academic_integrity: UJ's student guide says generative AI use depends on course, department, faculty rules and UJ policy, and students should familiarise themselves with those requirements before producing assignments and assessments.
5+Evidence (en, 3bb6efba73e2): How you use generative AI depends on your course, department, or faculty rules, as well as UJ policy. Familiarise yourself with the rules and requirements before you produce assignments and assessments.
6+teaching: UJ's staff guide frames appropriate generative AI use as including clear communication of institutional, departmental, and course regulations on generative AI, including referencing generated content.
7+Evidence (en, 71a0a789d9a8): Appropriate use of generative AI should apply the following parameters: Clear communication of the institutional/ departmental/ course regulations on the use of generative AI, including referencing generated content, developing proficiency in prompt generation, and harnessing the benefits of generative AI.

Release history

0 public release diffs

Claim changes

3 claim records

academic_integrity

UJ's practice note states that presenting the work of a generative AI tool, in whole or in part, as one's own is academic dishonesty, and says AI use should be acknowledged where used.

Review: Agent reviewedConfidence93%Evidence1Languagesen

teaching

UJ's staff guide frames appropriate generative AI use as including clear communication of institutional, departmental, and course regulations on generative AI, including referencing generated content.

Review: Agent reviewedConfidence88%Evidence1Languagesen

academic_integrity

UJ's student guide says generative AI use depends on course, department, faculty rules and UJ policy, and students should familiarise themselves with those requirements before producing assignments and assessments.

Review: Agent reviewedConfidence90%Evidence1Languagesen

Source snapshots

3 source attributions