Malpractice & Plagiarism
DAIS-POL-010
Malpractice and Plagiarism Policy
Data and AI School of London, NCFE Approved Centre
Policy Owner: Compliance Manager
Version: 1.0 | Date: April 2026 | Review: April 2027
NCFE Risk Level: High
Mode of Delivery: Fully Online
10.1 Purpose and Scope
The Data and AI School of London takes the integrity of all NCFE qualifications very seriously. This policy defines malpractice and plagiarism, sets out prevention and detection measures, and provides clear procedures for investigating and responding to suspected incidents. It applies to all learners and all staff.
10.2 Definitions
| Term | Definition |
|---|---|
| Plagiarism | Presenting another person's work, ideas, or words as one's own without proper acknowledgement, including copying from websites, books, other learners, or AI-generated text. |
| Collusion | Two or more learners working together on an individual assessment task without authorisation and submitting substantially similar work. |
| Contract cheating | Commissioning a third party (person or automated service) to complete assessed work and submitting it as one's own. |
| Impersonation | Having another person complete assessments on one's behalf, or attending/submitting as another learner. |
| AI-generated malpractice | Submitting content generated by an AI tool as original work, where such use has not been permitted for that specific task. |
| Assessor/staff malpractice | Any action by staff that gives learners an unfair advantage or compromises assessment integrity. |
10.3 Prevention Measures
All learners sign a digital declaration of authenticity with every major assessment submission.
All written submissions are checked using originality-detection software (Turnitin or equivalent).
Assessment tasks require personalised, contextualised responses difficult to plagiarise or AI-generate.
Assessors are trained to recognise AI-generated content, contract cheating, and collusion.
Learner identity may be re-verified at key assessment points through live video verification.
This policy is provided to all learners at induction and requires a digital acknowledgement.
10.4 Detection Indicators
Triggers for further investigation include:
Similarity scores above 25% on originality-detection software
A significant change in writing style, vocabulary, or quality compared to previous submissions
Content containing factual errors characteristic of AI hallucination
Identical or near-identical submissions from two or more learners
Metadata indicating submission was created by a different person or device
10.5 Investigation Procedure
1. The assessor reports the concern to the Compliance Manager in writing within 2 working days.
2. The Compliance Manager logs the concern in the Malpractice Register and notifies the Head of Centre.
3. The learner's submission is placed on hold; no grade is awarded pending investigation.
4. The learner is informed in writing and given the opportunity to respond and provide evidence.
5. The investigation is completed within 15 working days; the learner may be interviewed via video call.
6. Where malpractice is confirmed, the case is reported to NCFE under JCQ Suspected Malpractice Procedures.
7. The learner is informed of the outcome and advised of their right to appeal (Policy 1).
10.6 Sanctions
Available sanctions, applied proportionately:
A formal written warning on the learner's record
Resubmission under supervised conditions
Disqualification from the affected unit or the full qualification
Removal from the school's programme
10.7 Record Keeping
All concerns, investigations, and outcomes are recorded in the Malpractice Register regardless of whether the concern was upheld. Nil returns are recorded annually. The register is available to NCFE upon request.
This policy is reviewed annually. Next review: April 2027. Approved by: Head of Centre. © Data and AI School of London, April 2026. NCFE Approved Centre.