Recognition of Prior Learning for Credit

Turn proven experience into accredited credit.

RPLFC assesses a student's prior professional experience, certifications, and portfolio against the learning outcomes of their target degree. The output is a single, signed, auditable artefact: the RPLFC report — with every awarded ECTS mapped to evidence and justified for academic review.

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WoolfRecognition of Prior Learning for Credit
Report WU-RPLFC-2026-004417Prepared 03 June 2026

Woolf: A Report on the Application for the Recognition of Prior Learning

ApplicantAmara N. Okonkwo
Date of birth14 March 1996
Target degreeMaster of Science in Artificial Intelligence
CollegeAtlas Institute of Technology

1) Executive Summary

Identity confirmed. The names, date of birth, and qualification records on the submitted evidence match the enrolled student record without discrepancy.

18 ECTSTotal recommended exemption

Award 18 ECTS of exemption toward the Master of Science in Artificial Intelligence at Woolf, a degree-granting Higher Education Institution with license 2019-015. Allocation: 15 ECTS at course level (CH and MT courses per Step 4 priority) and 3 ECTS at overall degree level.

6) Step 5 — Final Mapping Table and Report

Learning sourceEvidence typeYearLicenceTarget course / degreeECTSStatus
Senior Machine Learning EngineerEmployment letter & reference2019–2022CHCloud Infrastructure & MLOps5Fully Allocated
AWS Certified Machine Learning — SpecialtyIndustry certification2021CHApplied Deep Learning4Partially Allocated
Lead Data ScientistEmployment contract2022–2024CHNatural Language Processing4Fully Allocated
Peer-reviewed workshop paper (NeurIPS)Portfolio evidence2023MTResponsible & Ethical AI2Fully Allocated
Open-source project maintainershipPortfolio evidence2020–2024DegreeOverall degree level3Fully Allocated
Total ECTS allocated18

7) Final Recommendation

15ECTS at course level
3ECTS at degree level
18ECTS total exemption

This report has been prepared for review by the Quality Assurance Enhancement and Technology Alignment Committee (QAETAC) by an Academic Compliance Officer of Woolf.

Quality Assurance Enhancement and Technology Alignment Committee (QAETAC)
How an assessment runs

Five evaluation steps, one defensible recommendation.

Every application moves through the same structured protocol. Credit is awarded only where evidence clearly demonstrates the equivalent learning outcome — and the total recognised maps directly to the target degree.

Step 1

Identity & document matching

Names, date of birth, and identifiers on every submitted document are matched against the enrolled student record. Any discrepancy is flagged before evidence is considered.

Step 2

Qualification & source classification

Each source is classified by type — formal qualification, professional certification, employment, or portfolio — and screened for verifiability and EQF level.

Step 3

Learning events & ECTS conversion

Verified learning events are converted into estimated learning hours and a defensible ECTS value, capped per Woolf policy and filtered to EQF 7 evidence.

Step 4

Target degree analysis & matching

Recognised learning is matched to the target degree, prioritising constituent-college (CH) and Malta (MT) course outcomes before any overall degree-level allocation.

Step 5

Final mapping table & recommendation

A final mapping table allocates ECTS course-by-course with written justification, producing the recommended exemption that the academic committee reviews and signs.

The artefact

The RPLFC report.

An AI-generated, structured document that walks from identity verification through ECTS conversion to a final mapping table. An academic compliance officer reviews and signs it; the signed PDF becomes the system of record for the credit applied.

  • Evidence-led. Every awarded ECTS is tied to a named source and evidence type — no unexplained credit.

  • Mapped to the degree. Recognised learning is matched to the specific courses and outcomes of the target degree.

  • Signed & auditable. Reviewed and signed by the academic committee (QAETAC), then stored as an auditable PDF.

WoolfRecognition of Prior Learning for Credit
Report WU-RPLFC-2026-004417Prepared 03 June 2026

Woolf: A Report on the Application for the Recognition of Prior Learning

ApplicantAmara N. Okonkwo
Date of birth14 March 1996
Target degreeMaster of Science in Artificial Intelligence
CollegeAtlas Institute of Technology

1) Executive Summary

Identity confirmed. The names, date of birth, and qualification records on the submitted evidence match the enrolled student record without discrepancy.

18 ECTSTotal recommended exemption

Award 18 ECTS of exemption toward the Master of Science in Artificial Intelligence at Woolf, a degree-granting Higher Education Institution with license 2019-015. Allocation: 15 ECTS at course level (CH and MT courses per Step 4 priority) and 3 ECTS at overall degree level.

6) Step 5 — Final Mapping Table and Report

Learning sourceEvidence typeYearLicenceTarget course / degreeECTSStatus
Senior Machine Learning EngineerEmployment letter & reference2019–2022CHCloud Infrastructure & MLOps5Fully Allocated
AWS Certified Machine Learning — SpecialtyIndustry certification2021CHApplied Deep Learning4Partially Allocated
Lead Data ScientistEmployment contract2022–2024CHNatural Language Processing4Fully Allocated
Peer-reviewed workshop paper (NeurIPS)Portfolio evidence2023MTResponsible & Ethical AI2Fully Allocated
Open-source project maintainershipPortfolio evidence2020–2024DegreeOverall degree level3Fully Allocated
Total ECTS allocated18

7) Final Recommendation

15ECTS at course level
3ECTS at degree level
18ECTS total exemption

This report has been prepared for review by the Quality Assurance Enhancement and Technology Alignment Committee (QAETAC) by an Academic Compliance Officer of Woolf.

Quality Assurance Enhancement and Technology Alignment Committee (QAETAC)
What students submit

Evidence the assessment accepts.

Curriculum vitae

A curriculum vitae outlining roles, responsibilities, and dates across each relevant position.

Employment proof

Offer letters, contracts, or reference letters that prove employment for each role you want recognised.

Certifications

Certified copies of industry qualifications and certifications, with certificate ID numbers where available.

Supporting profiles

Online profiles such as LinkedIn can support an application, but assessors require primary documents to verify learning.

Not to be confused with RPL for Admission. RPL for Admission qualifies a student to enter a program. RPLFC is a separate, post-enrollment product that awards course or tier credit against a degree the student is already enrolled in — they use different records and review flows. See admission pathways.

Recognise the learning that already happened

Give experienced learners a fair start.

Let Woolf assess prior learning against your degrees and produce a signed, defensible credit report — so students start where their experience says they should.

Book a consultationRead the RPLFC guide