RPL Document Processing Demo

AI-Powered Multi-Modal Document Analysis for Recognition of Prior Learning

AI Services PD Detection & Redaction 7 File Types Supported

⚠️ AI Services Not Configured

The system cannot process files because AI services are not properly configured. File uploads will fail until this is resolved.

Service Status:

✓ ✗ Blob Storage (Required)
✓ ✗ Azure OpenAI (Required)
✓ ✗ Document Intelligence
✓ ✗ Text Analytics (PD Detection)
✓ ✗ Speech Services

🔧 To Fix:

  1. Create a .env file in your project root
  2. Add your credentials (see README.md)
  3. Restart the Flask server
  4. Refresh this page
✓ AI Services Connected - System Ready

Drag & Drop Files Here or Click to Browse

Supported: PDF, Word, PowerPoint, Images (PNG/JPG), Python (.py), Audio (MP3), Video (MP4)

Maximum file size: 100MB per file

Processing Queue

RPL Information Validation

File Integrity Check

Uploaded documents are scanned for corruption or unreadable content before evaluation.

Files Submitted
Processed Successfully
Corrupted / Unreadable
No corrupted files detected — all uploads passed integrity validation.
file(s) could not be read and were excluded from evaluation.
APA Review
by APA ·

Name Matching

Candidate names extracted from the submitted documents, verified against the university student database record.

Names Extracted from Documents
University Database Record
Extracted Name Source Document(s) Result
No candidate names matching the student record were detected in the submitted documents.

Part of the database record () could not be verified from the submitted documents.

Names detected via Azure Text Analytics and matched against the university student record.

APA Review
by APA ·

Transcript Grade Extraction

Source: Educational_Transcript_Sarah_Chen.pdf · Singapore Polytechnic · Diploma in Information Technology (Software Development) · Apr 2019 – Mar 2022 ·

Transcript modules mapped to the applied micro-credential

Each highlighted transcript module is on the Pre-Identified List and is matched to the competency unit of Modern Software Engineering Practices it provides evidence toward.

All three competency units have at least one matching module on the transcript.

Pre-Identified List (PIL) — Exemption-Eligible Modules

Diploma modules pre-approved by the university as eligible to exempt a competency unit of Modern Software Engineering Practices. A transcript module is a match only if it appears on this list. Modules found in this applicant's transcript are highlighted.

Module Code Module Name Eligible to Exempt

Highlighted rows are modules found in this applicant's transcript ().

Module Code Module Name Credits Grade
Total Credits Earned
GPA
Final Classification
Diploma with Merit
Graduation Status
Graduated with Honors
APA Review
by APA ·

RPL Evaluation Analysis

Assessment Summary

Detailed Assessment

RPL Review & Approval Record

Student Id
2201234A
Name
Sarah Chen
Micro-Credential/Programme Component
Modern Software Engineering Practices
Credentials Units

Supporting Documents

  • No documents uploaded.

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Component Remarks

Approval Record

Approval Role Approver Status Remarks

Evidence Excerpt

Reviewer Feedback

Rate the accuracy of this AI-extracted evidence and leave an audit note (e.g. flag a hallucination or incorrect citation).

Feedback recorded ·