The objective of this task is to build an AI-driven validation engine that can automatically analyze and flag inconsistencies, missing fields, and formatting issues in academic data before submission to the Academic Bank of Credits (ABC) platform. Academic institutions frequently upload CSV/XML datasets containing student records, mark sheets, degrees, and transcripts, and even minor errors in these datasets can result in delays or data rejection.

This tool aims to ensure data quality at the source through AI-powered logic and pattern recognition, eliminating common human errors and making institutions submission-ready. The system should validate content (e.g., missing grades, total vs. subject-wise mismatches), structure (e.g., encoding errors, mandatory fields), and logic (e.g., invalid semester numbers, admission year > exam year).

It will also intelligently map uploaded column headers to the required ABC standard templates and offer data-cleaning suggestions or auto-corrections for issues like numeric formatting, duplicate rows, and character encoding problems.

• Build a backend validation tool (script, CLI, or microservice) to:

  • Parse CSV/XML academic records uploaded by institutions.

  • Run checks for:

    • Encoding and file format (e.g., UTF-8 CSV)

    • Presence of mandatory fields (e.g., Name, APAAR ID, Grade, Credits)

    • Format anomalies (e.g., exponential notation in IDs or marks)

    • Data consistency (e.g., total credits = sum of individual subject credits)

    • Logical inconsistencies (e.g., Admission Year > Exam Year, invalid Roman numerals in semesters)

    • Duplicate records or corrupted rows

  • Auto-suggest corrections or flag problematic entries with recommended fixes.

  • Map institutional headers to standard ABC headers using fuzzy logic or AI suggestions.

  • Provide a summary report of errors, warnings, and readiness score.

• Optionally create a dashboard or UI for uploading, validating, and downloading cleaned data.

A functional AI-based validator tool that:

• Accepts academic data files (CSV/XML) and validates them thoroughly.

• Detects structural, logical, and formatting errors.

• Provides actionable feedback or auto-correction options.

• Maps institutional field names to ABC-standard fields.

• Outputs a validation report highlighting errors, warnings, and suggestions.

• Helps reduce the manual back-and-forth between institutions and the ABC/NAD team during uploads.

• Includes usage instructions, test files, and error resolution guidance.

Deploy code via Git pull request.

https://openforge.gov.in/plugins/git/abc-collabwithgov/abc-data-validator

Organization Type: Government Publisher Name: Academic Bank of Credits (Digital India Corporation)