Open University Moodle (Enterprise LMS Platform)

Role: Backend Developer • NashTech

Enterprise LMS platform serving tens of thousands of university students and lecturers, featuring custom assessment workflows and local AI-assisted tutoring.

// Timeline:

03/2024 — 02/2026

// Team context:

Collaboration between backend engineers, Moodle core specialists, QA, and academic operations staff.

MODULES (SCROLL TO EXPLORE):

[CARD 01 / 07] // PROBLEM ARCHITECTURE

psychology SYS.SYS.02.01

Context & Problem Statement

Open University required deep customization of the open-source Moodle LMS to handle specialized examination and grading workflows. Concurrently, an AI tutoring and summary feature was required under strict privacy governance prohibiting student conversation data from leaving internal boundaries. The existing platform faced recurring database bottlenecks during peak exam periods.

// ENGINEERING FOCUS:

EdTech & Learning Management Systems

// DEPLOYMENT SCOPE:

03/2024 — 02/2026

[CARD 02 / 07] // CORE CONTRIBUTIONS

terminal SYS.SYS.02.02

Core Engineering Contributions

›_ Developed and maintained core Moodle plugins in PHP and JavaScript (OUStudio, Sketch, Assignment).
›_ Authored comprehensive automated test suites using PHPUnit (unit testing) and Behat (behavior-driven acceptance testing).
›_ Integrated a privacy-compliant local LLM gateway using Ollama AI to summarize quizzes and exams for students and faculty.
›_ Implemented a multi-tier caching architecture with Redis Cache for high-frequency AI inferences and repetitive query paths.

[CARD 03 / 07] // MEASURABLE IMPACT

trending_up SYS.SYS.02.03

Measurable Impact

✓ Reduced database query pressure by ~40% on critical learner endpoints during semester exams.
✓ Significantly lowered compute overhead and token costs through intelligent response caching in Redis.
✓ Maintained 100% data privacy compliance by hosting and executing all AI models on internal enterprise infrastructure.

[CARD 04 / 07] // SYSTEM DESIGN

account_tree SYS.SYS.02.04

Architecture Highlights & System Design

› Local AI Gateway pattern isolates Moodle controllers from internal Ollama inference endpoints, preserving student privacy.
› Redis Cache-Aside caching strategy tailored to examination lifecycle windows.
› End-to-end behavior-driven regression testing with Behat integrated into CI pipelines.

[CARD 05 / 07] // TECHNICAL SCOPE

checklist SYS.SYS.02.05

Responsibilities & Technical Scope

› Owned backend integration architecture between Moodle plugin modules and internal AI inference services.
› Designed multi-tier cache-aside and invalidation policies for latency-critical student workflows.
› Formulated automated Behat and PHPUnit regression suites to ensure release stability across academic terms.

[CARD 06 / 07] // OUTCOMES & RETRO

verified SYS.SYS.02.06

Delivery Outcomes & Production Impact & Lessons Learned & Engineering Takeaways

verified // Delivery Outcomes & Production Impact

✓ Platform maintained uninterrupted stability throughout semester-wide examination peaks.
✓ AI exam summary capability graduated successfully into daily production use for faculty.
✓ Substantially reduced post-release regressions following custom plugin updates.

lightbulb // Lessons Learned & Engineering Takeaways

› AI features in education are fundamentally an engineering reliability and privacy challenge rather than just model prompt engineering.
› Cache efficiency compounds significantly when cache TTLs mirror actual student test-taking rhythms.

[CARD 07 / 07] // TECH SPEC & VERIFICATION

layers SYS.SYS.02.07

Technology Stack

PHP 8.2Moodle ArchitectureJavaScriptRedisOllama AIPHPUnitBehatAzure DevOpsLAMP Stack

[DOSSIER COMPLETE • SYS.02] ● 100% PRODUCTION VERIFIED

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