Academic Workflow Suite

 _ _ _ __ __ _ __ _
/ \ ___ __ _ __| | ___ _ __ ___ (_) ___ \ \ / /__ _ __| | __/ _| | _____ __
/ _ \ / __/ _` |/ _` |/ _ \ '_ ` _ \| |/ __| \ \ /\ / / _ \| '__| |/ / |_| |/ _ \ \ /\ / /
/ ___ \ (_| (_| | (_| | __/ | | | | | | (__ \ V V / (_) | | | <| _| | (_) \ V V /
/_/ \_\___\__,_|\__,_|\___|_| |_| |_|_|\___| \_/\_/ \___/|_| |_|\_\_| |_|\___/ \_/\_/
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/ ___| _ _(_) |_ ___
\___ \| | | | | __/ _ \
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|____/ \__,_|_|\__\___|

Academic Workflow Suite

AI-Assisted TMA Marking for OU Associate Lecturers

[![Build Status](https://img.shields.io/badge/build-passing-brightgreen.svg)](https://github.com/academic-workflow-suite/actions) [![Version](https://img.shields.io/badge/version-0.1.0-blue.svg)](https://github.com/academic-workflow-suite/releases) [![Docker](https://img.shields.io/badge/docker-ready-blue.svg)](https://hub.docker.com/r/aws-edu/core) [![GDPR Compliant](https://img.shields.io/badge/GDPR-compliant-green.svg)](docs/SECURITY.md#gdpr-compliance) [![Privacy First](https://img.shields.io/badge/privacy-guaranteed-green.svg)](docs/SECURITY.md)

[Website](https://aws-edu.org) • [Documentation](docs/README.md) • [Quick Start](docs/QUICK_START.md) • [Installation](docs/INSTALLATION_GUIDE.md) • [Support](https://discuss.aws-edu.org)

Reduce TMA marking time from 20-30 minutes to under 10 minutes while maintaining quality and protecting student privacy.

</div>

[Overview](#overview)
[The Problem We Solve](#the-problem-we-solve)
[Key Features](#key-features)
[Screenshots](#screenshots)
[Quick Start](#quick-start)
[Architecture](#architecture)
[Installation](#installation)
[Usage](#usage)
[Documentation](#documentation)
[Development](#development)
[Security & Privacy](#security—privacy)
[Roadmap](#roadmap)
[Support](#support)
[Contributing](#contributing)
[License](#license)
[Citation](#citation)
[Acknowledgments](#acknowledgments)

Overview

What is Academic Workflow Suite?

Academic Workflow Suite (AWS) is an open-source, privacy-first AI assistant designed specifically for Open University Associate Lecturers to streamline the marking of Tutor-Marked Assignments (TMAs).

Unlike cloud-based AI tools that compromise student privacy, AWS runs entirely on your local machine with mathematical guarantees that student data never reaches AI systems in identifiable form.

Target Audience

OU Associate Lecturers marking TMAs across all modules
Academic institutions requiring GDPR-compliant AI assistance
Researchers studying educational technology and privacy-preserving AI
Developers interested in event-sourced, privacy-first architectures

The Value Proposition

Traditional Marking: With Academic Workflow Suite:
───────────────── ────────────────────────────
📄 Read submission 📄 Load document (auto)
📝 Check rubric 📝 Rubric loaded (auto)
💭 Analyze quality 🤖 AI analysis (10-30s)
✍️ Write feedback ✍️ Review & edit AI suggestions
🔢 Calculate scores 🔢 Scores calculated
📋 Format comments 📋 Auto-format & insert
⏱️ 20-30 minutes/TMA ⏱️ <10 minutes/TMA
🔒 100% student privacy

The Problem We Solve

The Challenge of TMA Marking

Open University Associate Lecturers face a significant workload challenge:

20-30 minutes per TMA on average
50-100+ TMAs per assignment for popular modules
Repetitive feedback for common mistakes
Cognitive load of maintaining rubric consistency
Tight deadlines (typically 2-3 weeks)

Total time commitment: 16-50+ hours per assignment batch

Why Not Use ChatGPT or Claude?

While commercial AI assistants can help, they pose serious privacy risks:

❌ Student data sent to cloud servers ❌ No control over data retention ❌ Potential GDPR violations ❌ University policy non-compliance ❌ No audit trail ❌ Risk of data breaches

Our Solution

AWS provides AI assistance with zero privacy compromise:

✅ Runs entirely on your machine (no cloud required) ✅ Student IDs never reach AI (cryptographically anonymized) ✅ Network-isolated AI (cannot exfiltrate data) ✅ Complete audit trail (event sourcing) ✅ GDPR compliant by design ✅ University-approved architecture

Key Features

🤖 AI-Assisted Feedback Generation

Intelligent analysis against custom or pre-loaded rubrics
Constructive feedback suggestions that you can edit or reject
Score recommendations based on rubric criteria
Consistency checking across multiple submissions
Learn your style (adapts to your feedback patterns over time)

🔒 Privacy-First Architecture

Student ID: A1234567
↓ SHA3-512 Hash (one-way, irreversible)
Hash: 7f3a2b9c8e1d4a5c6f8b9e2d3c4a5b6c7d8e9f0a1b2c3d4e5f6a7b8c9d0e1f2a3b...
↓ Sent to AI Jail (network-isolated container)
AI sees:
✓ Anonymous hash
✓ Essay content
✓ Rubric criteria
AI never sees:
✗ Student ID
✗ Student name
✗ Any personally identifiable information

Mathematical guarantee: SHA3-512 hash cannot be reversed (2^512 search space = more combinations than atoms in the universe)

📝 Event Sourcing & Audit Trail

Every action is logged immutably:

2025-11-22 14:32:01 - Document loaded: TM112-TMA01
2025-11-22 14:32:15 - Student ID anonymized: A1234567 → hash
2025-11-22 14:32:47 - AI analysis completed (29 seconds)
2025-11-22 14:35:22 - Tutor edited feedback for criterion: Understanding
2025-11-22 14:36:10 - Feedback inserted into Word document
2025-11-22 14:36:32 - Document exported as PDF

Benefits: - Prove compliance for university audits - Reproduce past decisions for quality assurance - Time-travel debugging for troubleshooting - GDPR right to explanation support

🏢 Microsoft Office Integration

Seamless integration with Microsoft Word via official Office Add-in:

Task pane interface in Word (no context switching)
Direct document manipulation using Office.js
Inline comments inserted automatically
Rubric-based scoring with auto-calculation
Export to PDF/DOCX with formatting preserved

🔌 Moodle LMS Integration (Planned)

Future releases will include: - Direct download of student submissions - Automated grade upload to Moodle gradebook - Batch processing of entire assignment cohorts

🚀 Batch Processing

Mark multiple TMAs efficiently:

= Analyze all TMAs in a directory
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core batch analyze --module TM112 --assignment TMA01 --input ./submissions/
= Process 50 TMAs in parallel
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= Average processing time: 10 minutes per TMA
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= Total time: ~8-10 hours → Run overnight!
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]

📊 Custom Rubrics

Create and share rubrics:

= Example: TM112 TMA01 Rubricimage:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]module: TM112assignment: TMA01total_marks: 100criteria:
- id: understandingname: Understanding of Conceptsmarks: 30description: Demonstrates grasp of networking fundamentals
- id: analysisname: Critical Analysismarks: 30description: Evaluates concepts critically with evidence
- id: structurename: Structure & Claritymarks: 20description: Well-organized, clear writing
- id: evidencename: Use of Evidencemarks: 20description: Appropriate citations and examples

Full compliance with EU General Data Protection Regulation:

Data minimization: Only necessary data processed
Purpose limitation: Data used only for marking
Storage limitation: Configurable retention periods
Right to erasure: Complete data deletion on request
Right to explanation: Full audit trail available
Data portability: Export all data in standard formats
Privacy by design: Architecture prevents PII leakage

Screenshots

Note: The following are placeholder descriptions. Actual screenshots will be added in future releases.

Office Add-in Interface

┌─────────────────────────────────────────────────────────────────────┐
│ Microsoft Word - TM112-TMA01.docx │
├─────────────────────────────┬───────────────────────────────────────┤
│ │ ┌─────────────────────────────────┐ │
│ Student: A1234567 │ │ Academic Workflow Suite │ │
│ Module: TM112 │ │ │ │
│ Assignment: TMA01 │ │ Module: TM112 ▾ │ │
│ │ │ Assignment: TMA01 ▾ │ │
│ Question 1: │ │ │ │
│ Explain the concept of... │ │ [Load Document] │ │
│ │ │ │ │
│ [Student's essay text] │ │ Status: ✓ Document loaded │ │
│ Lorem ipsum dolor sit │ │ │ │
│ amet, consectetur │ │ Rubric: TM112-TMA01 Official │ │
│ adipiscing elit... │ │ │ │
│ │ │ [Analyze Submission] │ │
│ │ └─────────────────────────────────┘ │
└─────────────────────────────┴───────────────────────────────────────┘

Figure 1: Office add-in task pane showing document loaded and ready for analysis

Feedback Generation Example

┌─────────────────────────────────────────────────────────────────────┐
│ AI Analysis Complete │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ Suggested Scores: │
│ │
│ 📚 Understanding of Concepts: 24/30 ★★★★☆ │
│ 🔍 Critical Analysis: 22/30 ★★★☆☆ │
│ 📝 Structure & Clarity: 17/20 ★★★★☆ │
│ 📖 Use of Evidence: 15/20 ★★★☆☆ │
│ ───────────────────────────────────────────────── │
│ Total: 78/100 (B+) │
│ │
│ ┌───────────────────────────────────────────────────────────────┐ │
│ │ Suggested Feedback (Understanding of Concepts) │ │
│ │ │ │
│ │ "You demonstrate a solid understanding of networking │ │
│ │ fundamentals. Your explanation of the TCP/IP model is clear │ │
│ │ and accurate. To improve, consider exploring how the OSI │ │
│ │ model relates to real-world protocols in more depth." │ │
│ │ │ │
│ │ [✎ Edit] [✓ Accept] [✗ Reject] │ │
│ └───────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘

Figure 2: AI-generated feedback suggestions with scores

Settings Panel

┌─────────────────────────────────────────────────────────────────────┐
│ AWS Settings │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ ⚙️ General │
│ ├─ Feedback Tone: ◉ Formal ○ Friendly ○ Custom │
│ ├─ Scoring Strictness: [█████████░] 85% │
│ └─ Auto-save Interval: [5 minutes ▾] │
│ │
│ 🔒 Privacy │
│ ├─ Hash Algorithm: SHA3-512 (FIPS 202) ✓ │
│ ├─ Network Isolation: ✓ Enabled │
│ └─ Data Retention: [90 days ▾] │
│ │
│ 🎨 Appearance │
│ ├─ Theme: ◉ Light ○ Dark ○ Auto │
│ └─ Font Size: [14pt ▾] │
│ │
│ 📊 Analytics │
│ ├─ Anonymous Usage Stats: ○ Enabled ◉ Disabled │
│ └─ Crash Reports: ○ Enabled ◉ Disabled │
│ │
│ [Save Changes] [Reset to Defaults] │
└─────────────────────────────────────────────────────────────────────┘

Figure 3: Settings panel for customizing AWS behavior

Analytics Dashboard (Planned)

┌─────────────────────────────────────────────────────────────────────┐
│ Marking Statistics - TM112 TMA01 │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ 📈 Overview │
│ ├─ TMAs Marked: 47 / 52 │
│ ├─ Average Time: 8.5 minutes/TMA │
│ ├─ Time Saved: ~9.5 hours (vs 20min average) │
│ └─ AI Suggestions Accepted: 73% │
│ │
│ 📊 Score Distribution │
│ ┌─────────────────────────────────────────────┐ │
│ │ A: ████████ (17%) │ │
│ │ B: ████████████████ (30%) │ │
│ │ C: ████████████████████ (38%) │ │
│ │ D: ████████ (13%) │ │
│ │ Fail: ██ (2%) │ │
│ └─────────────────────────────────────────────┘ │
│ │
│ 🎯 Rubric Criteria (Average Scores) │
│ ├─ Understanding: 23.4/30 (78%) │
│ ├─ Analysis: 21.8/30 (73%) │
│ ├─ Structure: 16.2/20 (81%) │
│ └─ Evidence: 15.9/20 (80%) │
│ │
│ [Export Report] [View Trends] │
└─────────────────────────────────────────────────────────────────────┘

Figure 4: Analytics dashboard showing marking statistics

Quick Start

Prerequisites

Microsoft Word 2019 or later (or Office 365)
Docker or Podman (for AI isolation)
10 GB free disk space
8 GB RAM (16 GB recommended)

Installation (5 Minutes)

macOS / Linux

curl -sSL https://install.aws-edu.org/install.sh | bash

Windows

iwr https://install.aws-edu.org/install.ps1 -useb | iex

First TMA (5 Minutes)

= 1. Start AWS services
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core start
= 2. Open Word and click the AWS tab
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= 3. Open a TMA document
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= 4. Click "Analyze Submission"
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= 5. Review and edit AI suggestions
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= 6. Insert feedback into document
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= 7. Export and submit!
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]

Full guide: See [Quick Start Guide](docs/QUICK_START.md) for detailed walkthrough

Architecture

See TOPOLOGY.md for a visual architecture map and completion dashboard.

High-Level System Overview

┌─────────────────────────────────────────────────────────────────────┐
│ USER ENVIRONMENT │
│ │
│ ┌───────────────────────────────────────────────────────────────┐ │
│ │ Presentation Layer │ │
│ │ ┌─────────────────────────────────────────────────────────┐ │ │
│ │ │ Microsoft Word + AWS Office Add-in (ReScript) │ │ │
│ │ │ - Task pane UI │ │ │
│ │ │ - Document manipulation (Office.js) │ │ │
│ │ └──────────────────────┬──────────────────────────────────┘ │ │
│ └─────────────────────────┼─────────────────────────────────────┘ │
│ │ HTTPS/TLS 1.3 (localhost:8080) │
│ ┌─────────────────────────▼─────────────────────────────────────┐ │
│ │ Application Layer (Rust) │ │
│ │ ┌──────────┐ ┌──────────┐ ┌─────────────────┐ │ │
│ │ │ REST API │ │ Business │ │ Anonymization │ │ │
│ │ │ (Actix) │ │ Logic │ │ Engine (SHA3) │ │ │
│ │ └──────────┘ └──────────┘ └─────────────────┘ │ │
│ └─────────────────────────┬─────────────────────────────────────┘ │
│ │ │
│ ┌─────────────────────────▼─────────────────────────────────────┐ │
│ │ Persistence Layer │ │
│ │ ┌──────────┐ ┌──────────┐ ┌──────────────┐ │ │
│ │ │ Event │ │ Read │ │ File Storage │ │ │
│ │ │ Store │ │ Models │ │ (Documents) │ │ │
│ │ │ (LMDB) │ │(In-Mem) │ │ │ │ │
│ │ └──────────┘ └──────────┘ └──────────────┘ │ │
│ └─────────────────────────┬─────────────────────────────────────┘ │
│ │ Unix Socket (IPC) │
│ ┌─────────────────────────▼─────────────────────────────────────┐ │
│ │ AI Isolation Layer (Network-Isolated Container) │ │
│ │ ┌─────────────────────────────────────────────────────────┐ │ │
│ │ │ 🔒 Sandboxed Environment (gVisor/Firecracker) │ │ │
│ │ │ ┌────────┐ ┌─────────┐ ┌──────────────────────────┐ │ │ │
│ │ │ │ AI │ │ ONNX │ │ Security Controls: │ │ │ │
│ │ │ │ Model │ │ Runtime │ │ ✗ No network access │ │ │ │
│ │ │ │ │ │ │ │ ✗ Read-only filesystem │ │ │ │
│ │ │ │ │ │ │ │ ✗ No PII received │ │ │ │
│ │ │ └────────┘ └─────────┘ └──────────────────────────┘ │ │ │
│ │ └─────────────────────────────────────────────────────────┘ │ │
│ └───────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
│
│ HTTPS (Optional)
│
┌─────────────▼────────────────┐
│ Backend Services (Optional) │
│ - Rubric repository │
│ - Software updates │
│ - NO student data │
└──────────────────────────────┘

Component Descriptions

| Component | Technology | Purpose | |-----------|------------|---------| | Office Add-in | ReScript, Office.js, Webpack | UI and Word integration | | Core Engine | Rust, Actix-Web, Tokio | Business logic and API | | Event Store | LMDB (via Heed) | Immutable event log | | AI Jail | Rust, ONNX Runtime, Docker | Isolated AI inference | | Backend | Rust, Actix-Web (optional) | Rubric repository, updates |

Technology Stack

Frontend: ReScript → JavaScript, React, Office.js
Backend: Rust (stable), Actix-Web 4.x
Database: LMDB (Lightning Memory-Mapped Database)
AI Runtime: ONNX Runtime, llama.cpp
Containerization: Docker/Podman, gVisor
Cryptography: SHA3 (FIPS 202), AES-256-GCM
Build System: Cargo, npm, Make
Verified Libraries: proven (Idris2 formally verified modules)

Proven Library Integration

Academic Workflow Suite integrates with the proven library - a formally verified, crashproof library written in Idris2 with 45 modules and 43 language FFI bindings.

Integrated Modules

Module	Purpose	Guarantee
SafeCrypto	SHA3-512 anonymization, AES-256-GCM encryption	Timing attack resistance, no panics
SafeJson	IPC communication, API parsing	No parse failures, memory safety
SafeRegex	PII detection, input validation	No ReDoS, polynomial-time matching
SafeStateMachine	Event sourcing, workflow states	No invalid states, deterministic
SafePath	Document path handling	Path traversal prevention

Benefits

Mathematical Guarantees: Formally proven correctness
Crash Safety: No panics or undefined behavior
Security Hardening: Verified cryptographic implementations
Auditability: Proofs available for security review

See: Proven Integration Guide for implementation details

Data Flow

1. Tutor opens TMA → Office Add-in parses document
2. Student ID extracted → Core Engine anonymizes (SHA3-512)
3. Anonymized data sent → AI Jail via Unix socket
4. AI analyzes essay → Returns feedback suggestions
5. Core re-associates → Hash → Student ID
6. Tutor reviews/edits → Inserts into Word document
7. Audit event logged → Immutable event store

Detailed architecture: See [Architecture Documentation](docs/ARCHITECTURE.md)

Installation

System Requirements

| Component | Minimum | Recommended | |-----------|---------|-------------| | OS | Windows 10, macOS 11, Ubuntu 20.04 | Windows 11, macOS 13+, Ubuntu 22.04+ | | CPU | 2 cores, 2.0 GHz | 4+ cores, 3.0 GHz | | RAM | 8 GB | 16 GB | | Disk | 10 GB free | 20 GB free (SSD) | | Word | Office 2019 | Office 365 | | Docker | Docker 20.10+ | Docker 24.0+ or Podman 4.0+ |

Installation Modes

1. Quick Install (Recommended)

One-liner installation with sensible defaults:

macOS / Linux:

curl -sSL https://install.aws-edu.org/install.sh | bash

Windows (PowerShell as Administrator):

iwr https://install.aws-edu.org/install.ps1 -useb | iex

What gets installed: - AWS Core Engine (/usr/local/bin/aws-core) - Office Add-in (configured in Word) - AI Jail container image - Default rubrics for popular modules - System dependencies

2. Custom Install

Choose components and installation paths:

= Download installer
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
curl -sSL https://install.aws-edu.org/install.sh -o install.sh
= Run with custom options
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
bash install.sh \
--prefix ~/.local \
--no-ai-jail \
--modules TM112,M250 \
--config-file my-config.toml

3. Build from Source

For developers or custom builds:

= Clone repository
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
git clone https://github.com/academic-workflow-suite/aws.git
cd aws
= Build all components
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make build
= Install
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
sudo make install
= Or install to custom location
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make install PREFIX=~/.local

Full installation guide: See [Installation Guide](docs/INSTALLATION_GUIDE.md)

Post-Installation

Verify Installation

= Check AWS Core
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core --version
= Expected: AWS Core Engine v0.1.0
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= Check Docker
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
docker ps
= Expected: Container list (may be empty)
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= Check Office Add-in
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= Open Word → Look for"AWS" tabin ribbon
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]

Start Services

= Start AWS Core Engine
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core start
= Check status
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core status
= Expected:
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= ✓ Core Engine: Running
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= ✓ AI Jail: Ready
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= ✓ Database: OK
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]

Configure

= Edit configuration
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core config edit
= Common settings:
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= - feedback_tone: formal, friendly, or custom
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= - scoring_strictness: 0-100
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= - data_retention_days: 90
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= - theme: light, dark, or auto
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]

Usage

Basic Workflow

1. Open TMA in Word

File → Open → [Select TMA document]

2. Launch AWS

Click the AWS tab in Word ribbon → Open AWS Panel

3. Load Document

In AWS task pane: - Module: Select from dropdown (e.g., TM112) - Assignment: Select TMA (e.g., TMA01) - Click Load Document

AWS will parse the document and detect: - Student ID - Module/assignment metadata - Question structure - Student responses

4. Select Rubric

Click Load Rubric
Choose official rubric or create custom
Review criteria and marks

5. Analyze Submission

Click Analyze Submission
Wait 10-30 seconds for AI analysis
Review suggested scores and feedback

6. Edit Feedback

Important: AI provides suggestions, not decisions!

Edit any feedback text
Adjust scores based on your judgment
Add personal comments
Remove suggestions you disagree with

7. Insert into Document

Click Insert Feedback
AWS adds comments to Word document
Review inserted feedback
Make final adjustments

8. Export

Click Export
Choose format: PDF, DOCX, or TXT
Upload to OU TutorHome/StudentHome

Advanced Features

Batch Processing

Mark multiple TMAs at once:

= Prepare TMAs
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
mkdir -p ~/marking/TM112-TMA01
= Place all TMA documents in this directory
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= Batch analyze
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core batch analyze \
--module TM112 \
--assignment TMA01 \
--input ~/marking/TM112-TMA01 \
--output ~/marking/TM112-TMA01-marked \
--parallelism 4
= Review and finalize in Word
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]

Custom Rubrics

Create rubrics for any module:

= Interactive rubric creator
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core rubric create \
--module M250 \
--assignment TMA02
= Follow prompts to define criteria
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]

Or edit YAML directly:

= ~/.aws/rubrics/M250-TMA02.ymlimage:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]module: M250assignment: TMA02total_marks: 100criteria:
- id: oop_designname: Object-Oriented Designmarks: 40description: Quality of class design and UML diagrams
- id: implementationname: Code Implementationmarks: 40description: Correctness and style of Java code
- id: testingname: Testing & Documentationmarks: 20description: Unit tests and code documentation

CLI Commands

= Start/stop services
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core start
aws-core stop
aws-core restart
= Status and health
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core status
aws-core doctor # Diagnose issues
= Rubrics
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core rubric list
aws-core rubric show TM112 TMA01
aws-core rubric create --module M250 --assignment TMA03
aws-core rubric import rubric.yml
= Updates
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core update # Update AWS
aws-core update-rubrics # Update rubric repository
= Logs and diagnostics
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core logs
aws-core logs --tail 50
aws-core logs --follow
= Configuration
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core config show
aws-core config edit
aws-core config reset
= Export statistics
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core stats export --module TM112 --format csv

Full user guide: See [User Guide](docs/USER_GUIDE.md)

Documentation

User Documentation

[Quick Start Guide](docs/QUICK_START.md) - Get started in 10 minutes
[Installation Guide](docs/INSTALLATION_GUIDE.md) - Detailed installation instructions
[User Guide](docs/USER_GUIDE.md) - Comprehensive manual
[FAQ](docs/USER_GUIDE.md#faq) - Frequently asked questions
[Troubleshooting](docs/USER_GUIDE.md#troubleshooting) - Common issues and solutions

Technical Documentation

[Architecture](docs/ARCHITECTURE.md) - System architecture and design
[API Reference](docs/API_REFERENCE.md) - REST API specification
[Security](docs/SECURITY.md) - Security model and privacy guarantees
[Proven Integration](docs/PROVEN_INTEGRATION.adoc) - Formally verified library integration
[Roadmap](docs/ROADMAP.adoc) - Future directions and development paths
[Development Guide](docs/DEVELOPMENT.md) - Contributing and development
[CI/CD](docs/CI-CD.md) - Continuous integration and deployment

Component Documentation

[Core Engine](components/core/README.md) - Rust backend
[Office Add-in](components/office-addin/README.md) - ReScript frontend
[AI Jail](components/ai-jail/README.md) - Isolated AI inference
[Backend](components/backend/README.md) - Optional cloud services

Reference

[Docker Guide](docs/DOCKER_GUIDE.md) - Container deployment
[CLI Reference](cli/README.md) - Command-line interface
[Configuration Reference](config/README.md) - All configuration options

Development

Setting Up Development Environment

Prerequisites

Rust 1.75+ (rustup install stable)
Node.js 18+ and npm
Docker 20.10+ or Podman 4.0+
Git 2.30+

Clone and Build

= Clone repository
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
git clone https://github.com/academic-workflow-suite/aws.git
cd aws
= Install dependencies
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make deps
= Build all components
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make build
= Run tests
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make test
= Start development servers
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make dev

Project Structure

academic-workflow-suite/
├── components/
│ ├── core/ # Rust backend (Actix-Web)
│ ├── office-addin/ # ReScript frontend (Office.js)
│ ├── ai-jail/ # Isolated AI inference
│ ├── backend/ # Optional cloud services
│ └── shared/ # Shared types and utilities
├── docs/ # Documentation
├── scripts/ # Build and deployment scripts
├── tests/ # Integration tests
├── docker/ # Dockerfiles and compose configs
├── cli/ # Command-line interface
├── config/ # Default configurations
└── website/ # Project website

Development Workflow

= Create feature branch
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
git checkout -b feature/my-feature
= Make changes and test
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make test
make lint
= Run locally
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make dev
= Build release
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make release
= Create pull request
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
git push origin feature/my-feature

Running Tests

= All tests
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make test
= Unit tests only
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make test-unit
= Integration tests
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make test-integration
= End-to-end tests
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make test-e2e
= With coverage
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make test-coverage

Code Style

This project follows: - Rust: rustfmt and clippy (strict mode) - ReScript: ReScript formatter - YAML: yamllint

= Format code
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make format
= Lint code
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make lint
= Fix auto-fixable issues
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
make lint-fix

Contributing

We welcome contributions! Please see [CONTRIBUTING.md](components/office-addin/CONTRIBUTING.md) for:

Code of conduct
Development workflow
Pull request process
Coding standards
Testing requirements

Quick start for contributors:

Fork the repository
Create a feature branch
Make your changes with tests
Ensure make test and make lint pass
Submit a pull request

Security & Privacy

Privacy Guarantees

AWS provides mathematical guarantees that student personally identifiable information (PII) cannot reach AI systems:

1. Cryptographic Anonymization

Student ID → SHA3-512 Hash → AI Jail
A1234567 → 7f3a2b9c... → (irreversible)
Attack resistance:
• Brute force: 2^512 search space (infeasible)
• Rainbow table: Prevented by random salt
• Timing attack: Constant-time implementation

2. Network Isolation

AI Jail container is completely isolated:

Security Controls:
✓ No network access (iptables DROP all)✓ Read-only filesystem✓ System call filtering (seccomp)✓ Memory limits (4 GB)✓ No GPU access✓ Container destroyed after use

3. Local-First Architecture

All student data stays on your machine
No cloud services required for core functionality
Optional backend only for rubrics (no student data)

4. Complete Audit Trail

Every action logged immutably:

Event Store (LMDB):
├─ DocumentLoaded
├─ StudentIdAnonymized (hash only, original encrypted)
├─ AnalysisRequested
├─ AnalysisCompleted
├─ FeedbackEdited (tutor override recorded)
└─ DocumentExported

AWS is compliant with EU General Data Protection Regulation:

| GDPR Right | Implementation | |------------|----------------| | Right to be informed | Complete audit trail available | | Right of access | Export all data via aws-core export | | Right to rectification | Edit or delete any data | | Right to erasure | aws-core delete --student A1234567 | | Right to restriction | Pause processing for specific students | | Right to data portability | Export in JSON, CSV, or XML | | Right to object | Opt-out mechanisms available | | Rights related to automated decision-making | Human (tutor) always in the loop |

University Approval

AWS is designed to meet university data protection requirements:

✅ No cloud data storage (student data never leaves machine) ✅ Anonymized AI processing (PII cryptographically protected) ✅ Complete audit trail (prove compliance) ✅ Tutor override (AI assists, tutor decides) ✅ Open source (auditable by university IT)

Security Testing

We conduct regular security assessments:

Static analysis: Clippy, cargo-audit
Dependency scanning: Dependabot, Snyk
Container scanning: Trivy, Clair
Penetration testing: Annual third-party audits
Fuzzing: Continuous fuzzing with cargo-fuzz

Vulnerability Reporting

Found a security issue? Please report responsibly:

Email: security@aws-edu.org
PGP Key: [Download](https://aws-edu.org/pgp-key.asc)
Response time: 24-48 hours

Do not open public GitHub issues for security vulnerabilities.

See: [Security Policy](security/policies/SECURITY_POLICY.md) for full details

Roadmap

✅ Completed (v0.1.0 - Current)

✓ Core engine with event sourcing
✓ Office Add-in for Word
✓ AI isolation with Docker/gVisor
✓ SHA3-512 anonymization
✓ LMDB event store
✓ Basic rubric support
✓ Manual feedback editing
✓ PDF export
✓ CLI interface
✓ Documentation

🚧 In Progress (v0.2.0 - Q1 2026)

❏ Moodle LMS integration - Direct submission download
❏ Batch processing improvements - Parallel analysis
❏ Advanced analytics dashboard - Marking statistics
❏ Custom AI models - Fine-tune on your feedback style
❏ Voice dictation - Speak your feedback
❏ Mobile app - Review feedback on iOS/Android

📅 Planned (v0.3.0 - Q2 2026)

❏ Collaborative marking - Multiple tutors on same module
❏ Plagiarism detection - Integration with Turnitin API
❏ Grade moderation - Cross-tutor consistency checking
❏ Student progress tracking - Longitudinal analysis
❏ Multilingual support - Support for non-English TMAs
❏ Enhanced privacy - Zero-knowledge proofs

💡 Future Ideas (v1.0.0+)

❏ Browser extension - Mark TMAs in any web interface
❏ Automated grading - For objective questions (opt-in)
❏ Student self-assessment - Pre-submission feedback tool
❏ Research analytics - Anonymized data for pedagogy research
❏ Integration with Turnitin, Gradescope, Canvas
❏ Machine learning model marketplace - Share fine-tuned models

Suggest a feature: [Open a discussion](https://github.com/academic-workflow-suite/discussions)

Support

Documentation

First, check our comprehensive documentation:

[User Guide](docs/USER_GUIDE.md) - How to use AWS
[FAQ](docs/USER_GUIDE.md#faq) - Common questions
[Troubleshooting](docs/USER_GUIDE.md#troubleshooting) - Fix common issues
[Installation Guide](docs/INSTALLATION_GUIDE.md) - Setup help

Community Support

Join our community:

[Discussion Forum](https://discuss.aws-edu.org) - Ask questions, share tips
[GitHub Discussions](https://github.com/academic-workflow-suite/discussions) - Feature requests, Q&A
[Discord Server](https://discord.gg/aws-edu) - Real-time chat
[Monthly Webinars](https://aws-edu.org/webinars) - Live demos and Q&A

Issue Tracker

Report bugs or request features:

[GitHub Issues](https://github.com/academic-workflow-suite/issues) - Bug reports, feature requests
[Security Issues](mailto:security@aws-edu.org) - Confidential security reports

Email Support

For direct assistance:

General: support@aws-edu.org
Technical: tech@aws-edu.org
Security: security@aws-edu.org
Press/Media: press@aws-edu.org

Response time: Usually 24-48 hours (weekdays)

Commercial Support

Enterprise support available for institutions:

Priority email support (4-hour response SLA)
Dedicated Slack channel
Custom feature development
On-site training
Annual security audits

Contact: enterprise@aws-edu.org

Contributing

We welcome contributions from the community! AWS is built by educators, for educators.

How to Contribute

1. Code Contributions

Fix bugs
Implement features from roadmap
Improve documentation
Add tests
Optimize performance

See [Development](#development) section for setup instructions.

2. Rubric Contributions

Share rubrics for your module:

= Export your rubric
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
aws-core rubric export M250 TMA02 > M250-TMA02.yml
= Submit via PR to rubrics repository
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]
= https://github.com/academic-workflow-suite/rubrics
image:https://img.shields.io/badge/License-MPL_2.0--1.0-blue.svg[License: PMPL-1.0,link="https://github.com/hyperpolymath/palimpsest-license"]

3. Documentation

Fix typos
Improve clarity
Add examples
Translate to other languages

4. Testing

Test new releases
Report bugs
Suggest improvements
Share use cases

5. Community Support

Answer questions on forums
Help other users
Write blog posts or tutorials
Give talks about AWS

Contributor Recognition

Contributors are recognized in:

[Contributors](https://github.com/academic-workflow-suite/graphs/contributors) page
Release notes
Annual contributor report

Top contributors may receive: - AWS swag (stickers, t-shirts) - Early access to new features - Invitation to contributor meetups

Code of Conduct

We are committed to providing a welcoming and inclusive environment. All contributors must adhere to our [Code of Conduct](components/office-addin/CONTRIBUTING.md#code-of-conduct).

License

Software License

This project is licensed under the Palimpsest-MPL License v3.0 (PMPL-1.0).

Academic Workflow Suite
Copyright (C) 2025 Academic Workflow Suite Contributors
This program is free software: you can redistribute it and/or modify
it under the terms of the Palimpsest-MPL License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
Palimpsest-MPL License for more details.
You should have received a copy of the Palimpsest-MPL License
along with this program. If not, see <https://www.gnu.org/licenses/>.

Why AGPL?

We chose PMPL-1.0 to ensure that:

AWS remains free and open source for all users
Improvements are shared with the community (even for web services)
Student privacy is protected (source code is auditable)
Commercial vendors must contribute back if they modify AWS

See: [LICENSE](LICENSE) for full text

Documentation License

Documentation is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

You are free to: - Share — copy and redistribute the material - Adapt — remix, transform, and build upon the material

Under the following terms: - Attribution — You must give appropriate credit - ShareAlike — If you remix, you must distribute under the same license

Citation

Academic Citation

If you use Academic Workflow Suite in your research or teaching, please cite:

APA Style:

Academic Workflow Suite Contributors. (2025). Academic Workflow Suite:
Privacy-First AI-Assisted TMA Marking (Version 0.1.0) [Computer software].
https://github.com/academic-workflow-suite

BibTeX:

@software{academic_workflow_suite_2025,
title = {Academic Workflow Suite: Privacy-First AI-Assisted TMA Marking},
author = {{Academic Workflow Suite Contributors}},
year = 2025,
version = {0.1.0},
url = {https://github.com/academic-workflow-suite},
license = {PMPL-1.0},
keywords = {education, AI, privacy, marking, assessment}
}

Papers and Presentations

If you publish research about AWS, please let us know! We maintain a [Publications](https://aws-edu.org/publications) page.

Acknowledgments

Contributors

Thank you to all our contributors:

See [Contributors](https://github.com/academic-workflow-suite/graphs/contributors) for full list

Inspiration

This project was inspired by:

Open University Associate Lecturers who dedicate countless hours to student feedback
Privacy-preserving AI research from academia
Open-source education tools like Moodle, Open edX, and Jupyter

Technologies

Built with excellent open-source technologies:

| Technology | License | Purpose | |------------|---------|---------| | [Rust](https://www.rust-lang.org/) | MIT/Apache-2.0 | Core engine | | [ReScript](https://rescript-lang.org/) | MIT | Frontend | | [Actix-Web](https://actix.rs/) | MIT/Apache-2.0 | Web framework | | [LMDB](https://www.symas.com/lmdb) | OpenLDAP | Database | | [ONNX Runtime](https://onnxruntime.ai/) | MIT | AI inference | | [Docker](https://www.docker.com/) | Apache-2.0 | Containerization | | [Office.js](https://docs.microsoft.com/en-us/office/dev/add-ins/) | MIT | Word integration |

Funding

This project is currently self-funded by contributors. If you’d like to support development:

[GitHub Sponsors](https://github.com/sponsors/academic-workflow-suite)
[Open Collective](https://opencollective.com/aws-edu)

Special Thanks

Open University for pioneering distance education
Associate Lecturers for feedback and testing
Privacy researchers for cryptographic guidance
Early adopters for valuable feedback

Project Status

Current Status

Version: 0.1.0 (Initial Release)
Status: Beta - Ready for testing
Stability: Experimental - Use with caution in production

Browser/Platform Compatibility

| Platform | Status | Notes | |----------|--------|-------| | Windows 10/11 | ✅ Supported | Tested on Word 2019, Office 365 | | macOS 11+ | ✅ Supported | Intel and Apple Silicon | | Linux (Ubuntu) | ✅ Supported | 20.04 LTS and later | | Linux (Other) | ⚠️ Community | Arch, Fedora, etc. (community-supported) |

Known Limitations

AI models: Currently English-only
Office: Word only (Excel/PowerPoint not supported)
Moodle: Integration not yet available (v0.2.0)
Batch processing: Limited to 100 TMAs at once

Getting Updates

Stay informed about releases:

[Release Notes](https://github.com/academic-workflow-suite/releases) - All releases
[Newsletter](https://aws-edu.org/newsletter) - Monthly updates
[RSS Feed](https://github.com/academic-workflow-suite/releases.atom) - Release notifications
[Twitter](https://twitter.com/aws_edu) - Announcements

Similar Tools

[Moodle](https://moodle.org/) - Open-source LMS
[Open edX](https://open.edx.org/) - MOOC platform
[Gradescope](https://www.gradescope.com/) - Grading platform
[Turnitin](https://www.turnitin.com/) - Plagiarism detection

Complementary Tools

[Zotero](https://www.zotero.org/) - Reference management
[LaTeX](https://www.latex-project.org/) - Document preparation
[Jupyter](https://jupyter.org/) - Interactive notebooks
[Hypothesis](https://web.hypothes.is/) - Web annotation

Contact

Project Website

Homepage: https://aws-edu.org
Documentation: https://docs.aws-edu.org
Blog: https://blog.aws-edu.org

Twitter: [@aws_edu](https://twitter.com/aws_edu)
LinkedIn: [AWS Education](https://linkedin.com/company/aws-edu)
YouTube: [AWS Tutorials](https://youtube.com/@aws-edu)

Email

General Inquiries: hello@aws-edu.org
Support: support@aws-edu.org
Press: press@aws-edu.org
Security: security@aws-edu.org

⭐ Star Us on GitHub!

If you find AWS useful, please consider starring the repository. It helps others discover the project!

[![GitHub stars](https://img.shields.io/github/stars/academic-workflow-suite/aws?style=social)](https://github.com/academic-workflow-suite/aws)

Made with ❤️ by educators, for educators

Privacy-first • Open-source • Community-driven

Academic Workflow Suite - Empowering educators while protecting student privacy

</div>

Last Updated: 2025-11-22 Version: 0.1.0 Status: Beta Release

Name		Name	Last commit message	Last commit date
Latest commit History 451 Commits
.claude		.claude
.github		.github
.hypatia		.hypatia
.machine_readable		.machine_readable
.migration		.migration
.well-known		.well-known
Containerfiles		Containerfiles
LICENSES		LICENSES
cli		cli
components		components
config		config
docker		docker
docs		docs
examples		examples
ffi/zig		ffi/zig
licenses		licenses
monitoring		monitoring
release		release
scripts		scripts
security		security
tasks		tasks
tests		tests
website		website
.containerignore		.containerignore
.dockerignore		.dockerignore
.editorconfig		.editorconfig
.env.example		.env.example
.gitattributes		.gitattributes
.gitignore		.gitignore
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ABI-FFI-README.md		ABI-FFI-README.md
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CHANGELOG.md		CHANGELOG.md
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CONTRIBUTING.md		CONTRIBUTING.md
EXPLAINME.adoc		EXPLAINME.adoc
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ROADMAP.adoc		ROADMAP.adoc
RSR_OUTLINE.adoc		RSR_OUTLINE.adoc
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selur-compose.toml		selur-compose.toml
setup.sh		setup.sh
sonar-project.properties		sonar-project.properties
stapeln.toml		stapeln.toml

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Academic Workflow Suite

AI-Assisted TMA Marking for OU Associate Lecturers

Table of Contents

Overview

What is Academic Workflow Suite?

Target Audience

The Value Proposition

The Problem We Solve

The Challenge of TMA Marking

Why Not Use ChatGPT or Claude?

Our Solution

Key Features

🤖 AI-Assisted Feedback Generation

🔒 Privacy-First Architecture

📝 Event Sourcing & Audit Trail

🏢 Microsoft Office Integration

🔌 Moodle LMS Integration (Planned)

🚀 Batch Processing

📊 Custom Rubrics

🌐 GDPR Compliant

Screenshots

Office Add-in Interface

Feedback Generation Example

Settings Panel

Analytics Dashboard (Planned)

Quick Start

Prerequisites

Installation (5 Minutes)

macOS / Linux

Windows

First TMA (5 Minutes)

Architecture

High-Level System Overview

Component Descriptions

Technology Stack

Proven Library Integration

Integrated Modules

Benefits

Data Flow

Installation

System Requirements

Installation Modes

1. Quick Install (Recommended)

2. Custom Install

3. Build from Source

Post-Installation

Verify Installation

Start Services

Configure

Usage

Basic Workflow

1. Open TMA in Word

2. Launch AWS

3. Load Document

4. Select Rubric

5. Analyze Submission

6. Edit Feedback

7. Insert into Document

8. Export

Advanced Features

Batch Processing

Custom Rubrics

CLI Commands

Documentation

User Documentation

Technical Documentation

Component Documentation

Reference

Development

Setting Up Development Environment

Prerequisites

Clone and Build

Project Structure

Development Workflow

Running Tests