@Digital-Learning-Companion

Digital Learning Companion

README.md

Digital Learning Companion (DLC)

Deterministic learning systems for writing and reasoning.

The Digital Learning Companion (DLC) is an open-source structured learning system built on the Emergent State Machine (ESM) architecture.

It demonstrates how instructional AI can be:

• deterministic
• auditable
• policy-governed
• replayable
• transparent to teachers and students

Rather than relying on opaque LLM behavior, the DLC models learning situations explicitly and applies structured instructional policies.

Start Here

➡ DLC Meta Repository

https://github.com/Digital-Learning-Companion/dlc

This repository launches the full system and contains the Bird + Brain architecture.

Architecture Stack

The DLC is a reference implementation of the following architecture stack.

Emergent State Machine (ESM)

Control architecture for situational reasoning systems.

https://github.com/emergent-state-machine
https://github.com/emergent-state-machine/esm-spec

Controlled Mutation Layer (CML)

Framework for policy mutation in deterministic systems.

https://github.com/controlled-mutation-layer

Digital Learning Companion (DLC)

Educational reference implementation demonstrating deterministic instructional guidance.

https://github.com/Digital-Learning-Companion/dlc

What the DLC Demonstrates

The DLC shows how AI learning systems can implement a full structured reasoning loop:

Observation
→ Signal extraction
→ State construction
→ Instructional gate
→ Policy projection
→ Instructional action
→ Learning evolution

This allows learning systems to provide guidance that is:

• interpretable
• stable
• inspectable
• versioned

instead of drifting through hidden model behavior.

Current Demonstration

The current DLC MVP implements a structured writing tutor that supports the development of:

Claim
Evidence
Reasoning

in argumentative writing.

The system analyzes writing structure, tracks learning signals, and delivers deterministic instructional guidance while recording interpretable telemetry.

Audience

This project is intended for:

• learning scientists
• education technologists
• engineers building structured AI systems
• organizations requiring auditability in AI-assisted workflows

Status

Active development.

The writing tutor MVP is operational and the control architecture is stable.

Popular repositories Loading

  1. dlc dlcPublic

    The Digital Learning Companion (DLC) is an open-source reference implementation of the Emergent State Machine (ESM) architecture for education, delivering deterministic, turn-based writing guidance…

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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@Digital-Learning-Companion

Digital Learning Companion

README.md
Digital Learning Companion (DLC)
Deterministic learning systems for writing and reasoning.
The Digital Learning Companion (DLC) is an open-source structured learning system built on the Emergent State Machine (ESM) architecture.
It demonstrates how instructional AI can be:
• deterministic

• auditable

• policy-governed

• replayable

• transparent to teachers and students
Rather than relying on opaque LLM behavior, the DLC models learning situations explicitly and applies structured instructional policies.
Start Here
➡ DLC Meta Repository
https://github.com/Digital-Learning-Companion/dlc
This repository launches the full system and contains the Bird + Brain architecture.
Architecture Stack
The DLC is a reference implementation of the following architecture stack.
Emergent State Machine (ESM)
Control architecture for situational reasoning systems.
https://github.com/emergent-state-machine
https://github.com/emergent-state-machine/esm-spec
Controlled Mutation Layer (CML)
Framework for policy mutation in deterministic systems.
https://github.com/controlled-mutation-layer
Digital Learning Companion (DLC)
Educational reference implementation demonstrating deterministic instructional guidance.
https://github.com/Digital-Learning-Companion/dlc
What the DLC Demonstrates
The DLC shows how AI learning systems can implement a full structured reasoning loop:
Observation

→ Signal extraction

→ State construction

→ Instructional gate

→ Policy projection

→ Instructional action

→ Learning evolution
This allows learning systems to provide guidance that is:
• interpretable

• stable

• inspectable

• versioned
instead of drifting through hidden model behavior.
Current Demonstration
The current DLC MVP implements a structured writing tutor that supports the development of:
Claim

Evidence

Reasoning
in argumentative writing.
The system analyzes writing structure, tracks learning signals, and delivers deterministic instructional guidance while recording interpretable telemetry.
Audience
This project is intended for:
• learning scientists

• education technologists

• engineers building structured AI systems

• organizations requiring auditability in AI-assisted workflows
Status
Active development.
The writing tutor MVP is operational and the control architecture is stable.

Popular repositories
Loading
  1. 
dlc
dlcPublic
    
The Digital Learning Companion (DLC) is an open-source reference implementation of the Emergent State Machine (ESM) architecture for education, delivering deterministic, turn-based writing guidance…

  2. 
.github
.githubPublic

Repositories

Showing 2 of 2 repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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@Digital-Learning-Companion

Digital Learning Companion

README.md
Digital Learning Companion (DLC)
Deterministic learning systems for writing and reasoning.
The Digital Learning Companion (DLC) is an open-source structured learning system built on the Emergent State Machine (ESM) architecture.
It demonstrates how instructional AI can be:
• deterministic

• auditable

• policy-governed

• replayable

• transparent to teachers and students
Rather than relying on opaque LLM behavior, the DLC models learning situations explicitly and applies structured instructional policies.
Start Here
➡ DLC Meta Repository
https://github.com/Digital-Learning-Companion/dlc
This repository launches the full system and contains the Bird + Brain architecture.
Architecture Stack
The DLC is a reference implementation of the following architecture stack.
Emergent State Machine (ESM)
Control architecture for situational reasoning systems.
https://github.com/emergent-state-machine
https://github.com/emergent-state-machine/esm-spec
Controlled Mutation Layer (CML)
Framework for policy mutation in deterministic systems.
https://github.com/controlled-mutation-layer
Digital Learning Companion (DLC)
Educational reference implementation demonstrating deterministic instructional guidance.
https://github.com/Digital-Learning-Companion/dlc
What the DLC Demonstrates
The DLC shows how AI learning systems can implement a full structured reasoning loop:
Observation

→ Signal extraction

→ State construction

→ Instructional gate

→ Policy projection

→ Instructional action

→ Learning evolution
This allows learning systems to provide guidance that is:
• interpretable

• stable

• inspectable

• versioned
instead of drifting through hidden model behavior.
Current Demonstration
The current DLC MVP implements a structured writing tutor that supports the development of:
Claim

Evidence

Reasoning
in argumentative writing.
The system analyzes writing structure, tracks learning signals, and delivers deterministic instructional guidance while recording interpretable telemetry.
Audience
This project is intended for:
• learning scientists

• education technologists

• engineers building structured AI systems

• organizations requiring auditability in AI-assisted workflows
Status
Active development.
The writing tutor MVP is operational and the control architecture is stable.

Popular repositories
Loading
  1. 
dlc
dlcPublic
    
The Digital Learning Companion (DLC) is an open-source reference implementation of the Emergent State Machine (ESM) architecture for education, delivering deterministic, turn-based writing guidance…

  2. 
.github
.githubPublic

Repositories

Showing 2 of 2 repositories
People

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@Digital-Learning-Companion

Digital Learning Companion

README.md
Digital Learning Companion (DLC)
Deterministic learning systems for writing and reasoning.
The Digital Learning Companion (DLC) is an open-source structured learning system built on the Emergent State Machine (ESM) architecture.
It demonstrates how instructional AI can be:
• deterministic

• auditable

• policy-governed

• replayable

• transparent to teachers and students
Rather than relying on opaque LLM behavior, the DLC models learning situations explicitly and applies structured instructional policies.
Start Here
➡ DLC Meta Repository
https://github.com/Digital-Learning-Companion/dlc
This repository launches the full system and contains the Bird + Brain architecture.
Architecture Stack
The DLC is a reference implementation of the following architecture stack.
Emergent State Machine (ESM)
Control architecture for situational reasoning systems.
https://github.com/emergent-state-machine
https://github.com/emergent-state-machine/esm-spec
Controlled Mutation Layer (CML)
Framework for policy mutation in deterministic systems.
https://github.com/controlled-mutation-layer
Digital Learning Companion (DLC)
Educational reference implementation demonstrating deterministic instructional guidance.
https://github.com/Digital-Learning-Companion/dlc
What the DLC Demonstrates
The DLC shows how AI learning systems can implement a full structured reasoning loop:
Observation

→ Signal extraction

→ State construction

→ Instructional gate

→ Policy projection

→ Instructional action

→ Learning evolution
This allows learning systems to provide guidance that is:
• interpretable

• stable

• inspectable

• versioned
instead of drifting through hidden model behavior.
Current Demonstration
The current DLC MVP implements a structured writing tutor that supports the development of:
Claim

Evidence

Reasoning
in argumentative writing.
The system analyzes writing structure, tracks learning signals, and delivers deterministic instructional guidance while recording interpretable telemetry.
Audience
This project is intended for:
• learning scientists

• education technologists

• engineers building structured AI systems

• organizations requiring auditability in AI-assisted workflows
Status
Active development.
The writing tutor MVP is operational and the control architecture is stable.

Popular repositories
Loading
  1. 
dlc
dlcPublic
    
The Digital Learning Companion (DLC) is an open-source reference implementation of the Emergent State Machine (ESM) architecture for education, delivering deterministic, turn-based writing guidance…

  2. 
.github
.githubPublic

Repositories

Showing 2 of 2 repositories
People

This organization has no public members. You must be a member to see who’s a part of this organization.

Top languages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
@Digital-Learning-Companion

Digital Learning Companion

README.md
Digital Learning Companion (DLC)
Deterministic learning systems for writing and reasoning.
The Digital Learning Companion (DLC) is an open-source structured learning system built on the Emergent State Machine (ESM) architecture.
It demonstrates how instructional AI can be:
• deterministic

• auditable

• policy-governed

• replayable

• transparent to teachers and students
Rather than relying on opaque LLM behavior, the DLC models learning situations explicitly and applies structured instructional policies.
Start Here
➡ DLC Meta Repository
https://github.com/Digital-Learning-Companion/dlc
This repository launches the full system and contains the Bird + Brain architecture.
Architecture Stack
The DLC is a reference implementation of the following architecture stack.
Emergent State Machine (ESM)
Control architecture for situational reasoning systems.
https://github.com/emergent-state-machine
https://github.com/emergent-state-machine/esm-spec
Controlled Mutation Layer (CML)
Framework for policy mutation in deterministic systems.
https://github.com/controlled-mutation-layer
Digital Learning Companion (DLC)
Educational reference implementation demonstrating deterministic instructional guidance.
https://github.com/Digital-Learning-Companion/dlc
What the DLC Demonstrates
The DLC shows how AI learning systems can implement a full structured reasoning loop:
Observation

→ Signal extraction

→ State construction

→ Instructional gate

→ Policy projection

→ Instructional action

→ Learning evolution
This allows learning systems to provide guidance that is:
• interpretable

• stable

• inspectable

• versioned
instead of drifting through hidden model behavior.
Current Demonstration
The current DLC MVP implements a structured writing tutor that supports the development of:
Claim

Evidence

Reasoning
in argumentative writing.
The system analyzes writing structure, tracks learning signals, and delivers deterministic instructional guidance while recording interpretable telemetry.
Audience
This project is intended for:
• learning scientists

• education technologists

• engineers building structured AI systems

• organizations requiring auditability in AI-assisted workflows
Status
Active development.
The writing tutor MVP is operational and the control architecture is stable.

Popular repositories
Loading
  1. 
dlc
dlcPublic
    
The Digital Learning Companion (DLC) is an open-source reference implementation of the Emergent State Machine (ESM) architecture for education, delivering deterministic, turn-based writing guidance…

  2. 
.github
.githubPublic

Repositories

Showing 2 of 2 repositories
People

This organization has no public members. You must be a member to see who’s a part of this organization.

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Loading…

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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@Digital-Learning-Companion

Digital Learning Companion

README.md
Digital Learning Companion (DLC)
Deterministic learning systems for writing and reasoning.
The Digital Learning Companion (DLC) is an open-source structured learning system built on the Emergent State Machine (ESM) architecture.
It demonstrates how instructional AI can be:
• deterministic

• auditable

• policy-governed

• replayable

• transparent to teachers and students
Rather than relying on opaque LLM behavior, the DLC models learning situations explicitly and applies structured instructional policies.
Start Here
➡ DLC Meta Repository
https://github.com/Digital-Learning-Companion/dlc
This repository launches the full system and contains the Bird + Brain architecture.
Architecture Stack
The DLC is a reference implementation of the following architecture stack.
Emergent State Machine (ESM)
Control architecture for situational reasoning systems.
https://github.com/emergent-state-machine
https://github.com/emergent-state-machine/esm-spec
Controlled Mutation Layer (CML)
Framework for policy mutation in deterministic systems.
https://github.com/controlled-mutation-layer
Digital Learning Companion (DLC)
Educational reference implementation demonstrating deterministic instructional guidance.
https://github.com/Digital-Learning-Companion/dlc
What the DLC Demonstrates
The DLC shows how AI learning systems can implement a full structured reasoning loop:
Observation

→ Signal extraction

→ State construction

→ Instructional gate

→ Policy projection

→ Instructional action

→ Learning evolution
This allows learning systems to provide guidance that is:
• interpretable

• stable

• inspectable

• versioned
instead of drifting through hidden model behavior.
Current Demonstration
The current DLC MVP implements a structured writing tutor that supports the development of:
Claim

Evidence

Reasoning
in argumentative writing.
The system analyzes writing structure, tracks learning signals, and delivers deterministic instructional guidance while recording interpretable telemetry.
Audience
This project is intended for:
• learning scientists

• education technologists

• engineers building structured AI systems

• organizations requiring auditability in AI-assisted workflows
Status
Active development.
The writing tutor MVP is operational and the control architecture is stable.

Popular repositories
Loading
  1. 
dlc
dlcPublic
    
The Digital Learning Companion (DLC) is an open-source reference implementation of the Emergent State Machine (ESM) architecture for education, delivering deterministic, turn-based writing guidance…

  2. 
.github
.githubPublic

Repositories

Showing 2 of 2 repositories
People

This organization has no public members. You must be a member to see who’s a part of this organization.

Top languages
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Most used topics
Loading…

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@Digital-Learning-Companion

Digital Learning Companion

README.md
Digital Learning Companion (DLC)
Deterministic learning systems for writing and reasoning.
The Digital Learning Companion (DLC) is an open-source structured learning system built on the Emergent State Machine (ESM) architecture.
It demonstrates how instructional AI can be:
• deterministic

• auditable

• policy-governed

• replayable

• transparent to teachers and students
Rather than relying on opaque LLM behavior, the DLC models learning situations explicitly and applies structured instructional policies.
Start Here
➡ DLC Meta Repository
https://github.com/Digital-Learning-Companion/dlc
This repository launches the full system and contains the Bird + Brain architecture.
Architecture Stack
The DLC is a reference implementation of the following architecture stack.
Emergent State Machine (ESM)
Control architecture for situational reasoning systems.
https://github.com/emergent-state-machine
https://github.com/emergent-state-machine/esm-spec
Controlled Mutation Layer (CML)
Framework for policy mutation in deterministic systems.
https://github.com/controlled-mutation-layer
Digital Learning Companion (DLC)
Educational reference implementation demonstrating deterministic instructional guidance.
https://github.com/Digital-Learning-Companion/dlc
What the DLC Demonstrates
The DLC shows how AI learning systems can implement a full structured reasoning loop:
Observation

→ Signal extraction

→ State construction

→ Instructional gate

→ Policy projection

→ Instructional action

→ Learning evolution
This allows learning systems to provide guidance that is:
• interpretable

• stable

• inspectable

• versioned
instead of drifting through hidden model behavior.
Current Demonstration
The current DLC MVP implements a structured writing tutor that supports the development of:
Claim

Evidence

Reasoning
in argumentative writing.
The system analyzes writing structure, tracks learning signals, and delivers deterministic instructional guidance while recording interpretable telemetry.
Audience
This project is intended for:
• learning scientists

• education technologists

• engineers building structured AI systems

• organizations requiring auditability in AI-assisted workflows
Status
Active development.
The writing tutor MVP is operational and the control architecture is stable.

Popular repositories
Loading
  1. 
dlc
dlcPublic
    
The Digital Learning Companion (DLC) is an open-source reference implementation of the Emergent State Machine (ESM) architecture for education, delivering deterministic, turn-based writing guidance…

  2. 
.github
.githubPublic

Repositories

Showing 2 of 2 repositories
People

This organization has no public members. You must be a member to see who’s a part of this organization.

Top languages
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Most used topics
Loading…

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
})();

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@Digital-Learning-Companion

Digital Learning Companion

README.md
Digital Learning Companion (DLC)
Deterministic learning systems for writing and reasoning.
The Digital Learning Companion (DLC) is an open-source structured learning system built on the Emergent State Machine (ESM) architecture.
It demonstrates how instructional AI can be:
• deterministic

• auditable

• policy-governed

• replayable

• transparent to teachers and students
Rather than relying on opaque LLM behavior, the DLC models learning situations explicitly and applies structured instructional policies.
Start Here
➡ DLC Meta Repository
https://github.com/Digital-Learning-Companion/dlc
This repository launches the full system and contains the Bird + Brain architecture.
Architecture Stack
The DLC is a reference implementation of the following architecture stack.
Emergent State Machine (ESM)
Control architecture for situational reasoning systems.
https://github.com/emergent-state-machine
https://github.com/emergent-state-machine/esm-spec
Controlled Mutation Layer (CML)
Framework for policy mutation in deterministic systems.
https://github.com/controlled-mutation-layer
Digital Learning Companion (DLC)
Educational reference implementation demonstrating deterministic instructional guidance.
https://github.com/Digital-Learning-Companion/dlc
What the DLC Demonstrates
The DLC shows how AI learning systems can implement a full structured reasoning loop:
Observation

→ Signal extraction

→ State construction

→ Instructional gate

→ Policy projection

→ Instructional action

→ Learning evolution
This allows learning systems to provide guidance that is:
• interpretable

• stable

• inspectable

• versioned
instead of drifting through hidden model behavior.
Current Demonstration
The current DLC MVP implements a structured writing tutor that supports the development of:
Claim

Evidence

Reasoning
in argumentative writing.
The system analyzes writing structure, tracks learning signals, and delivers deterministic instructional guidance while recording interpretable telemetry.
Audience
This project is intended for:
• learning scientists

• education technologists

• engineers building structured AI systems

• organizations requiring auditability in AI-assisted workflows
Status
Active development.
The writing tutor MVP is operational and the control architecture is stable.

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