SAGE MNKY Dojo Integration

The SAGE MNKY agent integrates deeply with the Dojo platform to enhance learning experiences, manage knowledge resources, and provide personalized educational support. This document details how SAGE MNKY capabilities are leveraged within the Dojo learning environment.

Integration Overview

The Dojo platform serves as MOOD MNKY’s personalized learning and development environment. SAGE MNKY enhances the knowledge and learning aspects of this platform by:

Curating personalized learning paths tailored to individual interests and goals
Providing contextual knowledge at the moment of need
Facilitating community learning and knowledge sharing
Adapting content difficulty based on learner progress
Offering multi-modal learning approaches to accommodate different learning styles

Key Integration Points

Learning Path Engine

SAGE MNKY powers the learning path engine with:

Personalized learning journeys
Adaptive sequencing of content
Progress tracking and milestone celebration
Just-in-time learning recommendations

Knowledge Repository

For knowledge management, SAGE MNKY enables:

Semantically organized content library
Intelligent search and discovery
Multi-modal content formats
Knowledge graph relationship mapping

Community Facilitation

SAGE MNKY enhances community learning with:

Guided discussion facilitation
Group learning activities
Knowledge sharing incentives
Expert matchmaking for mentorship

Learning Assessment

To measure progress, SAGE MNKY provides:

Comprehensive skill assessment
Knowledge retention verification
Practical application evaluation
Learning effectiveness metrics

Learning Path Personalization

SAGE MNKY creates deeply personalized learning experiences within the Dojo platform:

Adaptive Learning Paths

The agent dynamically constructs and adapts learning paths based on individual needs: The path personalization considers:

Learning preferences (visual, auditory, reading/writing, kinesthetic)
Pace preferences (comprehensive vs. accelerated)
Application goals (theoretical understanding vs. practical application)
Interest areas for contextually relevant examples
Learning history to avoid unnecessary repetition

Contextual Knowledge Delivery

SAGE MNKY delivers relevant knowledge exactly when needed:

import { useSageMnkyContext } from '@repo/sage-mnky-hooks';

function ContextualKnowledgePanel({ currentTopic, learnerContext }) {
  const {
    contextualContent,
    isLoading,
    relatedConcepts,
    practicalApplications,
    commonQuestions,
    expandTopic
  } = useSageMnkyContext({
    topic: currentTopic,
    learnerContext,
    depth: 'adaptive',
    format: 'mixed-media'
  });
  
  return (
    <div className="knowledge-panel">
      <div className="current-concept">
        <h2>{contextualContent.title}</h2>
        <p className="description">{contextualContent.description}</p>
        
        {contextualContent.primaryContent && (
          <div className="primary-content">
            {/* Renders appropriate content based on type */}
            <ContentRenderer 
              content={contextualContent.primaryContent}
              format={contextualContent.format}
            />
          </div>
        )}
      </div>
      
      <div className="knowledge-extensions">
        <div className="related-concepts">
          <h3>Related Concepts</h3>
          <ConceptList 
            concepts={relatedConcepts} 
            onSelect={(concept) => expandTopic(concept.id)}
          />
        </div>
        
        <div className="applications">
          <h3>Practical Applications</h3>
          <ApplicationList applications={practicalApplications} />
        </div>
        
        <div className="common-questions">
          <h3>Frequently Asked Questions</h3>
          <FAQAccordion questions={commonQuestions} />
        </div>
      </div>
    </div>
  );
}

Knowledge Repository Management

SAGE MNKY manages a comprehensive knowledge repository within the Dojo platform:

Content Organization and Retrieval

// Example content retrieval with SAGE MNKY
const knowledgeResults = await sageMnkyClient.retrieveKnowledge({
  query: "fragrance composition principles",
  learnerContext: {
    skillLevel: "intermediate",
    interestAreas: ["essential oils", "natural perfumery"],
    learningHistory: ["basic-scent-profiles", "fragrance-families"],
    preferredFormats: ["visual", "interactive"]
  },
  contentTypes: ["article", "video", "interactive", "assessment"],
  maxResults: 5,
  includeMetadata: true
});

// Returns structured content with intelligent organization
// {
//   primaryContent: { id: "frag-comp-101", type: "article", ... },
//   relatedConcepts: [
//     { id: "scent-layering", title: "Layering in Fragrance", relevance: 0.92 },
//     { id: "top-notes", title: "Understanding Top Notes", relevance: 0.87 },
//     ...
//   ],
//   supplementaryContent: [
//     { id: "video-blending", type: "video", title: "Blending Demonstration" },
//     ...
//   ],
//   practicalExercises: [
//     { id: "ex-identify-notes", type: "interactive", difficulty: "intermediate" },
//     ...
//   ],
//   knowledgeGraph: { nodes: [...], edges: [...] }
// }

SAGE MNKY creates and adapts learning content in multiple formats:

// Example content generation
const newContent = await sageMnkyClient.generateContent({
  topic: "Mindfulness in Daily Self-Care",
  targetAudience: {
    skillLevel: "beginner",
    interests: ["stress reduction", "morning routines"],
    demographicContext: "working professionals"
  },
  contentFormats: [
    { type: "article", length: "medium", style: "conversational" },
    { type: "infographic", focusAreas: ["key steps", "benefits"] },
    { type: "guided-practice", duration: "5min", complexity: "simple" }
  ],
  brandVoice: true,
  keyMessages: [
    "Simplicity in practice",
    "Integration with existing routines",
    "Scientific benefits of mindfulness"
  ]
});

The generated content includes:

Consistent messaging across different formats
Format-appropriate structures optimized for learning
Accessibility considerations for diverse learners
Brand-aligned voice and tone
Appropriate complexity for the target audience

Knowledge Graph Visualization

For complex topics, SAGE MNKY provides knowledge graph visualization:

import { useSageMnkyKnowledgeGraph } from '@repo/sage-mnky-hooks';

function KnowledgeGraphVisualization({ topicId, explorationDepth }) {
  const {
    graphData,
    isLoading,
    focusedNode,
    expandNode,
    collapseNode,
    pathToMastery,
    highlightPath
  } = useSageMnkyKnowledgeGraph({
    rootTopicId: topicId,
    depth: explorationDepth,
    learnerContext: getCurrentLearnerContext(),
    includeProgress: true
  });
  
  return (
    <div className="knowledge-graph">
      <div className="controls">
        <button onClick={() => highlightPath(pathToMastery)}>
          Show Path to Mastery
        </button>
        <ZoomControls />
        <FilterOptions />
      </div>
      
      <div className="graph-container">
        {isLoading ? (
          <LoadingIndicator />
        ) : (
          <InteractiveGraph
            nodes={graphData.nodes}
            edges={graphData.edges}
            focusedNode={focusedNode}
            onNodeClick={(node) => expandNode(node.id)}
            learnerProgress={graphData.progress}
          />
        )}
      </div>
      
      <div className="focused-content">
        {focusedNode && (
          <NodeDetailPanel 
            node={focusedNode}
            relatedContent={graphData.relatedContent[focusedNode.id]}
          />
        )}
      </div>
    </div>
  );
}

Community Learning Facilitation

SAGE MNKY enhances community-based learning within the Dojo platform:

Discussion Facilitation

The agent actively facilitates meaningful discussions and knowledge sharing: The facilitation includes:

Thoughtful prompts to deepen exploration
Connection building between related discussions
Expert identification to involve knowledgeable members
Respectful conflict mediation when opinions differ
Synthesis of key insights from complex discussions

Collaborative Learning Projects

SAGE MNKY guides collaborative projects for deeper learning:

import { useSageMnkyCollaboration } from '@repo/sage-mnky-hooks';

function CollaborativeLearningHub({ projectId, memberIds }) {
  const {
    project,
    isLoading,
    members,
    learningGoals,
    contributionAreas,
    suggestedResources,
    progress,
    nextActions,
    assignTask
  } = useSageMnkyCollaboration({
    projectId,
    memberIds,
    facilitationLevel: 'active'
  });
  
  return (
    <div className="collab-project">
      <div className="project-header">
        <h2>{project.title}</h2>
        <p className="description">{project.description}</p>
        <GoalsList goals={learningGoals} progress={progress.goalProgress} />
      </div>
      
      <div className="collaboration-space">
        <div className="member-section">
          <h3>Team Members</h3>
          <MemberList 
            members={members} 
            contributions={progress.memberContributions}
            expertiseAreas={project.expertiseMap}
          />
        </div>
        
        <div className="contribution-areas">
          <h3>Contribution Areas</h3>
          {contributionAreas.map(area => (
            <ContributionCard
              key={area.id}
              area={area}
              assignedMembers={area.assignedMembers}
              completionStatus={area.completionStatus}
              onAssign={(memberId) => assignTask(area.id, memberId)}
            />
          ))}
        </div>
      </div>
      
      <div className="facilitation-panel">
        <h3>SAGE Insights</h3>
        <ResourceSuggestions resources={suggestedResources} />
        <NextActionsList actions={nextActions} />
        <FacilitationChat projectId={projectId} />
      </div>
    </div>
  );
}

Learning Circles

SAGE MNKY supports ongoing learning communities focused on specific interest areas:

// Example learning circle creation
const learningCircle = await sageMnkyClient.createLearningCircle({
  topic: "Sustainable Fragrance Practices",
  description: "A community exploring environmentally responsible approaches to fragrance creation and use",
  learningObjectives: [
    "Understand sustainable sourcing principles",
    "Explore eco-friendly packaging innovations",
    "Develop natural preservation techniques",
    "Share waste reduction practices"
  ],
  membershipCriteria: {
    interestAreas: ["sustainability", "natural-fragrances"],
    minKnowledgeLevel: "beginner",
    commitmentExpectation: "moderate"
  },
  facilitationSettings: {
    sageMnkySupport: "active",
    contentCadence: "weekly",
    discussionPrompts: "twice-weekly",
    expertSessions: "monthly"
  },
  resources: {
    initialLibrary: ["sustainable-basics", "natural-ingredients-guide"],
    suggestedActivities: ["sourcing-challenge", "packaging-redesign"]
  }
});

Learning Assessment and Feedback

SAGE MNKY provides comprehensive assessment capabilities to measure learning effectiveness:

Knowledge Assessment

The agent offers various assessment approaches to evaluate understanding:

// Example assessment generation
const assessment = await sageMnkyClient.createAssessment({
  topicArea: "Aromatherapy Fundamentals",
  learnerProfile: {
    skillLevel: "intermediate",
    learningHistory: ["essential-oils-101", "scent-psychology"],
    preferredFormats: ["interactive", "application-based"]
  },
  assessmentType: "mixed",
  components: [
    { type: "knowledge-check", questionCount: 5, format: "multiple-choice" },
    { type: "case-analysis", scenario: "sleep-improvement", format: "short-answer" },
    { type: "practical-application", task: "blend-recommendation", format: "structured-response" }
  ],
  adaptiveDifficulty: true,
  showFeedback: "immediate",
  estimatedDuration: "15min"
});

The assessment includes:

Varied question types to evaluate different cognitive levels
Real-world application scenarios for authentic assessment
Adaptive difficulty based on performance
Immediate feedback with learning opportunities
Comprehensive results with growth recommendations

Progress Visualization

SAGE MNKY provides clear visualization of learning progress:

import { useSageMnkyProgress } from '@repo/sage-mnky-hooks';

function LearningProgressDashboard({ learnerId, focusAreas }) {
  const {
    progress,
    isLoading,
    skillMastery,
    learningPatterns,
    milestones,
    recommendations,
    setTimePeriod
  } = useSageMnkyProgress({
    learnerId,
    focusAreas,
    timePeriod: 'last-90-days'
  });
  
  return (
    <div className="progress-dashboard">
      <div className="period-selector">
        <TimeRangeSelector 
          currentPeriod="last-90-days"
          onChange={(period) => setTimePeriod(period)}
        />
      </div>
      
      <div className="mastery-overview">
        <h2>Knowledge Mastery</h2>
        <SkillRadarChart data={skillMastery} />
      </div>
      
      <div className="achievement-timeline">
        <h2>Learning Journey</h2>
        <MilestoneTimeline milestones={milestones} />
      </div>
      
      <div className="learning-insights">
        <h2>Learning Insights</h2>
        <PatternAnalysis patterns={learningPatterns} />
        <StrengthsWeaknesses data={progress.strengths} />
      </div>
      
      <div className="next-steps">
        <h2>Recommended Next Steps</h2>
        <RecommendationList recommendations={recommendations} />
      </div>
    </div>
  );
}

Adaptive Feedback

SAGE MNKY provides personalized feedback on learning activities:

// Example feedback generation
const feedback = await sageMnkyClient.generateFeedback({
  submissionId: "sub_12345",
  submissionType: "reflection-essay",
  topic: "Personal Wellness Planning",
  rubric: {
    understanding: { weight: 0.3, criteria: ["concept-accuracy", "depth"] },
    application: { weight: 0.4, criteria: ["personalization", "practicality"] },
    reflection: { weight: 0.3, criteria: ["insight-quality", "growth-orientation"] }
  },
  learnerContext: await getLearnerContext("user_789"),
  feedbackStyle: "growth-oriented",
  includeStrengths: true,
  includeDevelopmentAreas: true,
  suggestResources: true
});

The feedback approach focuses on:

Strengths-based commentary to reinforce positive aspects
Growth-oriented suggestions rather than deficiency-focused critique
Specific, actionable guidance for improvement
Contextually relevant examples to illustrate concepts
Forward-looking recommendations for continued development

Content Personalization Framework

SAGE MNKY implements a sophisticated framework for content personalization:

Learner Model

The model captures:

Knowledge state across different domains
Learning preferences for content format and pace
Interest patterns to guide topic selection
Time and engagement patterns for optimal delivery
Social learning preferences for community integration

Content Adaptation Engine

// Example content adaptation
const adaptedContent = await sageMnkyClient.adaptContent({
  baseContentId: "cont_wellness_intro",
  learnerContext: {
    id: "user_789",
    skillLevel: "beginner",
    attentionCapacity: "limited", // e.g., busy professional
    interestAreas: ["stress-management", "sleep-improvement"],
    learningPreferences: {
      format: ["visual", "brief-text"],
      examples: "practical",
      complexity: "accessible",
      tone: "encouraging"
    }
  },
  adaptationParameters: {
    lengthAdjustment: "condense",
    complexityAdjustment: "simplify",
    exampleStyle: "real-world-application",
    mediaPreference: "increased-visual"
  }
});

The adaptation includes:

Length modifications based on available time and attention
Complexity adjustments to match comprehension level
Example customization aligned with interests and goals
Format transformation to preferred learning styles
Tone calibration to motivational needs

Personalized Learning Sequences

import { useSageMnkySequence } from '@repo/sage-mnky-hooks';

function PersonalizedLearningModule({ moduleId, userId }) {
  const {
    sequence,
    isLoading,
    currentStep,
    progress,
    moveNext,
    movePrevious,
    jumpToStep,
    adaptStep
  } = useSageMnkySequence({
    moduleId,
    userId,
    adaptationLevel: 'dynamic'
  });
  
  return (
    <div className="learning-module">
      <div className="module-header">
        <h1>{sequence.title}</h1>
        <p className="description">{sequence.description}</p>
        <ProgressIndicator 
          steps={sequence.steps.length} 
          current={currentStep.index}
          completed={progress.completedSteps}
        />
      </div>
      
      <div className="content-container">
        <LearningStepContent
          content={currentStep.content}
          interactions={currentStep.interactions}
          onInteraction={(data) => adaptStep(data)}
          format={currentStep.format}
        />
      </div>
      
      <div className="navigation-controls">
        <button 
          onClick={movePrevious}
          disabled={currentStep.index === 0}
        >
          Previous
        </button>
        
        <StepSelector
          steps={sequence.steps}
          currentIndex={currentStep.index}
          completedSteps={progress.completedSteps}
          onSelectStep={(index) => jumpToStep(index)}
        />
        
        <button 
          onClick={moveNext}
          disabled={!currentStep.completed}
        >
          {currentStep.isLast ? 'Complete' : 'Next'}
        </button>
      </div>
    </div>
  );
}

Implementation Approaches

Embedded SAGE MNKY Interface

For learning environments, SAGE MNKY can be embedded directly in the interface:

import { SageMnkyProvider, useSageMnky } from '@repo/sage-mnky-hooks';

function LearningPage({ contentId, userId }) {
  return (
    <SageMnkyProvider
      userId={userId}
      context={{
        contentId,
        environment: 'learning'
      }}
    >
      <ContentDisplay />
      <SageMnkyAssistant />
    </SageMnkyProvider>
  );
}

function SageMnkyAssistant() {
  const {
    assistant,
    knowledgeContext,
    askQuestion,
    exploreRelated,
    findResources,
    isVisible,
    toggleVisibility
  } = useSageMnky();
  
  if (!isVisible) {
    return (
      <button 
        className="sage-mnky-toggle"
        onClick={toggleVisibility}
      >
        <SageMnkyIcon /> Learning Guide
      </button>
    );
  }
  
  return (
    <div className="sage-mnky-assistant">
      <div className="assistant-header">
        <img src={assistant.avatar} alt="SAGE MNKY" />
        <h3>Learning Guide</h3>
        <button onClick={toggleVisibility}>Minimize</button>
      </div>
      
      <div className="learning-assistance">
        <div className="common-actions">
          <button onClick={() => askQuestion("Can you explain this concept?")}>
            Explain This Concept
          </button>
          <button onClick={() => exploreRelated(knowledgeContext.currentTopic)}>
            Show Related Topics
          </button>
          <button onClick={() => findResources("practical applications")}>
            Find Practical Examples
          </button>
        </div>
        
        <div className="custom-question">
          <input 
            type="text"
            placeholder="Ask about this topic..."
            onKeyPress={(e) => e.key === 'Enter' && askQuestion(e.target.value)}
          />
        </div>
      </div>
    </div>
  );
}

Content API Integration

For seamless content personalization, the SAGE MNKY API can be integrated:

// Server-side content personalization
import { sageMnkyClient } from '@repo/sage-mnky-api';

async function getPersonalizedContent(contentId, userId) {
  // Fetch base content
  const baseContent = await dojoAPI.getContent(contentId);
  
  // Personalize with SAGE MNKY
  const personalizedContent = await sageMnkyClient.personalizeContent({
    content: baseContent,
    userId,
    personalizationTypes: [
      'examples',
      'difficulty',
      'format',
      'supplementary_resources'
    ],
    contentGoal: 'learning',
    preserveCore: true
  });
  
  return personalizedContent;
}

This approach allows:

Seamless enhancement of existing content
Server-side personalization for performance
Consistent content structure with personalized elements
Separation of concerns between content and personalization

Token Economy Integration

SAGE MNKY integrates with the token economy to incentivize learning:

Learning Rewards

// Example learning reward configuration
const rewardStructure = await sageMnkyClient.configureRewards({
  learningActivities: [
    {
      type: 'content_completion',
      tokenReward: 5,
      conditions: {
        assessmentRequired: true,
        minimumScore: 70
      }
    },
    {
      type: 'knowledge_sharing',
      tokenReward: 10,
      conditions: {
        qualityThreshold: 'medium',
        helpfulnessVotes: 3
      }
    },
    {
      type: 'streak_maintenance',
      tokenReward: 3,
      conditions: {
        daysRequired: 5,
        minimumTimePerDay: 10 // minutes
      }
    },
    {
      type: 'challenge_completion',
      tokenReward: 25,
      conditions: {
        difficultyLevel: 'intermediate',
        timeConstraint: '7days'
      }
    }
  ],
  bonusCategories: [
    {
      type: 'exceptional_contribution',
      tokenReward: 50,
      evaluationCriteria: ['originality', 'depth', 'community_impact']
    },
    {
      type: 'learning_milestone',
      tokenReward: 100,
      milestones: ['domain_mastery', 'certification_completion']
    }
  ],
  antiGamingMeasures: {
    qualityChecks: true,
    distributionLimits: {
      daily: 50,
      weekly: 250
    },
    reviewProcess: 'automated_with_oversight'
  }
});

Achievement Framework

import { useSageMnkyAchievements } from '@repo/sage-mnky-hooks';

function LearningAchievements({ userId }) {
  const {
    achievements,
    isLoading,
    inProgress,
    completed,
    tokenRewards,
    milestones,
    recentlyUnlocked
  } = useSageMnkyAchievements({
    userId,
    includeProgress: true
  });
  
  return (
    <div className="achievements-dashboard">
      <div className="achievements-header">
        <h2>Learning Achievements</h2>
        <div className="stats-summary">
          <StatCard label="Achievements Unlocked" value={completed.length} />
          <StatCard label="Tokens Earned" value={tokenRewards.total} />
          <StatCard label="Current Streak" value={achievements.currentStreak} />
        </div>
      </div>
      
      {recentlyUnlocked.length > 0 && (
        <div className="recently-unlocked">
          <h3>Recently Unlocked</h3>
          <AchievementList 
            achievements={recentlyUnlocked}
            showAnimation={true}
          />
        </div>
      )}
      
      <div className="achievement-categories">
        <AchievementCategory
          title="Knowledge Mastery"
          achievements={achievements.categories.knowledge}
          progress={inProgress.categories.knowledge}
        />
        
        <AchievementCategory
          title="Community Contribution"
          achievements={achievements.categories.community}
          progress={inProgress.categories.community}
        />
        
        <AchievementCategory
          title="Learning Habits"
          achievements={achievements.categories.habits}
          progress={inProgress.categories.habits}
        />
        
        <AchievementCategory
          title="Special Achievements"
          achievements={achievements.categories.special}
          progress={inProgress.categories.special}
        />
      </div>
      
      <div className="learning-journey">
        <h3>Learning Journey</h3>
        <MilestoneRoadmap milestones={milestones} />
      </div>
    </div>
  );
}

Implementation Considerations

Content Quality and Currency

When implementing SAGE MNKY in Dojo learning environments:

Regular content audits to ensure accuracy and relevance
Version control for learning materials with clear update history
Source verification for factual information
Diverse perspective inclusion for comprehensive understanding
Timely updates for rapidly evolving topics

Learning Experience Optimization

For optimal learning experiences:

Cognitive load management through appropriate content chunking
Interleaved practice for better long-term retention
Spaced repetition for knowledge reinforcement
Multi-modal presentation for different learning styles
Just-in-time support for learning challenges

Privacy and Ethical Considerations

Maintain ethical standards through:

Transparent learning data collection with clear purpose
Learner control over personal data and profiles
Bias monitoring in content recommendations
Inclusive content design for diverse audiences
Clear attribution for community-contributed content

Future Enhancements

The SAGE MNKY and Dojo integration roadmap includes:

Advanced knowledge visualization using AR/VR technologies
Emotional learning support adaptive to learner states
Integrated expert-learner matching for personalized mentorship
Cross-domain knowledge synthesis for holistic understanding
Predictive learning interventions to prevent challenges

Development Resources

For developers implementing SAGE MNKY in Dojo environments:

Agent Framework

Agent Database System

MOOD MNKY Agent

CODE MNKY Agent

SAGE MNKY Agent

Agent Styling & Integration

User Guides

​SAGE MNKY Dojo Integration

​Integration Overview

​Key Integration Points

Learning Path Engine

Knowledge Repository

Community Facilitation

Learning Assessment

​Learning Path Personalization

​Adaptive Learning Paths

​Contextual Knowledge Delivery

​Knowledge Repository Management

​Content Organization and Retrieval

​Multi-modal Content Generation

​Knowledge Graph Visualization

​Community Learning Facilitation

​Discussion Facilitation

​Collaborative Learning Projects

​Learning Circles

​Learning Assessment and Feedback

​Knowledge Assessment

​Progress Visualization

​Adaptive Feedback

​Content Personalization Framework

​Learner Model

​Content Adaptation Engine

​Personalized Learning Sequences

​Implementation Approaches

​Embedded SAGE MNKY Interface

​Content API Integration

​Token Economy Integration

​Learning Rewards

​Achievement Framework

​Implementation Considerations

​Content Quality and Currency

​Learning Experience Optimization

​Privacy and Ethical Considerations

​Future Enhancements

​Development Resources