🎯 UALS Hands-On Workshop: 2-Hour Introduction

Workshop Title: "UALS in Action: Experience Adaptive Learning Firsthand"

Duration: 2 hours

Target Audience: Educators, instructional designers

Format: Demo + Hands-on activities + Discussion

Learning Objectives

By the end of this workshop, participants will be able to:

  1. Understand the three core learning modes and their pedagogical foundations
  2. Experience all three modes through guided hands-on activities
  3. Analyze AI recommendations using "Show AI Thinking" visualization
  4. Compare curriculum-based vs. competency-based philosophies
  5. Plan implementation strategies for their educational context

Workshop Schedule (120 minutes)

Part 1: Introduction & System Overview (15 minutes)

0:00 - 0:15

Welcome & Icebreaker (3 min)

  • Quick introductions (name, role, institution)
  • Poll: "What's your biggest adaptive learning challenge?"

The Adaptive Learning Problem (5 min)

Three critical challenges:

  1. Philosophical rigidity: Systems designed for single pedagogy
  2. AI opacity: Black-box decisions without transparency
  3. ITS fragmentation: Narrow focus (only cognitive OR only assessment)

Real-world impact:

  • Students transfer institutions → learning history lost
  • Teachers can't validate AI recommendations
  • Systems can't adapt to diverse learner needs

UALS Solution (7 min)

Four innovations:

  1. Multi-Philosophy Framework:
    • Curriculum-based (domain → subdomain → concept)
    • Competency-based (category → competency → proficiency)
    • Identical UI, different content generation
  2. Three Complementary Modes:
    • Knowledge Explorer (exploration)
    • Socratic Playground (inquiry)
    • Scenario-Based CAT (assessment)
  3. 100-Agent ITS:
    • 30 production-ready, 70 extended
    • 17 categories (learner modeling, pedagogy, ethics, neuroscience, etc.)
    • 10 coordination levels (4-100 agents, 1-40s)
  4. Explainable AI:
    • "Show AI Thinking" workflow visualization
    • Complete transparency for trust-building
    • 5-minute cache for immediate replay
View Dashboard

Part 2: Knowledge Explorer (KE) - Theory + Hands-On (25 minutes)

0:15 - 0:40

Theory & Design Principles (7 min)

Pedagogical Foundation:

  • Constructivist learning (Piaget, Bruner)
  • Cognitive load theory (Sweller)
  • Schema construction through exploration

Design Features:

  • Interactive concept mapping
  • Progressive disclosure (prerequisite-based unlocking)
  • Multiple representations (definition, visual, examples, practice)
  • Learner-driven navigation (not forced sequence)

Agent Coordination (Level 2, 11 agents):

  1. Learner Model → Retrieves knowledge state
  2. Mastery Estimation → Identifies gaps/strengths
  3. Pedagogical Model → Selects exploration depth
  4. Adaptive Sequencing → Recommends next concept
  5. Affective State Monitoring → Assesses confidence
  6. Knowledge Tracing → Predicts trajectory
  7. Intervention Timing → Decides if scaffolding needed
  8. Domain Model → Provides concept relationships
  9. Misconception Detection → Identifies errors
  10. Explanation Generation → Adapts complexity
  11. Feedback Generation → Creates encouragement

Result: Personalized KE content with adaptive scaffolding

Hands-On Activity (15 min)

Instructions:

  1. Navigate to curriculum mode: Science → Biology → Photosynthesis
  2. Explore 3-4 concepts in depth
  3. Complete practice questions (note how hints adapt)
  4. Try to unlock an advanced topic by mastering prerequisites
  5. Switch to competency mode: Data Literacy → Statistical Analysis
  6. Compare navigation and structure

Observation Prompts:

  • How does content complexity change as you master concepts?
  • What happens when you get practice questions wrong?
  • How is prerequisite structure different in competency mode?
Try Knowledge Explorer Try Competency Mode

Group Discussion (3 min)

  • When would you use KE with your learners?
  • What subjects are ideal for KE?
  • How could KE support diverse learning styles?

Part 3: Socratic Playground (SPL) - Theory + Hands-On (30 minutes)

0:40 - 1:10

Theory & Design Principles (8 min)

Pedagogical Foundation:

  • Socratic method (Collins & Stevens, 1982)
  • EMT dialogue (Marineau et al., 2019)
  • Productive failure (Kapur, 2008)
  • Zone of Proximal Development (Vygotsky, 1978)

Design Features:

  • Authentic, scenario-based problems
  • Five hint agent types:
    1. Conceptual: "What principle applies?"
    2. Procedural: "What's the next step?"
    3. Reflection: "Why didn't that work?"
    4. Worked Example: Complete solution
    5. Error Analysis: Systematic mistake identification
  • Four interaction modes:
    • Socratic (questioning > telling)
    • Demonstrative (worked examples)
    • Practice-focused (repeated application)
    • Study Mate (peer-like conversation)

Agent Coordination (Level 3, 20 agents):

  • All 11 from KE PLUS:
  • Problem Generation, Worked Example Generation
  • Explanation Generation, Metacognitive Support
  • Domain Model, Curriculum Alignment
  • Misconception Detection, Assessment
  • Performance Analytics

EMT Dialogue Framework:

  1. Elicit expectation ("What do you predict?")
  2. Detect misconception (learner attempts problem)
  3. Provide tailored feedback (address specific error)

Hands-On Activity (18 min)

Instructions:

  1. From photosynthesis KE, click "Practice in SPL"
  2. Attempt Problem 1 (plant health scenario):
    • Try solving WITHOUT hints first
    • If stuck, request conceptual hint
    • If still stuck, request procedural hint
    • As last resort, view worked example
  3. Attempt Problem 2 (light intensity effects):
    • Use "Study Mate" mode for conversational support
    • Notice how difficulty adapts to your performance
  4. Attempt Problem 3 (advanced):
    • Try Socratic mode (minimal scaffolding)
    • Observe EMT dialogue when you make a mistake

Observation Prompts:

  • How do hints guide without giving answers?
  • What happens when you make a systematic error?
  • How does Study Mate mode differ from regular hints?
  • How does difficulty change based on success/failure?
Try Socratic Playground

Group Discussion (4 min)

  • How is SPL different from traditional problem sets?
  • When would you use demonstrative vs. Socratic mode?
  • How could SPL support struggling learners?
☕ BREAK (10 minutes) - 1:10 to 1:20

Part 4: Scenario-Based CAT (SBCAT) - Theory + Hands-On (20 minutes)

1:20 - 1:40

Theory & Design Principles (6 min)

Pedagogical Foundation:

  • Item Response Theory (Lord, 1980)
  • Computerized Adaptive Testing (Wainer, 2000)
  • ITS-inspired assessment (Hu et al., 2023)

Design Features:

  • IRT 2PL model: P(correct) = 1 / (1 + e^(-a(θ - b)))
    • θ = learner ability (estimated in real-time)
    • b = item difficulty
    • a = item discrimination
  • Adaptive algorithm:
    • Start at θ = 0 (or prior from learner model)
    • Select item with b ≈ θ ± 0.3 (maximum information)
    • Update θ after each response
    • Terminate when SE(θ) < 0.3
  • Scenario-based items (not multiple choice trivia)
  • Diagnostic feedback (not just correctness)
  • Formative guidance (hints available during test)
Key Innovation: Unlike traditional CAT (no feedback), SBCAT makes assessment a learning opportunity

Hands-On Activity (12 min)

Instructions:

  1. From SPL, click "Assess Mastery with SBCAT"
  2. Complete adaptive assessment (8-12 items)
  3. Observe:
    • How difficulty adjusts after each response
    • Feedback provided (not just "incorrect")
    • Option to request hints (with small θ penalty)
  4. Review final diagnostic report:
    • Final ability estimate (θ with confidence interval)
    • Mastery level (novice/intermediate/advanced)
    • Strengths identified
    • Gaps for remediation
    • Recommended next steps

Observation Prompts:

  • How does question difficulty change?
  • What feedback do you receive after wrong answers?
  • What insights does the diagnostic report provide?
  • How could you use this report to guide instruction?
Try Adaptive Assessment

Group Discussion (2 min)

  • When would you use SBCAT: pre-assessment, formative, summative?
  • How is SBCAT different from traditional quizzes?
  • What are ethical considerations of adaptive assessment?

Part 5: The 100-Agent Architecture + "Show AI Thinking" (20 minutes)

1:40 - 2:00

Agent Overview (5 min)

17 Agent Categories (100 total):

  • Core Learner Modeling (4)
  • Tutoring Interaction (11)
  • Content Generation (17)
  • Pedagogical Strategies (15)
  • Assessment (11)
  • Cognitive Support (10)
  • Social-Emotional Learning (9)
  • Social & Collaborative (6)
  • Domain-Specific (6)
  • Accessibility & Inclusion (3)
  • Teacher Support (4)
  • Analytics & Research (7)
  • AI Ethics & Fairness (4)
  • Longitudinal Learning (4)
  • Real-World Learning (4)
  • Parent & Community (3)
  • System Intelligence (2)

10 Coordination Levels:

Level Name Agents Time Use Case
1 Lightning 4 1-2s Quick checks
2 Quick 11 2-4s Recommended default
3 Standard 20 4-8s Comprehensive tutoring
5 Professional 50 12-18s Advanced analysis
10 Complete 100 25-40s Full ecosystem

"Show AI Thinking" Demo (12 min)

Activity Instructions:

  1. Click "Get AI Recommendation" from dashboard
  2. Enter goal: "Understand photosynthesis"
  3. Try Level 2 (Quick):
    • 11 agents, 2-4 seconds
    • Recommends SPL with rationale
    • Click "🧠 Show AI Thinking"
    • Explore workflow timeline
    • Play/pause, adjust speed
    • Click agents to see reasoning
  4. Try Level 5 (Professional):
    • 50 agents, 12-18 seconds
    • More comprehensive analysis
    • Includes multimodal, social intelligence
    • Compare workflow to Level 2
  5. Try Level 10 (Complete) (if time permits):
    • 100 agents, 25-40 seconds
    • Complete ecosystem consultation
    • Includes neuroscience, ethics, longitudinal

Key Insights:

  • Transparency builds trust in AI
  • Different levels for different use cases
  • Educators can validate recommendations
Try AI Recommendation

Group Discussion (3 min)

  • How does explainability help educators?
  • Which analysis level would you use most?
  • What research questions could workflow data answer?

Part 6: Wrap-Up, Q&A, Next Steps (15 minutes)

2:00 - 2:15

Q&A Session (10 min)

Common Questions (be prepared):

  • "What if students game the system?"
    → Agent monitoring detects patterns; system adapts
  • "How much do LLMs cost?"
    → <$0.01/student/hour (94% cache hit rate)
  • "What about incorrect content?"
    → Validation layers + educator review dashboard
  • "Can I customize content?"
    → Yes, custom prompts + domain model import
  • "How long to implement?"
    → Pilot: 2-4 weeks; Full deployment: 2-3 months
  • "LMS integration?"
    → LTI 1.3 (Canvas, Moodle, Blackboard, D2L)

Next Steps (5 min)

Takeaways:

  1. Workshop materials: Slides, recording (if virtual)
  2. Demo access: 30-day trial credentials
  3. Documentation: Architecture, API, deployment guides
  4. Implementation checklist: Steps for pilot program

Follow-Up Options:

  • 3-hour workshop: Deep dive into agents, analytics, customization
  • Office hours: Monthly Q&A sessions
  • Pilot program: 3-month institutional trial
  • Community: Join UALS educator Discord/Slack

Call to Action:

"Ready to transform adaptive learning at your institution? Schedule a 30-minute consultation to design your pilot program."

Contact:

  • Email: pilot@uals.edu
  • Office hours: First Friday/month, 10 AM PT
  • Community: discord.gg/uals-educators

Success Metrics