⚡ UALS Lightning Workshop: 1-Hour Introduction

Workshop Title: "UALS Essentials: A Quick Tour of Adaptive Learning"

Duration: 60 minutes

Target Audience: Educators, administrators (overview only)

Format: Fast-paced demo with minimal hands-on

Learning Objectives

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

  1. Understand the three core learning modes (KE, SPL, SBCAT) at a high level
  2. Experience one mode through brief hands-on activity
  3. Recognize when UALS could benefit their educational context
  4. Identify next steps for deeper exploration or pilot program

Workshop Schedule

Part 1: Introduction & The Adaptive Learning Problem (10 minutes)

0:00 - 0:10

Quick Poll

"What's your biggest challenge in personalizing learning?"

  • Too many students, not enough time
  • Can't diagnose individual gaps
  • Students at different levels
  • Keeping advanced students challenged

The Problem (5 minutes)

Three critical challenges:

  • Philosophy rigidity: One-size-fits-all pedagogical approach
  • AI opacity: Black-box decisions without transparency
  • Narrow focus: ITS that only do one thing well

UALS Solution (5 minutes)

Four key innovations:

  1. Multi-philosophy: Curriculum OR competency-based (same system!)
  2. Three modes: Exploration (KE), Inquiry (SPL), Assessment (SBCAT)
  3. 100 agents: Comprehensive AI support (vs. typical 3-5 agents)
  4. Explainable: "Show AI Thinking" reveals decision-making
View Dashboard

Part 2: Three Learning Modes - Live Demo (25 minutes)

0:10 - 0:35

Knowledge Explorer (KE) - 8 minutes

Theory (2 min):

  • Constructivist exploration with concept mapping
  • Visual knowledge structures + progressive disclosure
  • Adapts complexity (novice → intermediate → advanced)

Live Demo (6 min):

  1. Navigate to curriculum mode: Biology → Photosynthesis
  2. Show interactive concept map
  3. Click concept to see: definition, visual, examples, practice questions
  4. Demo prerequisite locking/unlocking
  5. Show how content adapts to mastery level
Key Takeaway: Self-directed exploration with just-in-time support
Try Knowledge Explorer

Socratic Playground (SPL) - 8 minutes

Theory (2 min):

  • Guided inquiry through problem-solving
  • Five hint agents (conceptual, procedural, reflection, worked example, error analysis)
  • Socratic questioning > telling answers

Live Demo (6 min):

  1. Launch SPL from photosynthesis topic
  2. Show authentic scenario (plant health problem)
  3. Demonstrate hint progression:
    • Try problem without hints (productive struggle)
    • Request conceptual hint (guiding question)
    • Request procedural hint (next step)
    • Show worked example if needed
  4. Demo misconception correction through EMT dialogue
Key Takeaway: Learning through scaffolded problem-solving
Try Socratic Playground

Scenario-Based CAT (SBCAT) - 9 minutes

Theory (2 min):

  • Adaptive assessment using Item Response Theory
  • Difficulty adjusts to learner (θ estimation)
  • Diagnostic feedback + formative guidance

Live Demo (7 min):

  1. Launch SBCAT assessment
  2. Show adaptive difficulty:
    • Answer easy question correctly → harder question
    • Answer hard question incorrectly → easier question
  3. Demonstrate feedback (not just "wrong"—why and how to improve)
  4. Show final diagnostic report:
    • Ability estimate (θ = 0.75, intermediate)
    • Strengths identified
    • Gaps for remediation
    • Recommended next steps (KE topics, SPL problems)
Key Takeaway: Assessment as learning opportunity, not just measurement
Try Adaptive Assessment

Part 3: The 100-Agent "Show AI Thinking" Feature (15 minutes)

0:35 - 0:50

Agent Architecture Overview (5 min)

100 specialized agents across 17 categories:

  • Learner modeling, pedagogical strategies, content generation
  • Assessment, cognitive support, social-emotional learning
  • AI ethics, neuroscience, domain specialists, longitudinal learning

10 coordination levels: Lightning (4 agents, 1s) → Complete (100 agents, 40s)

Production-ready: 30 core agents + 70 extended (4-year roadmap)

"Show AI Thinking" Demo (10 min)

Setup:

  1. Click "Get AI Recommendation" from dashboard
  2. Enter goal: "Understand photosynthesis"
  3. Select Analysis Level 2 (Quick, 11 agents, 2-4s)
  4. Submit

Results Screen:

  • System recommends: "Socratic Playground (SPL)"
  • Rationale: "You have foundational knowledge but need application practice"

Click "🧠 Show AI Thinking" button:

  • Visual workflow timeline appears
  • Shows 11 agent invocations in sequence
  • Playback controls: play/pause, speed adjustment (0.5x, 1x, 2x, 4x)
  • Click individual agents to see detailed reasoning
Key Takeaway: Complete transparency builds trust in AI recommendations
Try AI Recommendation

Part 4: Use Cases & Next Steps (10 minutes)

0:50 - 1:00

Quick Use Cases (3 min)

Classroom Supplementation:

  • KE for homework → SPL for in-class practice → SBCAT for exit tickets

Online Course:

  • Self-paced with AI recommendations guiding KE → SPL → SBCAT cycles

Remediation:

  • SBCAT pre-assessment identifies gaps → KE fills gaps → SPL builds skills

Corporate Training:

  • Competency-based onboarding → SPL job scenarios → SBCAT certification

Q&A (5 min)

Anticipated Questions:

  • "How much does it cost?" → LLM costs <$0.01/student/hour (94% cache hit rate)
  • "Can I customize content?" → Yes, custom prompts + domain model import
  • "What about incorrect LLM content?" → Validation layers + educator review dashboard
  • "How long to implement?" → Pilot in 2-4 weeks, full deployment 2-3 months

Next Steps (2 min)

Takeaways:

  1. Workshop slides: Sent via email today
  2. Demo access: 30-day trial account credentials
  3. Documentation: Links to guides, architecture, research articles

Call to Action:

"Interested in a pilot program? Let's schedule a 30-minute follow-up call to discuss your specific needs."

Contact:

  • Email: pilot@uals.edu
  • Office hours: First Friday of each month, 10 AM PT
  • Community: Join Discord/Slack for UALS educators

Success Metrics