AI Personalization: The Promises and Pitfalls

Week 5

Essential Question

How do AI-driven personalization systems reflect and shape individual learning experiences, and what are their promises and pitfalls?

Learning Objectives

  • Evaluate the research on personalized learning effectiveness
  • Analyze how AI systems model and respond to individual learner differences
  • Compare different approaches to AI-driven personalization
  • Identify potential unintended consequences of personalized learning systems

Today's Agenda

  1. Opening simulation: Experiencing personalization
  2. Interactive lecture: Personalization theories and evidence
  3. Break
  4. Applied learning: Designing an ideal personalization system
  5. Gallery walk and reflection
  6. Closing thoughts and preview

Opening Activity

Personalization Simulation

  • You've received different explanations of the same concept
  • Take a moment to review your materials
  • Compare with others around you
  • What differences do you notice?

Discussion Questions

  • How did your learning experience differ from others?
  • What advantages did personalization provide? What limitations?
  • What assumptions about you were embedded in your materials?
  • How might these differences compound over time?

Part 1: Understanding Personalization

Personalization Theories

  • Adaptive Content: Adjusting difficulty, format, or presentation
  • Adaptive Pace: Allowing learners to move at different speeds
  • Adaptive Path: Different sequences or routes through material
  • Adaptive Goals: Different learning objectives for different learners

Jigsaw Activity 🧩

Learner Modeling: What Do Systems Track?

  • Knowledge State: What learners know/don't know
  • Performance Patterns: Speed, accuracy, strategies
  • Learning Preferences: Format, pace, style
  • Engagement Metrics: Time on task, persistence
  • Affective State: Frustration, engagement, confidence
  • Goals & Interests: Personal relevance, aspirations

How AI Personalizes Learning

Adaptation Variables:

  • Content selection
  • Difficulty level
  • Pace
  • Feedback type
  • Support offered
  • Learning path
  • Interface/presentation

Research Evidence: What Do We Know?

Promising Findings:

  • Adaptive systems can improve learning efficiency
  • Personalized feedback increases engagement
  • Custom paths help struggling learners

Limitations:

  • Effect sizes often modest
  • Implementation quality matters more than technology
  • Many studies short-term or limited in scope
  • Concerns about research independence from vendors

Alpha School Case Study

  • AI-driven personalization across curriculum
  • Students progress at individual pace
  • Teachers as facilitators and mentors
  • Reported outcomes:
    • Increased engagement
    • Accelerated progress for some students
    • Mixed parent reactions
    • Questions about social development

Critical Perspectives

  • Isolation: Loss of shared learning experiences
  • Filter Bubbles: Narrowing of educational exposure
  • Data Privacy: Extensive tracking concerns
  • Equity Issues: Digital divide, algorithmic bias
  • De-professionalization: Changing teacher role
  • Narrowed Education: Focus on measurable outcomes
  • Community Loss: Reduced collective learning

Part 2: Personalization in Practice

Demo: Khan Academy's Mastery Approach

  • Knowledge map and prerequisite relationships
  • Mastery-based progression
  • Practice recommendations based on performance
  • Teacher dashboard showing progress and struggles

Demo: ALEKS Knowledge Space Approach

  • Knowledge space theory foundation
  • Initial assessment to determine precise knowledge state
  • Regularly reassesses to update model
  • Shows topics ready to learn vs. prerequisites needed

Demo: Newsela's Content Adaptation

  • Same content at different reading levels
  • Maintains same topics but adjusts text complexity
  • Preserves important information across levels
  • Assessments adapt to reading level

Demo: Century Tech's AI Pathways

  • Models memory, engagement, and understanding
  • Generates personalized learning pathways
  • Provides insights into misconceptions
  • Comprehensive dashboard for teachers

Design Framework

What Will You Create?

  • Learner model (what data is collected)
  • Adaptation engine (how it responds to differences)
  • Teacher interface (control and oversight)
  • Student experience (what learners see and do)

Design Considerations

  • What learner characteristics matter most?
  • What should adapt and what should remain constant?
  • How to balance personalization with community?
  • How to address isolation and filter bubbles?
  • What evidence supports your design choices?

Group Design Activity

  1. Form groups of 3-4
  2. Complete the design worksheet
  3. Create visual representations of your system
  4. Prepare a 2-3 minute presentation
  5. Be ready to give and receive feedback

I Like, I Wish, I Wonder

When giving feedback on designs:

  • "I like..." (strengths)
  • "I wish..." (suggestions)
  • "I wonder..." (questions)

Reflection Questions

  • What aspect of personalized learning do you find most promising?
  • What aspect do you find most concerning?
  • How has your thinking about personalization changed?
  • What questions remain unanswered for you?