Evolution of AI in Education

Evolution of AI: Symbolic to Deep Learning

  • Symbolic Systems (1950s–1980s)
  • Rule-based reasoning
  • Logic, if-then rules
  • Limited scalability

The Turing Test: A Philosophical Starting Point

  • Alan Turing (1950) posed the question:
    “Can machines think?”

  • Proposed the Imitation Game, now known as the Turing Test

  • A machine passes if a human evaluator cannot reliably distinguish it from a human in conversation

  • Still debated today:
    • Does passing the test mean a machine is intelligent?
    • Is mimicking enough in educational contexts, or do we need true understanding?

Machine Learning (1990s–2010s)

  • Pattern recognition from data
  • Requires labeled examples
  • Algorithms “learn” from experience

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Deep Learning (2010s–Present)

  • Neural networks & large-scale computation
  • Enables language, image, and speech processing
  • Powering today's generative AI (e.g., ChatGPT, image generators)

AI Capabilities in Education

Think-Pair-Share 🤔

What are the possible opportunities?

What are the limitations?

Debrief

Capabilities

  • Adaptive learning platforms
  • Automated grading (especially objective items)
  • Intelligent tutoring systems
  • Content generation
  • Predictive analytics for at-risk students

Limitations

  • Understanding nuance in student reasoning
  • Supporting emotional/social learning
  • Generalizing across educational contexts
  • Equity and bias concerns
  • Often “black box” systems with limited transparency

Frameworks for Evaluating AI Tools

Pedagogical Fit

Data Usage

Implementation Needs

Frameworks for Evaluating AI Toolsp

Pedagogical Fit

  • Does it align with constructivist, behaviorist, or other pedagogical models?
  • Is it enhancing or replacing human instruction?

Data Usage

  • What data is being collected and why?
  • Is it FERPA-compliant?
  • Are students and educators informed?

Implementation Needs

  • Infrastructure requirements
  • Teacher training and support
  • Integration with existing LMS or platforms
  • Cost and scalability

Educational Problems AI Can Help Solve

  • Scaling personalized feedback
  • Detecting disengagement early
  • Supporting multilingual or neurodiverse learners
  • Automating routine administrative tasks

Problems AI Struggles With

  • Fostering authentic student motivation
  • Teaching critical thinking and creativity
  • Supporting complex social-emotional learning
  • Adapting to rapidly changing classroom dynamics

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