Adaptive education: shaping teaching techniques to match individual students’ learning patterns and progress is the core goal of which AI-enabled approach?

Introduction / Context:
Personalized learning systems use models of student knowledge, preferences, and pace to deliver adaptive content. This question targets the AI-driven instructional paradigm designed to tailor pedagogy for each learner in real time.

Given Data / Assumptions:

• Objective: adapt teaching to individual learning patterns.
• Scope: instructional sequencing, hints, feedback, and assessment.
• Technology: AI models of learners and content.

Concept / Approach:
Intelligent Computer-Assisted Instruction (ICAI)—also called Intelligent Tutoring Systems (ITS)—combines a student model, a domain model, and a tutoring model to choose the right explanation or exercise at the right time. Decision support focuses on organizational choices; automatic programming concerns program synthesis; expert systems solve narrow expert tasks. ICAI specifically targets adaptive teaching.

Step-by-Step Solution:

Verification / Alternative check:
Empirical studies show ITS can match or exceed traditional classroom effectiveness by personalizing feedback and pacing.

Why Other Options Are Wrong:

Common Pitfalls:
Confusing general AI problem solving (expert systems) with pedagogical adaptivity which requires learner modeling.

Final Answer:
intelligent computer-assisted instruction