AI in Education: Adaptive Learning & Personalized Pathways

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AI in Education: Adaptive Learning & Personalized Pathways

Instructor: Hurix Digital

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Data-driven personalization uses behavior and performance metrics to scale tailored learning for diverse populations
AI content requires systematic bias detection and inclusivity validation to ensure equitable, high-quality learning for all.
L&D transformation pairs real-time analytics with content workflows to identify performance gaps and trigger proactive interventions
Adaptive learning succeeds when AI augments human expertise, requiring designers to master prompt engineering and curation.

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There are 3 modules in this course

AI is transforming education by creating truly personalized learning experiences that adapt to each learner's unique needs and progress. This Short Course was created to help Human Resources professionals accomplish adaptive learning implementation with quality controls and learner-centered analytics. By completing this course, you'll be able to configure adaptive engines that automatically route learners based on performance data, identify content gaps through engagement metrics, and ensure AI-generated materials meet inclusivity standards.

By the end of this course, you will be able to: • Apply adaptive learning engine settings to map curriculum objectives into personalized learner pathways with mastery checkpoints • Analyze engagement analytics such as drop-off rate and time-on-task to identify content segments requiring redesign • Evaluate AI-generated lesson drafts against bias and hallucination guardrails, revising prompts to meet quality and inclusivity standards This course is unique because it combines hands-on platform configuration with data-driven optimization and AI ethics, addressing real-world L&D implementation challenges. To be successful in this project, you should have experience with learning management systems and basic data interpretation skills.

Learners configure adaptive learning engines to create personalized pathways with mastery checkpoints.

What's included

2 readings2 assignments

Analyze learner engagement data to identify content segments requiring redesign and optimization.

What's included

1 video2 readings2 assignments

Evaluate AI-generated lesson drafts against bias and hallucination guardrails to ensure inclusive, high-quality learning materials.

What's included

1 video3 readings3 assignments

1 videoTotal 6 minutes

Understanding AI Bias Types and Detection Methods6 minutes

3 readingsTotal 30 minutes

The Critical Need for AI Content Quality Assurance10 minutes
Hallucination Patterns and Quality Evaluation Frameworks10 minutes
AI Content Generation and Revision Techniques10 minutes

3 assignmentsTotal 50 minutes

Course Assessment: AI in Education Implementation Mastery10 minutes
AI Content Quality Audit10 minutes
Knowledge Check: AI Content Quality Evaluation30 minutes

Instructor

Hurix Digital

Coursera

414 Courses35,180 learners

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