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AI in Education: Adaptive Learning & Personalized Pathways

位教师：Hurix Digital

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有3个模块

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.