T07
AI & Foundation Models in Learning
LLM tutors; generative feedback; AI-generated content; automated hint systems; risks and opportunities.
6 resources tagged with this topic
How to use this topic
Start with the resources below — they’re the practical methods, tools, and examples tagged to this area. Many can be adapted for resource-constrained and developing-country settings. When you need the underlying evidence and concept structure, switch the site lens to Researcher.
Concepts in this topic
AI & Foundation Models in Education Analyze
Builds on: Intelligent Tutoring Systems, Adaptive Learning Systems
AI Ethics & Responsible Use in Learning Evaluate
Builds on: AI & Foundation Models in Education
Resources
Reading List 3
Algorithmic Fairness in Education
Learning Engineering as an Ethical Framework
Van Campenhout frames learning engineering as an explicit ethical practice in adaptive instructional systems design and evaluation.
Events 3
Learning Engineering: A path to empowering learners in and for the Age of AI — Panel
Panel response to the McNamara and Garg keynotes above. Worth watching as a trio — the panel is where the rough edges of AI-in-LE show up (evaluation, equity, disciplinary identity).
Learning Engineering in the Age of AI — Kumar Garg
Garg (Walton-funded LE efforts; formerly OSTP) frames AI-and-LE as an evidence and infrastructure problem, not a pedagogical one. The pitch: the field needs many more studies, faster, to match AI's pace — and LE is the discipline set up to do that.
Leveraging AI and Learning Engineering in Large-Scale Learning Sciences — Danielle McNamara
McNamara's case for AI-enabled learning engineering at scale — grounded in her two decades of NLP-and-learning-sciences work. Argues that the most promising AI applications in LE aren't content generation but formative measurement and adaptive scaffolding. Technical but accessible.