AI and Adaptive Technologies

Michelle Derbenwick Barrett, Chair (Contact the Chair)

This SIG interacts asynchronously via a Google Group.

This SIG meets on the first Wednesday of each month at 1000 ET. The next meeting is on Wednesday, March 7. Connect to the meeting using this web conference URL.



The AI and Adaptive Technologies SIG supports the community of Learning Engineering professionals interested in implementing AI and adaptive technologies within learning systems.

The SIG creates bridges among engineers, researchers, and practitioners developing, using, and evaluating the effectiveness of artificial intelligence and adaptive technologies in support of formal and non formal lifelong learning. We provide actionable information to the Learning Engineering community through the dissemination of research, prototypes, use cases, and technical, evaluation, and communication frameworks.

We seek to advance the positive impact of AI technologies such as machine learning, natural language processing, and recommendation engines on personalized and adaptive learning experiences through:

  • Instructional and assessment content generation, discovery, extraction, classification, curation, and presentation, and the adaptive delivery of such content;
  • Modeling knowledge domain, competency, pedagogy, learning, learner engagement, and learner responses and applying those models to analyze and personalize the learning experience;
  • Considering learning environments and delivery modalities (e.g., mobile, augmented and virtual reality, AI-enabled edge devices); 
  • Characterizing data pipelines required for effective use of AI and adaptive technologies; and
  • Enabling wider understanding and potential use of AI and adaptive technology in learning such as but not limited to personalized assistants and intelligent tutors.

We believe these systems must address fairness, transparency, and explainability for learning communities and encourage efforts to develop frameworks to support the evaluation and communication of such.