Five new design concepts for AI in learning: orchestrating, augmenting, expanding natural interactions, broadening competencies, and revealing connections¶

Theory · All theories
Evidence · 1 claim (1 mixed) · 1 study (1 qualitative), q1 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

Beyond familiar personalized, adaptive, or blended learning, the panel identified five recurrent design concepts. The report offers that "design concepts of orchestrating, augmenting, expanding natural interactions, broadening competencies, and revealing connections may stimulate additional avenues of conversation with practitioners and policy makers." Orchestrating treats learning as coordinated social activity over time; augmenting extends human intelligence in partnership; naturalistic interaction escapes keyboard-and-pointer confines; broadening competencies goes beyond 20th-century measurement; revealing connections uncovers patterns such as cross-course equivalencies.

Design Implications¶

Context¶

Requirements¶

• Cross-sector conversations need shared design vocabulary beyond the limited language of personalized learning

Constraints¶

• The concepts are "not fully worked out today"; they are intended to illuminate possibilities and risks, not describe mature systems

Target Learners¶

• Learners in classrooms using AI-supported environments, including collaborative groups

Target Learning Objectives¶

• Expanding what learning experiences AI can support beyond individualization and adaptation

Claims¶

• Experts identified pervasive limitations of today's AI for learning, including biased data, inequity, non-graceful failure, and weak context grasp [~W]

Related Theories¶

• Technology-as-amplifier: learning technology intensifies and scales the impacts of design tradeoffs in either direction
• Intelligence Augmentation for Collaborative Learning: four verbs extending individualized EdTech
• Three-layer definition of AI for learning: computational intelligence, specific capabilities, and a futures toolkit
• Four most common AI technology types in cyberlearning: intelligent tutoring systems, machine learning, speech/vision/natural interaction, and social robotics and avatars

Examples¶

• Design AI as a classroom orchestration partner that helps teachers form groups, nurture conversations, and track participation without taking control
• Reorient AI assessment toward formative, holistic portraits of learner competencies rather than automated grading
• A co-orchestration technology ecosystem combining intelligent tutoring systems, peer tutoring, and a teacher-facing orchestration tool.

Key Sources¶

• Roschelle, J., Lester, J. & Fusco, J. (Eds.) (2020). AI and the future of learning: Expert panel report [Report]. Digital Promise. https://circls.org/reports/ai-report. doi:10.51388/20.500.12265/106