Three-layer definition of AI for learning: computational intelligence, specific capabilities, and a futures toolkit¶

Theory · All theories
Evidence · 2 claims (2 mixed) · 1 study (1 qualitative), q1 · 0 of 1 report an effect size · 2 claims rest on one study

Description¶

The report frames AI for educators through three layers: AI as computational intelligence brought to bear on educational challenges as an additional resource; AI as a set of specific rapidly advancing capabilities (perception, representation and reasoning, learning, natural interaction, societal impact) engineered into solutions; and AI as a toolkit for imagining futures. The report states: "we conceptualize three aspects of AI—as an ambitious leading edge of computing, as a set of specific capabilities that are rapidly advancing, and as a toolkit for synthesizing and exploring possible futures for learning and teaching." This framing underpins the panel's scenarios and design concepts.

Design Implications¶

Context¶

Requirements¶

• Educators and researchers adopt a shared multi-layer vocabulary rather than a single accepted definition of AI

Constraints¶

• AI does not have a single accepted definition; the layers are the report's own conceptualization

Target Learners¶

• K-12 and higher education learners supported by AI-enabled learning technologies

Target Learning Objectives¶

• Understanding what AI is and can contribute to learning among educational leaders and researchers

Claims¶

• Experts identified pervasive limitations of today's AI for learning, including biased data, inequity, non-graceful failure, and weak context grasp [~W]
• Weak grasp of context can make AI misdiagnoses systematically harmful when a flawed decision pattern is replicated across many students [~W]

Related Theories¶

• The electric bike vision of human-centered educational technology
• Five new design concepts for AI in learning: orchestrating, augmenting, expanding natural interactions, broadening competencies, and revealing connections
• Four most common AI technology types in cyberlearning: intelligent tutoring systems, machine learning, speech/vision/natural interaction, and social robotics and avatars

Examples¶

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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