Learning AI Capabilities for Real-World Application

The rise of artificial intelligence has been a powerful paradigm for learning science.

There is abundant research on the capabilities of learners for a world with AI, but not on learning AI capabilities for real-world application. By including the problem incubation advantage into the productive failure (PF) learning design, professionals can learn about AI capabilities at speed and depth to overcome novel problems.

PF is a learning design that gives learns opportunities to generate representations and solutions to a novel problem that targets a concept they have not learned yet, followed by consolidation and knowledge assembly where they learn the targeted concept. Compared to an instruction-first approach (instruction followed by problem-solving), PF focuses on a problem-solving first approach (problem-solving followed by instruction), which prepares professionals for future learning. Overwhelming research demonstrates the efficacy of PF facilitating conceptual knowledge and transfer over an instruction-first approach. Arguments in favor of PF as a learning design include the activation of prior knowledge, awareness of knowledge gaps, recognition of deep features and far transfer effects.

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