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Related Experiment Video

Updated: Apr 12, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
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AI Thinking: a framework for rethinking artificial intelligence in practice.

Denis Newman-Griffis1,2,3

  • 1Centre for Machine Intelligence, The University of Sheffield, Sheffield S1 3JD, UK.

Royal Society Open Science
|January 9, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) use requires a new framework, AI Thinking, to bridge disciplinary gaps. This approach models five key competencies for effective AI application across various contexts.

Keywords:
AI ThinkingAI applicationsartificial intelligencecritical data studiesinterdisciplinaritymachine learning

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Area of Science:

  • Computer Science
  • Information Science
  • Interdisciplinary Studies

Background:

  • Artificial intelligence (AI) is increasingly utilized across disciplines.
  • Conflicting understandings of AI exist, hindering effective interdisciplinary collaboration.
  • Novel approaches are needed to unify AI conceptualizations and guide its practical application.

Purpose of the Study:

  • To propose a novel conceptual framework, AI Thinking, for understanding and applying AI across diverse fields.
  • To address the need for interdisciplinary approaches in AI development and assessment.
  • To bridge competing conceptualizations of AI in practice and shape its future use.

Main Methods:

  • Development of the AI Thinking framework, modeling key decisions and considerations for AI use.
  • Identification of five practice-based competencies: motivating AI use, formulating AI methods, assessing tools/technologies, selecting data, and situating AI in sociotechnical contexts.
  • Illustration of the framework's application through a hypothetical case study.

Main Results:

  • The AI Thinking framework provides a structured approach to AI application.
  • The five competencies offer practical guidance for interdisciplinary AI implementation.
  • The framework connects AI Thinking to broader discourses on AI literacy and AI-driven innovation.

Conclusions:

  • AI Thinking facilitates interdisciplinary collaboration and consistent AI application.
  • The framework can guide AI efforts in education, industrial development, and policy.
  • It offers a unified perspective for diverse actors in the AI space.