# AI Thinking: A framework for rethinking artificial intelligence in practice

**Authors:** Denis Newman-Griffis  
**arXiv:** [2409.12922](https://arxiv.org/abs/2409.12922)  
**Date:** 2024-08-26  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2409.12922v1/

## Key points

- Artificial intelligence (AI) revolutionizing information utilization across disciplines and practical settings
- Conflicting interpretations of AI's nature and usage leading to disparities
- Need for interdisciplinary approaches to reconcile differing perspectives on AI in practice
- Introduction of a groundbreaking conceptual framework called AI Thinking by Denis Newman-Griffis
- Framework focusing on five core competencies for effective AI implementation: motivation, formulation, evaluation, selection, and embedding
- Practical application of the framework through a hypothetical case study presented by Newman-Griffis
- Framework serving as a bridge between academic disciplines and various contexts where AI is employed
- Potential of the AI Thinking concept to reshape how artificial intelligence is approached and utilized across different domains

## Summary

Artificial intelligence (AI) is revolutionizing the way information is utilized across various disciplines and practical settings. As more disciplines delve into studying, developing, and evaluating AI applications, conflicting interpretations of AI's nature and usage often arise. To address these disparities and shape the future of AI utilization, there is a pressing need for interdisciplinary approaches that can reconcile differing perspectives on AI in practice. In response to this challenge, Denis Newman-Griffis introduces a groundbreaking conceptual framework known as AI Thinking. This innovative model captures the essential decisions and considerations involved in utilizing AI from diverse disciplinary viewpoints. The  framework focuses on five core competencies crucial for implementing AI effectively within specific contexts: motivating the use of  in information processes, formulating appropriate , evaluating available tools and technologies, selecting relevant data sets, and embedding  within the sociotechnical environments where it operates. To illustrate the practical application of the  model, Newman-Griffis presents a hypothetical case study that showcases how this framework can be implemented in real-world scenarios. By situating  within broader cross-disciplinary dialogues surrounding artificial intelligence literacy and innovation driven by this framework serves as a bridge between academic disciplines and varied contexts where  is employed. Overall, the proposed concept of  has the potential to reshape how we approach and utilize artificial intelligence across different domains.