Developing a hetero-intelligence methodological framework for sustainable policy-making based on the assessment of large language models

Abstract

This work delves into the increasing relevance of Large Language Models (LLMs) in the realm of sustainable policy-making, proposing an innovative hetero-intelligence framework that blends human and artificial intelligence (AI) for tackling modern sustainability challenges. The research methodology includes a hetero-intelligence performance test, which juxtaposes human intelligence with AI in the formulation and implementation of sustainable policies. After testing this hetero-intelligence methodology, seven steps are rigorously described so that it can be replicated in any sustainability planning related context. The results underscore the capabilities and limitations of LLMs, underscoring the critical role of human intelligence in enhancing the efficacy of hetero-intelligence systems. This work fulfils the need of a rigorous methodological framework based on empirical steps that can provide unbiased outcomes to be integrated into sustainable planning and decision-making processes.•Assesses LLMs' limitations and capabilities regarding sustainable planning issues•A replicable methodology is proposed based on the combination of both human and artificial intelligence•It proposes and systematises the integration of a hetero-intelligent approach into the formulation of sustainability policies to be more efficient and effective.

Keywords: ChatGPT; Conversational generative AI; Hetero-intelligent performance testing; Human intelligence; Large language models; Sustainable planning and policy; hetero-intelligence methodological framework for sustainable policy-making.