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Effective forensic interviewing is crucial, particularly if the interview is the only source of evidence. Whilst there are a number of interview models that advocate for reflective practice, the evaluation of interviews rarely gets the time or attention it deserves. This is concerning, given that interviewer skills drop significantly after six months, and there are limited opportunities for refresher training. This study aims to explore how law enforcement officers reflect upon and evaluate their interviews and seek their insight into what they believe an effective evaluation tool would constitute.

A total of 32 officers from three police forces in England and Wales and six international security organisations completed a qualitative questionnaire that explored their views on how interview evaluation is conducted and the impact on the interview, and how technology could assist in this important stage in terms of usability, efficacy and capability of a new evaluation tool.

Thematic analysis was used, resulting in three overall themes being identified from the data. These focused on current evaluation methods, barriers to conducting evaluations and technological solutions.

The findings are discussed in relation to interviewing and the development of tools to assist in evaluations. Implications for practice include the need for much more effort to be put into (re)introducing the evaluation stage into training to ensure that officers are allowed to reflect upon and evaluate their interview performance.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.