Assessment and Development Matters

This article explores how Artificial Intelligence (AI) can enhance psychometric testing in the workplace. By leveraging natural language processing, machine learning algorithms, and data analytics, AI-driven psychometric testing offers greater efficiency, accuracy, and fairness. It discusses the potential of AI to revolutionise traditional testing methods and highlights its benefits for candidate selection, talent management, and employee development.

This article explores an innovative ensemble approach integrating multiple AI models to achieve consensus in psychological assessments in the educational psychology domain. Addressing the rapid advancements in Artificial General Intelligence (AGI), it emphasises the importance of principles over specific models in this fast-evolving field. The study compares responses from various AI models using a set of literacy and socioeconomic questions. The findings highlight the diversity of responses from individual models and the holistic ensemble perspective offered by DAIM. The article underscores the potential shift towards Artificial Specific Intelligence (ASI) in psychology, advocating for a focus on multi-model approaches and, potentially, a re-evaluation of humanconsensus in psychological research.

This article examines the expansive growth of ChatGPT and the implications for large-scale test design. The authors contend that the impressive test simulation results observed by Chat-GPT undergird ongoing construct validity concerns with student testing. In order to address these challenges, a set of strategies is proposed that emphasises authentic assessment, the importance of human elements in traditional paper-and-pencil questions, and the controversial issue of the stakes ascribed to test results. Collectively, these approaches are meant to help test developers more carefully consider existing limitations within traditional standardised and large-scale assessment programs. Ultimately, test design reforms that enhance validity are increasingly needed to address the challenges posed by AI applications.

The increase in digital evidence, especially in cases involving Indecent Images of Children (IIOC), presents a pressing challenge for law enforcement agencies. In this article, we discuss two of the most prominent types of Artificial Intelligence (AI) and how they can be used in digital forensic processes, providing examples, and highlighting potential challenges that are likely to be experienced in developing and adopting AI. The two main types are of Data-Driven Model (DDM) age classification and ModelBased Reasoning (MBR), and in this article, examples for both are provided and discussed in the contents of IIOC investigations.

A light-hearted magazine type article generated by Chat GPT 3.5 in the style of Sir David Attenborough describing some AI-related acronyms with information on their origins and popularity.

The article below summarises the key features of a new agreement between the Council of the European Union and the European Parliament regarding proposals to regulate AI.

Hiding behind a smartphone screen, online dating applications provide a playground of opportunity for fraudsters and scammers. With ease of access to artificial intelligence, the technological capabilities of nefarious individuals are quickly growing. From sophisticated chatbots designed to engage in conversations and extract personal data, to deepfake technology used to create convincing false personas. This article summarises the current and upcoming risks which artificial intelligence poses to dating application and social media users. Deepfake technology is a key risk; the world is experiencing greater use of attractive deepfake images to convince dating app users into involvement in a romance scam, face-swaps to target and blackmail social media users with their intimate images, and instant generation of child sexual abuse material. Other risks include stalkers tracking their victims with greater ease, and individuals downloading nefarious dating applications which utilise chatbots to gather information and get paid. Gaps in empirical research are identified and discussed.

This article explores essential managerial competencies in the context of integrating artificial intelligence (AI) into the modern workplace. Key skills include technical literacy for informed decision-making, ethical consideration to address biases, change management for seamless AI adoption, and effective communication to bridge technical and non-technical stakeholders. These competencies position managers as pivotal contributors to successful AI management, organisational resilience, and innovation.

Artificial Intelligence is evolving at a breathtaking pace. It offers huge opportunities, yet creates significant challenges to virtually every organisation. Even the leaders of the companies which are at the forefront of unleashing its capabilities seem unsure as to its power, and governmental authorities are unsure about how – or even if – Artificial Intelligence should be controlled. Against this uncertain backdrop, all organisations need urgently to be defining – or revising – their competencies, so that these opportunities can practically be maximised, and the threats managed. This article explores what competencies might be relevant for all organisations facing up to a new world of AI.