Conor Fahy

Artificial intelligence (AI) is increasingly transforming the game industry by reshaping development processes, production workflows, and career structures. In this study, we present a scoping literature review examining the impact of AI on game development and employment within the industry, with particular attention to the perspectives of displacement, task transformation, and productivity effects. Drawing on academic research and industry reports published between 2020 and 2026, while retaining earlier foundational literature where relevant, this study analyzes how AI technologies such as generative AI, machine learning, procedural content generation, and large language models are integrated across major stages of game production, including game art, production management, programming, and live operations. Our findings indicate that AI can support efficiency, accelerate development cycles, and enable new forms of personalization and creative experimentation. At the same time, AI adoption may alter workforce dynamics by increasing the automation potential of routine and entry-level tasks, especially in areas such as asset creation and quality assurance. Rather than fully replacing workers, however, AI more commonly reconfigures job roles and increases demand for hybrid skill sets that combine technical knowledge, creativity, and AI literacy. Our study also identifies emerging opportunities in AI-assisted design, data analytics, and machine learning integration. Despite these benefits, AI adoption introduces important challenges related to employment polarization, intellectual property, workforce adaptation, ethical governance, and market concentration. We conclude that AI is fundamentally reshaping the game industry and highlight the need for balanced strategies that support innovation while preserving creative diversity, sustainable career pathways, and equitable access to technological opportunities. Overall, the strongest evidence concerns AI capabilities, workflow changes, and task-level transformation, whereas direct evidence of actual employment outcomes and long-term occupational change remains limited and uneven.

Historic buildings and urban streetscapes face increasing threats from climate change, development, and aging infrastructure, creating a pressing need for accurate and scalable documentation methods. This review assesses the combined use of photogrammetry and unmanned aerial vehicle (UAV) technologies in preserving built cultural heritage. We systematically analyze the end-to-end workflow, from the sophisticated processing of imagery into highly detailed and accurate 3D models in photogrammetry software via data acquisition using diverse UAV platforms and sensor payloads. Through case studies, including the mapping of ancient Maya sites in the Yucatán Peninsula and the conservation of the Notre Dame Cathedral, the review highlights the accuracy, efficiency, and accessibility offered by this technological synergy, underscoring its significance for heritage conservation, research, and the development of digital twins. Furthermore, it explores how these advancements foster public engagement and virtual accessibility, enabling immersive experiences and enriched educational opportunities. The paper also critically assesses the inherent technical, ethical, and legal challenges associated with this methodology, offering a balanced perspective on its application. By synthesizing the current knowledge, this review proposes future research trajectories and advocates for best practices, aiming to guide heritage professionals in leveraging photogrammetry and UAVs for the effective documentation and safeguarding of global cultural heritage.

The integration of large language models (LLMs) into remote healthcare has the potential to revolutionize medication management by enhancing communication, improving medication adherence, and supporting clinical decision-making. This study aims to explore the role of LLMs in remote medication management, focusing on their impact. This paper comprehensively reviews the existing literature, medical LLM cases, and the commercial applications of LLMs in remote healthcare. It also addresses technical, ethical, and regulatory challenges related to the use of artificial intelligence (AI) in this context. The review methodology includes analyzing studies on LLM applications, comparing their impact, and identifying gaps for future research and development. The review reveals that LLMs have shown significant potential in remote medication management by improving communication between patients and providers, enhancing medication adherence monitoring, and supporting clinical decision-making in medication management. Compared to traditional reminder systems, AI reminder systems have a 14% higher rate in improving adherence rates in pilot studies. However, there are notable challenges, including data privacy concerns, system integration issues, and the ethical dilemmas of AI-driven decisions such as bias and transparency. Overall, this review offers a comprehensive analysis of LLMs in remote medication management, identifying both their transformative potential and the key challenges to be addressed. It provides insights for healthcare providers, policymakers, and researchers on optimizing the use of AI in medication management.

Co-creation is a collaborative process between students and staff. It aims to reposition students as active participants in the design and delivery of learning materials. In this article we propose a model for co-creation in block-delivery. This type of delivery is a departure from traditional, term-long modules, and the block mode we are concerned with is an academic year consisting of four 30 credit modules, where each module is delivered in a seven-week block. The proposed model focuses on embedding co-creation in traditional student voice processes, which typically happen at the end of each block. In our model, it is this end-of-module evaluation that provides the opportunity for students to contribute to the design of the learning experience for the next academic cycle. The student voice informs a set of action points for the following cycle and are fed back to the students. Our model requires a more active approach from the student, which in turn provides a more meaningful co-creation process. The model is evaluated in a pilot study with a cohort at level 6. We outline the design of this pilot, describe the results, and offer reflections on its implementation.

Recognising and reacting to change in non-stationary data-streams is a challenging task. The majority of research in this area assumes that the true class label of incoming points are available, either at each time step or intermittently with some latency. In the worse case this latency approaches infinity and we can assume that no labels are available beyond the initial training set. When change is expected and no further training labels are provided the challenge of maintaining a high classification accuracy is very great. The challenge is to propagate the original training information through several timesteps, possibly indefinitely, while adapting to underlying change in the data-stream. In this paper we conduct an initial study into the effectiveness of using an evolving, population-based approach as the mechanism for adapting to change. An ensemble of one-class-classifiers is maintained for each class. Each classifier is considered as an agent in the sub-population and is subject to selection pressure to find interesting areas of the feature space. This selection pressure forces the ensemble to adapt to the underlying change in the data-stream.

Data stream clustering can be performed to discover the patterns underlying continuously arriving sequences of data. A number of data stream clustering algorithms for finding clusters in arbitrary shapes and handling outliers, such as density-based clustering algorithms, have been proposed. However, these algorithms are often limited in their ability to construct and merge microclusters by measuring the Euclidean distances between high-dimensional data objects, e.g., transferring valuable knowledge from historical landmark windows to the current landmark window, and exploiting evolving subspace structures adaptively. We propose an online sparse representation clustering (OSRC) method to learn an affinity matrix for evaluating the relationships among high-dimensional data objects in evolving data streams. We first introduce a low-dimensional projection (LDP) into sparse representation to adaptively reduce the potential negative influence associated with the noise and redundancy contained in high-dimensional data. Then, we take advantage of the $l_{2,1}$ -norm optimization technique to choose the appropriate number of representative data objects and form a specific dictionary for sparse representation. The specific dictionary is integrated into sparse representation to adaptively exploit the evolving subspace structures of the high-dimensional data objects. Moreover, the data object representatives from the current landmark window can transfer valuable knowledge to the next landmark window. The experimental results based on a synthetic dataset and six benchmark datasets validate the effectiveness of the proposed method compared to that of state-of-the-art methods for data stream clustering.

With the rise of digital media, how we read and share information has undergone significant changes. However, existing XR reading projects still have barriers to promoting the reading experience. Thus, the study proposes using XR technology to enhance the reading experience by projecting a 3D reading UI into 3D space. Instead of simply displaying a 2D UI or media in reality, as seen in existing VR/AR reading samples, this study designs an adaptive UI environment covering four aspects: 3D layout, navigation, interactive data, and sharing. The project aims to facilitate efficient and immersive online reading by providing users with a seamless spatial reading experience. This study seeks to showcase the scalability of XR technology in online reading.

New Engineering Education (NEE) has become increasingly important in higher education in China. The COVID-19 pandemic has forced higher education institutions to adopt online and hybrid modes of instruction globally, with Massive Open Online Courses (MOOCs) becoming a primary educational tool. In this paper, we explore the impacts of online/hybrid modes on NEE courses in the context of the COVID-19 pandemic. We hypothesized that the rapid transition to online/hybrid modes may have negatively affected students due to insufficient preparation by teachers. This hypothesis was tested through an investigation involving 787 students conducted in 2020 and 2021. The results indicated that, while most students considered online/hybrid modes a helpful tool for their studies, 28.69–36.74% of participants reported significant issues. Through one-to-one interviews with negative/neutral attitude students, we found that insufficient and burdensome communication/interaction with classmates and teachers was the main contributor to these issues. We conclude by proposing a conceptual path to address the issues raised in the study, as well as offering the obtained results as a critical reference for educational researchers seeking to improve the quality of online/hybrid courses and contribute to the field of education.

Advances in digital interactive technologies have created a range of innovative products and services for the well-being of society. Extended Reality (XR) technology has shown enormous potential in the educational, commercial, and medical fields. Considering the potential of XR, we analyzed the use of XR technology for the improvement of web service user experiences. The paper discusses Web Extended Reality (XR) and its current circumstances. The study also discusses the advantages of web componentization and the Page Builder System, which is a famous framework for web componentization. Furthermore, the study analyzes the characteristics of XR. The research designs the Web XR User Interface framework with XR characteristics and componentization design. A 3D UI framework is proposed for providing an immersive, explorative, and readable user experience. The framework covers three aspects, including main content, a scrollbar, and navigation. In order to evaluate the performance efficiency of the framework, a proof-of-concept prototype was developed to examine the concept. We conducted a user study with 60 participants to evaluate the reading performance. Compared with the traditional web layout, the experimental results showed that the framework can improve the user experience in static websites. The paper indicates possible future views based on the experimental study.

In a dynamic stream there is an assumption that the underlying process generating the stream is non-stationary and that concepts within the stream will drift and change as the stream progresses. Concepts learned by a classification model are prone to change and non-adaptive models are likely to deteriorate and become ineffective over time. The challenge of recognising and reacting to change in a stream is compounded by thescarcity of labelsproblem. This refers to the very realistic situation in which the true class label of an incoming point is not immediately available (or might never be available) or in situations where manually annotating data points are prohibitively expensive. In a high-velocity stream, it is perhaps impossible to manually label every incoming point and pursue a fully supervised approach. In this article, we formally describe the types of change, which can occur in a data-stream and then catalogue the methods for dealing with change when there is limited access to labels. We present an overview of the most influential ideas in the field along with recent advancements and we highlight trends, research gaps, and future research directions.