Advances In Artificial Intelligence Research Articles

Generative AI insights in tourism and hospitality: A comprehensive review and strategic research roadmap

This study used bibliometric analysis and a systematic literature review (SLR) to examine how the tourism and hospitality industries use generative artificial intelligence (GAI), identifying developed patterns, theoretical frameworks, strengths and limitations, and future research challenges. We conducted a systematic review using the Scopus database, adhering to PRISMA principles. We analyzed a sample of 25 articles published between 2019 and 2023 through narrative synthesis and bibliometric analysis using the VOSviewer software, a tool for visualizing network analysis. The USA, China, India, and Saudi Arabia are the major countries engaged in GAI research in tourism and hospitality. Significant research topics emphasize decision-making, chatbots, deep learning, and sentiment analysis, mainly through the Technology Acceptance Model (TAM), Stimulus-Organism-Response (S-O-R), and Human-Computer Interaction (HCI) frameworks. GAI applications demonstrate strength in improving user experience and operational efficiency, though gaps exist in scope, ethics, technology performance, and collaboration between humans and AI. This study, therefore, provides a fundamental foundation for understanding the current status of GAI research in tourism and hospitality by pointing out some trends and areas that require further investigation to ensure the responsible and effective integration of AI within the industry.

Political-RAG: using generative AI to extract political information from media content

ABSTRACT In the digital era, media content is crucial for political analysis, providing valuable insights through news articles, social media posts, speeches, and reports. Natural Language Processing (NLP) has transformed Political Information Extraction (IE), automating tasks such as event extraction and sentiment analysis. Traditional NLP methods, while effective, are often task-specific and require specialized expertise. In contrast, Large Language Models (LLMs) powered by Generative Artificial Intelligence (GenAI) offer a more integrated solution. However, domain-specific challenges persist, which led to the development of the Retrieval-Augmented Generation (RAG) framework. RAG enhances LLMs by incorporating external data retrieval, addressing issues related to data availability. To demonstrate RAG’s capabilities, we introduce the Political-RAG system, designed to extract political event information from media content, including Twitter data and news articles. Initially developed for event extraction, the Political-RAG system lays the foundation for developing various complex Political IE tasks. These include detecting hate speech, analyzing conflicts, assessing political bias, and evaluating social trends, sentiment, and opinions.

Shaping integrity: why generative artificial intelligence does not have to undermine education

Shaping integrity: why generative artificial intelligence does not have to undermine education

A look at the emerging trends of large language models in ophthalmology.

As the surge in large language models (LLMs) and generative artificial intelligence (AI) applications in ophthalmology continue to expand, this review seeks to update physicians of the current progress, to catalyze further work to harness its capabilities to enhance healthcare delivery in ophthalmology. Generative AI applications have shown promising performance in Ophthalmology. Beyond native LLMs and question-answering based tasks, there has been increasing work in employing novel LLM techniques and exploring wider use case applications. In this review, we first look at existing LLM use case applications specific to Ophthalmology, followed by an overview of commonly used LLM techniques. We finally focus on the emerging trends of the generative AI space with an angle from ophthalmology.

Precise grading of non-muscle invasive bladder cancer with multi-scale pyramidal CNN

The grading of non-muscle invasive bladder cancer (NMIBC) continues to face challenges due to subjective interpretations, which affect the assessment of its severity. To address this challenge, we are developing an innovative artificial intelligence (AI) system aimed at objectively grading NMIBC. This system uses a novel convolutional neural network (CNN) architecture called the multi-scale pyramidal pretrained CNN to analyze both local and global pathology markers extracted from digital pathology images. The proposed CNN structure takes as input three levels of patches, ranging from small patches (e.g., 128×128\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$128 \ imes 128$$\\end{document}) to the largest size patches (512×512\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$512 \ imes 512$$\\end{document}). These levels are then fused by random forest (RF) to estimate the severity grade of NMIBC. The optimal patch sizes and other model hyperparameters are determined using a grid search algorithm. For each patch size, the proposed system has been trained on 32K patches (comprising 16K low-grade and 16K high-grade samples) and subsequently tested on 8K patches (consisting of 4K low-grade and 4K high-grade samples), all annotated by two pathologists. Incorporating light and efficient processing, defining new benchmarks in the application of AI to histopathology, the ShuffleNet-based AI system achieved notable metrics on the testing data, including 94.25% ± 0.70% accuracy, 94.47% ± 0.93% sensitivity, 94.03% ± 0.95% specificity, and a 94.29% ± 0.70% F1-score. These results highlight its superior performance over traditional models like ResNet-18. The proposed system’s robustness in accurately grading pathology demonstrates its potential as an advanced AI tool for diagnosing human diseases in the domain of digital pathology.

Transformative landscape of ChatGPT in higher education: unveiling the academic discourse

Purpose Transformative generative artificial intelligence (AI) tools, such as ChatGPT, have revolutionized various sectors, including higher education. Since its release in November 2022, it has gathered attention from academia, scholars, students, regulators and education policymakers. Opinions diverge on whether ChatGPT’s use in academia should be circumscribed or regulated. To offer insights into some of these, this study synthesizes 139 contributions (articles and blogs) authored by instructors, publishers, professors, editors and education journalists in the education domain. Design/methodology/approach The study uses sentiment analysis and topic modelling to examine the 139 articles and blogs. Drawing on their insights, it firstly explores emotional dimensions of the content. Subsequently, using critical discourse analysis, it integrates them with emerging concerns (what and why), and explicates key processes (how) to inform academia, research, practice and policy. Findings Drawing on the results obtained from sentiment analysis, the study identifies that the overall sentiments surrounding generative AI tools such as ChatGPT in higher education are more positive (33%) than negative (11%). Using topic modelling, the study further highlights four thematic areas to explore benefits and challenges: perceptions and biases, pedagogical impact, ethical considerations and implementation and adoption. Research limitations/implications Limitations include the absence of student perspectives, which may offer deeper insights into perception, pedagogical impacts, and integrity-related issues. Additionally, while findings are applicable across various domains, specialized areas may present differing insights that could refine the conclusions. Originality/value Overall, contributors acknowledge the capabilities of generative AI tools like ChatGPT in enhancing students’ productivity. The findings suggest that it is likely to offer significant gains in the education domain, providing several benefits to both teachers and students. Nevertheless, they also consider its limitations, discrimination and bias, copyright infringement, plagiarism, fabricated unauthentic textual content and assessment bias.

A qualitative survey on perception of medical students on the use of large language models for educational purposes.

Large language models (LLMs)-based chatbots use natural language processing and are a type of generative artificial intelligence (AI) that are capable of comprehending user input and generating output in various formats. They offer potential benefits in medical education. This study explored the student's feedback on the utilization of LLMs in medical education. We conducted an in-depth interview with open-ended questions with Indian medical students via telephone conversation. The recording (average time 55.28±18.04 min) was transcribed and thematically analyzed to find major themes and sub-themes. We used QDA Miner Lite v.2.0.8 (Provalis Research: Montreal, Canada) for the thematic analysis of the text. A total of 25 students from eight Indian states studying from the first to final year of studies participated in this study. Three major themes were identified about usage scenario, augmented learning, and limitation of LLMs. Students use LLMs for clarifying complex topics, searching for customized answers, solving MCQs, making simplified notes, and streamlining assignments. While they appreciated the ease of access, ready reference for getting clarity on doubts, lucid explanation of questions, and time-saving aspects of LLMs, concerns were raised regarding erroneous results, limited usage due to reliability and privacy issues, and the overreliance on chatbots for educational needs. Hence, they emphasized the need for training for the integration of LLM in medical education. In conclusion, according to students' perception, LLMs have the potential to enhance medical education. However, addressing challenges and leveraging the strengths of LLMs are crucial for optimizing their integration into medical education.

The Impact of Generative Artificial Intelligence on Education: A Comparative Study

Generative Artificial Intelligence (GAI), also known as creative AI, is a technology capable of independently producing original and creative content, such as text, images, videos, music, or code. It uses machine learning algorithms to analyze and learn from a vast amount of data to generate similar or even innovative content. This type of AI is used in several fields, such as health for the production of new drugs, design to generate adaptive designs, video games to create immersive environments, and artificial intelligence to generate training data for new networks. Seizing the opportunities offered by AI innovations in education is crucial. We cannot afford to let time pass without doing anything and without benefiting from this technique. It is time to enhance traditional learning methods and adopt more innovative approaches that take advantage of the potential of AI, like content and exam generation, and also improve students' learning experience by exploiting their closeness to the technology. This paper aims to answer the question of when and how to use artificial intelligence responsibly, considering it as a technology and limiting its excessive use. Additionally, we present the impact of GAI through a comparative study of student performance in three exam types: a classical MCQ exam written by the teacher, an adaptive MCQ exam generated by GAI from a topic, and a third MCQ exam generated by GAI based on the content of the provided documents. This study is conducted on students undergoing a software testing training program. By evaluating their performance in the AI-generated adaptive MCQ exams and the traditional MCQ exam, we can gain insights into the potential of this technology to enhance educational practices and provide personalized learning experiences. This research will contribute to the ongoing discussion on responsible and effective utilization of GAI in education, paving the way for future advancements in the field.

Enhancing Higher Education in Portugal: Leveraging Generative Artificial Intelligence for Learning-Teaching Process

Recent advancements in Generative Artificial Intelligence (GAI) have revolutionized numerous fields, including higher education. This study focuses on the integration of GAI in Portuguese higher education institutions and explores its multifaceted implications and potential. Our research, conducted through a comprehensive survey between April and June 2023, engages with higher education faculty to understand their perceptions and utilization of GAI tools in teaching and learning processes. The potential of GAI to enhance personalized learning and interactive teaching methodologies is significant. It enables the creation of customized educational content, interactive simulations, and real-time feedback mechanisms, thus reducing the educators' workload and enhancing the learning experience. However, this integration is not without challenges. The study identifies critical ethical concerns around the use of GAI, including data privacy, intellectual property, and the potential for bias in AI-generated content. Additionally, our findings indicate varying acceptance and readiness among educators to adopt these technologies. While some express enthusiasm for the potential of GAI to transform educational practices, others remain cautious, highlighting the need for comprehensive training and support to leverage GAI capabilities fully. This paper captures the essence of the study's findings, illustrating both the promising opportunities and the complex challenges associated with using Generative AI in higher education. Higher education institutions can significantly enhance teaching and learning landscapes by addressing these challenges and fostering an informed and ethical approach to GAI integration.

Digital Tools in the Thesis Process: A Case Study from Sweden

The emergence of autonomous learning has revolutionized educational systems, offering new pathways to enhance higher education. Across most Swedish universities, Bachelor's and Master's theses (final projects) represent an important part of students' academic journey. Embracing technology as a tool, rather than an end in itself, has become imperative in contemporary higher education and thesis processes. Departing from traditional instructional methods, the focus has shifted towards interactive and autonomous learning, propelled or supported by digitalization. Despite the growing emphasis on autonomous learning and the benefits of technology-enhanced learning (TEL), there are concerns about the misuse of digital tools such as Generative Artificial Intelligence (GAI) in educational contexts. The scarcity of knowledge to properly use available tools would potentially affect students' academic performance and future employment prospects, which has recently caused significant concern for educational authorities, educators, and even policymakers. This study reflects on students' perceptions of using digitalization and GAI to facilitate autonomous and interactive learning to support the thesis writing process. The focus is to understand how digital natives (predominantly Gen Z) view the integration of new digital tools in autonomous learning. A case-based approach in the form of an open survey conducted during the spring semester of 2024 gathered 52 responses from students within the informatics discipline at bachelor's and master's levels in a Swedish university. Results indicate that most students used digital tools such as GAI interactively, in the thesis process, mainly for constructive learning. They indicated that learning about digital tools as part of their formal learning leads them towards effective and more structured use of the tools for brainstorming, drawing inspiration, and developing interactive learning, which simulates real-world peer interactions or teacher-student dynamics. This instructional approach equips students with the skills to responsibly use tools as academic support rather than for any other inappropriate purposes. Effective utilization of digital tools promotes autonomous learning, leading to improved quality of bachelor's and master's theses and overall academic performance in higher education. Conversely, inadequate knowledge and improper use of these tools can negatively impact students' educational outcomes.

Recent Progress and Current Status of Artificial Intelligence in Skin Cancer Diagnosis: A Systematic Review—Where do we Stand?

Introduction: Skin cancer is one of the most prevalent forms worldwide, with a significant increase in recent decades. Real-time and accurate detection can reduce the burdens of invasive treatments. The advent of Artificial Intelligence (AI) and Machine learning (ML) has introduced multiple tools to aid accurate and early detection, categorizing dermatological images and proving especially valuable in regions with a shortage of specialists. However, the adoption of these AI-based tools requires consideration of efficacy, safety, and ethical implications. Objective: The systematic review aims to evaluate existing research on the detection, categorization, and assessment of skin cancer images. Methods: The systematic literature review is conducted based on studies published from 2018 to 2023 in PubMed, Scopus, Embase, Web of Science, IEEE Xplore, ACM DL, and Ovid MEDLINE. Study selection, data extraction, and inclusion are carried out after a proper evaluation of the studies. Results are presented in tables and figures using a narrative synthesis. Results: The search identified 687 studies from the database. However, after three phases of identification, screening, and evaluation, only 16 studies were chosen, focusing on developing and validating AI tools to detect, diagnose, and categorize skin cancer. This systematic review covers the selected studies in multiple dimensions. Conclusion: The use of AI and ML in dermatology has revolutionized the early detection of cancer, but it is necessary to validate and collaborate with healthcare professionals to ensure efficacy, safety, and effectiveness.

The Impact of AI Tools on Employment Trends and Skill Demands

Artificial intelligence (AI) is revolutionizing our work, lifestyles, and modes of communication. The integration of AI-driven technologies brings about automation of tedious tasks, enhances decision-making capabilities, and boosts overall productivity. Nonetheless, the adoption of AI carries profound implications for both the labor market and skill prerequisites. On one hand, the advent of AI is poised to usher in new job opportunities, particularly in domains such as machine learning, data science, and AI engineering. Conversely, AI's implementation is expected to automate numerous routine and manual job functions, resulting in the displacement of workers in sectors like manufacturing, transportation, and customer service. This transition underscores the necessity for workers to cultivate fresh skill sets and competencies. Skills such as data analysis, critical thinking, and advanced problem-solving will ascend in importance, whereas conventional proficiencies like manual dexterity and routine problem-solving will diminish in relevance. Furthermore, the effects of AI on employment prospects and skill requisites will vary across different industries and geographical regions. While certain sectors may undergo substantial job displacement, others may witness a surge in demand for adept personnel. Thus, policymakers and industry leaders must adopt a proactive stance toward reskilling and upskilling initiatives to ensure workforce competitiveness. Ultimately, the integration of AI tools is poised to catalyze transformative shifts in the labor market and skill landscape. While it presents novel opportunities and enhances productivity, it simultaneously necessitates adaptive measures from workers to thrive in an evolving job market.

The potential effects of deepfakes on news media and entertainment

AbstractDeepfakes are synthetic media, such as pictures, music and videos, created with generative artificial intelligence (GenAI) tools, a technique that builds upon machine learning and multi-layered neural networks trained on large datasets. Today, anyone can create deepfakes online, without any knowledge about the underpinning technology. This fact opens up creative opportunities at the same time as it creates individual and societal challenges. Some identified challenges are fake news, bullying, defamation, media manipulation and democracy damage. The aim of this study was to analyse and discuss how deepfakes could affect entertainment, education and digital news media in positive and negative ways. The chosen research strategy was a qualitative cross-sectional study to create a snapshot of current attitudes in blogs, online discussion fora and from a selected expert panel. Firstly, blogs and internet discussion fora on deepfakes and AI were analysed inductively. Secondly, results from the previous step were used to create a question scheme for semi-structured interviews with some selected experts that have relevant knowledge in the field of AI and deepfakes. Finally, interviews answered were analysed thematically using the Technology Affordances and Constraints Theory as an analytical lens. Findings show that deepfakes at the same time have a wide variety of affordances, but also some critical constraints to consider. In the affordance part, there are the potential use of deepfakes for speculative and provoking concepts, not only as entertainment, but also to illustrate educational scenarios that are hard or impossible to capture in traditional videos, images or sound recordings. Furthermore, there are many affordances for entertainment and the creation of environments where realism is not an important condition. Some examples are video game graphics, animated film background music in games and music videos. However, the conclusion is that the findings comprise more of constraints than of affordances, where the serious negative aspect of deepfakes can lead to psychological, financial and social harm. Many images and videos in news media have already been manipulated during the last decades, but deepfakes can take this to a new and more problematic level where the overall trust might disappear. The author’s recommendation is to further increase the focus on source criticism and that discussions on which sources to trust should be an early introduced part of educational programmes.

Generative AI in Higher Education: Educators' Perspectives on Academic Learning and Integrity

Generative Artificial Intelligence (Gen AI), exemplified by models such as ChatGPT, has rapidly advanced, becoming a significant force in various sectors, including higher education. ChatGPT, a leading application of Gen AI, utilizes large language models (LLMs) to generate human-like text, providing capabilities that range from answering complex questions to facilitating software development. As these tools become increasingly integrated into academic environments, their impact on teaching and learning processes has become more under focus. However, there are not many studies focusing on the impact of such systems on students’ performance and the use of such systems. This study therefore examines the impact of GPT tools on higher education, aiming to address the following research questions: How does the use of GPTs influence the teaching and learning process in higher education? What are the perceived impacts of GPTs on educational practices from university educators’ perspective? The research employs a qualitative methodology, incorporating semi-structured interviews with educators from Swedish universities who have integrated GPT into their curricula. Data were transcribed and analysed using OpenAI's Whisper to extract key themes and insights. Data analysis was done through thematic analysis and categorizing the data using codes. The study uncovers a dual impact of GPT on education, while it offers substantial opportunities for enhancing productivity and personalized learning, it also raises significant concerns about academic integrity, over-reliance on AI, and the potential influences on students’ soft skills. These findings contribute to the discourse on digital learning by highlighting the need for instructional and constructive integration of AI technologies such as GenAI, in educational settings. In addition, the rise and integration of GPT technology is irreversible, and we must adapt to it rather than return to old ways. Embracing AI's potential while addressing its challenges is essential for progress and innovation in this new era. The study emphasizes the importance of developing clear policies and guidelines to ensure that the benefits of GPT are realized without compromising the integrity of the educational process. As such, this research provides valuable insights for educators, policymakers, and scholars interested in the ethical and effective implementation of AI in higher education

Exploring the Perception of the Impact of Personal Learning Environments (PLEs) in the Generative AI Era in Engineering Education

Objective: This study explores students' perceptions of using Personal Learning Environments (PLEs) in the era of generative artificial intelligence as a learning resource in higher education. The specific objectives are: 1) to determine the current tools that students incorporate into their PLEs and 2) to identify students' perceptions of the use of PLEs for lifelong learning. Theoretical Framework: UNESCO and other institutions recognize lifelong learning as an essential competency for personal and professional development. Personal Learning Environments (PLEs) are considered effective tools for promoting self-directed learning, allowing students to manage their learning processes, especially with the advent of generative artificial intelligence technologies. Method: A qualitative study was conducted with 207 first-semester engineering students from a public university in Peru. Participants were introduced to a training program to develop their PLEs using various digital tools, including generative AI tools such as ChatGPT DALL-E. The instrument used to evaluate students' perceptions was the MUSIC model (Jones, 2009), which focuses on empowerment and usefulness. Results: The results indicate that more than 80% of the students positively perceive PLEs, considering them valuable and empowering tools for achieving their learning objectives. Integrating generative AI tools into PLEs is notable, as it provides a dynamic and personalized learning experience. Most students found PLEs helpful for their future and relevant for promoting lifelong learning skills. Research Implications: The research suggests that PLEs, especially when integrated with generative AI tools, positively impact self-directed learning and promote collaborative and social learning. These findings can inform the development of educational programs and policies that encourage using PLEs as a student-centered learning strategy. Originality/Value: This study contributes to the understanding of the role of PLEs in the context of higher education and generative artificial intelligence. It is one of the first studies to examine students' perceptions of using PLEs with generative AI tools, highlighting their potential to enhance learning autonomy and personalization.

The Adoption of Artificial Intelligence Technologies in Arab Newsrooms: Potentials and Challenges

Arab newsrooms and journalists are still attempting to process how to benefit from using artificial intelligence (AI) in their daily news production. Many have started experimenting with generative AI. However, in countries with strong economies, or in newsrooms funded by strong economies like in the UAE, Saudi Arabia, and Qatar, news organizations have endorsed AI technologies beyond ChatGPT and similar tools. The majority of Arab journalists, however, are yet to understand what is meant by AI. Very few news organizations in the region have strategically approached the question of AI. Arab newsrooms need to examine when and why they need to use AI technologies, whether they have the infrastructure or can afford the resources. Implementing AI in Arab newsrooms presents an additional set of challenges, including language complexities, cultural sensitivities, potential biases in algorithms, and the need for tailored solutions that resonate with local audiences. The ethical implications present its own set of challenges. ChatGPT has proven to generate inaccurate data in Arabic. Resources are to be put in place to ensure the model is trained on a diverse and comprehensive dataset of Arabic text. Implementing bias detection and mitigation techniques to ensure the models are free from biases and offensive content is crucial. Newsrooms operating in authoritarian regimes have to be aware of governments' attempts to use generative AI to whitewash and promote state-centered actions and policies. Maintaining editorial integrity remains an important consideration.

Student Learning Performance Evaluation: Mitigating the Challenges of Generative AI Chatbot Misuse in Student Assessments

Since the launch of ChatGPT, a growing number of generative artificial intelligence (AI) chatbots have entered the market. Although chatbots have the potential to help students learn, misusing them to complete assessments raises questions about the authenticity of the work and puts students at risk of academic misconduct. Given the crucial role of assessments in evaluating students’ learning performance, uncertainties about the authenticity of the work call into question the extent to which students have achieved the intended learning outcomes. This study conducted a thematic analysis to provide an overview of the challenges that chatbot misuse may pose to student learning performance evaluation, followed by the various mitigation strategies to overcome these challenges. This study searched the Education Resources Information Centre (ERIC) database for peer-reviewed articles published in scholarly journals after 30 November 2022 (the launch date of ChatGPT, as this study focuses on generative AI rather than other types of AI) and until 30 April 2024. The thematic analysis of 17 articles identified five major themes (and respective sub-themes) in the discussions of these articles, i.e., reasons students use chatbots for assessments, challenges that chatbots may pose to student learning performance evaluation, mitigation strategies, detection strategies, and counter-detection strategies. As chatbots become more prevalent and powerful, the study's findings provide education stakeholders with insightful information on the implications of students misusing chatbots for assessments and how this affects their learning performance evaluation.

Exploring the Impact of Artificial Intelligence Generative Tools on Research in Higher Education Institutions: A Perspective from Portugal

Artificial Intelligence (AI) generative tools have emerged as transformative instruments in various domains, including research and academia. It is important to see what is positive and what is not. This study focuses on the integration of GAI in Portuguese higher education institutions and explores its multifaceted implications and potential. Our research was conducted through a comprehensive survey between April and June 2023, garnering 77 responses. To this purpose, the analysis will have several insights into the research process, namely, to assess the frequency of use and objectives of using generative AI for higher education research and to explore possible trends and future directions in the adoption and application of generative AI in this field.

A Comprehensive Review of Artificial Intelligence

This paper explores the literature, types, applications, and challenges of Artificial Intelligence, providing a comprehensive overview of its current state and future potential. It traces the development of AI from early symbolic systems to modern machine learning and deep learning technologies, highlighting different AI types such as narrow AI, general AI, and the speculative concept of superintelligence. Through a literature review of various research papers, this paper examines the foundational theories and cutting-edge advancements in AI, including neural networks, reinforcement learning, and hybrid systems. The applications of AI across diverse industries, such as healthcare, finance, and education, demonstrate its transformative impact, while ethical concerns surrounding bias, privacy, and job displacement present significant challenges. The paper concludes by addressing the need for responsible AI development to ensure its benefits align with societal values and contribute to a sustainable future.

Exploring AI's Role in Enhancing Risk Assessment Models in Financial Quantitative Trading

The application of Artificial Intelligence (AI) algorithms to financial risk management has fundamentally transformed the nature of risk and the tools being used to control and manage it. This paper examines how AI is deployed to manage market risk, including volatility forecasting, portfolio optimisation, stress testing, and sentiment analysis. While many of these models are well-accepted across the industry and underpin many front-office and risk-management processes, they are often limited in dynamic market environments where traditional linearity doesnt exist and real-time data is king. AI has brought advanced tools that can be applied to large and complex data sets, and that adapt quickly as market conditions change. Reinforcement learning algorithms are now deployed to optimise portfolio weights dynamically. Natural-language processing is also used to augment sentiment analysis, which can be leveraged to assess potential risk. Other forms of AI can be deployed to address challenges that come with more advanced AI models. Explainable AI models provide the opportunity to produce financial outcomes that are more transparent and less opaque while also helping to validate regulatory compliance. Analyzing the applications of AI case studies sheds light on why financial professionals must continue to embrace AI tools, many of which are designed to help them manage risk in an increasingly volatile market environment.