AI Capabilities Research Articles

Innovating for the Future: AI and HRM Capabilities for Sustainability in Higher Education

Innovating for the Future: AI and HRM Capabilities for Sustainability in Higher Education

Enhancing customer experience through AI-driven language processing in service interactions

In today’s competitive service industries, delivering exceptional customer experiences is paramount. This paper explores how AI-driven language processing technologies enhance customer service interactions. Key technologies such as Natural Language Processing (NLP), chatbots, virtual assistants, speech recognition, and machine learning are crucial in transforming customer service. These technologies enable personalized, efficient, and consistent customer interactions, significantly improving overall customer satisfaction. However, implementing these technologies has challenges, including data quality, system integration, privacy concerns, user acceptance, and financial investment. Addressing these challenges is essential for successful AI adoption. Future trends indicate further advancements in AI capabilities, integration with IoT and blockchain technologies, and evolving ethical and regulatory considerations. The long-term impact of AI on service industries promises more efficient operations, cost savings, and superior customer experiences, giving companies a competitive advantage in the market. This paper provides insights into leveraging AI to enhance customer experience, outlining the benefits and challenges while speculating on future directions and implications.

Operationalizing responsible AI principles through responsible AI capabilities

AbstractResponsible artificial intelligence (RAI) has emerged in response to growing concerns about the impact of AI. While high-level principles have been provided, operationalizing these principles poses challenges. This study, grounded in recent RAI literature in organizational contexts and dynamic capability theory, and informed by literature on RAI principles and expert interviews in organizations deploying AI systems, (1) problematizes the high-level principles and low-level requirements and underscores the need for mid-level norms by adopting dynamic capability as a theoretical lens, and (2) develops five themes to capture firms’ RAI capability, including (i) understandable AI model, (ii) bias remediation, (iii) responsiveness, (iv) harmless, and vi) common good. As our contribution to the field of information systems (IS), this study extends the emerging literature on operationalizing RAI and dynamic capabilities, empirically elucidating the capabilities needed by firms. For IS practice, we provide organizations deploying AI with novel insights to aid in the responsible implementation of AI.

Korisnička percepcija modernih tehnologija integriranih u hotelskoj industriji

The hospitality and tourism industry has changed significantly through the use of modern technologies, both for travellers and service providers. Digitalisation, AI capabilities, IoT, and mobile technologies have enabled a personalised booking, travel, and accommodation experience that provides travellers with timely and useful suggestions, information, and assistance. They have also opened up new opportunities for marketing and customer engagement, providing advanced analytics that enable the hospitality industry to reach a wider audience and deliver tailored offers and services. The objective of this study is to investigate users' attitudes towards the use of modern technologies, their willingness to disclose their private data in order to receive personalised content through digital communication, their perception of the benefits of personalised content, and the impact on their purchase intentions. The influence of gender and level of education on the responses to the survey and the possibility of forming groups in relation to the respondents’ profiles were analysed in more detail. The methods used in this study were a survey, k-means clustering, one-way ANOVA and a two-sample t-test. A questionnaire with 15 statements was used, and 217 responses were collected. User behaviour was segmented using the k-means clustering method, resulting in two homogeneous clusters indicating two main user attitudes. A two-sample t-test was conducted to analyse whether there were significant differences between the responses of the different gender groups and between the different education groups. The results showed significant differences in both groups for several questions, as shown in the Results and Discussion section.

Artificial Intelligence Governance

Research on AI governance is important towards potentially useful and constraining affordable misuse, reduce new risks and economic trends that threaten to disrupt public political and economic trends, and drive off target as interest in advanced AI systems and the norms, focal points, and use of new AI research are potentially transformative and governance institutions aim to prevent. Potential public benefits from policy community re-using AI research are enormous, including reduced economic instability. A fundamental challenge in AI governance is a cognitive framing challenge: governing AI research requires understanding new kinds of safety risks, performance goals, and intended applications that advanced AI systems will make possible. Specifically, the letter focuses on how AI research could mitigate issues such as the possibility of AI capabilities getting concentrated within a small and hard-to-regulate group of actors, and ultimately recommends the prioritization of open research and collaboration, with concern for long-term social and economic looming to the forefront of coalitions if AI becomes an increasingly important aspect of the future economy and society.

Conceptualizing generative AI as style engines: Application archetypes and implications

The rise of generative AI has brought with it a surprising paradox: systems that excel at tasks once thought to be uniquely human, like fluent conversation or persuasive writing, while simultaneously failing to meet traditional expectations of computing, in terms of reliability, accuracy, and veracity (e.g., given the various issues with so-called ‘hallucinations’). We argue that, when generative AI is seen through a traditional computing lens, its development focuses on optimizing for traditional computing traits that remain in principle unattainable. This risks backgrounding what is most novel and defining about it. As probabilistic technologies, generative AIs do not store, in any traditional sense, any data or content. Rather, essential features of training data become encoded in deep neural networks as patterns, that become practically available as styles. We discuss what happens when the distinction between objects and their appearance dissolves and all aspects of images or text become understood as styles, accessible for exploration and creative combination and generation. For example, defining visual qualities of entities like ‘chair’ or ‘cat’ become available as ‘chair-ness’ or ‘cat-ness’ for creative image generation. We argue that, when understood as style engines, unique generative AI capabilities become conceptualized as complementing traditional computing ones. This will aid both computing practitioners and information systems researchers in reconciling and integrating generative AI into the traditional IS landscape. Our conceptualization leads us to propose four archetypes of generative AI application and use, and to highlight future avenues for information systems research made visible by this conceptualization, as well as implications for practice and policymaking.

Generative AI Chatbot for Engineering Scientific Journal

Abstract: This study focuses on exploring the potential of artificial intelligence as an alternative, effective, and user-preferred tool for answering inquiries, compared to traditional methods such as FAQs or email and ticketing systems. The study highlights how AI can enhance efficiency and accuracy in processing and responding to inquiries from readers, authors, and reviewers, by providing immediate and customized answers based on the analysis of information available on the journal's website and the data fed to the chatbot. Through in-depth discussions and an analysis of the inquiries received over a full six months, totaling about 3000 inquiries, the study demonstrates the good ability of the chatbot to understand complex inquiries and provide satisfactory answers. The study indicates that chatbots can reduce the workload on editorial teams of scientific journals by automating responses to routine inquiries, allowing staff to dedicate more time to editorial and academic tasks. One of the key aspects of training is teaching the chatbot to provide correct answers to various inquiries and to avoid responding to negative or redundant inquiries. The research explores the challenges of applying AI in this context, including the need to train smart models to understand specific academic language and ensure accuracy in responses, as well as addressing privacy concerns and data security. The importance of designing flexible and adaptable AI systems to meet the diverse requirements of different scientific journals and their users is emphasized. The study concludes that artificial intelligence is a promising tool for improving the interaction between academic journals and their communities, offering an effective alternative to traditional systems. It highlights the necessity for ongoing research and development to enhance AI capabilities. Notably, the AI tool currently lacks a direct method for correcting its wrong answers, which is one of the most effective learning tools used by parents to correct their children's answers. One of the key recommendations of the study is that AI training should be conducted in stages.

Existential risk from transformative AI: an economic perspective

The prospective arrival of transformative artificial intelligence (TAI) will be a filter for the human civilization – a threshold beyond which it will either strongly accelerate its growth, or vanish. Historical evidence on technological progress in AI capabilities and economic incentives to pursue it suggest that TAI will most likely be developed in just one to four decades. In contrast, theoretical problems of AI alignment, needed to be solved in order for TAI to be “friendly” towards humans rather than cause our extinction, appear difficult and impossible to solve by mechanically increasing the amount of compute. This means that transformative AI poses an imminent existential risk to the humankind which ought to be urgently addressed. Starting from this premise, this paper provides new economic perspectives on discussions surrounding the issue: whether addressing existential risks is cost effective and fair towards the contemporary poor, whether it constitutes “Pascal’s mugging”, how to quantify risks that have never materialized in the past, how discounting affects our assessment of existential risk, and how to include the prospects of upcoming singularity in economic forecasts. The paper also suggests possible policy actions, such as ramping up public funding on research on existential risks and AI safety, and improving regulation of the AI sector, preferably within a global policy framework.

Assessing the Ethical and Professional Capabilities of AI: A Study of ChatGPT and Google Gemini versus PREview (Situational Judgement Test) for Medical Student Applicant

Assessing the Ethical and Professional Capabilities of AI: A Study of ChatGPT and Google Gemini versus PREview (Situational Judgement Test) for Medical Student Applicant

AI-Counsellor Using Emerging Technique

Mental health is an integral part of overall well-being, encompassing emotional, psychological, and social well-being. It affects how we think, feel, and act. Yet, mental health concerns are on the rise globally. According to the World Health Organization (WHO), an estimated one in four people will experience a mental health condition in their lifetime. Depression, anxiety, and stress are amongst the most prevalent mental health issues. This paper aims to develop an AI Counsellor - a chatbot designed to use AI and ML capabilities to offer initial mental health support. Design the chatbot to understand user input effectively through sentiment analysis, intent recognition, and entity recognition. Finally develop a user-friendly interface for text-based interaction with the AI-Counsellor.

Innovating Cybersecurity Education Through AI-augmented Teaching

In traditional teaching frameworks, instructors face significant obstacles in offering current and synchronized learning materials and examples, especially when the course is taught by multiple instructors. This situation can affect the quality of the course's learning outcomes. These challenges become more pronounced in today’s higher education, because of the heightened complexity arising from the need to cover a range of course materials, diverse student backgrounds, varying skill levels, and different student expectations—all within the constraints of a fixed teaching and learning schedule. Furthermore, due to resource constraints, not every instructor has the availability of a teaching assistant (TA). Especially, while the demand for cybersecurity continues to rise, the dynamic nature of the cybersecurity field leads to the frequent emergence of new issues and incidents. To address these challenges, we examine the capabilities of generative AI to innovate teaching techniques and methods for cybersecurity curricula. We further explore the novel challenges introduced by generative AI, including issues related to privacy, data ownership, transparency, and other associated concerns, underscoring the need for comprehensive solutions. Our work further examines the teaching and learning capabilities of dynamically generated, up-to-date class materials in a personalized study environment augmented by AI. The adaptability of AI-augmented teaching across various disciplines will bring innovation to higher

Enhancing decision‐making in higher education: Exploring the integration of ChatGPT and data visualization tools in data analysis

AbstractThis chapter explores the potential of integrating conversational AI tools such as ChatGPT with data visualization (DV) tools such as Power BI in higher education settings. A brief history of chatbots is summarized and challenges and opportunities in higher education are outlined. The highlights include AI's prospects for enhancing data‐informed decision‐making while needing safeguards to mitigate risks. Through a pioneering exercise, we integrated ChatGPT's conversational capabilities with Power BI's interface via API and tested functionality. Suggestions for good practice and implications for higher education are discussed.Practical Takeaways The importance of data security, recognizing current limitations in accuracy, and good practices for integrating DV tools with ChatGPT. The exercise shows that AI tools have the potential to aid higher education leaders, but still face many challenges, and human judgment remains essential. Integration of ChatGPT with Power BI in higher education aims to enable natural language interaction with data visualizations, facilitating dynamic discussions and insights for leaders. Considerations include data security protocols, technical challenges, financial constraints, and the potential and limitations of AI. Technical challenges include limitations in ChatGPT's interpretation of complex queries, API performance issues, and data processing inefficiencies. Affordability of enterprise AI versions poses a barrier to adoption for institutions with budget constraints despite recognizing potential benefits. ChatGPT represents a significant advancement in AI capabilities but has limitations, including generating inaccurate information and lacking awareness of its limitations. Human oversight and critical evaluation are crucial in navigating the evolving AI landscape and leveraging its benefits effectively while mitigating risks.

Composing jazz music pieces using LSTM neural networks approach

The domain of artificial intelligence has increasingly extended into the creative arts, aiming to emulate and augment human creativity with automated processes. This is particularly evident in the field of music, where AI's ability to learn and produce intricate compositions has attracted significant attention. This study explores the challenge of generating jazz music using artificial intelligence, specifically focusing on the application of Long Short-Term Memory (LSTM) neural networks for jazz composition. By optimizing the model to address the genre's complexity, the research demonstrates the LSTM's capacity to capture and reproduce jazz's essential harmonic progressions and rhythmic nuances. Quantitative analyses show high accuracy and a deep understanding of musical structures, whereas qualitative feedback confirms the model's efficacy in producing compositions that embody jazz's spontaneity. Despite its achievements, the model's tendency to generate repetitive sequences suggests areas for improvement. This paper advances the field of AI in music, illustrating the potential of LSTM networks to mimic complex musical genres and emphasizing the necessity of ongoing model refinement to foster creativity. It highlights the evolving role of machine learning in music generation, proposing a foundation for future work aimed at diminishing the gap between AI capabilities and artistic expression.

Unsupervised Content Mining in CBIR: Harnessing Latent Diffusion for Complex Text-Based Query Interpretation.

The paper demonstrates a novel methodology for Content-Based Image Retrieval (CBIR), which shifts the focus from conventional domain-specific image queries to more complex text-based query processing. Latent diffusion models are employed to interpret complex textual prompts and address the requirements of effectively interpreting the complex textual query. Latent Diffusion models successfully transform complex textual queries into visually engaging representations, establishing a seamless connection between textual descriptions and visual content. Custom triplet network design is at the heart of our retrieval method. When trained well, a triplet network will represent the generated query image and the different images in the database. The cosine similarity metric is used to assess the similarity between the feature representations in order to find and retrieve the relevant images. Our experiments results show that latent diffusion models can successfully bridge the gap between complex textual prompts for image retrieval without relying on labels or metadata that are attached to database images. This advancement sets the stage for future explorations in image retrieval, leveraging the generative AI capabilities to cater to the ever-evolving demands of big data and complex query interpretations.

Conceptualizing the Dimensions of Digital Leadership towards Academicians’ Competencies in AI: A Systematic Literature Review for Instrument Design

This study investigates digital leadership's role in improving academics' AI skills and the crucial shift in teaching and learning techniques brought by digital and AI advancements. A learner-centric approach customizes, engages, and includes learning using digital and AI. It demands a change from traditional education and stresses digital leadership and AI capabilities for academicians. The study conducts a thorough literature review to identify key digital leadership attributes, including the ability to innovate, strategically use digital tools, and give equal access to educational technology. It also describes the AI capabilities academics require to personalize instruction, streamline administration, and boost student performance. Results show academicians require specific professional development to learn digital leadership and AI. These apps help teachers use AI to meet digital needs. Digital leadership is dynamic and involves technical, strategic, ethical, and collaborative abilities to transform education, according to the paper. Cooperation and research are essential to understanding digital leadership and creating effective assessment and training methods as the digital world changes. The initiative will assist academics navigate modern educational situations and promote digital integration and innovation. As implications, this study reveals how digital leadership and AI capabilities are altering education. This research emphasizes the role of digital leadership and AI in enhancing teaching and learning in the digital era, adding to the discourse about preparing academicians for the future.

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing prominent XAI solutions based on key attributes like explanation type, model dependence, and use cases. This resource equips users to navigate the diverse XAI landscape and select the most suitable framework for their specific needs. Furthermore, the study proposes a novel framework called XAIE (eXplainable AI Evaluator) for informed decision-making in XAI adoption. This framework empowers users to assess different XAI options based on their application context objectively. This will lead to more responsible AI development by fostering transparency and trust. Finally, the research identifies the limitations and challenges associated with the existing XAI frameworks, paving the way for future advancements. By highlighting these areas, the study guides researchers and developers in enhancing the capabilities of Explainable AI.

Will the AI Do Everything?

Abstract Are humans destined for mass redundancy? If Elon Musk is right, we may well be. Brian Runciman MBCS and ChatGPT team up to analyse the latest BCS research about AI's capabilities and find a very different story.

LawBot : From Documents to Answers, Unveiling A New Era In Real Estate Legal Assistance

In an era of technological advancement, the real estate sector is poised for a transformative leap with the introduction of an AI-powered chatbot that integrates LangChain technology and machine learning. The proposed method aims to redefine user experiences by providing comprehensive insights into land property, evaluating PDF documents, and offering invaluable guidance on real estate law. The core objectives of this endeavor are twofold : to enhance user trust and to optimize profitability within the real estate industry. Leveraging cutting-edge AI capabilities, our chatbot promises precision in legal knowledge and personalized interactions using Large Language Models (LLMS) and NLP driven approaches, ultimately reshaping the way individuals and businesses navigate the real estate landscape. Keywords - LangChain, AI, Chatbot, Real Estate, Large Language Models, NLP.

Strengths, weaknesses, opportunities and threats (SWOT) analysis of artificial intelligence adoption in nursing care

The primary objective of this commentary was to identify the strengths and weaknesses of AI technologies, uncover opportunities for improvement, and recognize potential threats that could impede their successful implementation in nursing care. This commentary involved constructing a SWOT matrix to analyze AI adoption, identifying internal strengths and weaknesses, and external opportunities and threats. The analysis revealed several strengths of AI adoption in nursing care, including enhanced data analysis capabilities, improved patient monitoring, and increased efficiency in routine tasks. However, weaknesses such as the high initial costs of implementation and concerns about data security were identified. Opportunities included the potential for AI to reduce healthcare costs and improve patient outcomes. Nonetheless, threats such as resistance to technological change and ethical dilemmas related to AI decision-making processes were recognized as potential barriers to successful adoption. This article sheds light on the intricate landscape of AI adoption in nursing care. While AI brings forth substantial strengths, it simultaneously poses challenges that healthcare systems should confront. To fully harness AI's potential, healthcare organizations should thoughtfully deliberate on the identified weaknesses and threats, actively seeking avenues for seamless integration. In this concerted effort, the healthcare industry is poised to unlock the transformative capabilities of AI, elevating nursing care standards, and ultimately, advancing patient outcomes.

Insights Unveiled: Harnessing AI to Explore Human Behaviour in Social Sciences

AI's evolution has expanded its potential to understand complex human behaviours, revealing new insights into human psychology and sociology. This essay explores AI's capabilities in detecting intricate behavioural patterns, its applications in social sciences, the challenges and ethical considerations associated with its use, and predictions for the future. AI's ability to uncover patterns invisible to the human mind offers an exciting frontier of exploration, particularly in understanding human behaviour. Human behaviour, characterized by its intricacy and influenced by sociocultural, economic, and psychological factors, has traditionally been studied through various methodologies. However, the unpredictability and dynamism of human behaviour often defy simple categorization or comprehension. AI has the potential to serve as a guide, decoding patterns beyond the reach of human cognition and traditional methodologies. AI can offer a new lens through which we view the rich tapestry of human experience, such as predicting social trends, understanding consumer behaviour, and exploring the underpinnings of political sentiment. Despite challenges such as feasibility, interpretability, and ethics, the potential impact of AI in understanding human behaviour is enormous. By shedding light on hidden patterns, we can foster a more nuanced understanding of our societies, inform policies, drive innovation, and potentially address some of the pressing challenges of our times.