Artificial Intelligence Capabilities Research Articles

Diagnostic accuracy of large language models in psychiatry

IntroductionMedical decision-making is crucial for effective treatment, especially in psychiatry where diagnosis often relies on subjective patient reports and a lack of high-specificity symptoms. Artificial intelligence (AI), particularly Large Language Models (LLMs) like GPT, has emerged as a promising tool to enhance diagnostic accuracy in psychiatry. This comparative study explores the diagnostic capabilities of several AI models, including Aya, GPT-3.5, GPT-4, GPT-3.5 clinical assistant (CA), Nemotron, and Nemotron CA, using clinical cases from the DSM-5. MethodsWe curated 20 clinical cases from the DSM-5 Clinical Cases book, covering a wide range of psychiatric diagnoses. Four advanced AI models (GPT-3.5 Turbo, GPT-4, Aya, Nemotron) were tested using prompts to elicit detailed diagnoses and reasoning. The models' performances were evaluated based on accuracy and quality of reasoning, with additional analysis using the Retrieval Augmented Generation (RAG) methodology for models accessing the DSM-5 text. ResultsThe AI models showed varied diagnostic accuracy, with GPT-3.5 and GPT-4 performing notably better than Aya and Nemotron in terms of both accuracy and reasoning quality. While models struggled with specific disorders such as cyclothymic and disruptive mood dysregulation disorders, others excelled, particularly in diagnosing psychotic and bipolar disorders. Statistical analysis highlighted significant differences in accuracy and reasoning, emphasizing the superiority of the GPT models. DiscussionThe application of AI in psychiatry offers potential improvements in diagnostic accuracy. The superior performance of the GPT models can be attributed to their advanced natural language processing capabilities and extensive training on diverse text data, enabling more effective interpretation of psychiatric language. However, models like Aya and Nemotron showed limitations in reasoning, indicating a need for further refinement in their training and application. ConclusionAI holds significant promise for enhancing psychiatric diagnostics, with certain models demonstrating high potential in interpreting complex clinical descriptions accurately. Future research should focus on expanding the dataset and integrating multimodal data to further enhance the diagnostic capabilities of AI in psychiatry.

Can ChatGPT read who you are?

The interplay between artificial intelligence (AI) and psychology, particularly in personality assessment, represents an important emerging area of research. Accurate personality trait estimation is crucial not only for enhancing personalization in human-computer interaction but also for a wide variety of applications ranging from mental health to education. This paper analyzes the capability of a generic chatbot, ChatGPT, to effectively infer personality traits from short texts. We report the results of a comprehensive user study featuring texts written in Czech by a representative population sample of 155 participants. Their self-assessments based on the Big Five Inventory (BFI) questionnaire serve as the ground truth. We compare the personality trait estimations made by ChatGPT against those by human raters and report ChatGPT's competitive performance in inferring personality traits from text. We also uncover a ‘positivity bias’ in ChatGPT's assessments across all personality dimensions and explore the impact of prompt composition on accuracy. This work contributes to the understanding of AI capabilities in psychological assessment, highlighting both the potential and limitations of using large language models for personality inference. Our research underscores the importance of responsible AI development, considering ethical implications such as privacy, consent, autonomy, and bias in AI applications.

The smart future for sustainable development: Artificial intelligence solutions for sustainable urbanization

AbstractFuture tools for supporting collaborations between technology and sustainable development include artificial intelligence (AI) applications in sustainable Urbanization roles. This article highlights the various applications of AI in advancing sustainable urbanization. From urban planning to disaster management, AI technology is revolutionizing the way cities are designed and managed. By leveraging data analytics, machine learning, and predictive modeling, AI is helping city officials make informed decisions, optimize resource usage, and improve quality of life for urban residents. Despite the immense potential of AI in sustainable urban development, there are still challenges and limitations to overcome. We show some of the most significant problems related to these issues. These include issues related to data privacy, algorithm bias, and ethical considerations. Continued research and innovation are needed to address these challenges and ensure that AI technology is used responsibly and effectively in shaping sustainable cities. As a result, AI has the power to transform urban environments and create more sustainable, resilient communities. By harnessing the capabilities of AI, cities can become more efficient, environmentally‐friendly, and prepared for the challenges of the future. It is essential for policymakers, urban planners, and technology developers to work together to harness the full potential of AI in sustainable urbanization and create a better future for all. Proactively addressing these challenges can unlock the full potential of AI in combating sustainable cities and building a sustainable future for all.

Research on the Advancement of Equity in Higher Education Driven by Artificial Intelligence

A midst the rapid evolution of artificial intelligence (AI) technology, its integration into higher education has gained significant momentum, presenting novel avenues for fostering educational equity. This paper comprehensively examines the utilization of AI in higher education, meticulously analyzing its specific contributions to the advancement of educational equity, and subsequently offers tailored strategies and recommendations for its optimal deployment. By harnessing the power of intelligent algorithms, big data analysis, and other AI capabilities, higher education institutions are able to personalize learning experiences, expand access to quality resources, and streamline administrative processes, thereby promoting a more equitable distribution of educational opportunities. The paper delves into these applications, underscores their potential impact, and provides practical guidance for leveraging AI to foster a more inclusive and equitable higher education landscape.

The interplay of intelligent manufacturing, innovation equilibrium and cost stickiness in the artificial intelligence era

AbstractThis study investigates the impact of intelligent manufacturing methods driven by artificial intelligence (AI) on cost stickiness in Chinese manufacturing enterprises. Leveraging the ABJ model， a regression analysis explores how different AI‐enabled intelligent manufacturing approaches influence cost stickiness through the lens of innovation equilibrium. The sample comprises manufacturing companies listed on China's A‐share market from 2013 to 2021. The findings reveal a negative correlation between intelligent manufacturing adoption and cost stickiness among these firms. Specifically, production‐based intelligent manufacturing exhibits a more significant effect on reducing cost stickiness compared with collaborative intelligent manufacturing methods. Moreover, intelligent manufacturing positively impacts both joint equilibrium innovation and matching equilibrium innovation. While joint equilibrium innovation is negatively associated with cost stickiness, matching equilibrium innovation shows no significant relationship with cost stickiness. The results indicate that innovation equilibrium plays a mediating role in the relationship between AI‐driven intelligent manufacturing and cost stickiness. Overall, this research sheds light on how AI capabilities enabling intelligent manufacturing processes and innovation equilibrium dynamics can help alleviate cost stickiness issues faced by manufacturing enterprises. It highlights the strategic value of adopting AI systems to enhance operational efficiency and cost management flexibility within manufacturing contexts.

Human-AI Teaming in Critical Care: A Comparative Analysis of Data Scientists' and Clinicians' Perspectives on AI Augmentation and Automation.

Artificial intelligence (AI) holds immense potential for enhancing clinical and administrative health care tasks. However, slow adoption and implementation challenges highlight the need to consider how humans can effectively collaborate with AI within broader socio-technical systems in health care. In the example of intensive care units (ICUs), we compare data scientists' and clinicians' assessments of the optimal utilization of human and AI capabilities by determining suitable levels of human-AI teaming for safely and meaningfully augmenting or automating 6 core tasks. The goal is to provide actionable recommendations for policy makers and health care practitioners regarding AI design and implementation. In this multimethod study, we combine a systematic task analysis across 6 ICUs with an international Delphi survey involving 19 health data scientists from the industry and academia and 61 ICU clinicians (25 physicians and 36 nurses) to define and assess optimal levels of human-AI teaming (level 1=no performance benefits; level 2=AI augments human performance; level 3=humans augment AI performance; level 4=AI performs without human input). Stakeholder groups also considered ethical and social implications. Both stakeholder groups chose level 2 and 3 human-AI teaming for 4 out of 6 core tasks in the ICU. For one task (monitoring), level 4 was the preferred design choice. For the task of patient interactions, both data scientists and clinicians agreed that AI should not be used regardless of technological feasibility due to the importance of the physician-patient and nurse-patient relationship and ethical concerns. Human-AI design choices rely on interpretability, predictability, and control over AI systems. If these conditions are not met and AI performs below human-level reliability, a reduction to level 1 or shifting accountability away from human end users is advised. If AI performs at or beyond human-level reliability and these conditions are not met, shifting to level 4 automation should be considered to ensure safe and efficient human-AI teaming. By considering the sociotechnical system and determining appropriate levels of human-AI teaming, our study showcases the potential for improving the safety and effectiveness of AI usage in ICUs and broader health care settings. Regulatory measures should prioritize interpretability, predictability, and control if clinicians hold full accountability. Ethical and social implications must be carefully evaluated to ensure effective collaboration between humans and AI, particularly considering the most recent advancements in generative AI.

A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide

As artificial intelligence (AI) begins to take center stage in technological innovations, it is essential to understand the business value of AI innovation efforts and investments. While some early work at the firm level exists, there is a shortage of literature that takes a larger country-level perspective. This study investigated the effect of AI innovation efforts on production efficiency across countries using stochastic production frontier approaches. In addition, our model also included the traditional economic inputs of capital and labor. We used both the Cobb–Douglas function and Constant Elastic Substitution model specifications. The significant findings of this study are as follows: Innovation efforts in AI measured by the number of AI-related patents and capital investment in AI have a substantial effect on economic output. The significance of AI investments indicates the need for a robust digital infrastructure as a prerequisite for harnessing AI capabilities. The complementary relationship between labor and AI-related patents implies that high-skilled labor is often necessary to incorporate AI inputs into production. However, as AI capabilities develop, they weaken the effect on labor input. The study also distinguishes between AI innovation (research and development activities indicated by AI patents) and the production efficiency of AI investments (return on every dollar invested), highlighting that more AI innovation does not always translate into better production efficiency. The findings indicate that while the United States leads innovation in AI, the UK has the best production efficiency. China ranked fourth in AI innovation and has the lowest production efficiency among the countries included in the study.

Revolutionizing Healthcare and Drug Discovery: The Impact of Artificial Intelligence on Pharmaceutical Development

Artificial intelligence (AI) is reshaping drug discovery and delivery in the pharmaceutical industry, fundamentally transforming traditional methods. In drug discovery, AI algorithms rapidly analyze vast biological and chemical datasets to identify potential drug candidates with unprecedented accuracy. Machine learning models predict compound efficacy and safety, accelerating earlystage drug development. AI also facilitates drug repurposing, uncovering new therapeutic uses for existing medications. At the drug delivery front, AI optimizes formulations and systems, enabling targeted and personalized approaches. Intelligent algorithms enhance the understanding of pharmacokinetics and pharmacodynamics, guiding the development of precision medicine strategies. This integration of AI not only expedites innovative drug discovery but also refines delivery mechanisms, promising more effective and tailored treatments with the potential to revolutionize patient care. The data-processing capabilities of AI drive digitalization and widespread utilization. Applications in drug discovery, development, repurposing, and clinical trials aim to alleviate human workload, expedite objectives, and foster innovation. Despite promising prospects, concerns about job displacement and stringent regulations accompany AI implementation. Emphasizing the intent to augment human labor rather than replace it entirely, the industry anticipates that AI will become a pivotal resource, propelling efficiency, innovation, and advancements in healthcare. This review emphasizes the role of AI in transforming drug discovery and delivery

Perceptions of artificial intelligence system's aptitude to judge morality and competence amidst the rise of Chatbots

This paper examines how humans judge the capabilities of artificial intelligence (AI) to evaluate human attributes, specifically focusing on two key dimensions of human social evaluation: morality and competence. Furthermore, it investigates the impact of exposure to advanced Large Language Models on these perceptions. In three studies (combined N = 200), we tested the hypothesis that people will find it less plausible that AI is capable of judging the morality conveyed by a behavior compared to judging its competence. Participants estimated the plausibility of AI origin for a set of written impressions of positive and negative behaviors related to morality and competence. Studies 1 and 3 supported our hypothesis that people would be more inclined to attribute AI origin to competence-related impressions compared to morality-related ones. In Study 2, we found this effect only for impressions of positive behaviors. Additional exploratory analyses clarified that the differentiation between the AI origin of competence and morality judgments persisted throughout the first half year after the public launch of popular AI chatbot (i.e., ChatGPT) and could not be explained by participants' general attitudes toward AI, or the actual source of the impressions (i.e., AI or human). These findings suggest an enduring belief that AI is less adept at assessing the morality compared to the competence of human behavior, even as AI capabilities continued to advance.

Impact of Internet of Things, Cloud Computing, Artificial Intelligence, Digital Capabilities, Digital Innovation, IT Flexibility on Firm Performance in Saudi Arabia Islamic Bank

This study delves into the impact of technological advancements on firm performance within the Islamic banking sector in Saudi Arabia, focusing on the roles of Internet of Things (IoT) capabilities, Cloud Computing Capabilities (CCC), and Artificial Intelligence (AI) capabilities. It explores how these technological dimensions influence operational efficiency, customer engagement, and competitive positioning, thereby affecting overall firm performance. The research further investigates the mediating role of digital innovation in translating technological capabilities into performance outcomes and examines the moderating effects of digital capabilities and IT flexibility on this relationship. Through empirical analysis, the study demonstrates that IoT, CCC, and AI capabilities are crucial drivers of firm performance, offering significant opportunities for process optimization, service innovation, and market differentiation. Digital innovation is identified as a key mediator that enables Islamic banks to harness the full potential of these technologies, suggesting that the strategic integration of digital innovation practices is vital for realizing performance gains. Additionally, the findings highlight the importance of robust digital capabilities and a flexible IT infrastructure in maximizing the benefits of technological advancements, underscoring their role in facilitating adaptation to technological changes and fostering a culture of continuous innovation. The study contributes to the literature on technology management and banking performance by providing insights into the strategic value of technological capabilities and digital innovation within the Islamic banking context. It offers practical implications for banking executives and policymakers in Saudi Arabia, emphasizing the need for a comprehensive approach that encompasses technological adoption, innovation management, and capability development to achieve enhanced firm performance in the digital era.

Artificial intelligence (AI) for supply chain collaboration: implications on information sharing and trust

PurposeManagers and scholars alike claim that artificial intelligence (AI) represents a tool to enhance supply chain collaborations; however, existing research is limited in providing frameworks that categorise to what extent companies can apply AI capabilities and support existing collaborations. In response, this paper clarifies the various implications of AI applications on supply chain collaborations, focusing on the core elements of information sharing and trust. A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company’s AI journey.Design/methodology/approachUsing existing literature on AI technology and collaboration and its effects of information sharing and trust, we present two frameworks to clarify (a) the interrelationships between information sharing, trust and AI capabilities and (b) develop a model illustrating five AI application stages how AI can be used for supply chain collaborations.FindingsWe identify various levels of interdependency between trust and AI capabilities and subsequently divide AI collaboration into five stages, namely complementary AI applications, augmentative AI applications, collaborative AI applications, autonomous AI applications and AI applications replacing existing systems.Originality/valueSimilar to the five stages of autonomous driving, the categorisation of AI collaboration along the supply chain into five consecutive stages provides insight into collaborations practices and represents a practical management tool to better understand the utilisation of AI capabilities in a supply chain environment.

A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions

This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase and renewable energy sources become more integrated, maintaining the stability and reliability of both conventional power systems and smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s power grids’ complex, dynamic nature. This paper discusses the adoption of advanced intelligence methods, including artificial intelligence (AI), deep learning (DL), machine learning (ML), metaheuristic optimization algorithms, and other AI techniques such as fuzzy logic, reinforcement learning, and model predictive control to address these challenges. It underscores the critical importance of power system stability and the new challenges of integrating diverse energy sources. The paper reviews various intelligent methods used in power system analysis, emphasizing their roles in predictive maintenance, fault detection, real-time control, and monitoring. It details extensive research on the capabilities of AI and ML algorithms to enhance the precision and efficiency of protection systems, showing their effectiveness in accurately identifying and resolving faults. Additionally, it explores the potential of fuzzy logic in decision-making under uncertainty, reinforcement learning for dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering valuable insights into practical applications. The review concludes by identifying current limitations and suggesting areas for future research, highlighting the need for more robust, flexible, and scalable intelligent systems in the power sector. This paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection.

Advancing macroeconomic models through artificial intelligence integration

The integration of Artificial Intelligence (AI) into macroeconomic models marks a significant evolution in the field of economic forecasting and analysis. Traditional macroeconomic models, often constrained by linear assumptions and a limited set of variables, struggle to accurately capture the complexities of the global economy. This paper explores how AI, particularly machine learning algorithms like Random Forests and Neural Networks, enhances these models by processing and interpreting vast and diverse datasets, including unstructured data such as social media sentiment and news analysis. AI's capability to adapt and learn from new data enables dynamic models that remain relevant and accurate amidst changing economic conditions. By addressing non-linearities and enhancing model robustness, AI provides a more nuanced understanding of economic dynamics, uncovering intricate patterns missed by traditional analyses. The implications of AI-enhanced macroeconomic models are profound, offering more reliable foundations for economic research and policy-making. This paper argues that the integration of AI into economic modeling not only improves the precision of economic forecasts but also enriches the field of economic research and the formulation of more effective policy interventions. Through examples such as inflation rate forecasting and the identification of complex, non-linear economic relationships, this study highlights the transformative potential of AI in macroeconomic analysis and policy formulation.

Computer Vision―The Frontier of Modern Environmental Diagnostics: A Review

Computer vision (CV), in combination with various sensors and image analysis algorithms, is a frontier direction in diagnosing the state of the environment and its biogenic and abiogenic objects. The work generalizes scientific achievements and identifies scientific and technical problems in this area of research based on the conceptual system of analysis on the time axis: from implemented achievements as part of the past and present to original new solutions—the future. Our work gives an idea of three areas of application of CV in diagnosing the state of the environment: phenotype recognition in digital images, monitoring of living and abiogenic objects, and development of new methods for identifying pollution and its consequences. The advantages of CV, which can be attributed to scientific achievements in this field of research, are shown: an increase in the volume of analyzed samples, simultaneous analysis of several parameters of the object of observation, and leveling of subjective evaluation factors. The main CV problems currently solved are the accuracy of diagnostics and changing quality of the survey, identification of the object of analysis with minimal operator participation, simultaneous monitoring of objects of different quality, and development of software and hardware systems with CV. A promising direction for the future is to combine the capabilities of CV and artificial intelligence. Thus, the review can be useful for specialists in environmental sciences and scientists working in interdisciplinary fields.

Generative AI And New Challenges For Public Leaders: The Implications For Citizen Communications And Foundations Of Government Leadership

This brief commentary describes some of the less discussed implications of generative artificial intelligence (GenAI) for changes in government communications, citizen experiences and the very foundations of government. It focuses on the current and developing capabilities of artificial intelligence to handle and generate text and images in a way that seems virtually human.

Revisiting e-commerce platforms’ strategies of exercising channel power: a contingency perspective

PurposeThe integration of e-commerce platforms and artificial intelligence (AI) into the marketing channel ecosystem is challenging the explanatory capacity of traditional channel power theories, indicating a significant yet unaddressed research gap concerning the impact of these digital entities and AI on channel power exercise dynamics. This study adopts a contingency perspective to critically revisit how e-commerce platforms exercise channel power and the ensuing effects on channel conflicts. The purpose of this study is to extend the boundaries of traditional channel power theories, enhancing their relevance in today’s digital marketplace.Design/methodology/approachBuilding on channel power theories, the authors developed a framework tested with survey data collected from 262 sellers. This framework incorporates three key contingent variables: inter-platform competition, AI capabilities and platform value co-creation. Regression analysis was used to perform the analyses.FindingsThis study finds that intense inter-platform competition mitigates the (positive) negative relationship between platform channel power and the exercise of (non-) coercive power. Moreover, a platform’s AI capabilities and value co-creation activities diminish the potential for channel conflicts induced by the exercise of coercive power. AI capabilities can also strengthen the negative relationship between the exercise of non-coercive power and channel conflicts.Originality/valueThis study contributes to the advancement of traditional channel power theories by integrating contemporary digital elements like AI and platform dynamics. This study provides theoretical and practical insights on navigating channel power in modern marketing environments, offering strategic guidelines for optimizing channel relationships.

Leveraging Artificial Intelligence for Enhanced Performance in Matrix Organizations: A Research Perspective

In today's complex business landscape, matrix organizations represent a prevalent structural framework characterized by overlapping reporting structures and cross-functional project teams. This organizational model, while offering flexibility and specialization, also presents unique challenges in resource allocation, decision-making, collaboration, and project management. This research paper investigates the potential of artificial intelligence (AI) to address these challenges and enhance the performance of matrix organizations. Drawing upon an extensive review of existing literature and relevant case studies, the paper provides a comprehensive analysis of AI's role in optimizing various aspects of matrix organization operations. Specifically, it examines how AI technologies can streamline resource allocation processes by analyzing project requirements, team capabilities, and individual skill sets to ensure optimal staffing levels and skill matching across projects. Additionally, the paper explores AI's capabilities in providing decision support to managers and team leaders by leveraging data analytics to offer insights into project progress, risk assessment, and performance evaluation.

The Diversity of Artificial Intelligence Applications in Marine Pollution: A Systematic Literature Review

Marine pollution, a major disturbance to the sustainable use of oceans, is becoming more prevalent around the world. Multidimensional and sustainable ocean governance have become increasingly focused on managing, reducing, and eliminating marine pollution. Artificial intelligence has been used more and more in recent years to monitor and control marine pollution. This systematic literature review, encompassing studies from the Web of Science and Scopus databases, delineates the extensive role of artificial intelligence in marine pollution management, revealing a significant surge in research and application. This review aims to provide information and a better understanding of the application of artificial intelligence in marine pollution. In marine pollution, 57% of AI applications are used for monitoring, 24% for management, and 19% for prediction. Three areas are emphasized: (1) detecting and responding to oil pollution, (2) monitoring water quality and its practical application, and (3) monitoring and identifying plastic pollution. Each area benefits from the unique capabilities of artificial intelligence. If the scientific community continues to explore and refine these technologies, the convergence of artificial intelligence and marine pollution may yield more sophisticated solutions for environmental conservation. Although artificial intelligence offers powerful tools for the treatment of marine pollution, it does have some limitations. Future research recommendations include (1) transferring experimental outcomes to industrial applications in a broader sense; (2) highlighting the cost-effective advantages of AI in marine pollution control; and (3) promoting the use of AI in the legislation and policy-making about controlling marine pollution.

AI’s Bipolar Effect on Mitigating and Motivating Frauds

In the time of digital innovation, Artificial Intelligence (AI) stands at the forefront, signaling new capabilities in fraud management but also new vulnerabilities. This paper aims to dissect AI's paradoxical influence on fraud, portraying its roles in both promoting and mitigating fraudulent activities. The research seeks to bridge the gap in understanding the dual nature of AI, highlighting the need for ethical and regulatory frameworks to traverse the complications AI introduces into fraud detection and prevention. Utilising secondary data from Google News and academic databases with the keyword 'AI fraud,' this study adopts a keyword-based analysis to sift through the most relevant literature. The approach is designed to capture a comprehensive snapshot of the current discourse, underlining the bipolar impact of AI on fraud. The analysis reveals AI's significant potential in enhancing fraud detection systems through rapid data analysis and pattern recognition. However, AI technologies can be exploited to facilitate sophisticated fraud schemes. The study underscores an urgent need for evolving practices and policies that counteract AI's potential for misuse, weighing in the emerging concept of self-regulatory AI systems as a promising direction for future research. This paper contributes insights into the dualistic role of AI in fraud, adding depth to the discourse on its implications for security, ethical considerations, and regulatory challenges. It advocates for a balanced perspective on AI's capabilities.

Intelligent Conversations: A Theoretical Framework for Understanding Natural Language Processing within Artificial Intelligence Systems

The present study paper presents a theoretical framework for understanding the language capabilities of artificial intelligence (AI) systems, with a particular emphasis on natural language processing (NLP). The proposed framework, known as the Framework of Key Performative Attributes in AI Discourse (FKPA-AID), is based on a survey of pertinent linguistic theories and AI literature. It specifies six essential dimensions for modeling intelligent conversations: linguistic precision, contextual adaptation, fluid dialogue management, self-learning capacities, socio-emotional sensitivity, and continuity. The study also uses FKPA-AID to assess the strengths and weaknesses of present AI chat capabilities, and uses this analysis to propose prospective research directions for improving machine-based language mastery to better mimic human discourses. The study conducts a literature review of current breakthroughs in AI, notably in the field of natural language processing. It investigates AI's disruptive influence across industries and its role in driving digital transformation, with a particular emphasis on mobile technology applications. The paper also dives into the development of Generative AI (GAI) and Natural Language Processing (NLP), explaining its intricacies, models, applications, and recent advances. Furthermore, it emphasizes the use of AI and NLP in voice-controlled homes and healthcare, with a focus on designing intuitive interfaces and analyzing health data. The primary goal of this literature review is to provide a complete knowledge of the emerging landscape shaped by the combination of AI and NLP. The findings from the studied literature highlight the disruptive potential, ethical difficulties, and practical applicability across multiple disciplines. The paper emphasizes the importance of responsible development in the dynamic and ever-changing world of AI and NLP, and calls for collaborative efforts from interdisciplinary experts such as linguists, computer scientists, psychologists, sociologists, and philosophers to guide future research in this field. The proposed FKPA-AID framework is positioned as a theory-based systematic tool for assessing AI conversation capabilities, contributing to overall improvements in the field.