Advances In Artificial Intelligence Research Articles

Negotiating Meaning with Machines: AI's Role in Doctoral Writing Pedagogy

AbstractThis paper examines the integration of generative artificial intelligence (AI) in doctoral writing pedagogy. It explores how AI augments traditional teaching and composition processes, fosters a new paradigm of cognitive engagement and collaborative academic writing, and the broader ethical and social implications of human-AI writing in doctoral writing pedagogy. A community-engaged participatory research methodology was employed within a Doctor of Healthcare Administration program. Data were collected through discussion board messages, self-assessment papers, student reflections, and a focus group interview, and analyzed using thematic analysis. The research unearthed a hybrid human-AI writing process characterized by dynamic brainstorming, continuous negotiation of meaning, and comparative evaluation. These practices enhanced students' cognitive and metacognitive engagement, confidence, and learner agency, signifying a shift toward a collaborative approach to academic writing. The findings highlight the need for academic institutions to adapt policies and curricula to incorporate AI technologies ethically and responsibly. Emphasis on AI literacy and academic integrity is crucial for preparing graduates for an AI-integrated workforce. This study contributes to the understanding of AI's role in doctoral education, specifically doctoral writing development, presenting a novel perspective on the synergistic collaboration between students and AI in academic writing and its implications for institutional policies and writing pedagogy.

2024 practice analysis: a comparison of expectations vs actual performance of essential competencies in public relations

PurposeWe conducted the Universal Accreditation Board (UAB) practice analysis to examine perceptions of US public relations practitioners and educators regarding the essential competencies for entry-level and mid-career professionals. This is a trend analysis survey that is conducted every five years to assess changes in required competencies.Design/methodology/approachThe survey was distributed via email to organizations affiliated with the UAB. Two variations of the survey were available to differentiate between practitioners and educators. The study was conducted from February through March of 2024.FindingsWe found practitioners’ expectations for others exceeded their own actual performance levels. This may be attributed to social comparison bias. We have provided recommendations for updating the Accreditation in Public Relations (APR) and certificate exams based on our findings. For example, we recommend senior professionals adopt a nurturing leadership style when mentoring young professionals. The study also revealed that educators overestimated generative artificial intelligence (AI) use in the workplace, as practitioners exhibited a slower rate of adoption of AI. The literature and theories that guided the paper were AI use, the history of accreditation in public relations and the diffusion of innovation theory.Originality/valueThis research paper provides insights related to the diffusion of AI competencies in the workplace. Additionally, this research adds to public relations literature by revealing the gap in expectations of senior professionals for beginning and mid-career professionals and their own job performance.

What are the differences? A comparative study of generative artificial intelligence translation and human translation of scientific texts

Generative artificial intelligence translation (GenAIT) brings convenience yet also imposes severe challenges on the dissemination of knowledge. The respective (dis)advantages of GenAIT and human translation (HT), and the ways to promote their effective interaction have not been sufficiently explored yet. This study investigates the linguistic features of GenAIT and HT of scientific texts rendered from English to Chinese from lexical and syntactic levels. The GenAIT is generated by ChatGPT 3.5, a representative GenAI platform, while HTs are done by 19 Master-of-Translation-and-Interpreting students in China. Data shows that GenAIT and HTs present distinguished linguistic features in both levels. At the lexical level, HT exhibits lengthier texts with a lower average word diversity; GenAIT presents higher accuracy in translating terminology. At the syntactic level, the average sentence count in HT is greater, whereas its average sentence length measured in tokens is shorter. Moreover, human translators tend to transform sentences from passive voice into active voice more frequently than ChatGPT 3.5 does. Furthermore, human translators exhibit superior skills in deconstructing lengthy and complex sentences into shorter, more comprehensible clauses. This study reveals that ChatGPT 3.5 and human translators exhibit complementary capabilities in the translation of scientific texts, thereby suggesting an optimisation of the strengths inherent to both parties. Implications are provided for future translator training, language service providers and the ongoing development of GenAIT and HT.

The Two Word Test as a semantic benchmark for large language models

Large language models (LLMs) have shown remarkable abilities recently, including passing advanced professional exams and demanding benchmark tests. This performance has led many to suggest that they are close to achieving humanlike or “true” understanding of language, and even artificial general intelligence (AGI). Here, we provide a new open-source benchmark, the Two Word Test (TWT), that can assess semantic abilities of LLMs using two-word phrases in a task that can be performed relatively easily by humans without advanced training. Combining multiple words into a single concept is a fundamental linguistic and conceptual operation routinely performed by people. The test requires meaningfulness judgments of 1768 noun-noun combinations that have been rated as meaningful (e.g., baby boy) or as having low meaningfulness (e.g., goat sky) by human raters. This novel test differs from existing benchmarks that rely on logical reasoning, inference, puzzle-solving, or domain expertise. We provide versions of the task that probe meaningfulness ratings on a 0–4 scale as well as binary judgments. With both versions, we conducted a series of experiments using the TWT on GPT-4, GPT-3.5, Claude-3-Optus, and Gemini-1-Pro-001. Results demonstrated that, compared to humans, all models performed relatively poorly at rating meaningfulness of these phrases. GPT-3.5-turbo, Gemini-1.0-Pro-001 and GPT-4-turbo were also unable to make binary discriminations between sensible and nonsense phrases, with these models consistently judging nonsensical phrases as making sense. Claude-3-Opus made a substantial improvement in binary discrimination of combinatorial phrases but was still significantly worse than human performance. The TWT can be used to understand and assess the limitations of current LLMs, and potentially improve them. The test also reminds us that caution is warranted in attributing “true” or human-level understanding to LLMs based only on tests that are challenging for humans.

Comparing emotions in ChatGPT answers and human answers to the coding questions on Stack Overflow.

Recent advances in generative Artificial Intelligence (AI) and Natural Language Processing (NLP) have led to the development of Large Language Models (LLMs) and AI-powered chatbots like ChatGPT, which have numerous practical applications. Notably, these models assist programmers with coding queries, debugging, solution suggestions, and providing guidance on software development tasks. Despite known issues with the accuracy of ChatGPT's responses, its comprehensive and articulate language continues to attract frequent use. This indicates potential for ChatGPT to support educators and serve as a virtual tutor for students. To explore this potential, we conducted a comprehensive analysis comparing the emotional content in responses from ChatGPT and human answers to 2000 questions sourced from Stack Overflow (SO). The emotional aspects of the answers were examined to understand how the emotional tone of AI responses compares to that of human responses. Our analysis revealed that ChatGPT's answers are generally more positive compared to human responses. In contrast, human answers often exhibit emotions such as anger and disgust. Significant differences were observed in emotional expressions between ChatGPT and human responses, particularly in the emotions of anger, disgust, and joy. Human responses displayed a broader emotional spectrum compared to ChatGPT, suggesting greater emotional variability among humans. The findings highlight a distinct emotional divergence between ChatGPT and human responses, with ChatGPT exhibiting a more uniformly positive tone and humans displaying a wider range of emotions. This variance underscores the need for further research into the role of emotional content in AI and human interactions, particularly in educational contexts where emotional nuances can impact learning and communication.

Exploring the Utilisation of Generative AI Tools by Undergraduate First-Year Mechanical Engineering Students in Programming Assessments.

Integrating the fundamentals of computer science and programming skills into the undergraduate engineering curriculum has been a primary focus for many educational institutions worldwide. Learning the basics of programming from the beginning of undergraduate engineering education allows students to incorporate such skills into their future work easily. Therefore, an introductory programming course for first-year undergraduate students has been running since 2021 in the Mechanical Engineering Department at University College London intending to teach the fundamentals of Python programming language. However, it is well-known that generative artificial intelligence (Gen AI) tools in higher education are moving so fast that a wait-and-see approach cannot be taken. These applications have received much global attention from academics on their impact and proper use within the teaching-learning process. This paper investigates first-year undergraduate mechanical engineering students' use of Gen AI tools in their programming assessment. The results show that 60% of the cohort used tools that helped mainly to check their code, improve their English language, and understand error messages. However, 40% abstained from using any. Based on these findings, recommendations on how Gen AI tools can be utilised by undergraduate students in ways that support their learning and enhance their ability to achieve learning outcomes are made.

Genre‐based writing in the German classroom in the age of generative AI

AbstractGenre, defined as a “staged goal‐oriented social process” (Martin, 2009), has long stood as a key principle in second‐language writing instruction. This article presents the principles underpinning genre theory as it relates to the language curriculum and the five phases of genre‐based L2 writing pedagogy. This pedagogy provides the foundation for a case study in which students in an intermediate, fifth‐semester German class at a small liberal arts college received instruction in the film review genre. Student products written prior to classroom work with film reviews are compared with those written after students had gained an awareness and understanding of the film review genre, revealing the positive effects of genre‐based instruction on student writing. In light of the future impact of generative artificial intelligence (AI) on writing pedagogy, the final section addresses how ChatGPT and similar platforms can potentially enhance genre‐based pedagogies. The article suggests ways in which generative AI can be incorporated into each phase of genre‐based second language writing instruction. As technologies such as ChatGPT become established in language classrooms, it is clear that the writing process must share the stage with written products, both in instruction and assessments.

Artificial Intelligence-Driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine.

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims to dissect the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic strategies can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. The main issues are ethical considerations related to patient privacy, biases, and the lack of reliability and generalizability. Furthermore, there is a need for high-quality real-world validation and the development of standardized protocols and policies to guide the implementation of these technologies in diverse clinical settings.

Artificial Intelligence (AI) Coaching: Redefining People Development and Organizational Performance

The confluence of organizational coaching and artificial intelligence (AI), specifically generative AI is set to permanently change how employees are developed and supported. This could result in significant benefits to individual and organizational learning, wellness, and performance. The benefits of organizational coaching are well documented through rigorous research, while the efficacy of AI coaching shows early signs of promise. The challenge now is how to optimally leverage AI in organizational coaching for exponential gains. In this article, I will explore the current and potential future applications of AI coaching in organizations, what we must be cognizant of, and which concerns are overhyped.

Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions

Background: ChatGPT is a generative artificial intelligence that has numerous professional applications. Applications in medical education are currently being explored. ChatGPT 4.0 performance on image-based dermatology boards-style practice questions has not been assessed. Objective: The objective of this study was to determine the accuracy with which ChatGPT can answer dermatology boards examination practice questions. Methods: 150 multiple-choice questions from the popular question bank DermQbank were inputted into ChatGPT. Of these, 83 were text-only questions and 67 had associated images. These same questions were inputted into ChatGPT again in July 2024. An additional 150 questions were inputted for a total of 300 different questions where 169 were text-only and 133 had associated images. Results: Of the aggregate 300 question data, ChatGPT answered 232 questions correctly (77.3%). ChatGPT performed significantly better with text-only questions than with questions that included images (85.2% (144/169) vs 67.7% (90/133), P<.001). Of image-based questions, ChatGPT performed better with clinical image questions than with dermatopathology questions (69.0% (78/133) vs. 58.8% (10/17), P=.40), but this difference was not statistically significant partially due to the sample size of the dermatopathology questions. Compared to post-graduate year 4 (PGY-4) residents, ChatGPT performed above the 46th percentile. ChatGPT agreed with the answer choice picked by the majority of question bank users 75.3% of the time. Multivariable regression demonstrated that significant predictive variables for ChatGPT answering a question correctly included the percent of dermatology trainees who answered a question correctly and whether the question was text-based (P<.001and P=.004, respectively). Conclusions: ChatGPT answered 77.3% of dermatology board examination practice questions correctly, performing above the 46th percentile of PGY-4 question bank users. If using ChatGPT as a study resource for dermatology board examination preparation, residents should be judicious with exactly how they employ ChatGPT to avoid learning incorrect information.

AI-Driven Innovations in Earthquake Risk Mitigation: A Future-Focused Perspective

This study explores the transformative potential of artificial intelligence (AI) in revolutionizing earthquake risk mitigation across six key areas. Unlike traditional approaches, this paper examines how AI-driven innovations can uniquely enhance early warning systems, enabling real-time structural health monitoring, and providing dynamic, multi-hazard risk assessments that seamlessly integrate seismic data with other natural hazards such as tsunamis and landslides. It introduces groundbreaking applications of AI in earthquake-resilient design, where generative design algorithms and predictive analytics create structures that optimally balance safety, cost, and sustainability. The study also presents a novel discussion on the ethical implications of AI in this domain, stressing the critical need for transparency, accountability, and bias mitigation. Looking forward, the manuscript envisions the development of advanced AI platforms capable of delivering real-time, personalized risk assessments, immersive public training programs, and collaborative design tools that adapt to evolving seismic data. These innovations promise not only to significantly enhance current earthquake preparedness but also to pave the way toward a future where the societal impact of earthquakes is drastically reduced. This work underscores the potential of AI’s role in shaping a safer, more resilient future, emphasizing the importance of continued innovation, ethical governance, and collaborative efforts.

The use of generative artificial intelligence (AI) in teaching and assessment of postgraduate students in pathology and microbiology

The use of generative artificial intelligence (AI) in teaching and assessment of postgraduate students in pathology and microbiology

Optimizing solar photovoltaic farm-based cogeneration systems with artificial intelligence (AI) and Cascade compressed air energy storage for stable power generation and peak shaving: A Japan-focused case study

This study proposes a novel solar cogeneration system that integrates compressed air energy storage units (CAES) and gas turbines (GT) with a solar farm consisting of photovoltaic panels. The primary objective of this research is to address the instability of solar energy production and help during peak energy consumption by utilizing CAES. The proposed system is modeled using EES software, and its performance is optimized using advanced artificial intelligence (AI) methods, including artificial neural networks and intelligent algorithms. The analysis identifies five critical decision variables that significantly impact system performance: the number of photovoltaic panels, CAES pressure ratio, CAES inlet pressure, gas turbine efficiency, and compressor efficiency. The results demonstrate that the optimized solar cogeneration system can achieve exergy efficiency of 36.44 % and a cost rate of 13.76 $/hour. The exergy analysis of the system indicates that the most significant destruction is related to the solar farm, gas turbine, and compressors. Furthermore, this study investigates the effect of weather in eight Japanese cities on system performance, considering two operating modes: with and without using system electricity (PV mode). The results show that the proposed solar cogeneration system has significant potential for clean electricity generation and CAES applications to overcome the instability of the solar system and help during peak energy consumption throughout the year. The study's findings highlight the attractive potential of integrating CAES and AI technologies in solar photovoltaic systems for stable and efficient power generation in Japan.

Human vs. AI: can ChatGPT improve tourism product descriptions?

ABSTRACT Generative artificial intelligence (AI) is a transformative technology with the potential to impact tourism in diverse ways. One important possible application of AI entails tourism suppliers employing AI-powered chatbots, such as ChatGPT, to generate marketing content. It is unclear, however, how such marketing content compares to content created by humans. Consequently, this study’s purpose was to compare perceptions of actual tour descriptions from TripAdvisor with ChatGPT-optimised versions of those descriptions. An experiment was conducted in which subjects (N = 579) were randomly provided either the actual description or a ChatGPT-optimised description of Panamanian indigenous cultural day-tours. Subjects then completed a questionnaire assessing variables including the text’s perceived tone, the information’s perceived usefulness and credibility, and the subjects’ desire to take the tour. Findings revealed ChatGPT improved the product descriptions along various dimensions, but did not significantly increase desire to take the tours. The findings make an important contribution to the incipient empirical tourism literature examining ChatGPT, and yield numerous theoretical and practical implications, which are discussed.

Implementing ChatGPT to Support Teachers and Promote Learning

ChatGPT, a type of generative artificial intelligence, recently became the fastest-growing app of all time, with over 100 million users. The increasing use of this chatbot has led educators to fear that its use will cause various problems, including cheating. However, using ChatGPT can lead to desirable outcomes. Teachers can use this technology to make their tasks easier to complete and to encourage students to think critically. This article focuses on how educators can implement ChatGPT so that students benefit. Three strategies are included to achieve this goal. The first is to prohibit the use of ChatGPT when its use will likely impede learning. The second focuses on using this chatbot to encourage students to learn and think critically, and the third involves the importance of implementing an ethical approach.

Results and implications for generative AI in a large introductory biomedical and health informatics course

Generative artificial intelligence (AI) systems have performed well at many biomedical tasks, but few studies have assessed their performance directly compared to students in higher-education courses. We compared student knowledge-assessment scores with prompting of 6 large-language model (LLM) systems as they would be used by typical students in a large online introductory course in biomedical and health informatics that is taken by graduate, continuing education, and medical students. The state-of-the-art LLM systems were prompted to answer multiple-choice questions (MCQs) and final exam questions. We compared the scores for 139 students (30 graduate students, 85 continuing education students, and 24 medical students) to the LLM systems. All of the LLMs scored between the 50th and 75th percentiles of students for MCQ and final exam questions. The performance of LLMs raises questions about student assessment in higher education, especially in courses that are knowledge-based and online.

Durably reducing conspiracy beliefs through dialogues with AI.

Conspiracy theory beliefs are notoriously persistent. Influential hypotheses propose that they fulfill important psychological needs, thus resisting counterevidence. Yet previous failures in correcting conspiracy beliefs may be due to counterevidence being insufficiently compelling and tailored. To evaluate this possibility, we leveraged developments in generative artificial intelligence and engaged 2190 conspiracy believers in personalized evidence-based dialogues with GPT-4 Turbo. The intervention reduced conspiracy belief by ~20%. The effect remained 2 months later, generalized across a wide range of conspiracy theories, and occurred even among participants with deeply entrenched beliefs. Although the dialogues focused on a single conspiracy, they nonetheless diminished belief in unrelated conspiracies and shifted conspiracy-related behavioral intentions. These findings suggest that many conspiracy theory believers can revise their views if presented with sufficiently compelling evidence.

Managing Currency Risk in Global Supply Chains: Resilience Strategies

Currency risk was a significant difficulty for firms involved in international commerce in the current global economy. The profitability and competitiveness of organisations with supply chains that span across borders were greatly affected by fluctuations in exchange rates. This introduction examined the intricacies of managing currency risk in global supply chains, investigating the most recent solutions used by organisations to strengthen their operations. Over the last 10 years, there has been an extraordinary increase in globalisation, characterised by more market integration and increased international trade. Nevertheless, globalisation brings with it not just benefits but also inherent concerns, with currency volatility being the most prominent among them. According to projections by Grand View Research, Inc., the global currency exchange market was expected to reach a value of $28.8 trillion by 2025. This highlights the significant currency risk that firms throughout the globe were facing. The emergence of the COVID-19 pandemic emphasised the need to effectively handle currency risk in worldwide supply networks. Amidst trade interruptions and shifting currency rates, economies faced increased uncertainty and volatility, which in turn affected enterprises. Research conducted by the Foreign Monetary Fund had shown that there was a significant increase of 59% in currency volatility during the early phase of the epidemic. This has worsened the difficulties faced by companies that heavily depend on foreign commerce. In light of this situation, companies were obligated to reevaluate their methods for managing currency risk to strengthen their ability to withstand challenges and reduce the possibility of financial losses. Conventional methods such as forward contracts and currency options, while somewhat successful, may not be enough to handle the intricacies of contemporary supply chains. As a result, organisations are using novel methods and technology to effectively manage currency risk. Recent developments in Financial Technology have created new opportunities for innovative currency risk management strategies. Blockchain technology had the potential to provide transparent and secure cross-border transactions, which helped decrease the risk of currency exposure. Moreover, the advent of Artificial Intelligence and Machine Learning algorithms enabled organisations to analyse extensive information and forecast currency fluctuations with enhanced precision. Collaborative alliances within supply chains provided a strategic opportunity to reduce currency risk. Companies may enhance risk mitigation and improve their ability to adapt to market dynamics by developing stronger connections with suppliers and consumers. This can be achieved via the exploration of collaborative hedging methods and the sharing of risk burdens. Nevertheless, effectively managing currency risk in international supply chains requires the ability to quickly adapt, come up with new ideas, and work together. This summary summarised important findings from the following conversation, emphasising the need for proactive risk management techniques in protecting against the uncertainties of the global economy. Keywords: Currency risk management, Global supply chains, Exchange rate volatility, International commerce, Financial technology (Fintech).

China’s Legal Practices Concerning Challenges of Artificial General Intelligence

The artificial general intelligence (AGI) industry, represented by ChatGPT, has impacted social order during its development, and also brought various risks and challenges, such as ethical concerns in science and technology, attribution of liability, intellectual property monopolies, data security, and algorithm manipulation. The development of AI is currently facing a crisis of trust. Therefore, the governance of the AGI industry must be prioritized, and the opportunity for the implementation of the Interim Administrative Measures for Generative Artificial Intelligence Services should be taken. It is necessary to enhance the norms for the supervision and management of scientific and technological ethics within the framework of the rule of law. Additionally, it is also essential to continuously improve the regulatory system for liability, balance the dual values of fair competition and innovation encouragement, and strengthen data-security protection systems in the field of AI. All of these will enable coordinated governance across multiple domains, stakeholders, systems, and tools.

Role of Generative Artificial Intelligent In Indian Banking Sector: Challenges & Opportunities

Generative Al offers a considerable improvement over the traditional Al models which are mostly employed for classification or prediction tasks. In addition to supporting data augmentation in situations where data is scarce, it exhibits creativity in producing original text, images, or music. It also enables personalization to produce tailored content, greatly improving user experience and opening up new avenues for increased productivity for business and technology teams. Gen AI is completely changing the banking industry by producing content, mimicking human behavior, enhancing client interactions, offering real-time support, and increasing operational efficiency. Through deep learning algorithms, it improves client experiences, expedites procedures, and strengthens risk management. By putting ethical standards into place, encouraging openness, and guaranteeing data security and privacy, the sector can realize this potential. Banks are using generative artificial intelligence (AI) to solve persistent problems like fraud detection, fraud prevention, customer targeting, data summarization, enhancing conversational bots, and product integration. This technology can supply digital items through digital channels, increase data summarization, and improve user experience.