Advances In Artificial Intelligence Research Articles

Figure-Based Approach in Creating ChatGPT-4o-Resistant Multiple-Choice Questions for Introductory Biology Courses: An Instructional Guide

Online assessment is an essential part of online education, and if conducted properly, has been found to effectively gauge student learning. Generally, text-based questions have been the cornerstone of online assessment. Recently, however, the emergence of generative artificial intelligence has added a significant challenge to the integrity of online assessments. In particular, it has been reported that large language models, like ChatGPT-4o, show high performance on text-based questions. In comparison, ChatGPT-4o exhibited significantly reduced performance on figure-based questions in our study. In an effort to counter the recent encroachment of ChatGPT-4o into online assessment, we propose a step-by-step instructional guide for a method in creating figure-based multiple-choice questions that are resistant to ChatGPT-4o. This involves generation of a ChatGPT-4o-resistant figure, writing the question text based on it, and evaluating the final question on ChatGPT-4o. If successfully created, ChatGPT-4o response could be subject to random guessing. Our results showcase four representative examples for introductory biology courses and illustrate a systematic approach to compose questions based on qualitative analysis of ChatGPT-4o responses. In combination with other assessment methods, our method aims to serve as a tool to alleviate the current challenge that educators face for online assessments.

Artificial intelligence and pharmacy education: a survey to assess the knowledge, application, and perspective of B. Pharm. students from India

This study aimed to understand the knowledge, application, and perspective of B. Pharm students regarding artificial intelligence (AI) tools. This study used a quantitative cross-sectional observational design, and data were collected using a questionnaire. The questionnaire was distributed among B. Pharm students in India, and 305 participants were included in the study. The results of the study showed that the majority of participants were female, and the mean age of the participants was 20.51 years. The study also found that the participants had limited knowledge of AI and its applications in pharmacy education. Only 34.6% of participants attempted to integrate AI tools into their academic activities. The most commonly used integrated tools were ChatGPT (42.2%) and Google Bard (Gemini) (30.8%). The survey strongly indicated the inclination of the pharmacy student community towards generative AI tools for academic purposes. However, the participants showed positive attitudes towards the use of AI in pharmacy education and believed that it would improve the quality of their education. A significant majority (82.3%) of respondents express interest in attending workshops or training sessions to enhance their knowledge about AI, The study concluded that there is a need for educators and technology creators to develop strategies to integrate AI into pharmacy education to improve the learning experience of students.

Ethical and Regulatory Challenges of Generative Artificial Intelligence in Healthcare: A Chinese Perspective.

To provide practical insights that delve into the ethical issues and regulatory implications of generative artificial intelligence (GenAI) in healthcare. Ethical Challenges and Regulatory Impact in China is used as an example. Despite China's efforts to strike a delicate balance between protecting public welfare and promoting technological advancement, numerous unresolved issues persist in the practical integration of generative artificial intelligence into healthcare settings. Key issues such as data application, privacy protection, cost-effectiveness and regulatory remain areas of ambiguity that require clarification. Stringent ethical guidelines, data privacy protection measures and continuous supervision and evaluation of artificial intelligence decisions will help enhance the expected benefits of GenAI in healthcare. The potential use of GenAI in healthcare has garnered widespread attention, emerging as a significant global research topic. However, its application in this domain presents substantial ethical and regulatory challenges. Compared to other fields, GenAI's role in healthcare is more sensitive and complex, necessitating an urgent assessment of its ethical implications for future development and deployment. Challenges and ethical considerations are particularly pronounced in developing countries with limited healthcare resources.

From Backlogs to Bots: Generative AI's Impact on Agile Role Evolution

ABSTRACTThis position paper investigates the transformative impact of generative artificial intelligence (GenAI) on Agile roles. Focusing on the product owner, developer, and scrum master, we analyze how GenAI redefines traditional tasks, encouraging a shift towards more strategic and creative functions. Through practical experience, we illustrate AI's role in enhancing Agile processes, its practices and emphasize the need for Agile practitioners to integrate AI understanding. These results highlight the balance between GenAI's efficiencies and Agile's human‐centric principles, proposing research directions for AI‐enriched Agile practices that promise to drive innovation and maintain the agility in a technologically progressive era.

AI-integrated network for RNA complex structure and dynamic prediction.

RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures. With their roots in the graph theory, these methods have a long history of providing insight into the static and dynamic properties of RNA molecules. These approaches have been effective in identifying functional binding sites and analyzing the dynamic behavior of RNA complexes. Recently, the advent of artificial intelligence (AI) has brought transformative changes to the field. These technologies have been increasingly applied to studying RNA complex structures, providing new avenues for understanding the complex interactions within RNA complexes. By integrating AI with traditional network analysis methods, researchers can build more accurate models of RNA complex structures, predict their dynamic behaviors, and even design RNA-based inhibitors. In this review, we introduce the integration of network-based methodologies with AI techniques to enhance the understanding of RNA complex structures. We examine how these advanced computational tools can be used to model and analyze the detailed interface information and dynamic behaviors of RNA molecules. Additionally, we explore the potential future directions of how AI-integrated networks can aid in the modeling and analyzing RNA complex structures.

Navigating the Future of Pedagogy: The Integration of AI Tools in Developing Educational Assessment Rubrics

ABSTRACTThe advent of artificial intelligence (AI) technologies heralds a transformative era in education. This study investigates the integration of AI tools in developing educational assessment rubrics within the ‘Curriculum Design Development and Evaluation’ course at the University of A Coruña during the 2023–2024 academic year. Employing an action–research methodological approach, 27 comprehensive evaluation rubrics were developed using AI tools like ChatGPT. The findings highlight AI's ability to enhance rubric precision, efficiency and alignment with curricular objectives, thus facilitating personalised learning experiences. This research underscores AI's transformative potential in education, advocating for broader adoption and the necessity for educators to acquire AI proficiency. The study demonstrates how AI tools can streamline the rubric development process, making it more accurate and time efficient while also ensuring the inclusion of essential curricular elements. Through this approach, AI can support innovative assessment strategies that are both effective and adaptable to diverse educational contexts.

Towards Achilles Tendon Injury Prevention in Athletes with Structural MRI Biomarkers: A Machine Learning Approach.

Recent advancements in artificial intelligence have proven their effectiveness in orthopaedic settings, especially in tasks like medical image analysis. This study compares human musculoskeletal radiologists to artificial intelligence in a novel, detailed, short, and cost-effective examination of Achilles tendon magnetic resonance images to uncover potential disparities in their reasoning approaches. Aiming to identify relationships between the structured assessment of the Achilles tendon and its function that could support injury prevention. We examined 72 athletes to investigate the link between Achilles tendon structure, as visualised in magnetic resonance images using a precise T2*-weighted gradient echo sequence with very short echo times, and its functional attributes. The acquired data were analysed using advanced artificial intelligence techniques and reviewed by radiologists. Additionally, we conducted statistical assessments to explore relationships with functional studies in four meaningful groups: dynamic strength, range of motion, muscle torque and stabilography. The results show notable linear or non-linear relationships between functional indicators and structural alterations (maximal obtained Spearman correlation coefficients ranged from 0.3 to 0.36 for radiological assessment and from 0.33 to 0.49 for artificial intelligence assessment, while maximal normalised mutual information ranged from 0.52 to 0.57 for radiological assessment and from 0.42 to 0.6 for artificial intelligence assessment). Moreover, when artificial intelligence-based magnetic resonance assessment was utilised as an input, the associations consistently proved more robust, or the count of significant relationships surpassed that derived from radiological assessment. Ultimately, utilising only structural parameters as inputs enabled us to explain up to 59% of the variance within specific functional groups. This analysis revealed that structural parameters influence four key functional aspects related to the Achilles tendon. Furthermore, we found that relying solely on subjective radiologist opinions limited our ability to reason effectively, in contrast to the structured artificial intelligence assessment. Cross-sectional studies.

What drives economics students to use generative artificial intelligence?

The increasing integration of Artificial Intelligence (AI) into education requires studying the motives for its use among students. This study aims to identify the key motivations for economics students to use AI and compare these motivations by grade level and gender. The study examines satisfaction with the use of AI and analyzes the number of AI tools used. An anonymous empirical study was conducted among 264 students from the Faculty of Economics at Taras Shevchenko National University of Kyiv, Ukraine. Data analysis included descriptive statistical methods, non-parametric statistical methods, and exploratory factor analysis. The study found that students’ main motivations for using AI are the automation of routine tasks (34.2%) and the need to save time (21.5%), while 18.7% use AI to compensate for lack of experience. Among Bachelor’s students, motivations such as automating routine tasks and saving time increased from 53% to 58% over the course of their studies, while lack of experience decreased from 22% to 15%. In contrast, Master’s students showed a decrease in routine automation (from 36% to 28%) but an increase in the need to compensate for lack of experience (from 15% to 28%) and to save time (from 18% to 25%). In terms of gender, men are more likely to use AI for learning and personal development, while women are slightly more likely to use AI for work. More than 38% of respondents say they need to use at least 2 AIs to achieve their goals. Acknowledgment This publication is based upon work from 24-PKVV-UM-002, ‘Strengthening the Resilience of Universities: Czech-Ukrainian Partnership for Digital Education, Research Cooperation, and Diversity Management,’ supported by the Czech Development Agency and the Ministry of Foreign Affairs under the initiative ‘Capacity Building of Public Universities in Ukraine 2024.’

Navigating AI Transformation: Human Resource Development Strategies for Corporate Learning

Artificial Intelligence (AI) has revolutionised knowledge work through generative AI like ChatGPT, impacting coordination, creativity, and problem-solving. As AI adoption accelerates, understanding its implications for Human Resource Development (HRD) becomes crucial. This paper introduces a conceptual framework for HRD strategies in AI transformation, addressing the dearth of research in this area. Utilising a case study approach, we distinguish five strategies: Acceleration, Culture-driven Transformation, Data-Driven Agility, Personalized Learning, and Immersive, emotional Learning at the workplace. Each strategy caters to distinct organisational needs. Challenges include balancing rapid upskilling, managing cultural shifts, ensuring data quality, promoting self-directed learning, and implementing immersive technologies. The framework offers insights for organisations navigating AI-induced transformations in HRD practices. Further research and refinement are essential as AI technology evolves.

Assessing the Determinants of Generative AI Integration: A Study on Google's Gemini Adoption Among Thai University Graduate Students

The advent of Generative Artificial Intelligence (Gen AI) in educational tools like Google's Gemini represents a significant evolution. These technologies aim to revolutionize learning by creating human-like content in various formats, enhancing personalized and engaging educational experiences. This research investigates the factors influencing Thai graduate students' adoption of Google's Gemini, employing a quantitative approach with data from 826 respondents via closed-ended questionnaires through convenience sampling. The analysis included demographic, socio-economic, and media platform preference variables. Using binary logistic regression, the study evaluated how these factors affect graduate students' intentions to use Google’s Gemini in Thailand. Key determinants identified include age, education level, occupational status, and preferences for social media platforms like Facebook and YouTube. The study provides insights into the factors influencing Thai graduate students' use of Google's Gemini, useful for stakeholders in digital literacy and technology adoption. It stresses the importance of tailoring digital environments to learners' diverse needs, influenced by demographics and social media, and offers strategies for enhancing digital literacy and engagement. The research also sets the stage for further academic inquiry into educational technology, advocating for an interdisciplinary approach to better understand technology's educational impact.

Beyond boundaries: exploring a generative artificial intelligence assignment in graduate, online science courses.

Generative artificial intelligence (GAI) offers increased accessibility and personalized learning, though the potential for inaccuracies, biases, and unethical use is concerning. We present a newly developed research paper assignment that required students to utilize GAI. The assignment was implemented within three online, asynchronous graduate courses for medical laboratory sciences. Student learning was assessed using a rubric, which rated students' effective integration and evaluation of GAI-generated content against peer-reviewed research articles, thus demonstrating their critical thinking and synthesis skills, among other metrics. Overall rubric scores were high, suggesting that learning outcomes were met. After field testing, we administered a 16-item survey about GAI utilization, contribution to learning, and ethical concerns. Data (n = 32) were analyzed, and free-response answers were thematically coded. While 93.8% of respondents found the GAI-generated content to be "very good" or "excellent," 28.1% found inaccuracies, and 68.8% "strongly agreed" or "agreed" that GAI should be allowed to be used as a tool to complete academic assignments. Interestingly, however, only 28.1% "strongly agreed" or "agreed" that GAI may be used for assignments if not explicitly authorized by the instructor. Though GAI allowed for more efficient completion of the project and better understanding of the topic, students noted concerns about academic integrity and the lack of citations in GAI responses. The assignment can easily be modified for different learning preferences and course environments. Raising awareness among students and faculty about the ethical use and limitations of GAI is crucial in today's evolving pedagogical landscape.

Open Science at the Generative AI Turn: An Exploratory Analysis of Challenges and Opportunities

Abstract Technology influences Open Science (OS) practices, because conducting science in transparent, accessible, and participatory ways requires tools/platforms for collaboration and sharing results. Due to this relationship, characteristics of employed technologies directly impact OS objectives. Generative Artificial Intelligence (GenAI) is increasingly used by researchers for tasks such as text refining, code generation/editing, reviewing literature, data curation/analysis. Nevertheless, concerns about openness, transparency and bias suggest that GenAI may benefit from greater engagement with OS. GenAI promises substantial efficiency gains but is currently fraught with limitations that could negatively impact core OS values such as fairness, transparency and integrity, and harm various social actors. In this paper, we explore possible positive and negative impacts of GenAI on OS. We use the taxonomy within the “UNESCO Recommendation on Open Science” to systematically explore the intersection of GenAI and OS. We conclude that using GenAI could advance key OS objectives by broadening meaningful access to knowledge, enabling efficient use of infrastructure, improving engagement of societal actors, and enhancing dialogue among knowledge systems. However, due to GenAI limitations, it could also compromise the integrity, equity, reproducibility, and reliability of research. Hence, sufficient checks, validation and critical assessments are essential when incorporating GenAI into research workflows. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00337

Human-Machine Engagement (HME): Conceptualization, Typology of Forms, Antecedents, and Consequences

Artificial intelligence (AI) applications in customer-facing settings are growing rapidly. The general shift toward robot- and AI-powered services prompts a reshaping of customer engagement, bringing machines into engagement conceptualizations. In this paper, we build on service research around engagement and AI, incorporating computer science, and socio-technical systems perspective to conceptualize human-machine engagement (HME), offering a typology and nomological network of antecedents and consequences. Through three empirical studies, we develop a typology of four distinct forms of HME (informative, experimenting, praising, apprehensive), which differ in valence and intensity, underpinned by both emotional (excitement) and cognitive (concern, advocacy) drivers. We offer empirical evidence which reveals how these HME forms lead to different cognitive and personality-related outcomes for other users (perceived value of HME, perceived risk, affinity with HME) and service providers (willingness to implement in services, perceived value of HME). We also reveal how outcomes for service providers vary with the presence and absence of competitor pressure. Our findings broaden the scope of engagement research to include non-human actors and suggest both strategic and tactical guidance to service providers currently using and/or seeking to use generative AI (GenAI) in services alongside an agenda to direct future studies on HME.

Developing a computer-based tutor utilizing Generative Artificial Intelligence (GAI) and Retrieval-Augmented Generation (RAG)

Developing a computer-based tutor utilizing Generative Artificial Intelligence (GAI) and Retrieval-Augmented Generation (RAG)

Generative artificial intelligence (GAI): From large language models (LLMs) to multimodal applications towards fine tuning of models, implications, investigations

This research explores the transformative integration of artificial intelligence (AI), robotics, and language models, with a particular emphasis on the PaLM-E model. The exploration aims to assess PaLM-E’s decision-making processes and adaptability across various robotic environments, demonstrating its capacity to convert textual prompts into very precise robotic actions. In addition, the research investigates Parameter-Efficient Fine-Tuning (PEFT) techniques, such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), providing a historical overview of PEFT and highlighting their significance in enhancing task performance while reducing the number of trainable parameters. The broader scope of Generative AI is examined through an analysis of influential models like GPT-3, GPT-4, Copilot, Bard, LLaMA, Stable Diffusion, Midjourney, and DALL-E. These models’ abilities to process natural language prompts and generate a wide range of outputs are thoroughly investigated. The research traces the historical evolution of AI, from its roots in science fiction to its practical applications today, with a focus on the rise of Generative AI in the 21st century. Furthermore, the research delves into the various modalities of Generative AI, covering applications in text, code, images, and more, and assesses their real-world impact on robotics, planning, and business intelligence. The implications of synthetic data generation for business analytics are also explored. The research inspects within both software and hardware landscapes, comparing local deployment on consumer-grade hardware along with cloud-based services, and underscores the benefits of local model deployment in terms of privacy protection, intellectual property security, and censorship resistance. Ethical considerations are central to this research, addressing concerns related to privacy, security, societal impact, biases, and misinformation. The research proposes ethical guidelines for the responsible development and deployment of AI technologies. Ultimately, this work reveals the deep interconnections between vision, language, and robotics, pushing the boundaries of AI capabilities and providing crucial insights for future AI model development and technological innovation. These findings are intended to guide the field through the emerging challenges of the rapidly evolving Generative AI landscape.

Redefining educational paradigms: Integrating generative AI into society 5.0 for sustainable learning outcomes

The digital era has ushered in significant advancements in Generative Artificial Intelligence (GAI), particularly through Generative Models and Large Language Models (LLMs) like ChatGPT, revolutionizing educational paradigms. This research, set against the backdrop of Society 5.0 and aimed at sustainable educational practices, utilizes qualitative analysis to explore the impact of Generative AI in various learning environments. It highlights the potential of LLMs to offer personalized learning experiences, democratize education, and enhance global educational outcomes. The study finds that Generative AI revitalizes learning methodologies and supports educational systems’ sustainability by catering to diverse learning needs and breaking down access barriers. In conclusion, the paper discusses the future educational strategies influenced by Generative AI, emphasizing the need for alignment with Society 5.0’s principles to foster adaptable and sustainable educational inclusion.

Utility of Generative Artificial Intelligence for Patient Care Counseling for Mandibular Fractures.

To determine the readability and accuracy of information regarding mandible fractures generated by Chat Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4o. Patients are increasingly turning to generative artificial intelligence to answer medical queries. To date, the accuracy and readability of responses regarding mandible fractures have not been assessed. Twenty patient questions regarding mandible fractures were developed by querying AlsoAsked (https://alsoasked.com), SearchResponse (https://searchresponse.io), and Answer the Public (https://answerthepublic.com/). Questions were posed to ChatGPT 3.5 and 4o. Readability was assessed by calculating the Flesch Kincaid Reading Ease, Flesch Kincaid Grade Level, number of sentences, and percentage of complex words. Accuracy was assessed by a board-certified facial plastic and reconstructive otolaryngologist using a 5-point Likert Scale. No significant differences were observed between the two versions for readability or accuracy. Readability was above recommended levels for patient education materials. Accuracy was low, and a majority of responses were deemed inappropriate for patient use with multiple inaccuracies and/or missing information. ChatGPT produced responses written at a high level inappropriate for the average patient, in addition to containing several inaccurate statements. Patients and clinicians should be aware of the limitations of generative artificial intelligence when seeking medical information regarding mandible fractures.

Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for portfolio selection

In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization of asset allocation. With the advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, the black-box nature of these tools renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning to align with users’ expected outcomes. This study presents a structured prompt framework specifically designed for stock selection, aiming to provide direct and interpretable stock-selecting tools for investors of various levels. By creating representative scenarios and combining them into different cases for experimentation, we can explore how the construction of prompts influences the responses generated by generative AI tools. Additionally, this paper proposes a novel algorithm that combines the Nonlinear-Activated Beetle Antennae Search strategy with the Egret Swarm Optimization Algorithm (NBESOA) to address the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC), utilizing real stock market data to construct portfolios based on generative AI tools recommendations. Simulation results indicate that, compared to other algorithms, NBESOA prefers optimizing portfolio configurations to achieve the highest Sharpe Ratio with the strictest constraints, bringing the outcomes closer to the portfolio’s efficient frontier.

Readability Metrics in Patient Education: Where Do We Innovate?

The increasing use of digital applications in healthcare has led to a greater need for patient education materials. These materials, often in the form of pamphlets, booklets, and handouts, are designed to supplement physician–patient communication and aim to improve patient outcomes. However, the effectiveness of these materials can be hindered by variations in patient health literacy. Readability, a measure of text comprehension, is a key factor influencing how well patients understand these educational materials. While there has been growing interest in readability assessment in medicine, many studies have demonstrated that digital texts do not frequently meet the recommended sixth-to-eighth grade reading level. The purpose of this opinion article is to review readability from the perspective of studies in pediatric medicine, internal medicine, preventative medicine, and surgery. This article aims to communicate that while readability is important, it tends to not fully capture the complexity of health literacy or effective patient communication. Moreover, a promising avenue to improve readability may be in generative artificial intelligence, as there are currently limited tools with similar effectiveness.

Accuracy of Prospective Assessments of 4 Large Language Model Chatbot Responses to Patient Questions About Emergency Care: Experimental Comparative Study.

Recent surveys indicate that 48% of consumers actively use generative artificial intelligence (AI) for health-related inquiries. Despite widespread adoption and the potential to improve health care access, scant research examines the performance of AI chatbot responses regarding emergency care advice. We assessed the quality of AI chatbot responses to common emergency care questions. We sought to determine qualitative differences in responses from 4 free-access AI chatbots, for 10 different serious and benign emergency conditions. We created 10 emergency care questions that we fed into the free-access versions of ChatGPT 3.5 (OpenAI), Google Bard, Bing AI Chat (Microsoft), and Claude AI (Anthropic) on November 26, 2023. Each response was graded by 5 board-certified emergency medicine (EM) faculty for 8 domains of percentage accuracy, presence of dangerous information, factual accuracy, clarity, completeness, understandability, source reliability, and source relevancy. We determined the correct, complete response to the 10 questions from reputable and scholarly emergency medical references. These were compiled by an EM resident physician. For the readability of the chatbot responses, we used the Flesch-Kincaid Grade Level of each response from readability statistics embedded in Microsoft Word. Differences between chatbots were determined by the chi-square test. Each of the 4 chatbots' responses to the 10 clinical questions were scored across 8 domains by 5 EM faculty, for 400 assessments for each chatbot. Together, the 4 chatbots had the best performance in clarity and understandability (both 85%), intermediate performance in accuracy and completeness (both 50%), and poor performance (10%) for source relevance and reliability (mostly unreported). Chatbots contained dangerous information in 5% to 35% of responses, with no statistical difference between chatbots on this metric (P=.24). ChatGPT, Google Bard, and Claud AI had similar performances across 6 out of 8 domains. Only Bing AI performed better with more identified or relevant sources (40%; the others had 0%-10%). Flesch-Kincaid Reading level was 7.7-8.9 grade for all chatbots, except ChatGPT at 10.8, which were all too advanced for average emergency patients. Responses included both dangerous (eg, starting cardiopulmonary resuscitation with no pulse check) and generally inappropriate advice (eg, loosening the collar to improve breathing without evidence of airway compromise). AI chatbots, though ubiquitous, have significant deficiencies in EM patient advice, despite relatively consistent performance. Information for when to seek urgent or emergent care is frequently incomplete and inaccurate, and patients may be unaware of misinformation. Sources are not generally provided. Patients who use AI to guide health care decisions assume potential risks. AI chatbots for health should be subject to further research, refinement, and regulation. We strongly recommend proper medical consultation to prevent potential adverse outcomes.