Artificial Intelligence Tools Research Articles

Artificial Intelligence Integration in Teacher Education: Navigating Benefits, Challenges, and Transformative Pedagogy

This article explores the potential uses, benefits, and challenges of artificial intelligence (AI) tools for teacher educators and their teacher candidates. It begins with a brief introduction to the topic, followed by a discussion of existing literature concerning the impact of AI on K–12 education; the importance of preparing AI-literate teachers; and specific issues related to AI’s use in teacher education, which includes studies on perceptions about AI utilization in education. The author also examines the challenges of AI implementation in the preparation of teachers; poses critical questions and ethical, pedagogical, and philosophical concerns faculty and students must consider; and provides ideas on using AI tools in courses through exemplar activities. The various activities illustrate the integration of AI- and non-AI-based activities to promote AI literacy among teachers. The goal of these activities is to support candidates’ competency development to become effective teachers while understanding the potential of AI tools for assisting in planning instruction and creating curricula. The exemplars also focus on developing critical thinking, creativity, and collaboration in carrying out pedagogical tasks. The author advocates for embracing AI tools in education cautiously and strategically by recognizing their power to transform teaching and learning for teachers and students.

Utilising Artificial Intelligence (AI) in Vocabulary Learning by EFL Omani Students: The Effect of Age, Gender, and Level of Study

AI tools have enabled FLLs to navigate their journeys of learning languages. This poses several questions, particularly about the most common AI tools used in learning vocabulary and the attitudes of EFL Omani students toward using AI tools in learning English. A mixed-method research design was utilised, and the sampling included 236 respondents studying in the Sultanate of Oman. An SPSS version 29 was employed in analysing the quantitative data, whereas the qualitative data were analysed thematically. The qualitative data revealed that EFL Omani students depended heavily on Google Translation (44%) as the highest AI tool, followed by the Dictionary Application (32%), ChatGPT (22%), Chat Bot (17.40%), and Duolingo (15.70%). In addition, translating the meaning of a new word occupied the highest learning strategy (frequency: 141), followed by learning new vocabulary (frequency: 134), translating the meaning of a full sentence (frequency: 91), and learning the correct pronunciation of strange words (frequency: 90), whereas learning grammar, enhancing writing and reading skills had the lowest frequency. The quantitative data showed that the overall mean is (3.67), which reveals a high frequency of use of AI tools in learning vocabulary. The lowest mean (3.45) is associated with trusting the new vocabulary recommended by AI tools, whereas the positive effectiveness of AI tools has the highest mean (3.92). However, it was found that age, gender, and level of study do not affect EFL Omani students’ use of AI tools to learn vocabulary.

Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.

Urogenital schistosomiasis is considered a Neglected Tropical Disease (NTD) by the World Health Organization (WHO). It is estimated to affect 150 million people worldwide, with a high relevance in resource-poor settings of the African continent. The gold-standard diagnosis is still direct observation of Schistosoma haematobium eggs in urine samples by optical microscopy. Novel diagnostic techniques based on digital image analysis by Artificial Intelligence (AI) tools are a suitable alternative for schistosomiasis diagnosis. Digital images of 24 urine sediment samples were acquired in non-endemic settings. S. haematobium eggs were manually labeled in digital images by laboratory professionals and used for training YOLOv5 and YOLOv8 models, which would achieve automatic detection and localization of the eggs. Urine sediment images were also employed to perform binary classification of images to detect erythrocytes/leukocytes with the MobileNetv3Large, EfficientNetv2, and NasNetLarge models. A robotized microscope system was employed to automatically move the slide through the X-Y axis and to auto-focus the sample. A total number of 1189 labels were annotated in 1017 digital images from urine sediment samples. YOLOv5x training demonstrated a 99.3% precision, 99.4% recall, 99.3% F-score, and 99.4% mAP0.5 for S. haematobium detection. NasNetLarge has an 85.6% accuracy for erythrocyte/leukocyte detection with the test dataset. Convolutional neural network training and comparison demonstrated that YOLOv5x for the detection of eggs and NasNetLarge for the binary image classification to detect erythrocytes/leukocytes were the best options for our digital image database. The development of low-cost novel diagnostic techniques based on the detection and identification of S. haematobium eggs in urine by AI tools would be a suitable alternative to conventional microscopy in non-endemic settings. This technical proof-of-principle study allows laying the basis for improving the system, and optimizing its implementation in the laboratories.

Predictive Model to Evaluate University Students' Perception and Attitude Towards Artificial Intelligence

Artificial Intelligence is emerging as a transformative tool impacting various industries, including education As Artificial Intelligence continues to develop and gain prominence in classrooms, understanding how students perceive this integration and how it affects their educational experience becomes crucial. The aim of this research was to develop a model to predict the perception of students at Bolívar State University regarding the use and potentialities of Artificial Intelligence in the educational field. The methodology employed a factorial analysis, which represents the relationships among a set of variables. From this, a logistic regression was performed, generating an equation to identify predictors that allowed understanding student behavior based on specific characteristics such as attitude, perception, and satisfaction. As a technique for information gathering, a questionnaire composed of 25 items on a Likert scale was used, statistically validated with a Cronbach's alpha value of 0.925. The results of the model show that all covariates, except "Insecurity and fear of using artificial intelligence tools", are significant (p < 0.001). This suggests that the remaining variables are related to the dependent variable "Positive Perception of the Usefulness of Artificial Intelligence in Learning". It is concluded that students have limited knowledge about Artificial Intelligence, and this may cause them to have unrealistic expectations. Training can help students learn about AI and how to use it effectively and ethically. Received: 9 May 2024 / Accepted: 12 September 2024 / Published: 05 November 2024

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.

Artificial intelligence tools trained on human-labeled data reflect human biases: a case study in a large clinical consecutive knee osteoarthritis cohort

Humans have been shown to have biases when reading medical images, raising questions about whether humans are uniform in their disease gradings. Artificial intelligence (AI) tools trained on human-labeled data may have inherent human non-uniformity. In this study, we used a radiographic knee osteoarthritis external validation dataset of 50 patients and a six-year retrospective consecutive clinical cohort of 8,273 patients. An FDA-approved and CE-marked AI tool was tested for potential non-uniformity in Kellgren-Lawrence grades between the right and left sides of the images. We flipped the images horizontally so that a left knee looked like a right knee and vice versa. According to human review, the AI tool showed non-uniformity with 20–22% disagreements on the external validation dataset and 13.6% on the cohort. However, we found no evidence of a significant difference in the accuracy compared to senior radiologists on the external validation dataset, or age bias or sex bias on the cohort. AI non-uniformity can boost the evaluated performance against humans, but image areas with inferior performance should be investigated.

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.’

SMART DENGUE CONTROL

This research presents a novel approach to combating dengue by integrating Biological Control methods with Artificial Intelligence (AI). The objective of the study is to reduce the Aedes aegypti mosquito population and decrease dengue transmission through sustainable strategies. A comprehensive research methodology was used, involving extensive literature review, case studies, and data analysis to identify the main factors driving the high incidence of dengue in Brazil. The research applied AI tools to analyze epidemiological, meteorological, and environmental data, allowing for early outbreak predictions and targeted interventions. AI-enhanced surveillance systems were also employed to monitor mosquito populations and identify breeding sites more accurately. These strategies were combined with biological control methods, such as using natural predators and genetically modified mosquitoes, to manage mosquito populations effectively. The study demonstrate that the integration of AI and biological control can significantly improve the efficiency, accuracy, and sustainability of dengue control measures. The study concludes that this data-driven approach, when adapted to local contexts, can reduce reliance on chemical pesticides and offer a more resilient framework for long-term dengue management.

The Role of Principal Leadership Management as a Driving Force in Implementing the Independent Curriculum Using AI

In the millennial era, characterized by constant change and progress, schools that thrive possess specific qualities and characteristics. Education not only functions as a place to transfer knowledge but also as a place to develop students' skills, character, and critical thinking abilities. In this increasingly complex world, educational institutions must adapt curricula and teaching methods that are relevant and able to meet students' diverse needs. Schools that innovate and provide captivating learning experiences will be better able to attract the attention of students and parents. In addition, educational institutions must realize the importance of building an identity and characteristics that distinguish them from other schools. This includes developing superior programs, adequate facilities, and a positive and supportive learning environment. This study investigates the role of principal leadership in implementing the Merdeka Curriculum while integrating artificial intelligence (AI) in a kindergarten/early childhood education environment. Through a qualitative approach, this study explores the experiences of a principal and staff at TK IT Tunas Mulia. Findings indicate that principals play a critical role in fostering a culture of innovation and adaptation to AI, providing the necessary training and resources for teachers to utilize AI tools effectively, and collaborating with stakeholders to ensure successful implementation. However, the study has identified challenges such as limited teacher proficiency in AI and the need for more comprehensive AI-integrated curriculum materials. While principal leadership plays a critical role, the study concludes that further support and professional development are necessary to fully harness the potential of AI in improving early childhood education.

Perceptions of Artificial Intelligence and ChatGPT by Speech-Language Pathologists and Students.

This project explores the perceived implications of artificial intelligence (AI) tools and generative language tools, like ChatGPT, on practice in speech-language pathology. A total of 107 clinician (n = 60) and student (n = 47) participants completed an 87-item survey that included Likert-style questions and open-ended qualitative responses. The survey explored participants' current frequency of use, experience with AI tools, ethical concerns, and concern with replacing clinicians, as well as likelihood to use in particular professional and clinical areas. Results were analyzed in the context of qualitative responses to typed-response open-ended questions. A series of analyses indicated participants are somewhat knowledgeable and experienced with GPT software and other AI tools. Despite a positive outlook and the belief that AI tools are helpful for practice, programs like ChatGPT and other AI tools are infrequently used by speech-language pathologists and students for clinical purposes, mostly restricted to administrative tasks. While impressions of GPT and other AI tools cite the beneficial ways that AI tools can enhance a clinician's workloads, participants indicate a hesitancy to use AI tools and call for institutional guidelines and training for its adoption.

The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education.

In the field of medicine, uncertainty is inherent. Physicians are asked to make decisions on a daily basis without complete certainty, whether it is in understanding the patient's problem, performing the physical examination, interpreting the findings of diagnostic tests, or proposing a management plan. The reasons for this uncertainty are widespread, including the lack of knowledge about the patient, individual physician limitations, and the limited predictive power of objective diagnostic tools. This uncertainty poses significant problems in providing competent patient care. Research efforts and teaching are attempts to reduce uncertainty that have now become inherent to medicine. Despite this, uncertainty is rampant. Artificial intelligence (AI) tools, which are being rapidly developed and integrated into practice, may change the way we navigate uncertainty. In their strongest forms, AI tools may have the ability to improve data collection on diseases, patient beliefs, values, and preferences, thereby allowing more time for physician-patient communication. By using methods not previously considered, these tools hold the potential to reduce the uncertainty in medicine, such as those arising due to the lack of clinical information and provider skill and bias. Despite this possibility, there has been considerable resistance to the implementation of AI tools in medical practice. In this viewpoint article, we discuss the impact of AI on medical uncertainty and discuss practical approaches to teaching the use of AI tools in medical schools and residency training programs, including AI ethics, real-world skills, and technological aptitude.

Use of Artificial Intelligence (AI) Technologies in Education According to Primary School Teachers: Opportunities and Challenges

Artificial intelligence (AI), which refers to technologies that mimic human cognition, affects many industries. Education is one of these sectors. Artificial intelligence affects many educational environments, from lectures to homework. In this process, both academic and ethical concerns call into question the future of artificial intelligence. These inquiries are essential as they show that the human factor will continue as an integral part of education. Because AI tools, even when best designed, can only partially replace human interaction or quality teaching. However, they can make the teacher's job easier and contribute to more effective learning. Therefore, teachers' awareness of this technology has become essential. This research aims to determine primary school teachers' opinions about using AI tools in education. The research was conducted using a case study. The participants are 16 primary school teachers determined by the criterion sampling method. Data were collected through a semi-structured interview form and analyzed with content analysis. According to the findings, teachers stated that AI tools may have advantages and disadvantages in educational environments. While teachers are concerned about the adverse effects of artificial intelligence tools on students, they also recognize their cognitive and socio-emotional contributions. Teachers also stated that artificial intelligence can make teachers' jobs easier but can only partially replace them. The results help understand primary school teachers' opinions regarding using artificial intelligence tools in the learning process.

The Impact of Big Data and Artificial Intelligence on Influencer Marketing

Influencer marketing has rapidly emerged as a cornerstone of digital advertising strategies, enabling brands to connect authentically with target audiences through trusted voices on social media platforms. However, measuring the effectiveness of these campaigns has long been a challenge due to the complexity of digital ecosystems and the sheer volume of data generated by user interactions. Big Data and Artificial Intelligence (AI) are revolutionizing the way brands evaluate influencer marketing campaigns, offering unprecedented insights into key performance metrics such as engagement, audience sentiment, and conversion rates. This paper explores how data analytics and AI tools are transforming the identification of influencers, the optimization of campaign performance, and the prediction of future trends. Furthermore, we address the ethical considerations surrounding data privacy and the transparency of AI-driven decisions in influencer marketing. Ultimately, this study highlights how Big Data and AI are not only enhancing the efficiency of influencer marketing but also reshaping the future of digital advertising as a whole.

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.

The Influence of Artificial Intelligence Tools on Student Performance in e-Learning Environments: Case Study

This study investigated the impact of AI-powered personalized learning tools on the academic performance and perceptions of pre-service student teachers enrolled in the Educational Technology course, a compulsory component of the Professional Postgraduate Diploma in Teaching program at Ajman University. A quasi-experimental design was employed with 55 students in the experimental group and 55 in the control group. The experimental group utilized AI-powered tools within the Moodle platform, while the control group received traditional instruction. Pre- and post-tests, along with a rubric-based assessment and a questionnaire, were administered to assess performance, knowledge retention, critical thinking, motivation, and engagement. Statistical analysis revealed significant differences between the groups, favoring the experimental group in terms of academic performance and knowledge retention. Additionally, students in the experimental group demonstrated higher levels of critical thinking, motivation, and engagement. These findings underscore the transformative potential of AI-powered personalized learning tools in teacher education. By integrating AI into educational practices, institutions can revolutionize e-learning by providing personalized instruction, intelligent tutoring, automated grading, and adaptive learning experiences. This can enhance student engagement, improve learning outcomes, and reduce the workload of educators. Ultimately, AI empowers institutions to create more effective and inclusive learning environments that cater to the diverse needs of students and prepare them for the future.

Artificial intelligence in management education: transformative potential and challenges

Purpose The purpose of this paper is to define artificial intelligence (AI) and examine its history, positive and negative impacts, ethical and social implications and implementation within management education. This paper offers various suggestions for the use of AI, as well as context surrounding the current AI landscape. Design/methodology/approach The paper uses a narrative review (Sylvester et al., 2013). Findings This paper identifies several areas of AI innovation, including AI tutoring systems, feedback systems for student papers, utilization of AI for innovative lesson plans and the use of AI to predict potential student dropout from a course or institution. In addition, there are significant concerns regarding the lack of ethical guidelines with current AI. Practical implications Practical implications include the ability to immediately use certain AI tools to enhance lesson plans as well as enhance student work using AI as a tool. Originality/value This paper was originally created as a conference presentation and presented at the society for advancement of management (SAM) International Business Conference before being reworked to be submitted to the journal. All content in this paper is original in their creation.

Decoding Silence in Digital Cross-Cultural Communication: Overcoming Misunderstandings in Global Teams

This study investigates the role of silence in digital cross-cultural communication, emphasizing its potential for misinterpretation due to cultural differences and the absence of non-verbal cues. Silence, often perceived differently across high- and low-context cultures, plays a crucial role in conveying emotions and intentions. In digital spaces, the lack of immediate feedback and non-verbal richness increases the complexity of silence interpretation. This research employs a comprehensive literature review to explore how silence is used in virtual communication platforms like Zoom and Slack and its impact on global teams. The study analyzes various existing research on silence in intercultural communication and digital communication tools. Key findings reveal that silence in high-context cultures, such as Japan and China, is seen as reflective and respectful, while in low-context cultures like the United States, it is often interpreted negatively, leading to miscommunication. Furthermore, the absence of non-verbal cues in digital environments exacerbates these misunderstandings. The study also explores the potential of emerging technologies like artificial intelligence (AI) to mitigate these communication barriers by detecting and interpreting silence more effectively. The results underscore the need for cultural sensitivity in virtual teams and highlight the limitations of current AI tools in understanding cultural nuances. This research contributes to the ongoing discourse on improving intercultural communication in digital settings, offering practical implications for global teams and suggesting future directions for AI integration in digital communication platforms.

Artificial Intelligence, Discrimination, Fairness, and Other Moral Concerns

Should the input data of artificial intelligence (AI) systems include factors such as race or sex when these factors may be indicative of morally significant facts? More importantly, is it wrong to rely on the output of AI tools whose input includes factors such as race or sex? And is it wrong to rely on the output of AI systems when it is correlated with factors such as race or sex (whether or not its input includes such factors)? The answers to these questions are controversial. In this paper, I argue for the following claims. First, since factors such as race or sex are not morally significant in themselves, including such factors in the input data, or relying on output that includes such factors or is correlated with them, is neither objectionable (for example, unfair) nor commendable in itself. Second, sometimes (but not always) there are derivative reasons against such actions due to the relationship between factors such as race or sex and facts that are morally significant (ultimately) in themselves. Finally, even if there are such derivative reasons, they are not necessarily decisive since there are sometimes also countervailing reasons. Accordingly, the moral status of the above actions is contingent.

ARTIFICIAL INTELLIGENCE AND TEXT-PROCESSING GENERATIVE TOOLS: EFFECTIVE USE OF CHATGPT IN WORKING WITH SCIENTIFIC TEXTS

Today, artificial intelligence tools have opened the door to fantastic opportunities for scientists conducting scientific research. In particular, artificial intelligence tools such as ChatGPT make it very convenient to write scientific articles. Using ChatGPT, you can quickly and easily write a scientific article, develop an article plan, add facts and evidence, and improve the style of the article. However, using ChatGPT also has its restrictions and rules because artificial intelligence cannot reflect the full human potential. In this thesis, several issues, such as why researchers should use ChatGPT, how to write an influential scientific article using ChatGPT, and the limitations of this artificial intelligence tool, are analyzed.

Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model

The rapid development of generative artificial intelligence (GenAI) tools has given rise to a growing discussion of the potential challenges and benefits that the use of these technologies may present in the field of education. This study examines the acceptance of the use of GenAI tools for teaching and learning among primary and secondary school teachers in Hong Kong. It uses an extension of the technology acceptance model (TAM) with a modified framework that incorporates two key factors: self-efficacy and subjective norm. Data were collected from a sample of 367 primary and secondary school teachers in Hong Kong using questionnaires containing items for six constructs: self-efficacy, perceived usefulness, perceived ease of use, attitude towards using, subjective norm, and behavioural intention. The results show that fostering teachers’ self-efficacy, perceived usefulness, and attitude is essential for successfully increasing their behavioural intention to use GenAI tools. Subjective norm was also found to influence teachers’ behavioural intention. To enhance teachers’ effective use of GenAI for teaching, teacher development programmes should focus on equipping teachers with comprehensive conceptual knowledge and skills and an understanding of the application of these tools to teaching and learning. Policy support to create a conducive environment for the use of GenAI in teaching and learning would also be beneficial. The study has theoretical implications in its extension of the TAM model as well as implications for enhancing teachers’ AI literacy and developing pedagogies for the meaningful use of GenAI tools for teaching and learning in K–12 settings.