Advances In Artificial Intelligence Research Articles

Discourse analysis on academic integrity generative AI: Perspectives from science and mathematics students in higher education

Artificial Intelligence (AI) has significantly influenced the current educational system, offering various benefits to teachers and students. However, the integration of AI in education also poses challenges, particularly in maintaining academic integrity. This study investigates the perspectives of 24 science and mathematics students on the impact of Generative AI (GAI) on academic integrity, a core principle in higher education. Using an exploratory design, the findings reveal a dual perspective: GAI can enhance learning and research by providing advanced tools and resources, yet it also risks undermining ethical standards if misused. The study highlights the importance of balancing technological advancements with the maintenance of academic integrity, emphasizing the need for responsible AI usage and the development of policies to mitigate potential negative impacts. Further research is needed to explore the impact of GAI on academic integrity across different disciplines and educational contexts.

Blended Learning Approach in TVET Colleges: Lecturers and Students Perceptions of Teaching and Learning Practical Subjects Online

With the advent of artificial intelligence, institutions are now adopting the Blended learning approach, which infuses face-to-face strategies and online teaching methodologies. A blended learning approach is a way to enhance the quality of teaching and learning in higher education institutions, but how feasible is that in TVET Colleges? This study thus investigated the global perceptions of lecturers and students in Technical Vocational Education (TVET) colleges regarding Blended learning for teaching practical subjects online. It examined the challenges and opportunities of moving practical learning online, considering the unique requirements of first-hand subjects like Electrical Engineering, Tourism, Hospitality, Art, and Design. The study utilised articles from Google Scholar and Science Direct databases to comprehensively understand the effectiveness of Blended learning in the TVET context. Using Blended learning as a theoretical framework, a new methodological approach, and a promising blueprint for transforming higher education, the study adopted an integrative literature review methodology to synthesise identified cases and draw themes from the survey. The study brings new knowledge regarding the significant shift in online learning usage during and after the COVID-19 pandemic, particularly in vocational education, providing valuable insights for designing effective online learning strategies. Keywords: Artificial Intelligence, Blended Learning, Integrative Literature Review, Technical Vocational Education (TVET) Colleges

A Novel Artificial General Intelligence Security Evaluation Scheme Based on an Analytic Hierarchy Process Model with a Generic Algorithm

The rapid development of Artificial General Intelligence (AGI) in recent years has provided many new opportunities and challenges for human social production. However, recent evaluation methods have some problems with regard to consistency, subjectivity and comprehensiveness. In order to solve the above problems, in this paper, we propose an Artificial General Intelligence Security Evaluation scheme (AGISE), which is based on analytic hierarchy process (AHP) technology with a genetic algorithm, to comprehensively evaluate the AGI security based on multiple security risk styles and complex indicators. Firstly, our AGISE combines AHP technology with a genetic algorithm to realize reliable, consistent and objective evaluation for AGI security. Secondly, in our AGISE, we propose implementing more effective AGI security evaluation classification and indicator settings. Finally, we demonstrate the effectiveness of our AGISE through experiments.

Revisiting Four Conversations in Technical and Professional Writing Scholarship to Frame Conversations About Artificial Intelligence

This article explores four different topics of conversation in technical and professional communication (TPC) scholarship that overlap and connect with contemporary issues in generative artificial intelligence (AI): process and iteration, theory and power, actors and activity, and the social justice turn. The authors offer four nonexhaustive reviews of these conversations, offering insight into key issues and texts that have animated discourse in the field and can directly or indirectly address the complex relationship between TPC work and generative AI.

Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings

BackgroundConventional MR acceleration techniques, such as compressed sensing, parallel imaging, and half Fourier often face limitations, including noise amplification, reduced signal-to-noise ratio (SNR) and increased susceptibility to artifacts, which can compromise image quality, especially in high-speed acquisitions. Artificial intelligence (AI)-assisted compressed sensing (ACS) has emerged as a novel approach that combines the conventional techniques with advanced AI algorithms. The objective of this study was to examine the imaging quality of the ACS approach by qualitative and quantitative analysis for brain, spine, kidney, liver, and knee MR imaging, as well as compare the performance of this method with conventional (non-ACS) MR imaging.MethodsThis study included 50 subjects. Three radiologists independently assessed the quality of MR images based on artefacts, image sharpness, overall image quality and diagnostic efficacy. SNR, contrast-to-noise ratio (CNR), edge content (EC), enhancement measure (EME), scanning time were used for quantitative evaluation. The Cohen’s kappa correlation coefficient (k) was employed to measure radiologists’ inter-observer agreement, and the Mann Whitney U-test used for comparison between non-ACS and ACS.ResultsThe qualitative analysis of three radiologists demonstrated that ACS images showed superior clinical information than non-ACS images with a mean k of ~ 0.70. The images acquired with ACS approach showed statistically higher values (p < 0.05) for SNR, CNR, EC, and EME compared to the non-ACS images. Furthermore, the study’s findings indicated that ACS-enabled images reduced scan time by more than 50% while maintaining high imaging quality.ConclusionIntegrating ACS technology into routine clinical settings has the potential to speed up image acquisition, improve image quality, and enhance diagnostic procedures and patient throughput.

Comparing The Effectiveness Of Multiple-Answer And Single-Answer Multiple-Choice Questions In Assessing Student Learning

ABSTRACT Driven by technological innovations, continuous digital expansion has transformed fundamentally the landscape of modern higher education, leading to discussions about evaluation techniques. The emergence of generative artificial intelligence raises questions about reliability and academic honesty regarding multiple-choice assessments in online education. In this context, this study investigates multiple-answer questions (MAQs) versus traditional single-answer questions (SAQs) in online higher-education assessments. A mixed-methods study involving quantitative field experiments and qualitative interviews was conducted with students enrolled in an online Marketing M.Sc. program. The students were divided randomly and assessed using either SAQs or MAQs, and the impacts on test performance of variables such as grade averages, study times, perceived workload, and difficulty were evaluated using independent-sample t-tests and ordinary least-squares regression analysis. The results show that although grades were lower and MAQs were perceived as being more difficult, study times and perceived workload did not differ significantly between the two formats. These findings suggest that despite their challenge, MAQs can promote deeper understanding and greater learning retention. Furthermore, even with their higher perceived difficulty and impact on performance, MAQs hold potential for dealing with academic-integrity concerns related to artificial intelligence.

Language and Education: Ideologies of Correctness

This review illustrates how language ideologies about correctness, in speaking and writing, have been discussed in research on the role of language in education. Research illustrates a give-and-take between the interests of multilingual speakers and advocates of language diversity on the one hand and, on the other, the correctness ideologies embedded in institutional demands for correctness and standardization (in schools and language policies) and commodification (in the global educational marketplace). More subtle than ideologies of correctness, language ideologies about “appropriate” language emerge as related to race, ethnicity, gender, and other embodied biases, and the nuanced mechanisms of language socialization illuminate the persistent dynamics of these appropriateness ideologies. Finally, we discuss the relevance of postcolonial epistemologies, the collaborative participatory research methods that are reframing what counts as correct and appropriate for the study of language and education, and the emerging role of generative artificial intelligence for language in education.

Opportunities for the use of artificial intelligence in supply chain management

Aim. To evaluate the potential of generative artificial intelligence technology in the context of supply chain management.Objectives. To analyze how artificial intelligence is changing traditional management practices and structures in supply chain management; to identify fundamental shifts brought about by AI, including automation, data-driven decision making, and human empowerment; to examine current statistics on the effectiveness of artificial intelligence in management processes and conclude the potential, promise of this technology in the logistics industry; to provide strategic insight and practicalMethods. The research combines qualitative and quantitative methods to provide a deeper understanding of the problems considered. Based on these methods, an array of relevant statistical data and expert opinions in the field of generative artificial intelligence has been studied.Results. In today’s business environment, supply chains face a number of challenges: globalization, market volatility, changing customer expectations, and force majeure (natural disasters, pandemics, or wars). To meet these challenges and strengthen their market position, companies are increasingly turning to new technologies and innovations. These technologies have the potential to revolutionize the approach to supply chain management, increasing transparency, efficiency and responsiveness. All emerging technologies, from artificial intelligence and blockchain to the Internet of Things (IoT) and robotics, potentially offer opportunities to streamline operations, improve decision-making, and enhance supply chain collaboration.Conclusions. In this article, the authors have shown how artificial intelligence is changing traditional governance structures in supply chain management. Current statistical data on the effectiveness of artificial intelligence in management processes are analyzed, and a conclusion is made about the potential and prospects of this technology in the logistics sphere. Strategic understanding is formed and practical recommendations for organizations seeking to implement new tools of generative artificial intelligence are given.

Meta Ads digital advertising in social networks

A progressive tool for achieving the marketing goals of retail enterprises in digital advertising in social networks, taking into account the current situation in Ukraine, is Meta Ads. Advanced targeting parameters based on demographics, interests and own audience make Meta Ads a powerful tool for digital advertising professionals, allowing them to reach a wide audience with targeted messages. The study was conducted using the following methods: data analysis, synthesis and logical generalization of results; systemic, structural and statistical analysis with the use of graphical methods. The main sources of information are scientific papers, the results of marketing research, expert blogs in the field of digital advertising, and official statistical information from Internet resources. In the article there were considered the functions of generative artificial intelligence in Meta Ads, such as image enhancement, background generation, and ad text variations, which can reduce the time required to create advertising materials. Advantage+ tools that automate the process of creating advertising campaigns and targeting have been analysed, in particular Advantage+ Shopping Campaigns and Advantage+ Creative. The development trends of Meta Ads in the Ukrainian market, which will affect the results and efficiency of work with digital advertising, are substantiated. It was determined that the use of existing artificial intelligence technologies will allow enterprises to better find the target audience and work with advertisements

Enhancing data privacy in federated learning using artificial intelligence

Federated Learning (FL) is a decentralised approach to machine learning that enables model training on local devices without the need to share raw data. While FL inherently provides some level of privacy, significant challenges remain in fully protecting sensitive information. This article explores advanced artificial intelligence (AI) techniques for improving privacy in federated learning. I propose novel methods that include differential privacy, homomorphic encryption, and secure multiparty computation, enhanced by AI-driven optimizations. My empirical studies and theoretical analyses demonstrate the effectiveness and efficiency of these techniques in maintaining data protection.

Generative artificial intelligence (GenAI) and decision-making: Legal & ethical hurdles for implementation in mental health

This article argues that significant risks are being taken with using GenAI in mental health that should be assessed urgently. It recommends that guidelines for using generative artificial intelligence (GenAI) in mental health care must be established promptly. Currently, clinicians using chatbots without appropriate approval risk undermining legal protections for patients. This could harm the patient and undermine the standards of the profession, undermining trust in an area where human involvement in decision-making is critical. To explore these concerns, this paper is divided into three parts. First, it examines the needs of patients in mental health. Second, it explores the potential benefits of GenAI in mental health and highlights the risks of its use as it pertains to patient needs. Third, it notes the ethical and legal concerns around data use and medical liability that require careful attention. The impact of the European Union's (EU) Artificial Intelligence Act (AI-Act) is also considered. It will be seen that these laws are insufficient in the context of mental health. As such, the paper recommends that guidelines should be developed to help resolve the existing legal gaps until codified rules are established.

AI-Powered E-Learning for Lifelong Learners: Impact on Performance and Knowledge Application

The widespread integration of artificial intelligence (AI) technologies, such as generative AI tools like ChatGPT, in education and workplaces requires a clear understanding of the factors that influence their adoption and effectiveness. This study explores how the ease of using AI tools, the ability to apply knowledge gained from them, and users’ confidence in learning with AI impact individuals’ performance and frequency of use. We also examine how these factors affect academic success and job performance among adults engaged in lifelong learning. Using data from 300 participants analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM), we found that, when AI tools are easy to use, individuals experience greater benefits and are more likely to use them regularly. Applying knowledge from AI tools enhances both personal performance and usage frequency. Additionally, having confidence in one’s ability to learn with AI leads to significant improvements in personal outcomes and an increased use of AI tools. These findings highlight the importance of designing user-friendly AI technologies, promoting the practical application of AI-generated knowledge, and building users’ confidence to maximize the benefits of AI. Educators, policymakers, and AI developers can use these insights to develop strategies that enhance academic and job performance through effective AI integration. Future research should consider other influencing factors and employ longitudinal studies to further validate these findings.

The interconnectedness between Ubuntu principles and generative artificial intelligence in distance higher education institutions

The interconnectedness of African philosophical principles of Ubuntu with generative artificial intelligence may provide an inclusive approach to distance higher education. This fusion may improve and provide customised learning, access quality education, collaboration, inclusivity, strengthens lecturer support and development, and a culturally relevant experience that acknowledges the communal interests of all stakeholders. Generative artificial intelligence promotes epistemological access for all students irrespective of their diverse social, financial, and linguistic backgrounds and redefines higher education experiences and practices while the principles of Ubuntu prioritise the well-being of stakeholders. The objective of this conceptual article was to explore the interconnectedness between Ubuntu principles and generative artificial intelligence perspectives in distance higher education institutions to reimagine methods of teaching and learning. This article is grounded in the theory of the Ubuntu principles and advocates for a paradigm shift in higher education practices. It contends that integrating African philosophical principles into the implementation of generative artificial intelligence’s learning provides a more inclusive and culturally relevant approach to education. The notable highlights confirm that Ubuntu principles dismantle oppressive systems, restore self-determination for all students and promote change and reconciliation through the use of generative artificial intelligence. This approach can be applied to distance higher education institutions to create inclusive learning experiences that gives precedence to a sense of belonging, mutual support, and ethical considerations.

Assessment and learning outcomes for generative AI in higher education: A scoping review on current research status and trends

Generative artificial intelligence (GenAI) impacts higher education assessment and learning outcomes, which are closely related and intertwined. Literature suggests that educators and researchers have many varied concerns regarding student assessment in the higher education GenAI context, such as how to assess students’ learning and the new (refocused) learning outcomes that emerged in GenAI-facilitated learning environments. To provide evidence-based insights into and answers to these concerns, we conducted a scoping review by collating literature in relevant research areas. Following a five-stage scoping review framework, we collaboratively collected and coded 34 studies. The three assessment approaches identified in the review were traditional assessment, innovative and refocused assessment and GenAI-incorporated assessment. The new, refocused learning outcomes identified were career-driven competencies and lifelong learning skills. The review also revealed that most research designs were qualitatively oriented (e.g., with exploratory design, descriptive research, ethnographic research and phenomenological research). This study proposes a holistic diagram showing the current research status and trends. It suggests five future research directions: innovative assessment designs, collaborations among assessment approaches, new learning outcomes, relationships between assessment approaches and learning outcomes, and quantitative or mixed research studies. Implications for practice or policy: Traditional assessment methods in higher education do not operate effectively in the GenAI era. Innovative and refocused assessment and GenAI-incorporated assessment are promising strategies to assess student learning. Career-driven competencies and lifelong learning skills are new focused learning outcomes evolved from the use of GenAI. More quantitative and mixed research studies should be conducted to provide additional empirical evidence on the impact of GenAI on student assessment and learning outcomes.

Using Generative Artificial Intelligence in Exams: A Research on KPSS with ChatGPT

ChatGPT is a language model developed by OpenAI that uses generative artificial intelligence (GenAI) algorithms for natural language processing (NLP) tasks. It is a popular Chatbot due to its impressive ability to respond consistently to users' various prompts. In this study, the Public Personnel Selection Examination (KPSS) questions, organized by ÖSYM (Turkish Student Selection and Placement Center) every year, were asked to ChatGPT, and its performance was evaluated. The evaluation was made based on fundamental text questions asked in the last three years (2021-2023) in Biology, History, Geography, Turkish Language Literature, and Social Studies in KPSS. This study also aims to investigate the accuracy and reliability of the questions asked to ChatGPT in the field of KPSS educational sciences. According to the research results, ChatGPT's KPSS knowledge and interpretation skills are significantly lower than the university graduate candidates who took the exam. Some suggestions are included for better Chatbot performance, more effective prompt engineering and ethical use of these tools.

Describing the Framework for AI Tool Assessment in Mental Health and Applying It to a Generative AI Obsessive-Compulsive Disorder Platform: Tutorial.

As artificial intelligence (AI) technologies occupy a bigger role in psychiatric and psychological care and become the object of increased research attention, industry investment, and public scrutiny, tools for evaluating their clinical, ethical, and user-centricity standards have become essential. In this paper, we first review the history of rating systems used to evaluate AI mental health interventions. We then describe the recently introduced Framework for AI Tool Assessment in Mental Health (FAITA-Mental Health), whose scoring system allows users to grade AI mental health platforms on key domains, including credibility, user experience, crisis management, user agency, health equity, and transparency. Finally, we demonstrate the use of FAITA-Mental Health scale by systematically applying it to OCD Coach, a generative AI tool readily available on the ChatGPT store and designed to help manage the symptoms of obsessive-compulsive disorder. The results offer insights into the utility and limitations of FAITA-Mental Health when applied to "real-world" generative AI platforms in the mental health space, suggesting that the framework effectively identifies key strengths and gaps in AI-driven mental health tools, particularly in areas such as credibility, user experience, and acute crisis management. The results also highlight the need for stringent standards to guide AI integration into mental health care in a manner that is not only effective but also safe and protective of the users' rights and welfare.

Awareness About Artificial Intelligence Among Dental Practitioners And Dental Students

BACKGROUND Artificial intelligence (AI) is defined as acquisition of intelligence by computers or machines to perform complex tasks that generally require human intelligence. Nowadays usage of AI in various fields is appreciated because of its time effectiveness and less laborious.In dentistry, AI has been trending, specifically in diagnostic imaging and early detection of diseases,benefiting both dental graduates and practitioners. AIM The main aim is to assess the awareness of Artificial intelligence as an effective tool among dental trainees and dental practitioners. MATERIALS AND METHOD An online cross-sectional survey was conducted among dental trainees and dental practitioners in various institutions. A Google questionnaire form was developed and circulated to assess participant’s awareness about Artificial intelligence in dentistry. Data were collected in Microsoft excel and statistical analysis were performed. RESULTS More than 60% of the participants showed confidence about their familiarity towards AI in dentistry (p&lt;0.01197). Among those, 60.5% answered that they are well aware of ChatGPT and 28% of them to Microsoft Bing (p&lt;0.04395). 53% answered that machine learning is an important form of AI (p&lt;0.00216). Over the advantages of AI, 43% believed AI can improve diagnostics, access to disease screening, cost effectiveness, reduced treatment time (p&lt;0.0001) and 53% agreed that AI can be useful in day-to-day dental practices (p&lt;0.0001). CONCLUSION The present study shows that awareness of AI among the dental fraternity is satisfactory. The field of AI is emerging rapidly and various other new applications in AI are being utilised. AI could act as a valuable tool in supporting clinicians delivering effective dental care and supplementing education for dental trainees. To enhance the future of AI in dentistry, the present curriculum needs to be approachable enough and various hands-on-training are essential. KEYWORDS:artificial intelligence, chatbots, dental caries, radiographic diagnosis, teledentistry.

The Future of Generative AI: Copyright and Infringement Issues

Rapid advancements in generative artificial intelligence (Generative AI) have sparked a global revolution, ushering in a new era of accelerated automation that promises to upend industries, boost productivity, and potentially displace significant portions of the white- and blue-collar workforce. However, the rise of Generative AI also presents numerous challenges, particularly in the realm of copyright and infringement. The use of copyrighted works as training data for AI models has sparked fierce debates among creators, artists, writers, and programmers who object to their creations being utilized without attribution or compensation. Whether AI-generated content should be protected under copyright law is contentious, as the traditional copyright framework was designed for human creators. This paper delves into the copyright and infringement challenges posed by Generative AI, examining the legal framework, case studies, and potential solutions. It argues that a strategic approach is necessary to navigate the complexities of this rapidly evolving technology while ensuring the protection of intellectual property rights. The paper concludes by advocating for the development of comprehensive legal frameworks that balance the benefits of Generative AI with the need to safeguard creators rights.

Substantive and Emotional: The Anthropomorphic Evolution of Generative AITaking GPT-4o as an Example

Under the background of the rapid development of artificial intelligence technology, "personal publishing" represented by blog in the past has transformed into "human-computer co-creation", showing a new human-computer interaction relationship, and generative artificial intelligence has become the "collaborator" of human content creation. The flagship multimodal large language model "anthropomorphism" represented by GPT-4o is evolving from "linguistic anthropomorphism" to "emotional anthropomorphism". Based on the anthropomorphic theory, this paper aims to reveal that GPT-4o, while demonstrating its linguistic intelligence in the human-machine co-created dialogue, simulates human emotional response, showing empathy and "resonance", that is, the "anthropomorphic" process at the two levels of "words have things" and "words have feelings". It also reflects on the potential ethical challenges of the anthropomorphic evolution of generative AI to future human society.

Challenges, Benefits and Recommendations for Using Generative Artificial Intelligence in Academic Writing - A Case of ChatGPT

Challenges, Benefits and Recommendations for Using Generative Artificial Intelligence in Academic Writing - A Case of ChatGPT