Advances In Artificial Intelligence Research Articles

Targeting Lin28: Insights into Biology and Advances with AI-Driven Drug Development

Lin28, a conserved RNA-binding protein, promotes cancer stem cell features, epithelial-to-mesenchymal transition, and treatment resistance. Lin28 regulates mRNA translation, RNA stability, and transcription, which improve tumor plasticity and treatment resistance, in addition to suppressing let-7 microRNA production. Lin28 is an interesting cancer target due to its many functions; however, its structural complexity makes treatment development difficult. Lin28's proven and new non-canonical activities in cancer biology are examined in this work, focusing on cancer stem cell maintenance, metastasis, and treatment resistance. We highlight computational drug discovery advances targeting Lin28 utilizing virtual screening and machine learning. Generative artificial intelligence has made it easier to develop inhibitors for challenging targets like Lin28. This work integrates current knowledge and technology to demonstrate Lin28's therapeutic potential and outline future techniques to overcome RNA-binding protein targeting challenges.

Bringing Historical Management Theories to Life: An Experiential Exercise Using Generative AI

Foundational management theories, such as the Principles of Scientific Management, Bureaucratic Management, or Systems Theory, provide frameworks for understanding and organizing the extensive body of management knowledge. Despite their significance, students often find it difficult to grasp and apply historical concepts in today’s workplace. In this experiential exercise, we use generative artificial intelligence (AI) to create an interactive chatbot through which students “interview” historical figures. Through this exercise, students achieve four key learning objectives: (a) demonstrating a critical understanding of historical management frameworks; (b) analyzing how historical, social, and economic contexts influenced management thinking; (c) evaluating the applicability of historical management frameworks to contemporary business settings; and (d) critically analyzing and interpreting information generated by AI tools to enhance AI literacy. The activity includes tailored open-access chatbots and resources to assist these learning outcomes across various class formats.

The Purity Myth: Why Stigmatizing GAI in Academic Writing Is Harmful

This article explores the evolving role of generative artificial intelligence (GAI) in academic writing, addressing the challenges, opportunities, and ethical considerations surrounding its adoption. While GAI tools like ChatGPT enhance efficiency and accessibility, their use in writing remains stigmatized due to traditional notions of authorship and intellectual rigor. This article argues against this stigma, advocating for a balanced perspective that values content quality over outdated ideals of textual purity. By embracing GAI responsibly and transparently, academia can foster innovation, democratize access to publishing, and redefine the collaborative potential of human–machine partnerships in advancing knowledge.

Method for assessing visual saliency in children with cerebral/cortical visual impairment using generative artificial intelligence

Method for assessing visual saliency in children with cerebral/cortical visual impairment using generative artificial intelligence

Study on the Changes in College Students' Learning Behaviors under ChatGPT-based Interventions: A Case Study of Hunan University of Technology

Generative artificial intelligence, exemplified by ChatGPT, has garnered significant attention from the global technology community. Scholars suggest that this technology could lead to profound transformations in areas such as education and research. As digital information continues to evolve, the disruption of traditional educational models by AI-driven innovations appears to be an inevitable progression. This study aims to explore whether generative AI facilitates or impedes the development of education, its impact on traditional offline educational models, and its potential to enhance student learning outcomes. Utilizing sampling techniques, surveys, data mining, convolutional neural networks, and logistic regression, this research systematically investigates and critically analyzes college students' perceptions of the advantages and disadvantages of ChatGPT compared to conventional learning methods, the degree of its influence, and potential future implications.

Neurological history both twinned and queried by generative artificial intelligence

Neurological history both twinned and queried by generative artificial intelligence

The effectiveness of ChatGPT in assisting high school students in programming learning: evidence from a quasi-experimental research

ABSTRACT Programming education gains importance in high schools as the digital age progresses. However, the openness and adaptability of programming languages present unique challenges for instructional practices compared to other subjects. While traditional instructional tools offer limited support, ChatGPT, a groundbreaking Generative Artificial Intelligence, has shown impressive capabilities in natural language processing and knowledge generation. This study explored whether ChatGPT can transcend existing limitations and improve programming education through a quasi-experimental approach with post-hoc interviews in high school classrooms. A total of 153 students participated, and the results from MANCOVA and ANCOVA analyses revealed that students using ChatGPT reported lower levels of flow experience, self-efficacy, and learning achievement compared to those utilizing conventional methods. Post-hoc interviews further revealed that students felt ChatGPT's effectiveness in facilitating their programming learning fell short of their initial expectations. These findings highlight the need to carefully consider the complexity of programming learning tasks and students' cognitive, affective, and interactive dimensions when integrating AI technologies into education. We discuss the implications and provide thorough pedagogical strategies, specifically the guidance–practice–transformation (G–P–T) mode, to maximize the potential of AI tools and support high school programming education, emphasizing the balance of technological innovation with learning best practices.

Exploring the Role of Generative AI in Advancing Pre-service Teachers' Digital Literacy Through Educational Technology Courses

This study investigates the role of generative artificial intelligence (AIGC), particularly large language models, in enhancing the digital literacy of pre-service teachers. With the rapid growth of AI technologies, integrating generative AI into education has gained significant attention. The research focuses on how varying frequencies of generative AI usage affect pre-service teachers’ skills in information processing, problem-solving, and critical thinking. Using a polynomial regression model, we analyze the relationship between factors such as AI usage frequency, problem-solving time, feedback quality, and digital literacy scores. The results indicate that frequent use of generative AI substantially improves digital literacy, with the high-frequency group achieving higher and more consistent scores compared to the low-frequency group. Personalized feedback and project-based tasks, provided by generative AI, enhance students’ comprehension and application of digital technologies. This research shows that incorporating generative AI into teacher training programs not only supports personalized learning but also fosters essential digital competencies. The findings provide valuable insights for enhancing pre-service teachers' digital literacy and lay a foundation for future educational practices involving AI technologies.

AI-Enhanced Social Work: Developing and Evaluating Retrieval-Augmented Generation (RAG) Support Systems

ABSTRACT This article introduces the application of retrieval-augmented generation (RAG) technology, aiming to enhance decision making and client support through artificial intelligence (AI)–enhanced tools. RAG addresses the critical challenge of ensuring accuracy and reliability in AI-generated content by integrating generative AI with domain-specific knowledge bases. Through a case study, we explore the development and assessment of a RAG-powered tool designed to improve faculty advising, highlighting the importance of collaboration between social workers and technologists. The adaptability of RAG to various social work challenges is discussed alongside considerations for the maintenance of the knowledge base. Our work underscores the potential of RAG to revolutionize social work practices by providing accurate and reliable information to serve individuals and communities better.

The ChatGPT Fact-Check: Exploiting the Limitations of Generative AI to Develop Evidenced-Based Reasoning Skills in College Science Courses.

Generative large language models (LLMs) like ChatGPT can quickly produce informative essays on various topics. However, the information generated cannot be fully trusted as artificial intelligence (AI) can make factual mistakes. This poses challenges for using such tools in college classrooms. To address this, an adaptable assignment called the ChatGPT Fact-Check was developed to teach students in college science courses the benefits of using LLMs for topic exploration while emphasizing the importance of validating its claims based on evidence. The assignment requires students to use ChatGPT to generate essays, evaluate AI-generated sources, and assess the validity of AI-generated scientific claims (based on experimental evidence in primary sources). The assignment reinforces student learning around responsible AI use for exploration while maintaining evidence-based skepticism. The assignment meets objectives around efficiently leveraging beneficial features of AI, distinguishing evidence types, and evidence-based claim evaluation. Its adaptable nature allows integration across diverse courses to teach students to responsibly use AI for learning while maintaining a critical stance.

Adoption and impact of generative artificial intelligence on blockchain-enabled supply chain efficiency

Purpose The purpose of this study is to investigate the primary determinants influencing the acceptance of generative artificial intelligence (GAI) adoption within Blockchain-enabled environments. Further research will examine the impact of GAI adoption on supply chain efficiency (SCE) through the enhancement of Blockchain. Design/methodology/approach Drawing on innovation diffusion theory (IDT), this study used partial least square structural equation modelling (PLS-SEM) to look into the hypotheses. The data were gathered via online questionnaires from employers of Chinese supply chain enterprises that have already integrated Blockchain. Findings The findings of this study demonstrate that relative advantages (RAs), compatibility, trialability and observability have a significant positive effect on GAI adoption, while complexity harms GAI adoption. Above all, the GAI adoption has significantly enhanced Blockchain, thus effectively improving SCE. Practical implications The outcomes from this study furnish enterprises and organizations with valuable insights to proficiently integrate GAI and Blockchain capability, optimize supply chain management and bolster market competitiveness. Also, this study will help accelerate the successful integration of business processes and attain Sustainability Development Goals 9, industrial growth and industrial diversification. Originality/value To the extent of the author’s knowledge, the current status of the GAI study remains largely exploratory, and there is limited empirical evidence on integrating Blockchain capability and GAI. This research bridges the knowledge gap by fully revealing the optimal integration of these two transformative technologies to leverage their potential advantages in supply chain management.

Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency

Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.

Ethical Guidelines for the Application of Generative AI in German Journalism

Generative Artificial Intelligence (genAI) holds immense potential in revolutionizing journalism and media production processes. By harnessing genAI, journalists can streamline various tasks, including content creation, curation, and dissemination. Through genAI, journalists already automate the generation of diverse news articles, ranging from sports updates and financial reports to weather forecasts. However, this raises ethical questions of high relevance for media organizations and societies especially when genAI is used for more sensitive topics and at larger scale. To not jeopardize trustworthiness in journalistic organizations, it is important that the use of genAI in journalism is guided by moral principles. We therefore conducted 18 interviews with researchers and practitioners with expertise in AI-based technologies, journalism, and ethics from a German perspective in order to identify guidelines for the ethical use of genAI in media organizations. We derived requirements for the ethical introduction of genAI and actionable guidelines which explain how decision makers in media organizations should address ethical principles for the use of AI in the news production life cycle, in order to contribute to trustworthiness of journalistic organizations and products.

The role of large language models in the peer-review process: opportunities and challenges for medical journal reviewers and editors

The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.

Future Directions for Quantitative Systems Pharmacology.

In this chapter, we envision the future of Quantitative Systems Pharmacology (QSP) which integrates closely with emerging data and technologies including advanced analytics, novel experimental technologies, and diverse and larger datasets. Machine learning (ML) and Artificial Intelligence (AI) will increasingly help QSP modelers to find, prepare, integrate, and exploit larger and diverse datasets, as well as build, parameterize, and simulate models. We picture QSP models being applied during all stages of drug discovery and development: During the discovery stages, QSP models predict the early human experience of in silico compounds created by generative AI. In preclinical development, QSP will integrate with non-animal "new approach methodologies" and reverse-translated datasets to improve understanding of and translation to the human patient. During clinical development, integration with complementary modeling approaches and multimodal patient data will create multidimensional digital twins and virtual populations for clinical trial simulations that guide clinical development and point to opportunities for precision medicine. QSP can evolve into this future by (1) pursuing high-impact applications enabled by novel experimental and quantitative technologies and data types; (2) integrating closely with analytical and computational advancements; and (3) increasing efficiencies through automation, standardization, and model reuse. In this vision, the QSP expert will play a critical role in designing strategies, evaluating data, staging and executing analyses, verifying, interpreting, and communicating findings, and ensuring the ethical, safe, and rational application of novel data types, technologies, and advanced analytics including AI/ML.

Synthetic [18F]FET-PET Generation and Hotspot Prediction Via Preoperative MRI Fusion of Glioma Lacking Radiographic High-Grade Characteristics

Abstract Background Limited amino acid availability for Positron Emission Tomography (PET) imaging hinders therapeutic decision-making for gliomas without typical high-grade imaging features. To address this gap, we evaluated a generative artificial intelligence (AI) approach for creating synthetic [18F]FET-PET and predicting high [18F]FET-uptake from magnetic resonance imaging (MRI). Methods We trained a deep learning (DL)-based model to segment tumors in MRI and extracted radiomic features using the pyradiomics package and classification with a Random Forest classifier. To generate [18F]FET-PET images, we employed a Generative Adversarial Network (GAN) framework and utilized a split-input fusion module for processing different MRI sequences through feature extraction, concatenation, and self-attention. Results We included MR and PET images from 215 studies for the hotspot classification and 211 studies for the synthetic PET generation task. The top-performing radiomic features achieved 80% accuracy for hotspot prediction. From the synthetic [18F]FET-PET, 85% were classified as clinically useful by senior physicians. Peak Signal-to-Noise Ratio analysis indicated high signal fidelity with a peak at 40 dB, while Structural Similarity Index values showed structural congruence. Root Mean Square Error analysis demonstrated lower values below 5.6. Most Visual Information Fidelity scores ranged between 0.6 and 0.7. This indicates that synthetic PET images retain the essential information required for clinical assessment and diagnosis. Conclusion For the first time, we demonstrate that predicting high [18F]FET-uptake and generating synthetic PET images from preoperative MRI in LGG and HGG is feasible. Advanced MRI modalities and other generative AI models will be used to improve the algorithm further in future studies.

Evaluación cualitativa de modelos de inteligencia artificial generativa para resolución de preguntas clínicas de rehabilitación infantil

Introduction: The use of artificial intelligence in the healthcare field has shown multiple potentials. Among them, using generative artificial intelligence models, such as ChatGPT, to support the creation of clinical documents like reviews, clinical guidelines, or protocols. The objective of this study is to evaluate and compare the responses to clinical questions defined by experts on two rehabilitation topics, provided by the most widely used generative AI models on the market, in order to analyze their role in the creation of clinical documents, such as clinical guidelines or protocols. Material or Patients and Methods: Qualitative descriptive study. Through prompts of various types of clinical questions, an expert evaluates and rates, using a scale design for this study, the responses provided by 5 generative artificial intelligence models: ChatGPT, Gemini, Claude, Perplexity, and a customized GPT. Results: All generative artificial intelligence models are capable of delivering well-structured and coherent responses, but with some shortcomings in their technical content and updates according to evidence-based medicine. ChatGPT received the highest ratings, using the study rating scale. Discussion: Generative artificial intelligence models can play a role in the creation of clinical documents, such as reviews, guidelines, and protocols, providing a quick and effective tool. However, each step in the creation of these documents should be supervised by a human expert to identify possible errors, hallucinations and protection of ethic and security issues.

The Impact of Generative Artificial Intelligence Like Chatgpt on Media Literacy Among Users

With the rapid development of artificial intelligence technology, people are increasingly encountering intelligent assistants and generated content in their daily studies and lives. This paper demonstrates the behaviour patterns of users when using AI tools and their impact on various dimensions of media literacy, focusing on the influence of generative AI like ChatGPT on users' media literacy. The research finds that while AI can assist users in quickly retrieving information and stimulating creativity, a significant issue remains: the lack of evaluation of information sources and critical analysis skills. Therefore, this paper aims to provide recommendations for educators and editors to cultivate users' media literacy, helping them effectively utilize these technologies in the digital age.

Generative artificial intelligence and sustainable higher education: Mapping the potential

Generative artificial intelligence (GAI) becomes widespread in higher education, and it creates new educational possibilities, with a potential to transform the educational process and promote sustainability. This study aims to explore the potential of GAI tools such as ChatGPT in promoting sustainable higher education. ChatGPT was utilized to aid the investigation at the initial stage, while the output generated was reviewed and edited by the researcher. It is indicated that GAI’s integration into higher education can lead to advancements in sustainability, such as enhancing educational practices (e.g., personalized learning, automated assessment and feedback, and educators’ professional development), optimizing resource utilization (e.g., digital learning resources, and efficient energy use), supporting inclusive and accessible education, and promoting environmental awareness and sustainability. Through these contributions, GAI can assist in the creation of more efficient, inclusive, and sustainable educational environments. It is suggested that higher education policies are modified and re-formulated to serve sustainable development, while empirical research on GAI implementation in higher education is a necessity (most publications are theoretical/conceptual). Limitations and ethical considerations should also be addressed. The study contributes to the ongoing debate on the role of GAI for sustainability in higher education.

Intersezione tra intelligenza artificiale generativa e educazione: un’ipotesi

INTERSECTION BETWEEN GENERATIVE ARTIFICIAL INTELLIGENCE AND EDUCATION: A HYPHOTHESIS Abstract This study explores the impact of integrating Generative Artificial Intelligence (GenAI) into adaptive and personalized learning environments, focusing on its diverse applications in the field of education. It begins with an examination of the evolution of GenAI models and frameworks, establishing selection criteria to curate case studies that showcase the applications of GenAI in education. The analysis of these case studies highlights the tangible benefits of integrating GenAI, such as increased student engagement, improved test scores, and accelerated skill development. Ethical, technical, and pedagogical challenges are also identified, emphasizing the need for careful collaboration between educators and computer science experts. The findings underscore the potential of GenAI to revolutionize the field of education. By addressing technological challenges and ethical concerns, and embracing human-centered approaches, educators and computer science experts can leverage GenAI to create innovative and inclusive learning environments. Finally, the study also highlights the importance of socio-emotional learning and personalization in the evolutionary process that will revolutionize the future of education.