AI Language Research Articles

Using AI-Enabled Tools to Support Minority Students’ Success in HE

Generative AI and large language models (LLMs) are transforming workplace literacy practices in the fourth industrial revolution. This study explored how master's students in educational leadership and teacher education programs at a minority-serving university in a low SES urban metroplex in the south-central USA perceive and experience using LLMs and AI-assisted software. The focus was on how these tools support their learning and help them demonstrate knowledge. Key themes include equitable access to AI tools, diverse applications in academic and professional contexts, building student confidence through support and training, using AI to enhance academic readiness and skills, identifying individual needs, enabling higher order thinking opportunities, and promoting ethical use and academic integrity.By addressing various issues of AI usage, institutions can better support underserved students in using disruptive technologies, contributing to their academic success and professional preparedness. Faculty must ensure students' needs are met before requiring the use of new technologies like generative AI and AI-enabled tools.

Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models.

Malaria and Typhoid fever are prevalent diseases in tropical regions, and both are exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of similar symptoms. Although machine learning (ML) models offer accurate predictions, they operate as "black boxes" with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like GPT to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics by describing which symptoms had the greatest impact on the model's decisions and providing clear, understandable explanations. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed the other tested models; in addition, important features were identified with the LIME plots while ChatGPT 3.5 had a comparative advantage over other LLMs. The study integrates RF, LIME, and GPT in building a mobile app to enhance the interpretability and transparency in malaria and typhoid diagnosis system. Despite its promising results, the system's performance is constrained by the quality of the dataset. Additionally, while LIME and GPT improve transparency, they may introduce complexities in real-time deployment due to computational demands and the need for internet service to maintain relevance and accuracy. The findings suggest that AI-driven diagnostic systems can significantly enhance healthcare delivery in environments with limited resources, and future works can explore the applicability of this framework to other medical conditions and datasets.

Ethical ChatGPT: Concerns, Challenges, and Commandments

Large language models, e.g., Chat Generative Pre-Trained Transformer (also known as ChatGPT), are currently contributing enormously to making artificial intelligence even more popular, especially among the general population. However, such chatbot models were developed as tools to support natural language communication between humans. Problematically, it is very much a “statistical correlation machine” (correlation instead of causality), and there are indeed ethical concerns associated with the use of AI language models including ChatGPT, such as bias, privacy, and abuse. This paper highlights specific ethical concerns about ChatGPT and articulates key challenges when ChatGPT is used in various applications. Practical recommendations for different stakeholders of ChatGPT are also proposed that can serve as checklist guidelines for those applying ChatGPT in their applications. These best practice examples are expected to motivate the ethical use of ChatGPT.

Unveiling AI's Accent in Spoken English Education

Viewed against the backdrop of the technological landscape, artificial intelligence has become the shining point of the 21st century. In recent years, AI has been pervasively applied into various realms in modern society, among which the incorporation into spoken English education has become one of the most heated topics. To navigate the application of AI in spoken English learning, this paper evaluates the effectiveness based on the Interaction Hypothesis proposed by Michael Long. To be specific, this research focuses on the comprehensive application of AI in the realm of spoken English acquisition, underscoring why AI language models have gained in popularity in innumerable apps concerning language learning and how AI serves as a double-edged sword in spoken English education. By illustrating the language acquisition process, a strong connection between the Interaction Hypothesis and AI-based spoken English learning is constructed and clarified. Through all-round evaluation and analysis, the article concludes that despite challenges, AI has not only acted as a knowledgeable mentor for individual learners,but also provided an effective driving force for the innovation and improvement of spoken English education. In the future, more advancements concerning human-like interactions and immersive learning environment are likely to emerge.

Can my writing be polished further? When ChatGPT meets human touch

Abstract Recent attention on ChatGPT, a prominent AI language model, highlights its potential in assisting EFL writing. Although ChatGPT’s capabilities involve grammar correction, vocabulary enrichment and sentence structuring, its full potential alongside human scaffolding in EFL writing classrooms remains unexplored. This study aims to fill this gap by adopting the cognitive process model of writing as a theoretical framework. Four groups of 19 EFL learners were engaged in think-aloud sessions and interviews. The collaborative writing process with ChatGPT progressed through iterative stages of planning, translating, and reviewing, incorporating various embedded subprocesses. Guidance from the teacher and collaborative work among peers facilitated the effective use of ChatGPT for writing refinement by creating an environment of emotional reassurance and enhancing metacognitive awareness among the learners. The findings emphasise the importance of collaborative work, orchestrated by the teacher and peers in using ChatGPT in the EFL writing class.

UKRAINIAN PHD STUDENTS' ATTITUDES TOWARD AI LANGUAGE PROCESSING TOOLS IN THE CONTEXT OF ENGLISH FOR ACADEMIC PURPOSES

Mastering academic writing skills in English is essential for future researchers. At present, AI language processing tools provide high-quality, accessible, and fast assistance for translation, editing, and stylistic enhancement of scientific texts. However, their use within English for Academic Purposes (EAP) courses generates mixed reactions among educators and raises ethical concerns. Our study aimed to explore the predominant perceptions of AI language processing tools by PhD students of the National Academy of Sciences of Ukraine (NASU) from the viewpoint of their integration into the academic English course taught in the first year of their PhD studies. The study involved 52 PhD students from various NASU institutes. They completed a survey with both closed-ended and open-ended questions regarding their previous and expected use of online translators, writing enhancement tools, and ChatGPT for research writing purposes. The results of the survey show that NASU PhD students have extensive experience with online translators, but are less familiar with writing enhancement tools and less certain about their potential use in the future. Almost a third of the respondents expressed reservations about using ChatGPT for academic writing due to academic integrity concerns. Most of the respondents (66%) agree that the basics of ethical use of AI writing and editing tools should be incorporated into EAP courses. One subgroup of the participants (n = 11) took part in a small-scale additional intervention focused on writing enhancement tools. They were asked to apply Grammarly, QuillBot, and ChatGPT to edit their course projects (presentations of the current state of their dissertation research) and compare these tools according to various criteria. The feedback provided by this subgroup indicates that they were most satisfied with the quality of editing provided by ChatGPT but found Grammarly and QuillBot easier to use and more suitable for superficial grammar checks. We found out that the AI tools helped participants achieve improvements primarily in such aspects as the use of articles, punctuation, use of prepositions, and elimination of redundancy. The study has significant pedagogical implications, promoting the wider use of AI tools in the context of teaching English for Academic Purposes and addressing appropriate teaching techniques and methods.

Open Brain AI and language assessment.

Neurolinguistic assessments play a vital role in neurological examinations, revealing a wide range of language and communication impairments associated with developmental disorders and acquired neurological conditions. Yet, a thorough neurolinguistic assessment is time-consuming and laborious and takes valuable resources from other tasks. To empower clinicians, healthcare providers, and researchers, we have developed Open Brain AI (OBAI). The aim of this computational platform is twofold. First, it aims to provide advanced AI tools to facilitate spoken and written language analysis, automate the analysis process, and reduce the workload associated with time-consuming tasks. The platform currently incorporates multilingual tools for English, Danish, Dutch, Finnish, French, German, Greek, Italian, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, and Swedish. The tools involve models for (i) audio transcription, (ii) automatic translation, (iii) grammar error correction, (iv) transcription to the International Phonetic Alphabet, (v) readability scoring, (vi) phonology, morphology, syntax, semantic measures (e.g., counts and proportions), and lexical measures. Second, it aims to support clinicians in conducting their research and automating everyday tasks with "OBAI Companion," an AI language assistant that facilitates language processing, such as structuring, summarizing, and editing texts. OBAI also provides tools for automating spelling and phonology scoring. This paper reviews OBAI's underlying architectures and applications and shows how OBAI can help professionals focus on higher-value activities, such as therapeutic interventions.

Exploring the integration of IoT and Generative AI in English language education: Smart tools for personalized learning experiences

English language education is undergoing a transformative shift, propelled by advancements in technology. This research explores the integration of the Internet of Things (IoT) and Generative Artificial Intelligence (Generative AI) in the context of English language education, with a focus on developing a personalized oral assessment method. The proposed method leverages real-time data collection from IoT devices and Generative AI's language generation capabilities to create a dynamic and adaptive learning environment. The study addresses historical challenges in traditional teaching methodologies, emphasizing the need for AI approaches. The research objectives encompass a comprehensive exploration of the historical context, challenges, and existing technological interventions in English language education. A novel, technology-driven oral assessment method is designed, implemented, and rigorously evaluated using datasets such as Librispeech and L2Arctic. The ablation study investigates the impact of training dataset proportions and model learning rates on the method's performance. Results from the study highlight the importance of maintaining a balance in dataset proportions, selecting an optimal learning rate, and considering model depth in achieving optimal performance.

Integrating vision-based AI and large language models for real-time water pollution surveillance.

Water pollution has become a major concern in recent years, affecting over 2 billion people worldwide, according to UNESCO. This pollution can occur by either naturally, such as algal blooms, or man-made when toxic substances are released into water bodies like lakes, rivers, springs, and oceans. To address this issue and monitor surface-level water pollution in local water bodies, an informative real-time vision-based surveillance system has been developed in conjunction with large language models (LLMs). This system has an integrated camera connected to a Raspberry Pi for processing input frames and is further linked to LLMs for generating contextual information regarding the type, causes, and impact of pollutants on both human health and the environment. This multi-model setup enables local authorities to monitor water pollution and take necessary steps to mitigate it. To train the vision model, seven major types of pollutants found in water bodies like algal bloom, synthetic foams, dead fishes, oil spills, wooden logs, industrial waste run-offs, and trashes were used for achieving accurate detection. ChatGPT API has been integrated with the model to generate contextual information about pollution detected. Thus, the multi-model system can conduct surveillance over water bodies and autonomously alert local authorities to take immediate action, eliminating the need for human intervention. PRACTITIONER POINTS: Combines cameras and LLMs with Raspberry Pi for processing and generating pollutant information. Uses YOLOv5 to detect algal blooms, synthetic foams, dead fish, oil spills, and industrial waste. Supports various modules and environments, including drones and mobile apps for broad monitoring. Educates on environmental healthand alerts authorities about waterpollution.

AI Based Multilingual Chatbot: Advancing Higher Education in Rural Communities

The project "AI Based Multilingual Chatbot: Advancing Higher Education in Rural Communities" addresses the pressing need for educational support in rural areas. With advancements in AI and natural language processing (NLP), chatbots have emerged as promising tools to bridge educational gaps. However, existing solutions often lack multilingual support and fail to cater to the unique needs of rural communities. This project aims to develop a multilingual chatbot tailored specifically for rural contexts, enabling access to educational resources and support in local languages. By leveraging AI and NLP technologies, the chatbot will provide personalized assistance to rural students, empowering them to pursue higher education aspirations. Through this project, we aim to address the challenges faced by rural communities in accessing quality education and contribute to their educational advancement. The methodology of the project involves leveraging machine learning algorithms and natural language processing techniques to develop the chatbot. Objectives include designing a user-friendly interface, implementing multilingual support, and ensuring robust data storage and retrieval. Design and experimental tools such as Python, Flask, NLTK, and scikit-learn will be utilized. Key specifications include handling diverse user queries, ensuring data security, and optimizing response generation algorithms. The sequence involves initial data gathering, followed by algorithm development, system testing, and iterative refinement based on user feedback. The key findings of the project showcase significant improvements in user interaction and system performance. Experimental data demonstrates high accuracy in query processing and response generation. The developed chatbot exhibits a working efficiency of over 90%, as indicated by user satisfaction surveys and performance metrics. These outcomes validate the effectiveness of the implemented methodologies and design choices. Key Words: User Experience, Product Recommendation, Neural Network (CNN’s)

Contrastive adversarial gender debiasing

This research contributes a comprehensive analysis of gender bias within contemporary AI language models, specifically examining iterations of the GPT series, alongside Gemini and Llama. The study offers a systematic investigation, encompassing multiple experiments spanning sentence completions, generative narratives, bilingual analysis, and visual perception assessments. The primary objective is to scrutinize the evolution of gender bias in these models across iterations, explore biases in professions and contexts, and evaluate multilingual disparities. Notably, the analyses reveal a marked evolution in GPT iterations, with GPT4 showcasing significantly reduced or negligible biases, signifying substantial advancements in bias mitigation. Professions and contexts exhibit model biases, indicating associations with specific genders. Multilingual evaluations demonstrate subtle disparities in gender bias tendencies between English and Spanish narratives. To effectively mitigate these biases, we propose a novel Contrastive Adversarial Gender Debiasing (CAGD) method that synergistically combines contrastive learning and adversarial training techniques. The CAGD method enables language models to learn gender-neutral representations while promoting robustness against gender biases, consistently outperforming original and adversarially debiased models across various tasks and metrics. These findings underscore the complexity of gender bias in AI language models, emphasizing the need for continual bias mitigation strategies, such as the proposed CAGD approach, and ethical considerations in AI development and deployment.

Special Issue on Multisensory eXtended Reality Integrating GPT as an Assistant for Low-Cost Virtual Reality Escape-Room Games.

This work explores the integration of GPT, an AI language model developed by OpenAI, as an assistant in low-cost virtual escape games. The study focuses on the synergy between Virtual Reality (VR) and GPT, aiming to evaluate its performance in helping solve logical challenges within a specific context in the virtual environment while acting as a personalized assistant through voice interaction. The findings from user evaluations revealed both positive perceptions and limitations of GPT in addressing highly complex challenges, indicating its potential as a valuable tool for providing assistance and guidance in problem-solving situations. The study also identified areas for future improvement, including adjusting the difficulty of puzzles and enhancing GPT's contextual understanding. Overall, the research sheds light on the opportunities and challenges of integrating AI language models like GPT in virtual gaming environments, offering insights for further advancements in this field.

Communicating the cultural other: trust and bias in generative AI and large language models

Abstract This paper is concerned with issues of trust and bias in generative AI in general, and chatbots based on large language models in particular (e.g. ChatGPT). The discussion argues that intercultural communication scholars must do more to better understand generative AI and more specifically large language models, as such technologies produce and circulate discourse in an ostensibly impartial way, reinforcing the widespread assumption that machines are objective resources for societies to learn about important intercultural issues, such as racism and discrimination. Consequently, there is an urgent need to understand how trust and bias factor into the ways in which such technologies deal with topics and themes central to intercultural communication. It is also important to scrutinize the ways in which societies make use of AI and large language models to carry out important social actions and practices, such as teaching and learning about historical or political issues.

The Use of ChatGPT in Language Education

This study explores the multifaceted role of ChatGPT, an AI language model, in language teaching and learning contexts. Drawing upon a comprehensive review of literature and empirical studies, the benefits and disadvantages of integrating ChatGPT into language education are examined. Findings reveal that ChatGPT offers personalized feedback, facilitates customized lesson planning, and enhances language proficiency through individualized learning experiences. However, concerns regarding misinformation, lack of human connection, and ethical implications necessitate careful consideration. Implications for language teaching underscore the importance of strategic integration and educator training, while recommendations for further research highlight the need for addressing ChatGPT's limitations and exploring its potential in-depth. By addressing these considerations, educators can harness ChatGPT's capabilities to enhance language instruction and foster more effective learning outcomes.

When geoscience meets generative AI and large language models: Foundations, trends, and future challenges

AbstractGenerative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi‐criteria decision‐making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super‐resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data‐driven modelling and uncertainty quantification.

Clinical decision making by ChatGPT vs medical oncologists: A retrospective concordance study.

e13634 Background: This research explores the application of AI language models in clinical decision-making within the context of oncology, focusing on the concordance between AI-generated recommendations and actual treatment decisions made by physicians. Leveraging ChatGPT version 4.0, a large language model, we conducted a concordance study using retrospective patient cases from Yeolyan Hematology and Oncology Center. The hypothesis, centered on the alignment of AI chatbots with human decisions, spurred an in-depth exploration of concordance rates. Methods: A total of 228 adult solid malignancy cases from Yeolyan Hematology and Oncology Center (Jan 2020 - Sep 2021) were analyzed, focusing on primary cancers and data from multidisciplinary tumor board meetings. We scored concordance and analyzed the response times as well. Results: The study unveiled an overall 62.3% concordance rate, with varying rates among different cancer types: lung (50%), breast (50%), gastrointestinal (66%), and gynecological (81.8%) cancers. Despite promising results, challenges emerged, including variability in ChatGPT-4 responses and concerns about reliability and reproducibility. The study acknowledges the influence of prior assessments from other healthcare facilities, financial disparities impacting treatment decisions, and potential oversimplification of chemotherapy regimens. Conclusions: AI-based language models like ChatGPT present intriguing possibilities for healthcare, yet further research, controlled trials, and prospective studies are imperative to comprehensively understand and enhance their applicability in personalized patient care. This research sheds light on the complexities and considerations associated with integrating AI into oncological decision-making, offering valuable insights for future advancements in this evolving field.

Investigating the Evolving Landscape of Deepfake Technology: Generative AI's Role in it's Generation and Detection

The world of artificial intelligence is constantly changing, with Generative AI and Large Language Models (LLMs) leading the way in bringing new technological advancements. This paper offers a detailed look at these groundbreaking technologies and how they are shaping the digital world today. We explore the technical aspects of Generative AI and LLMs, explain their unique features, and compare them to traditional AI models.One of the key focuses of our research is the growing issue of DeepFakes—artificial intelligence-generated media that presents a significant challenge in verifying content. We conduct a thorough examination of few deepfake detection techniques out of which we will be implementing and analyzing one of them. Our research implements a framework for Deep Fake Image Detection. The suggested solution utilizes a RESNET-50(Residual Network with 50 layers) and MTCNN (Multi-task Cascaded Convolutional Networks) models for detecting whether the images are real or fake. This study conducts the Hypothesis testing for the proposed solution taking in consideration that the current Deepfake detection algorithms are less effective in detecting highly realistic Deepfakes compared to less sophisticated manipulations. By investigating the convergence of deep learning, neural networks, and sophisticated algorithms, we set the stage for advancements in AI-based content verification.

War of Words: Harnessing the Potential of Large Language Models and Retrieval Augmented Generation to Classify, Counter and Diffuse Hate Speech

This paper explores the emergence of divergent narratives in the wake of the Russian-Ukraine war, which began on February 24, 2022, and the innovative application of AI language models, specifically RetrievalAugmented Generation (RAG) and instruction-based large language models (LLMs), in countering hateful speech on social media. We design a pipeline to automatically discover and then respond to hateful content trending on social media platforms. Monitoring via traditional topic/narrative modeling often focuses on lowlevel content, which is difficult to interpret. In addition, workflows for prioritization and response generation are often highly manual. We utilize several large language models (LLMs) throughout our pipeline to detect and summarize topics, to determine whether tweets contain hate speech and to generate counter narratives. We test our approach on Ukraine Bio-Lab Tweet Corpus of 500k Tweets and evaluate the counter-narrative generation performance across several dimensions: relevance, grammaticality, factuality, and diversity. Our approach outperforms existing state of the art algorithms for hate speech detection and promising counter-narrative generation performance scores across our metrics reflect effectiveness of our pipeline in addressing hateful social media posts

Democratizing Data Visualization and Insights Extraction with Pandas, Generative AI, and CSV Data

Data visualization and insights extraction are crucial components of modern data-driven decision-making processes. However, traditional methods often require extensive coding knowledge, creating barriers for non-technical users. This whitepaper presents a comprehensive solution that integrates the powerful data manipulation capabilities of the Pandas library with cutting-edge Generative AI and natural language processing techniques. By leveraging a fine-tuned GPT-3 model trained on a diverse corpus of data analysis and visualization resources, our approach enables users to upload CSV data files and receive automated insights, default visualizations, and the ability to generate custom visualizations through intuitive natural language prompts. The solution streamlines the workflow, eliminating the need for coding expertise while ensuring data privacy and integrity within a secure execution environment. User studies and benchmarking demonstrate increased productivity, time savings, and high user satisfaction. This solution has the potential to democratize data analysis and visualization, empowering decision-makers across various industries with data-driven insights and informed decision-making processes.

Enhancing College Learners' Writing Skills through The Integration of ChatGPT: Strategies, Benefits, And Concerns

Abstract. Writing instructions pose challenges for educators, as students often struggle to effectively express their ideas in writing. First language influence further complicates the writing process. With the emergence of ChatGPT, this paper aims to explore its potential application in the writing classroom. This study investigates the strategies employed by students in integrating ChatGPT, an AI language model, into their writing process. Seven outstanding undergraduate students of the Applied English program preparing themselves for Indonesian International Student Mobility Awards (IISMA) were asked to write some simple essays. They are encouraged to utilize ChatGPT to help them improve their writing. Their portfolio of essays was used to explore how students utilize this technology to enhance their writing skills and evaluate the effectiveness of such integration. Some in-depth interviews were also conducted with these students who actively engaged with ChatGPT to understand how they enhance their writing skills through the help of ChatGPT: students' experiences and approaches to incorporating ChatGPT into their writing workflow, including idea generation, improving sentence structure and grammar, enhancing coherence and flow, and expanding vocabulary. Perceived benefits and challenges associated with integrating ChatGPT are examined, including: effects on confidence, efficiency, and writing quality. Concerns regarding overreliance, plagiarism, and authenticity of the written work are also addressed. This study's findings highlight the efficacy of integrating AI language models in writing classes through ChatGPT's writing suggestions, enhancing students' expression, structure, and style. Further the study shows that while ChatGPT improves students' writing skills , it raises concerns about overreliance and plagiarism. Ethical guidelines and long-term studies are needed to address dependency issues and maintain authenticity. Assessing ChatGPT's adaptability across writing contexts is crucial for responsible integration in education, advancing AI-assisted writing understanding. Keywords: college writing, ChatGPT, learning strategies