Artificial Intelligence Solutions Research Articles

AI-based R&D for frozen and thawed meat: Research progress and future prospects.

Frozen and thawed meat plays an important role in stabilizing the meat supply chain and extending the shelf life of meat. However, traditional methods of research and development (R&D) struggle to meet rising demands for quality, nutritional value, innovation, safety, production efficiency, and sustainability. Frozen and thawed meat faces specific challenges, including quality degradation during thawing. Artificial intelligence (AI) has emerged as a promising solution to tackle these challenges in R&D of frozen and thawed meat. AI's capabilities in perception, judgment, and execution demonstrate significant potential in problem-solving and task execution. This review outlines the architecture of applying AI technology to the R&D of frozen and thawed meat, aiming to make AI better implement and deliver solutions. In comparison to traditional R&D methods, the current research progress and promising application prospects of AI in this field are comprehensively summarized, focusing on its role in addressing key challenges such as rapid optimization of thawing process. AI has already demonstrated success in areas such as product development, production optimization, risk management, and quality control for frozen and thawed meat. In the future, AI-based R&D for frozen and thawed meat will also play an important role in promoting personalization, intelligent production, and sustainable development. However, challenges remain, including the need for high-quality data, complex implementation, volatile processes, and environmental considerations. To realize the full potential of AI that can be integrated into R&D of frozen and thawed meat, further research is needed to develop more robust and reliable AI solutions, such as general AI, explainable AI, and green AI.

Democratising artificial intelligence in healthcare: community-driven approaches for ethical solutions

The rapid advancement and widespread adoption of artificial intelligence (AI) has ushered in a new era of possibilities in healthcare, ranging from clinical task automation to disease detection. AI algorithms have the potential to analyse medical data, enhance diagnostic accuracy, personalise treatment plans and predict patient outcomes among other possibilities. With a surge in AI's popularity, its developments are outpacing policy and regulatory frameworks, leading to concerns about ethical considerations and collaborative development. Healthcare faces its own ethical challenges, including biased datasets, under-representation and inequitable access to resources, all contributing to mistrust in medical systems. To address these issues in the context of AI healthcare solutions and prevent perpetuating existing inequities, it is crucial to involve communities and stakeholders in the AI lifecycle.This article discusses four community-driven approaches for co-developing ethical AI healthcare solutions, including understanding and prioritising needs, defining a shared language, promoting mutual learning and co-creation, and democratising AI. These approaches emphasise bottom-up decision-making to reflect and centre impacted communities' needs and values. These collaborative approaches provide actionable considerations for creating equitable AI solutions in healthcare, fostering a more just and effective healthcare system that serves patient and community needs.

From Pursuit of the Universal AGI Architecture to Systematic Approach to Heterogeneous AGI (SAGI): Addressing Alignment, Energy & AGI Grand Challenges

Artificial intelligence (AI) faces a trifecta of grand challenges: the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. Contemporary AI solutions consume unsustainable amounts of energy during model training and daily operations. Making things worse, the amount of computation required to train each new AI model has been doubling every 2 months since 2020, directly translating to unprecedented increases in energy consumption. The leap from AI to AGI requires multiple functional subsystems operating in a balanced manner, which requires a system architecture. However, the current approach to AI lacks system design; even though system characteristics play a key role in the human brain; from the way it processes information to how it makes decisions. In this paper, we posit that system design is the missing piece in overcoming current AI the grand challenges. We present a Systematic Approach to AGI (SAGI) that utilizes system design principles to overcome the energy wall and the alignment challenges. This paper asserts that artificial intelligence can be realized through a multiplicity of design-specific pathways, rather than a singular, overarching AGI architecture. AGI systems may exhibit diverse architectural configurations and capabilities, contingent upon their intended use cases. We argue that AI alignment, the most difficult among the grand challenges, is not attainable without a way to reflect the complexity of the human moral system and its subsystems in the AGI architectures. We claim that AGI approaches such as symbolicism, connectionism and others are not fundamental to AGI but emergent from the system design processes. Hence, we focus on employing system design principles as a guiding framework, rather than solely concentrating on a universal AGI architecture.

Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care

Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.

Enabling AI in Radiology: Evaluation of an AI Deployment Process.

Artificial intelligence (AI) is expected to transform healthcare systems and make them more sustainable. Despite the increased availability of AI tools for disease detection, evidence of their impact on healthcare organisations and patient care remains limited. Drawing on previous research underscoring the need for comprehensive evaluations of real-world AI deployments, this paper explores the challenges and opportunities encountered while procuring and implementing AI solutions for radiology. The paper aims to contribute to a better understanding of the complexities surrounding AI deployments in real-world clinical settings through a process evaluation study.

GenAI-Assisted Database Deployment for Heterogeneous Indigenous–Native Ethnographic Research Data

In ethnographic research, data collected through surveys, interviews, or questionnaires in the fields of sociology and anthropology often appear in diverse forms and languages. Building a powerful database system to store and process such data, as well as making good and efficient queries, is very challenging. This paper extensively investigates modern database technology to find out what the best technologies to store these varied and heterogeneous datasets are. The study examines several database categories: traditional relational databases, the NoSQL family of key-value databases, graph databases, document databases, object-oriented databases and vector databases, crucial for the latest artificial intelligence solutions. The research proves that when it comes to field data, the NoSQL lineup is the most appropriate, especially document and graph databases. Simplicity and flexibility found in document databases and advanced ability to deal with complex queries and rich data relationships attainable with graph databases make these two types of NoSQL databases the ideal choice if a large amount of data has to be processed. Advancements in vector databases that embed custom metadata offer new possibilities for detailed analysis and retrieval. However, converting contents into vector data remains challenging, especially in regions with unique oral traditions and languages. Constructing such databases is labor-intensive and requires domain experts to define metadata and relationships, posing a significant burden for research teams with extensive data collections. To this end, this paper proposes using Generative AI (GenAI) to help in the data-transformation process, a recommendation that is supported by testing where GenAI has proven itself a strong supplement to document and graph databases. It also discusses two methods of vector database support that are currently viable, although each has drawbacks and benefits.

Elevating workforce stability: augmented reality and artificial intelligence solutions for overcoming employee turnover challenges in hospitality and tourism

PurposeThe aim of this article is to produce alternative solution suggestions at a conceptual level, by utilizing technologies in the field of Augmented Reality (AR) and Artificial Intelligence (AI), to address the increasing personnel shortages encountered in the tourism and hospitality industry. The discussion is based on a review of the literature.Design/methodology/approachThis article presents a qualitative study investigating the impact of Artificial Intelligence (AI) and Augmented Reality (AR) technologies on the workforce turnover rate in the tourism and hospitality industry in general.FindingsAlthough Artificial Intelligence (AI) and Augmented Reality (AR) technologies have both positive and negative aspects for the hospitality and tourism industry and its employees, these technologies can be used to reduce the factors that cause employee turnover. In particular, it leads to improvements in job satisfaction, job commitment and career opportunities of sector employees, reduction of job stress, and selection and retention of the right employees.Originality/valueThis study examines the factors that tourism sector employees encounter in the sector and that cause the workforce turnover rate to increase, and emphasizes the importance of the possible benefits of the use of Artificial Intelligence (AI) and Augmented Reality (AR) technologies in reducing these factors that cause the workforce turnover rate.

Machine learning and deep learning prediction of patient specific quality assurance in breast IMRT radiotherapy plans using Halcyon specific complexity indices

IntroductionNew radiotherapy machines such as Halcyon are capable of delivering dose-rate of 600 monitor-units per minute, allowing large numbers of patients treated per day. However, patient-specific quality assurance (QA) is still required, which dramatically decrease machine availability. Innovative artificial intelligence (AI) algorithms could predict QA result based on complexity metrics. However, no AI solution exists for Halcyon machines and the complexity metrics to be used have not been definitively determined. The aim of this study was to develop an AI solution capable of firstly determining the complexity indices to be obtained and secondly predicting patient-specific QA in a routine clinical setting. MethodsThree hundred and eighteen beams from 56 patients with breast cancer were used. The seven complexity indices named Modulation-Complexity-Score (MCS), Small-Aperture-Score (SAS10), Beam-Area (BA), Beam-Irregularity (BI), Beam-Modulation (BM), Gantry and Collimator angles were used as input to the AI model. Machine learning (ML) and deep learning (DL) models using tensorflow were set up to predict DreamDose QA conformance. ResultsMCS, BI, gantry and collimator angle are not correlated with QA compliance. Therefore, ML and DL models were trained using SAS10, BA and BM complexity indices. ROC analyses enabled to find best predicted probability threshold to increase specificity and sensitivity. ML models did not show satisfactory performance with an area under-the-curve (AUC) of 0.75 and specificity and sensitivity of 0.88 and 0.86. However, optimised DL model showed better performance with an AUC of 0.95 and specificity and sensitivity of 0.98 and 0.97. ConclusionThe DL model demonstrated a high degree of accuracy in its predictions of the quality assurance (QA) results. Our online predictive QA-platform offers significant time savings in terms of accelerator occupancy and working time.

Artificial intelligence for literature reviews: opportunities and challenges

This paper presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates prior research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in this field shows great potential in providing more effective support for researchers, moving towards the semi-automatic creation of literature reviews. Our study focuses on how AI techniques are applied in the semi-automation of SLRs, specifically in the screening and extraction phases. We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features. We also analyse 11 recent tools that leverage large language models for searching the literature and assisting academic writing. Finally, the paper discusses current trends in the field, outlines key research challenges, and suggests directions for future research. We highlight three primary research challenges: integrating advanced AI solutions, such as large language models and knowledge graphs, improving usability, and developing a standardised evaluation framework. We also propose best practices to ensure more robust evaluations in terms of performance, usability, and transparency. Overall, this review offers a detailed overview of AI-enhanced SLR tools for researchers and practitioners, providing a foundation for the development of next-generation AI solutions in this field.

Innovation and challenges of artificial intelligence technology in personalized healthcare

As the burgeoning field of Artificial Intelligence (AI) continues to permeate the fabric of healthcare, particularly in the realms of patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care. Through the introduction of innovative instruments such as virtual assistant chatbots, wearable monitoring devices, predictive analytic models, personalized treatment regimens, and automated appointment systems, AI is not only amplifying the quality of care but also empowering patients and fostering a more interactive dynamic between the patient and the healthcare provider. Yet, this progressive infiltration of AI into the healthcare sphere grapples with a plethora of challenges hitherto unseen. The exigent issues of data security and privacy, the specter of algorithmic bias, the requisite adaptability of regulatory frameworks, and the matter of patient acceptance and trust in AI solutions demand immediate and thoughtful resolution .The importance of establishing stringent and far-reaching policies, ensuring technological impartiality, and cultivating patient confidence is paramount to ensure that AI-driven enhancements in healthcare service provision remain both ethically sound and efficient. In conclusion, we advocate for an expansion of research efforts aimed at navigating the ethical complexities inherent to a technology-evolving landscape, catalyzing policy innovation, and devising AI applications that are not only clinically effective but also earn the trust of the patient populace. By melding expertise across disciplines, we stand at the threshold of an era wherein AI's role in healthcare is both ethically unimpeachable and conducive to elevating the global health quotient.

AI-enabled FinTech for innovative sustainability: promoting organizational sustainability practices in digital accounting and finance

Purpose The purpose of this study is to delve into the intricate terrain of assimilating sustainability practices into digital accounting and finance, centring on the transformative dynamics introduced by artificial intelligence (AI)-enabled FinTech. The primary objective is to scrutinize critical lacunae in existing literature, exploring how organizations can meticulously construct comprehensive sustainability frameworks. Simultaneously, the study investigates the protracted repercussions of AI-enabled FinTech on the enduring sustainability paradigms. Design/methodology/approach Executing a systematic literature review, the research engaged in the meticulous identification and assessment of a voluminous pool of 1,158 articles. Using a judicious two-phase strategy, the scrutiny distilled a mere 64 pertinent articles, subjecting them to rigorous evaluation encompassing methodologies, contributions and overall quality. The Fuzzy Delphi method was used to elicit expert opinions and facilitate consensus-building, leveraging fuzzy logic to accommodate uncertainties in the data. Findings The review navigates the convoluted impact of AI across diverse sectors, accentuating its transformative imprint on realms such as health care, finance and transportation. Specifically, in the financial domain, the discerning eye of AI-enabled FinTech optimizes investment portfolios, augments risk assessment, propels financial inclusion and streamlines the intricate landscape of sustainability reporting. The study meticulously pinpoints research gaps encompassing investment optimization, risk management, financial inclusion, sustainability reporting and ethical considerations within the intricate milieu of AI-enabled FinTech. This research contributes to the existing body of knowledge by synthesizing intricate thematic strands, discerning overarching trends and spotlighting critical voids in the synthesis of sustainability practices and AI-enabled FinTech. The findings resonate with far-reaching implications, emphasizing the exigency of comprehensive investigations into the longitudinal sustainability ramifications instigated by AI-enabled FinTech. Originality/value The study underscores the imperative of crafting robust ethical frameworks for the equitable and transparent deployment of AI solutions within the intricate landscape of FinTech. Moreover, this research stands poised to shape organizational strategies, inform regulatory frameworks and guide investment decisions, thereby catalyzing the cultivation of conscientious and sustainable financial practices.

Bridging the gap for underserved populations: Personalized AI solutions for college access and learning support

AbstractThe evolution of artificial intelligence (AI) technology offers promising solutions to underserved populations in the United States. AI tools can reduce college access and academic barriers for low‐income students, first‐generation college students, racial and ethnic minorities, individuals with disabilities, and students living in rural areas. While AI tools offer support to college students from all demographics, they offer uniquely individualized assistance to underserved students to overcome the complex postsecondary schooling challenges they face. Examples of the nuanced, often complex, obstacles faced by these vulnerable students are discussed along with brief descriptions of AI tools that can help. AI tools leverage the power of high‐speed computing, expansive access to information, machine learning capabilities, and natural language processing. Utilizing the power of AI tools, vulnerable learners can increase their access to financial, academic, and social resources needed to attend and be successful in postsecondary education. AI tools also help close opportunity gaps as they provide means by which college students gain increased independence and control over their educational and career journeys. Note that the AI tools referenced in this article are included as examples only. We encourage ongoing discussion of AI‐powered tools and solutions to help underserved students gain more access to higher education and get individualized support for vulnerable learners.Practical Takeaways Highlighting the potential of AI in addressing educational inequalities. Using AI in higher education settings for educators to better understand and respond to student needs. Implementing AI usage in higher education settings for underserved populations to reduce dropout rates. Leveraging technology to offer tailored guidance, bridging gaps, and fostering community among the underserved populations in higher education.

A generalist vision-language foundation model for diverse biomedical tasks.

Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs. However, existing biomedical generalist AI solutions are typically heavyweight and closed source to researchers, practitioners and patients. Here, we describe BiomedGPT, the first open-source and lightweight vision-language foundation model, designed as a generalist capable of performing various biomedical tasks. BiomedGPT achieved state-of-the-art results in 16 out of 25 experiments while maintaining a computing-friendly model scale. We also conducted human evaluations to assess the capabilities of BiomedGPT in radiology visual question answering, report generation and summarization. BiomedGPT exhibits robust prediction ability with a low error rate of 3.8% in question answering, satisfactory performance with an error rate of 8.3% in writing complex radiology reports, and competitive summarization ability with a nearly equivalent preference score to human experts. Our method demonstrates that effective training with diverse data can lead to more practical biomedical AI for improving diagnosis and workflow efficiency.

AI-based methods for detecting and classifying age-related macular degeneration: a comprehensive review

This paper explores the advancements and achievements of artificial intelligence (AI) in computer vision (CV), particularly in the context of diagnosing and grading age-related macular degeneration (AMD), one of the most common leading causes of blindness and low vision that impact millions of patients globally. Integrating AI in biomedical engineering and healthcare has significantly enhanced the understanding and development of the CV application to mimic human problem-solving abilities. By leveraging AI-based models, ophthalmologists can improve the accuracy and speed of disease diagnosis, enabling early treatment and mitigating the severity of the conditions. This paper presents a comprehensive analysis of many studies on AMD published between 2014 and 2024, with more than 80% published after 2020. Various methodologies and techniques are examined, particularly emphasizing utilizing different retinal imaging modalities like color fundus photography and optical coherence tomography (OCT), where 66% of the studies used OCT datasets. This review aims to compare the efficacy of these AI-based approaches, including machine learning and deep learning, in detecting and diagnosing different stages and grades of AMD based on the evaluation of different performance metrics using different private and public datasets. In addition, this paper introduces some suggested AI solutions for future work.

Performance of AI for preoperative CT assessment of lung metastases: Retrospective analysis of 167 patients

ObjectivesTo evaluate the performance of artificial intelligence (AI) in the preoperative detection of lung metastases on CT. Materials and methodsPatients who underwent lung metastasectomy in our institution between 2016 and 2020 were enrolled, their preoperative CT reports having been performed before an AI solution (Veye Lung Nodules, version 3.9.2, Aidence) became available as a second reader in our department.All CT scans were retrospectively processed by AI. The sensitivities of unassisted radiologists (original CT radiology reports), AI reports alone and both combined were compared. Ground truth was established by a consensus reading of two radiologists, who analyzed whether the nodules mentioned in the pathology report were retrospectively visible on CT. Multivariate analysis was performed to identify nodule characteristics associated with detectability. ResultsA total of 167 patients (men: 62.9 %; median age, 59 years [47–68]) with 475 resected nodules were included. AI detected an average of 4 nodules (0–17) per CT, of which 97 % were true nodules. The combination of radiologist plus AI (92.4 %) had significantly higher sensitivity than unassisted radiologists (80.4 %) (p < 0.001). In 27/57 (47.4 %) patients who had multiple preoperative CT scans, AI detected lung nodules earlier than the radiologist. Vascular contact was associated with non-detection by radiologists (OR:0.32[0.19, 0.54], p < 0.001), whilst the presence of cavitation (OR:0.26[0.13, 0.54], p < 0.001) or pleural contact (OR:0.10[0.04, 0.22], p < 0.001) was associated with non-detection by AI. ConclusionAI significantly increases the sensitivity of preoperative detection of lung metastases and enables earlier detection, with a significant potential benefit for patient management.

Opportunities and challenges for using artificial intelligence in academic continuity: Case of Georgia

This article examined how higher education institutions in the Republic of Georgia responded to the challenges of the COVID-19 pandemic. Focusing on the context of the digital revolution and centering upon the utilization of artificial intelligence (AI), it aimed to discern how these institutions sustained the continuity of the learning process and implemented innovative measures. Based on the research findings, the solutions proposed in this article present AI tools for personalized learning, adaptive assessment, and intelligent tutoring. As institutions navigated the post-pandemic era, the integration of AI into education proved viable. This research provided tangible insights into the digital revolution affecting education and informing strategic decision-making in Georgia's evolving higher education landscape. Recognizing the difficulties caused by the pandemic and the inherent challenges associated with strategic decision-making, a qualitative research approach was used to gain nuanced insights. It relied on in-depth interviews, recognizing the spontaneous and time-sensitive nature of strategic decisions made by universities during the pandemic, often precluding extensive pre-planning. The authors provided critical findings in terms of the pros and cons of distance learning and proposed AI solutions for each challenge that Universities faced during and after this significant disruption, giving real successful examples.

Decolonizing global AI governance: assessment of the state of decolonized AI governance in Sub-Saharan Africa.

Global artificial intelligence (AI) governance must prioritize equity, embrace a decolonial mindset, and provide the Global South countries the authority to spearhead solution creation. Decolonization is crucial for dismantling Western-centric cognitive frameworks and mitigating biases. Integrating a decolonial approach to AI governance involves recognizing persistent colonial repercussions, leading to biases in AI solutions and disparities in AI access based on gender, race, geography, income and societal factors. This paradigm shift necessitates deliberate efforts to deconstruct imperial structures governing knowledge production, perpetuating global unequal resource access and biases. This research evaluates Sub-Saharan African progress in AI governance decolonization, focusing on indicators like AI governance institutions, national strategies, sovereignty prioritization, data protection regulations, and adherence to local data usage requirements. Results show limited progress, with only Rwanda notably responsive to decolonization among the ten countries evaluated; 80% are 'decolonization-aware', and one is 'decolonization-blind'. The paper provides a detailed analysis of each nation, offering recommendations for fostering decolonization, including stakeholder involvement, addressing inequalities, promoting ethical AI, supporting local innovation, building regional partnerships, capacity building, public awareness, and inclusive governance. This paper contributes to elucidating the challenges and opportunities associated with decolonization in SSA countries, thereby enriching the ongoing discourse on global AI governance.

Harnessing Ai for Socio-economic Equity in Uganda: Bridging the Digital Divide through Agricultural Innovation

The paper explores the application of artificial intelligence (AI) technologies in addressing digital divide challenges and enhancing socio-economic equity in Uganda. The digital divide, characterized by disparities in access to digital resources, internet connectivity, and digital literacy, significantly impacts socio-economic development. This research focuses on AI technologies such as machine learning, image recognition, natural language processing (NLP), and GIS-integrated AI, which are mildly actively used in Uganda’s agricultural sector. Studies have shown that digital technologies can address key challenges in agriculture, such as access to extension services, marketing systems, and financial inclusion. For example, mobile money has significantly increased financial inclusion among Uganda's unbanked populations, including women, youth, and rural households. However, barriers such as low digital literacy, limited smartphone ownership, and inadequate internet access hinder the adoption of these technologies, especially among disadvantaged groups. Therefore, while digital innovations have the potential to improve productivity and inclusiveness in agriculture, their limited use could exacerbate existing inequalities by leaving behind those who cannot easily access or afford these technologies. Leveraging these technologies, the study examines how they contribute to improving crop yield predictions, pest and disease management, and precision agriculture. Through the analysis of initiatives undertaken by the National Agricultural Research Organization (NARO), including the use of AI-driven image recognition for plant disease identification and NLP for farmer support systems, highlighting the practical applications and benefits. The findings emphasize the critical infrastructure requirements, ethical considerations, and potential challenges in deploying AI solutions in low-resource communities [9]. Ultimately this research aims to provide a comprehensive framework for utilizing AI to foster digital inclusivity and promote socio-economic growth in developing regions, using Uganda as a case study

Use of artificial intelligence in geographic atrophy in age-related macular degeneration

The first regulatory approval of treatment for geographic atrophy (GA) secondary to age-related macular degeneration in the USA constitutes an important milestone; however, due to the nature of GA as anon-acute, insidiously progressing pathology, the ophthalmologist faces specific challenges concerning risk stratification, making treatment decisions, monitoring of treatment and patient education. Innovative retinal imaging modalities, such as fundus autofluorescence (FAF) and optical coherence tomography (OCT) have enabled identification of typical morphological alterations in relation to GA, which are also suitable for the quantitative characterization of GA. Solutions based on artificial intelligence (AI) enable automated detection and quantification of GA-specific biomarkers on retinal imaging data, also retrospectively and over time. Moreover, AI solutions can be used for the diagnosis and segmentation of GA as well as the prediction of structure and function without and under GA treatment, thereby making a valuable contribution to treatment monitoring and the identification of high-risk patients and patient education. The integration of AI solutions into existing clinical processes and software systems enables the broad implementation of informed and personalized treatment of GA secondary to AMD.

A PERSPECTIVE INTO THE FUTURE OF TEACHING AND LEARNING IN THE CONTEXT OF THE RISING INTEREST IN ARTIFICIAL INTELLIGENCE IN EDUCATION. OPPORTUNITIES AND ETHICAL CHALLENGES

: The paper aims to provide an image of the future of teaching and learning in the context of artificial intelligence transforming various industries, including sports, education, and construction. Its place in education is a frequently discussed topic. While some argue that artificial intelligence will revolutionize education, others worry that it will take over to the harm of educators and students. Though robotics in the classroom is still a ways off, artificial intelligence is finding its way into the classroom. AI has the power to improve teaching and learning methods, solve some of the largest issues facing education today, and hasten the achievement of inclusive and equitable quality education. In addition to delivering artificial intelligence courses, EdTech businesses are increasingly using eLearning solutions to personalize learning experiences, pinpoint knowledge gaps, and give focused feedback. Also, AI-driven education is upending conventional teaching methods and influencing how this field will use technology in the future. With the use of complex algorithms and massive data sets, artificial intelligence solutions for education may provide a lot of advantages, yet as with the use of artificial intelligence in any context, there are significant ethical considerations which are a hot topic of discussion in the technology world and beyond, and the majority of university degree programs are including courses on AI ethics in their curricula. Therefore, the paper presents the benefits of AI in the classroom, such as engagement and assistance for students, assessment and evaluation, and individualized learning but also about the difficulties and worries associated with AI in education, including prejudice and privacy issues, as well as the moral issues raised by AI-powered learning. It also discusses the possible effects on the educational system and how students are trained for the workforce of the future as potential applications of AI in education are explored.