Ability Of Artificial Intelligence Research Articles

Navigating the Future of Pedagogy: The Integration of AI Tools in Developing Educational Assessment Rubrics

ABSTRACTThe advent of artificial intelligence (AI) technologies heralds a transformative era in education. This study investigates the integration of AI tools in developing educational assessment rubrics within the ‘Curriculum Design Development and Evaluation’ course at the University of A Coruña during the 2023–2024 academic year. Employing an action–research methodological approach, 27 comprehensive evaluation rubrics were developed using AI tools like ChatGPT. The findings highlight AI's ability to enhance rubric precision, efficiency and alignment with curricular objectives, thus facilitating personalised learning experiences. This research underscores AI's transformative potential in education, advocating for broader adoption and the necessity for educators to acquire AI proficiency. The study demonstrates how AI tools can streamline the rubric development process, making it more accurate and time efficient while also ensuring the inclusion of essential curricular elements. Through this approach, AI can support innovative assessment strategies that are both effective and adaptable to diverse educational contexts.

Global longitudinal strain measurement in the modern era - is artificial intelligence the answer ?

Abstract Background Global longitudinal strain (GLS) enhances the assessment of left ventricular (LV) function beyond ejection fraction (EF) in 2D echocardiography (Echo). However, manual GLS measurement, the most common technique, is operator dependent. A novel vendor-independent artificial intelligence (AI)-based software streamlines automated 2D-Echo exam evaluation and GLS calculation. Purpose We sought to evaluate the ability of the AI to automatically identify LV 4-, 2-, and 3-chamber views (CV) from 2D-Echo exams and to validate GLS against the conventional manual method. Methods 540 patients underwent standard 2D-Echo exams. The server-based AI recognized the optimal LV CVs from 2D-Echo exams and automatically calculated triplane GLS by endocardial delineation (n=489, 91%). Manual LV GLS was performed independently by accredited cardiologists. Pearson’s correlation (R) and Bland-Altman analysis were performed to assess bias and limits of agreement (LOA) for GLS. Multivariate between-method regression was performed using ordinary least squares to disclose possible sources of possible bias. Significance was defined as a 2-tailed P value < 0.05. Results Patients’ median age was 70 years [18-93], 59% were males. The 2D-Echo indications were coronary artery disease (36%), valvular heart disease (15%), non-ischemic heart failure (15%) and others (34%). The feasibility of the AI to calculate GLS was 91% (n=489) due to endocardial border delineation issues. AI correctly classified all CVs. An excellent correlation (r=0.92, p<0.001) was observed between AI and manual GLS, with low bias (0.5%) and narrow LOAs (-3.1 and +4.2%) (Figure 1, top row). We identified LV end-diastolic volume and EF as statistically significant confounders (p < 0.05) with only small effect as depicted in Figure 1 (bottom row). Neither sex, age, acoustic window, body mass index, nor heart rate, along with the interaction with sex, end-diastolic volume, or EF, were significant (p > 0.23). Conclusion AI-based LV GLS calculation in 2D-Echo shows promising feasibility and accuracy. Strong agreement between AI-derived GLS and manual measurements supports its reliability. The AI's ability to identify optimal LV views underscores its robustness. Further validation and refinement are warranted, highlighting the transformative potential of AI in advancing cardiovascular diagnostics.

Predicting physical fitness levels from resting ECG data: a machine learning approach in cardiovascular assessment

Abstract Background Exercise Stress Testing (EST), an inexpensive noninvasive modality, is widely used for preliminary cardiovascular evaluation and ongoing monitoring of cardiac patients. The MET (Metabolic Equivalent of Task) score from EST, indicating physical fitness, is associated with overall mortality risk and is extremely helpful for preoperative evaluation. Artificial Intelligence (AI) advances, particularly Deep Learning (DL) for resting ECG analysis, have significantly improved cardiac diagnostic accuracy. Objectives This study explores AI's ability to predict MET scores from resting ECGs, aiming to streamline fitness assessments and detect early changes in fitness levels. Methods The current analysis encompasses 45,518 consecutive EST evaluations from 31,532 individual patients from 2017 to 2023. MET scores were automatically computed using an established formula, while interpretations by certified cardiologists provided a benchmark for the AI model. Poor fitness was defined as being within the lowest quartile of MET scores, adjusted for sex and age. A deep learning framework was developed to process a 10-second snippet of resting phase ECG in raw waveform, predicting the likelihood of diminished fitness and thus acting as a binary classifier. The efficacy of this Poor Fitness AI model (PF-AI model) was assessed through metrics such as the area under the receiver operating characteristic curve (AUROC), along with sensitivity, specificity, and accuracy rates. Results The average age of the study group was 55±10 years, predominantly male (68.8%, n = 31,337). Among them, 1,152 were identified as having poor fitness. Applying the model to the entire cohort through 5-fold cross-validation yielded a sensitivity of 78%, a specificity of 81%, and an AUROC of 0.87 in predicting poor fitness. Conclusions Leveraging merely 10 seconds of resting ECG data, the PF-AI model proves to be a robust tool in predicting poor fitness levels, comparable to the outcomes of comprehensive stress testing.

Integrating AI and Blockchain Technologies for Enhanced Risk Management and Operational Efficiency in Indonesian Airport Customs: Challenges and Opportunities

The rapid growth of global trade has increased the need to modernize customs operations, especially in Indonesia, where traditional methods struggle with fraud and inefficiency. This study explores the integration of disruptive technologies, specifically Artificial Intelligence (AI) and blockchain, to enhance risk management and operational efficiency in Indonesian customs. Using a qualitative methodology, including literature reviews, case studies, and interviews with customs officials, the research highlights AI's ability to automate risk assessments and fraud detection, while blockchain ensures secure, transparent transaction records. Despite the significant benefits, challenges such as personnel resistance, outdated infrastructure, and regulatory uncertainties hinder implementation. The study emphasizes the need for comprehensive training for customs officers and collaboration with Cross-Border Regulatory Agencies (CBRAs) to overcome these obstacles. Case studies from Singapore and the UAE provide valuable insights into best practices for integrating these technologies. With strategic investment and strong stakeholder commitment, Indonesia can modernize its customs systems, improve efficiency, and strengthen national security. The study concludes with recommendations for creating a comprehensive integration roadmap, developing skills initiatives, and strengthening regulatory frameworks to support the successful adoption of AI and blockchain in Indonesian customs.

Artificial Intelligence & the Capacity for Discrimination: The Imperative Need for Frameworks, Diverse Teams & Human Accountability

The increasing integration of Artificial Intelligence (AI) in various industries has led to concerns about how these systems can perpetuate discrimination, particularly in fields like employment, healthcare, and public policy. Multiple academic and business perspectives on AI discrimination, focusing on the need for global policy coordination and ethical oversight to mitigate biased outcomes, ask for our technical innovators to create contingencies that will better protect humanity’s experience with AI’s ever-expanding reach. Central to the key constructs such as biased datasets, algorithmic transparency, and the global governance of AI systems can function as a harmful drawback to these systems. Without adequate data governance and transparency, AI systems can perpetuate discrimination. AI's ability to discriminate stems primarily from biased data and the opacity of machine learning models, necessitating proactive research and policy implementation on a global scale. These frameworks must transcend the limitations of the experiences or perspectives of their programmers to ensure that AI innovations are ethically sound and that their use in global organizations adheres to principles of fairness and accountability. This synthesis will explore how these articles advocate for comprehensive, continuous monitoring of AI systems and policies that address both local and international concerns, offering a roadmap for organizations to innovate responsibly while mitigating the risks of AI-driven discrimination.

ARTIFICIAL INTELLIGENCE IN KAZAKHSTAN'S EDUCATION SYSTEM: ANALYSIS AND PROSPECTS

. The article explores the impact of artificial intelligence (AI) on Kazakhstan's education system and its potential to fundamentally transform learning and administrative processes. AI's ability to analyze big data allows educators to gain deeper insights into student performance, predict outcomes, and develop strategies to improve academic achievement. It also enhances personalized learning by offering students tailored educational content that matches their individual knowledge levels and learning pace. The automation of administrative tasks such as scheduling and grading significantly improves the efficiency of educational institutions, allowing teachers more time for direct engagement with students. The article thoroughly examines both the benefits and challenges of AI integration, including issues of digital inequality, access to modern technologies, and the need for ongoing professional development for teachers. Ethical considerations related to the protection of student data are also emphasized. Examples of successful AI implementation in international education platforms are provided, offering valuable insights for Kazakhstan. The article discusses opportunities for further investment in educational infrastructure and outlines key areas for digital development.

Assessment of ChatGPT-4 in Family Medicine Board Examinations Using Advanced AI Learning and Analytical Methods: Observational Study.

This research explores the capabilities of ChatGPT-4 in passing the American Board of Family Medicine (ABFM) Certification Examination. Addressing a gap in existing literature, where earlier artificial intelligence (AI) models showed limitations in medical board examinations, this study evaluates the enhanced features and potential of ChatGPT-4, especially in document analysis and information synthesis. The primary goal is to assess whether ChatGPT-4, when provided with extensive preparation resources and when using sophisticated data analysis, can achieve a score equal to or above the passing threshold for the Family Medicine Board Examinations. In this study, ChatGPT-4 was embedded in a specialized subenvironment, "AI Family Medicine Board Exam Taker," designed to closely mimic the conditions of the ABFM Certification Examination. This subenvironment enabled the AI to access and analyze a range of relevant study materials, including a primary medical textbook and supplementary web-based resources. The AI was presented with a series of ABFM-type examination questions, reflecting the breadth and complexity typical of the examination. Emphasis was placed on assessing the AI's ability to interpret and respond to these questions accurately, leveraging its advanced data processing and analysis capabilities within this controlled subenvironment. In our study, ChatGPT-4's performance was quantitatively assessed on 300 practice ABFM examination questions. The AI achieved a correct response rate of 88.67% (95% CI 85.08%-92.25%) for the Custom Robot version and 87.33% (95% CI 83.57%-91.10%) for the Regular version. Statistical analysis, including the McNemar test (P=.45), indicated no significant difference in accuracy between the 2 versions. In addition, the chi-square test for error-type distribution (P=.32) revealed no significant variation in the pattern of errors across versions. These results highlight ChatGPT-4's capacity for high-level performance and consistency in responding to complex medical examination questions under controlled conditions. The study demonstrates that ChatGPT-4, particularly when equipped with specialized preparation and when operating in a tailored subenvironment, shows promising potential in handling the intricacies of medical board examinations. While its performance is comparable with the expected standards for passing the ABFM Certification Examination, further enhancements in AI technology and tailored training methods could push these capabilities to new heights. This exploration opens avenues for integrating AI tools such as ChatGPT-4 in medical education and assessment, emphasizing the importance of continuous advancement and specialized training in medical applications of AI.

Investigating Data Diversity and Model Robustness of AI Applications in Palliative Care and Hospice: Protocol for Scoping Review.

Artificial intelligence (AI) has become a pivotal element in health care, leading to significant advancements across various medical domains, including palliative care and hospice services. These services focus on improving the quality of life for patients with life-limiting illnesses, and AI's ability to process complex datasets can enhance decision-making and personalize care in these sensitive settings. However, incorporating AI into palliative and hospice care requires careful examination to ensure it reflects the multifaceted nature of these settings. This scoping review aims to systematically map the landscape of AI in palliative care and hospice settings, focusing on the data diversity and model robustness. The goal is to understand AI's role, its clinical integration, and the transparency of its development, ultimately providing a foundation for developing AI applications that adhere to established ethical guidelines and principles. Our scoping review involves six stages: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; (5) collating, summarizing, and reporting the results; and (6) consulting with stakeholders. Searches were conducted across databases including MEDLINE through PubMed, Embase.com, IEEE Xplore, ClinicalTrials.gov, and Web of Science Core Collection, covering studies from the inception of each database up to November 1, 2023. We used a comprehensive set of search terms to capture relevant studies, and non-English records were excluded if their abstracts were not in English. Data extraction will follow a systematic approach, and stakeholder consultations will refine the findings. The electronic database searches conducted in November 2023 resulted in 4614 studies. After removing duplicates, 330 studies were selected for full-text review to determine their eligibility based on predefined criteria. The extracted data will be organized into a table to aid in crafting a narrative summary. The review is expected to be completed by May 2025. This scoping review will advance the understanding of AI in palliative care and hospice, focusing on data diversity and model robustness. It will identify gaps and guide future research, contributing to the development of ethically responsible and effective AI applications in these settings. DERR1-10.2196/56353.

Advanced AI and Augmented Reality (AR) Integration in Medical and Surgical Practice

The future of artificial intelligence (AI) in diagnosing rare genetic disorders is poised to transform precision medicine by accelerating the identification of conditions that are often difficult to diagnose. Rare genetic disorders, which affect millions of people worldwide, typically involve complex symptoms and lengthy diagnostic processes. AI's ability to process vast amounts of genomic, phenotypic, and clinical data positions it as a game-changer in this field. By detecting subtle patterns and correlations in large datasets, machine learning algorithms can deliver diagnoses faster and more accurately than traditional methods. AI-powered tools are proving valuable in whole genome and exome sequencing, automating the identification of pathogenic variants linked to rare diseases. By integrating clinical and phenotypic data, these systems can offer personalized insights, reduce diagnostic delays and improve genetic counseling and treatment development. However, the use of AI in rare disease diagnosis poses challenges, such as the need for diverse datasets to train algorithms and concerns over data privacy and equal access. Ensuring that AI tools are validated in diverse populations and effectively integrated into healthcare systems is crucial to their success. This summary will focus on the potential of AI to improve diagnostic accuracy, personalize treatments, and improve the management of rare diseases.

Advanced Computer Vision Techniques for Accurate Measurement in Unmanned Mobile Robots

Abstract For years, researchers have been studying computer vision, i.e. the ability of artificial intelligence (AI) systems to perceive and interpret visual data like humans. This study is gaining increasing attention as researchers aim to develop tools that automate visual tasks and replicate human visual awareness. However, the interpretation of images is very complex due to the vast amount of multi-resolution information they contain, making the development of AI technologies for visual recognition particularly challenging. This article provides an overview of digital image processing, highlighting the main concepts and introducing key algorithms. These methods are designed to capture, process, and interpret digital images and enable the extraction of important data from real-world environments. We conduct rigorous image processing tests and compare AI-driven recognition systems with human analysis. The results show that computer vision technology significantly outperforms human observation in terms of accuracy and consistency. These results highlight the potential of computer vision to revolutionize various industries by automating complex visual tasks and offer promising future applications in areas such as healthcare, security, and manufacturing. The paper provides valuable insights into current advances in digital image processing and the role of AI in improving visual recognition capabilities, paving the way for further innovation in this area.

Enhancing peer review efficiency: A mixed‐methods analysis of artificial intelligence‐assisted reviewer selection across academic disciplines

AbstractThis mixed‐methods study evaluates the efficacy of artificial intelligence (AI)‐assisted reviewer selection in academic publishing across diverse disciplines. Twenty journal editors assessed AI‐generated reviewer recommendations for a manuscript. The AI system achieved a 42% overlap with editors' selections and demonstrated a significant improvement in time efficiency, reducing selection time by 73%. Editors found that 37% of AI‐suggested reviewers who were not part of their initial selection were indeed suitable. The system's performance varied across disciplines, with higher accuracy in STEM fields (Cohen's d = 0.68). Qualitative feedback revealed an appreciation for the AI's ability to identify lesser‐known experts but concerns about its grasp of interdisciplinary work. Ethical considerations, including potential algorithmic bias and privacy issues, were highlighted. The study concludes that while AI shows promise in enhancing reviewer selection efficiency and broadening the reviewer pool, it requires human oversight to address limitations in understanding nuanced disciplinary contexts. Future research should focus on larger‐scale longitudinal studies and developing ethical frameworks for AI integration in peer‐review processes.

Investigating the Applications of AI in Oncology from NIH Funded Projects: Case study of Lung Cancer and Pancreatic Cancer

ABSTRACTThis poster aims to conduct an examination of the applications of Artificial Intelligence (AI) in oncology. We collect projects from National Institutes of Health (NIH) which delve into how AI applications and techniques are used in cancer research between 2018 to 2024. Results show AI's ability to enhance lung and pancreatic cancer through novel imaging techniques and data processing. We find that refined medical imaging can be done with AI‐driven techniques such as machine learning or deep learning to improve diagnostic accuracy potentially leading to better survival rates for patients involved. Additionally, this reveals how AI transformed oncology studies underlining the movement toward complex diagnostic algorithms that allow accurate stage assignments. Consequently, these forms of teamwork are essential when determining as well as making reliable Cancer care systems by developing strong AI systems with interpretation abilities using supportive NIH funding.

Transforming personalized chronic pain management with artificial intelligence: A commentary on the current landscape and future directions

Artificial intelligence (AI) has the potential to revolutionize chronic pain management by guiding the development of effective treatment strategies that are tailored to individual patient needs. This potential comes from AI's ability to analyze large and heterogeneous datasets to identify hidden patterns. When applied to clinical datasets of a particular patient population, AI can be used to identify pain subtypes among patients, predict treatment responses, and guide the clinical decision-making process. However, integrating AI into the clinical practice requires overcoming challenges such as data quality, the complexity of human pain physiology, and validation against diverse patient populations. Targeted, collaborative efforts among clinicians, researchers, and AI specialists will be needed to maximize AI's capabilities and advance current management and treatment of chronic pain conditions.

Review of farmer-centered AI systems technologies in livestock operations

Abstract The assessment of livestock welfare aids in keeping an eye on the health, physiology, and environment of the animals in order to prevent deterioration, detect injuries, stress, and sustain productivity. Because it puts more consumer pressure on farming industries to change how animals are treated to make them more humane, it has also grown to be a significant marketing tactic. Common visual welfare procedures followed by experts and vets could be expensive, subjective, and need specialized staff. Recent developments in artificial intelligence (AI) integrated with farmers’ expertise have aided in the creation of novel and cutting-edge livestock biometrics technologies that extract important physiological data linked to animal welfare. A thorough examination of physiological, behavioral, and health variables highlights AI's ability to provide accurate, rapid, and impartial assessments. Farmer-focused strategy: an emphasis on the crucial role that farmers play in the skillful adoption and prudent application of AI and sensor technologies, as well as conversations about developing logical, practical, and affordable solutions that are specific to the needs of farmers.

Evaluating Generative AI's Ability to Identify Cancer Subtypes in Publicly Available Structured Genetic Datasets.

Genetic data play a crucial role in diagnosing and treating various diseases, reflecting a growing imperative to integrate these data into clinical care. However, significant barriers such as the structure of electronic health records (EHRs), insurance costs for genetic testing, and the interpretability of genetic results impede this integration. This paper explores solutions to these challenges by combining recent technological advances with informatics and data science, focusing on the diagnostic potential of artificial intelligence (AI) in cancer research. AI has historically been applied in medical research with limited success, but recent developments have led to the emergence of large language models (LLMs). These transformer-based generative AI models, trained on vast datasets, offer significant potential for genetic and genomic analyses. However, their effectiveness is constrained by their training on predominantly human-written text rather than comprehensive, structured genetic datasets. This study reevaluates the capabilities of LLMs, specifically GPT models, in performing supervised prediction tasks using structured gene expression data. By comparing GPT models with traditional machine learning approaches, we assess their effectiveness in predicting cancer subtypes, demonstrating the potential of AI models to analyze real-world genetic data for generating real-world evidence.

Innovative Content Production in Marketing Communication Through AI

The integration of artificial intelligence (AI) into marketing communications has changed the way businesses communicate with their customers. The present paper explores the potential of AI-driven systems to automate content creation, highlighting the role of AI in increasing customer engagement and strategic decision making. AI offers significant benefits by leveraging customer data to predict their behaviour, optimize marketing strategies, and personalize content. By systematically creating and evaluating content, AI enables marketers to customize messages to specific audiences, increasing the effectiveness of campaigns. The research employs a mixed methods approach that combines qualitative and quantitative analyses to assess the potential use of AI in content marketing. The qualitative aspect involves a thorough review of existing literature, identifying key trends and relationships in the use of AI for marketing communications. On the other hand, the quantitative aspect of the study involves a process of experimental validation using Python and APIs such as OpenAI and Graph APIs to automate content creation and measure its effectiveness. The findings confirm that AI can be utilized to personalize content using the input data in the form of brand-voice, target group, and the evaluation of current communication. The iterative process allows for continuous improvement of quality and relevance of content, which is important in the dynamic environment of marketing communications. The study highlights the importance of accurately defining the target audience and brand-voice, as the effectiveness of AI depends heavily on the accuracy of the input data. However, the study also acknowledges limitations such as the potential inconsistency of the content generated through AI and the challenges associated with maintaining a coherent brand-voice. Future research should focus on developing advanced machine learning methods to better understand context and improve the alignment of content with brand values and customer expectations. In conclusion, the integration of AI into marketing communications offers a powerful tool for businesses to optimize content creation, increase customer engagement, and achieve strategic goals in an increasingly competitive digital environment. AI's ability to create, evaluate and refine content in real-time provides a significant advantage and ensures that marketing communications remain adaptive and effective.

Application and Evaluation of the Effectiveness of Artificial Intelligence in Personalized Learning

The application of artificial intelligence (AI) in personalized learning has demonstrated remarkable advantages, including intelligent assessment, resource recommendation, and teaching optimization, effectively enhancing learning outcomes and engagement. However, this process has also revealed several issues. Firstly, technical challenges and limitations hinder AI's ability to fully adapt to each student's individual needs, requiring further improvements in algorithm accuracy and reliability. Secondly, data privacy and security issues have become increasingly prominent, necessitating robust measures to protect student data from misuse and leakage. Lastly, challenges in the educational system and mindset must be addressed, focusing on how to integrate AI technology into traditional education models while maintaining teaching quality. To address these issues, continuous research and development, enhanced data protection, and the updating of educational systems and mindsets are required to fully harness the potential of AI in personalized learning.

Research on Artificial Intelligence in Game Strategy Optimization

This paper provides a comprehensive review of the current state and future directions of artificial intelligence (AI) in game strategy optimization. It explores the key AI techniques driving advancements in this field, including machine learning, reinforcement learning, neural networks, and Monte Carlo tree search. Through detailed case studies of landmark AI systems such as Deep Blue, AlphaGo, Libratus, and AlphaStar, the paper illustrates the remarkable progress made in domains ranging from chess and go to poker and real-time strategy games. Despite these achievements, significant challenges remain, including multi-domain generalization, explainability, and effective human-AI collaboration. The paper also delves into promising future research directions, such as developing more flexible AI architectures and improving AI's ability to work alongside human players. Finally, it addresses the ethical considerations surrounding the advancement of AI in game strategy, including issues of fairness in competitive gaming, potential societal impacts, and the responsible development of these technologies. This research not only highlights the transformative potential of AI in gaming but also its broader implications for strategic decision-making in real-world scenarios.

Making sense of negotiation and AI: The blossoming of a new collaboration

The integration of artificial intelligence (AI), including the recent appearance of revolutionary large language models (LLMs), marks a transformative era in the field of negotiations, reshaping traditional practices and presenting a range of opportunities and challenges. This article delves into the evolving interplay between negotiation and various AI technologies, as they now combine massive computational power with user-friendly interfaces capable of fluent, multi-topic conversations. The article categorizes AI's role in negotiation into assistance, semi-automation, and automation, each offering unique advantages and addressing different negotiation needs. While AI's ability to compensate for human limitations in rationality, emotion, and computational capacity is promising, it also raises concerns regarding biases, ethical considerations, and the reliability of automated decision-making. The burgeoning AI and negotiation collaboration necessitates a balanced approach, harnessing AI's potential to enhance negotiation outcomes while conscientiously navigating its challenges. This article aims to foster understanding of and influence the future trajectory of negotiation and AI, highlighting the need for ongoing research and development to ensure ethical, effective, and equitable negotiation practices in an AI-augmented future.

ROLE OF ARTIFICIAL INTELLIGENCE FOR ADAPTIVE LEARNING ENVIRONMENTS IN HIGHER EDUCATION BY 2030

This study focuses on the ability of Artificial Intelligence (AI) to redesign learning experience of higher education by making learning adaptable by the year 2030. Machine learning and natural language processing afford the possibility of developing adaptive learning environment for students. The research also focuses on AI's present and future uses in the learning & issues of realizing those uses. The purposive sampling technique selected 5 faculty members from different universities. A semi-structured interview guide was developed to get data from the participants. Data was analyzed thematically by facilitation of NVivo 14. The potential of AI for enhancing personalized tasks, automated tasks related to administration, and creating interactive learning experiences. The concerns of data confidentiality and ethical considerations were also addressed. By analyzing the improvement of adaptive learning technologies, the study presents views of how AI can improve educational outcomes. Therefore, the findings also emphasize the diverse implications of equalizing technological innovation with keeping important human fundamentals in education while highlighting the justice and inclusivity.