An overview of visual intelligent tools based on artificial intelligence

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

Comprehensive chart documentation is a key competency in dental hygiene education. Providers spend a significant portion of their day on record-keeping, over 12% in primary care settings [1]. Accurate charting supports decisions, billing, and legal protection, but excessive documentation reduces time for patient education. It also limits communication, leadership, and collaboration by restricting feedback and team interaction. At the same time, dental education must adapt to emerging artificial intelligence (AI) tools designed to improve efficiency and support learning. AI-assisted charting and automated note generation show promise in reducing documentation time [2, 3]. Despite the growing interest in AI, little is known about how such systems can be effectively integrated into dental hygiene training to enhance both perceived efficiency and patient communication [4]. Students continue to learn traditional, manual charting methods. AI-based documentation tools can help create record periodontal charts, clinical notes, analyze data, and improve record accuracy [5]. The lack of such training limits students’ and teachers’ awareness and readiness to use AI systems. To address these challenges, a dental hygiene program piloted DentalBee, an AI-powered tool that converts clinical speech into structured dental notes. The project aimed to determine whether AI-assisted documentation could improve workflow efficiency and patient engagement while integrating digital tools into dental hygiene education. The pilot involved 2nd-year students during clinical rotations and included faculty training, AI-assisted modules, and integration with the patient management system (PMS) (Table 1). Launched in Spring 2025, the initiative received administrative and IT support. Outcome measures focused on students’ perceived efficiency and patient interaction, assessed through a structured survey. Faculty also observed reduced documentation time, though exact times were not recorded because implementation occurred alongside student training, conditions that would have artificially inflated time data and misrepresented tool performance. This pilot established baseline perceptions of efficiency and engagement, providing a foundation for future studies to measure objective time savings once system use becomes routine. Institutional review board exemption was granted for this educational quality-improvement project. A quantitative survey assessed student perceptions of documentation efficiency and patient communication at three timepoints: pre-implementation (n = 23), mid-rotation (1 month; n = 23), and post-rotation (3 months; n = 20). At baseline, 74% of participants reported that documentation significantly impacted their perceived appointment efficiency (Figure 1). Following AI integration, 12.5% of students reported noticeable improvement at mid-rotation, increasing to 50% by post-rotation—suggesting a gradual adaptation and perceived benefit over time. In terms of patient communication, 50% of students initially indicated that note-taking limited their ability to connect with patients, while 27% reported a moderate burden and 23.5% a high burden (Figure 2). After using the AI system, 15.6% reported high improvement in patient connection at mid-rotation, increasing to 41.9% by post-rotation. Although self-reported, these findings suggest that with continued use, AI-assisted documentation may improve perceived workflow efficiency and facilitate more meaningful patient engagement. Future studies should include objective measurement of documentation time and direct assessment of patient outcomes to strengthen these preliminary results. The authors would like to acknowledge the support of Catherine Ford, Dean of Owens Community College School of Nursing and Health Professions. Acknowledgment to Kanza Javed for the logistic support.

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

The integration of artificial intelligence (AI) in local governments has gained increasing attention as a means to enhance civic engagement and streamline public participation. This study examines how local governments utilize AI-powered platforms for civic engagement, exploring the perspectives of public servants and developers on the implementation of AI, its benefits, challenges, and ethical considerations. With a qualitative approach, semi-structured interviews were conducted with developers, product designers, public servants, and decision-makers involved in developing and using the AI-powered platform for civic engagement initiatives. Thematic analysis was employed to identify key themes related to AI functionalities, user experiences, and governance challenges. The preliminary results reveal that four distinct types of AI applications have been employed, including AI-powered optical character recognition (OCR), AI-based content moderation, AI sensemaking, and AI-driven translation. These AI tools facilitate efficiency in data processing, enhance accessibility, and support decision-making. However, concerns regarding AI transparency, data privacy, and public trust remain significantly challenging. Additionally, public servants emphasized the need for AI literacy training and the development of ethical guidelines to ensure the responsible use of AI in local governance. This study contributes to the growing discourse on AI in civic engagement by offering insights into the practical and ethical dimensions of AI adoption in local governments. The findings underscore the need for policy frameworks, digital inclusivity measures, and ongoing capacity-building efforts to enhance AI-driven public participation.

The sketch phase in architectural design is a process where design requirements are not fully defined, but decisions regarding the architectural product largely become clear. The integration of artificial intelligence (AI) into this process can be considered an exploration of potential design products that address early-stage design decisions. This paper examines how AI technologies can be utilized in the field of architecture, starting from the sketch phase and continuing through 3D visualization and rendering processes. Aim: Inspired by the sketches of pioneering modernist architects, the aim is to demonstrate how "sketch-to-sketch rendering" and "sketch-to-3D rendering" can be produced using AI-based tools. Method: Based on the selected architects' own sketches and actual visuals of the buildings as references, prompts obtained from ChatGPT were used to generate sketch-to-sketch and sketch-to-3D render visuals in the artificial intelligence tools Air for SketchUp and Leonardo AI, and the results were presented comparatively. Results: It demonstrates that Air for SketchUp provides more realistic and visually successful results, especially in the 3D rendering phase, while Leonardo AI highlights the contribution of artificial intelligence, remaining more limited in adherence to sketches and overall visual qualities. The findings suggest that human-machine collaboration can offer new potential in architectural design processes and that artificial intelligence tools can transform the traditional sketch process, taking on a supportive role for designers. Conclusion: It was determined that AI tools contribute to architectural design processes in terms of creativity, reflecting environmental context in design, and developing design ideas.

The advent of Artificial Intelligence (AI) has revolutionized various domains, and Content Management Systems (CMS) are no exception. Traditional CMS workflows, often reliant on manual processes, have struggled to keep up with the growing demands for scalability, speed, and personalization in managing digital content. AI - powered CMS address these challenges by introducing automation, enhancing efficiency, and delivering personalized user experiences. Through the integration of AI technologies such as Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision, modern CMS have evolved into intelligent platforms capable of automating workflows, optimizing content delivery, and driving data - driven decision - making. This paper explores the transformative potential of AI in CMS, focusing on its applications in workflow automation and user experience enhancement. AI enables the automation of repetitive tasks like content tagging, categorization, and scheduling, significantly reducing manual effort and human error. At the same time, it enhances user engagement by offering personalized content recommendations, optimizing search functionalities, and dynamically adapting content to user contexts. These advancements allow businesses to manage content more effectively and cater to diverse audience needs in real time. However, the integration of AI into CMS is not without challenges. Issues such as data privacy concerns, bias in AI models, and the complexity of integrating AI into legacy systems remain significant barriers. This paper addresses these challenges and proposes actionable recommendations, including the development of explainable AI systems, robust data security frameworks, and scalable architectures that accommodate diverse business needs. As businesses increasingly adopt AI - powered CMS, their potential to transform content management is undeniable. These systems not only streamline workflows and improve operational efficiency but also empower organizations to deliver meaningful and personalized experiences to users. By addressing current challenges and advancing AI technologies, the future of content management will be defined by intelligent, adaptive, and user - centric platforms that bridge the gap between technology and human creativity. This comprehensive review highlights the opportunities and challenges of leveraging AI in CMS and underscores the importance of continued research and innovation to fully realize its potential. AI - powered CMS are not merely tools for managing content but catalysts for digital transformation, enabling organizations to thrive in an increasingly dynamic digital landscape.

Social media analytics has emerged as a critical tool for understanding consumer behavior in today’s digital landscape. This study explores the integration of social media analytics with Power BI to gain actionable insights into consumer preferences, behaviors, and trends at Cryptex Technologies, Nagpur. Leveraging the capabilities of Power BI, the research examines how data visualization and analytics tools can translate vast amounts of social media data into meaningful insights that drive decision-making. The study employs a mixed-methods approach, combining qualitative and quantitative data analysis, to evaluate the effectiveness of Power BI in analysing social media metrics such as engagement rates, sentiment analysis, and demographic segmentation. Results indicate that social media analytics, when effectively integrated with advanced visualization tools, significantly enhances the understanding of consumer behavior, enabling businesses to make data-driven marketing decisions. The findings further reveal that the use of interactive dashboards and real-time data processing capabilities of Power BI facilitates a deeper comprehension of market trends, helping organizations remain competitive in a rapidly evolving business environment. This research contributes to the growing body of knowledge on the strategic application of social media analytics in consumer behavior studies and offers practical insights for businesses aiming to optimize their social media strategies using data visualization tools.

Background: Artificial intelligence (AI) is increasingly embedded in routine primary care, yet how the levels of integration might affect its acceptability is unknown, especially in relationship-based service models where patients expect visible human stewardship. Prior experimental studies often treat AI adoption as a binary condition, leaving the "intensity gradient" of automation and the role of model specialization under-explored. We examine whether increasing AI integration in the clinical encounter erodes patients' intention to seek care from family doctors in China, and whether labeling the AI as a medical-specific model buffers such erosion. Methods: We conducted a nationwide online survey experiment in China (N = 2790). Participants were randomly assigned to vignettes that varied by (i) the level of AI integration (low, medium, high) and (ii) the AI type (general-purpose vs. medical-specific large language model), with a human-only care scenario as a reference. Care-seeking intention from family doctors was assessed immediately after exposure. We estimated treatment effects using OLS regression with heteroskedasticity-robust standard errors, and examined the buffering hypothesis through an interaction term between AI integration intensity and AI type. Results: Care-seeking intention declined steadily as AI integration increased (p < 0.001), with the sharpest drop under high-intensity AI integration where clinical decisions were delegated to the AI system. Across all intensity levels, framing the system as a medical-specific AI consistently resulted in higher care-seeking intention than a general-purpose model. However, the interaction between AI intensity and the AI type was not statistically significant (p = 0.508). Conclusions: Patient acceptance of AI in primary care depends not only on whether AI is involved, but on how deeply AI is positioned in the encounter. Medical-specific AI labeling may enhance acceptance across all AI integration levels. The findings underscore the need to preserve human clinical agency in AI-embedded primary care. The results contribute to research on healthcare systems, digital health, and AI-patient interaction.

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

Amidst the current wave studies of artificial intelligence (AI) in education, this longitudinal case study, spanning Spring 2023 to Spring 2024, delves into the integration of AI in the UI/UX web design classroom. By introducing both text-based and image-based AI tools to students with varying levels of skill in introductory web design and user experience (UX) courses, the study observed a significant enhancement in student creative capabilities and project outcomes. The utilization of text-based generators markedly improved writing efficiency and coding, while image-based tools facilitated better ideation and color selection. These findings underscore the potential to augment traditional educational methods, providing students with novel avenues for creativity and innovation. At the same time, the goal of this study was also to ascertain the factors that led to the adoption of AI tools in the educational workflow, specifically focusing on student major and background, thereby illuminating how AI can be tailored to meet diverse educational needs and foster a more adaptive and innovative learning environment. The findings reveal that students were more receptive to integrating AI tasks into their workflows when these tasks did not directly relate to their major field of study. For example, Computer Science students exhibited less resistance to using AI for selecting color palettes, a task outside their primary focus, compared to utilizing AI for coding. Additionally, the study observed a significant growth in both awareness and usage of AI tools among students throughout the duration of the research. This trend suggests an increasing incorporation of AI technologies into their standard toolkit, highlighting a broader acceptance and integration of AI in educational practices.

In recent years, there has been a growing interest in the integration of artificial intelligence (AI) into various fields. Artificial Intelligence (AI) also has become a prominent technology in various fields, including education. One area where Artificial Intelligent (AI) has shown great potential is in English Language Teaching (ELT). With the rapid development of technology, Artificial Intelligent (AI) tools and applications have been integrated into language learning platforms, classrooms, and online resources, transforming the way English is taught and learned. In the realm of language teaching, Artificial Intelligent offers significant potential for enhancing the learning process and improving outcomes. English language teaching is no exception, as educators are exploring the potential of Artificial Intelligent (AI) to enhance language learning experiences. English language teaching is no exception, as Artificial Intelligent (AI) can enhance the learning experience for English language learners by providing personalized instruction, immediate feedback, and immersive language practice. This research addresses the extent to use artificial intelligence in English language teaching. Researcher used descriptive qualitative approach with questionnaire as technique of data collection. The results of this research are the Artificial Intelligence is needed in English language teaching. Artificial Intelligence also has negative and positive impact in English language teaching depend on utilized correctly and at the appropriate timing. Besides that, Artificial Intelligence also help students in improving their English skill like speaking, writing, and reading using product of Artificial Intelligence like Chat GPT, Quillbot, Grammarly, Plagiarism Checker, Paraphrasingtool.com. So, the use of Artificial Intelligent in English language teaching is very important or useful and also reducing the burden of teachers and improving teaching quality.

In the modern business landscape, data visualization has become an indispensable element of business intelligence (BI), particularly within the context of US companies. The aim of this research paper is to examine how data visualization technologies might improve business intelligence and decision making in American businesses by examining adaptation, advantages and disadvantages. This research paper explores the transformative contribution of data visualization tools to improving business intelligence capabilities and their significant influence on the processes of decision-making. The study begins by examining the adoption of various data visualization tools and platforms, such as Tableau, Power BI, and Elkview within US companies. It delves into how these tools facilitate the conversion of complex data sets into visually intuitive formats, allowing stakeholders at all levels to grasp trends, patterns, and anomalies with greater ease. By integrating these visualization tools into their BI systems, companies can achieve a more nuanced understanding of their operational landscapes and market dynamics. The paper further evaluates the impact of data visualization on decision-making processes. Through an analysis of case studies and empirical data, it highlights several key benefits, including improved accuracy in data interpretation, enhanced ability to identify actionable insights, and accelerated decision-making timelines. The paper discusses strategies to overcome these challenges, such as ensuring data integrity and investing in user training to maximize the efficacy of visualization tools. Overall, the findings underscore that while data visualization is not a panacea, it significantly augments the decision-making capabilities of US companies by making data more accessible and actionable. The integration of advanced visualization techniques into BI practices is increasingly seen as a strategic advantage, driving both operational efficiencies and competitive differentiation. As data continues to proliferate, the role of data visualization in shaping business strategies and outcomes will likely become even more pronounced, making it a critical area of focus for future research and development in the field of business intelligence.

The integration of artificial intelligence (AI) tools in the workflow of scholarly writing, including scientific and medical writing, offers transformative advantages while raising critical ethical and practical concerns. Among the pros, AI substantially enhances efficiency by automating several time-consuming tasks such as literature review, data synthesis and text editing. In particular, tools such as ChatGPT, Grammarly, and SciSpace Copilot remarkably empower researchers when they draft, refine, and format manuscripts, increasing precision and speed. In addition, AI tools may foster inclusivity by assisting non-native English speakers with seamless translations and also enabling interdisciplinary collaboration, thereby hopefully democratizing access and boosting scientific communication and cooperation. However, the rapid adoption of novel AI tools brings significant challenges. First, there is a distinct risk of perpetuating biases in training datasets, and other key issues include ambiguity in authorship accountability and the potential erosion of critical thinking skills. In addition, AI tools could be purposefully misused to generate mock datasets and fraudulent papers (e.g. by paper mills), and this clearly poses a threat to academic integrity. This challenge is all too pressing given that traditional plagiarism detection tools often fall short against sophisticated AI-generated content. We hereby explore the pros and cons of AI on medical writing, poignantly leveraging Sergio Leone’s The Good, The Bad, and The Ugly movie as a metaphor. By providing general concepts as well as focusing on several key AI tools, we are hopeful this overview may prove useful to anyone wishing to conscientiously adopt AI tools for scientific writing. In addition, we make a compelling case for transparent guidelines, robust and freely available detection mechanisms, and ongoing critical oversight to ensure AI will be able to serve as a catalyst for innovation and dissemination without compromising the credibility of scholarly endeavors.

The rise of artificial intelligence (AI) heralds a significant revolution in healthcare, particularly in mental health.AI's potential spans diagnostic algorithms, data analysis from diverse sources, and real-time patient monitoring. It is essential for clinicians to remain informed about AI's progress and limitations. The inherent complexity of mental disorders, limited objective data, and retrospective studies pose challenges to the application of AI. Privacy concerns, bias, and the risk of AI replacing human care also loom. Regulatory oversight and physician involvement are needed for equitable AI implementation. AI integration and use in psychotherapy and other services are on the horizon. Patient trust, feasibility, clinical efficacy, and clinician acceptance are prerequisites. In the future, governing bodies must decide on AI ownership, governance, and integration approaches. While AI can enhance clinical decision-making and efficiency, it might also exacerbate moral dilemmas, autonomy loss, and issues regarding the scope of practice. Striking a balance between AI's strengths and limitations involves utilizing AI as a validated clinical supplement under medical supervision, necessitating active clinician involvement in AI research, ethics, and regulation. AI's trajectory must align with optimizing mental health treatment and upholding compassionate care.