Automation Of Tasks Research Articles

Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling.

This paper explores the potential for human-AI collaboration in the context of data storytelling for data workers. Data storytelling communicates insights and knowledge from data analysis. It plays a vital role in data workers' daily jobs since it boosts team collaboration and public communication. However, to make an appealing data story, data workers need to spend tremendous effort on various tasks, including outlining and styling the story. Recently, a growing research trend has been exploring how to assist data storytelling with advanced artificial intelligence(AI). However, existing studies focus more on individual tasks in the workflow of data storytelling and do not reveal a complete picture of humans' preference for collaborating with AI. To address this gap, we conducted an interview study with 18 data workers to explore their preferences for AI collaboration in the planning, implementation, and communication stages of their workflow. We propose a framework for expected AI collaborators' roles, categorize people's expectations for the level of automation for different tasks, and delve into the reasons behind them. Our research provides insights and suggestions for the design of future AI-powered data storytelling tools.

Smart Farming Technologies: Exploring the Factors of Adoption Among Smallholder Farmers in Peninsular Malaysia

The impact of climate change on global agriculture has increased the need for sustainable and resource-efficient farming practices to ensure food security. Smart farming, which incorporates the use of artificial intelligence (AI) and the Internet of Things (IoT), allows farmers to monitor changes in climatic factors and soil conditions, optimize farming operations and increase crop yields. This study aimed to identify the factors influencing the adoption of smart farming among smallholders in Peninsular Malaysia. Twelve participants were recruited using the snowball sampling method and thematic analysis was used to identify patterns and themes in the qualitative data obtained from interviews. The analysis revealed that participants desired to use smart farming technologies due to the lack of foreign labour, cost savings, time flexibility, awareness and education campaigns and government support. Utilizing smart farming technologies can result in cost savings through efficient use of inputs, reduction of waste and automation of tedious tasks. The use of solar energy can provide a dependable and affordable energy source to increase sustainability, while precision planting and seeding techniques can improve the efficient use of resources. The government's provision of information and funding has helped participants establish smart farming in their farms, with some receiving grants and collaborating with universities to develop new technologies. Overall, smart farming technologies can help farmers manage their crops and resources more effectively, increase yields and optimize farm operations

Beyond Snippet Assistance: A Workflow-Centric Framework for End-to-End AI-Driven Code Generation

Recent AI-assisted coding tools, such as GitHub Copilot and Cursor, have enhanced developer productivity through real-time snippet suggestions. However, these tools primarily assist with isolated coding tasks and lack a structured approach to automating complex, multi-step software development workflows. This paper introduces a workflow-centric AI framework for end-to-end automation, from requirements gathering to code generation, validation, and integration, while maintaining developer oversight. Key innovations include automatic context discovery, which selects relevant codebase elements to improve LLM accuracy; a structured execution pipeline using Prompt Pipeline Language (PPL) for iterative code refinement; self-healing mechanisms that generate tests, detect errors, trigger rollbacks, and regenerate faulty code; and AI-assisted code merging, which preserves manual modifications while integrating AI-generated updates. These capabilities enable efficient automation of repetitive tasks, enforcement of coding standards, and streamlined development workflows. This approach lays the groundwork for AI-driven development that remains adaptable as LLM models advance, progressively reducing the need for human intervention while ensuring code reliability.

Assessing the impact of artificial intelligence integration on educational processes in higher education institutions of Ukraine and Kazakhstan

The study focuses on assessing the impact of artificial intelligence on educational processes in higher education institutions. It considers aspects of administrative task automation, personalization of learning, and ethical challenges. The topic's relevance is driven by global trends in the digital transformation of education and the need to adapt systems to modern challenges. A descriptive approach was used, using secondary data from scientific publications, statistical reports, and analytical studies. The data were analyzed using statistical and correlation methods, allowing us to identify the key patterns of implementing artificial intelligence in higher education in Ukraine and Kazakhstan. Integration of artificial intelligence increases the efficiency of administrative processes by 40%, reducing the time spent on routine tasks. Personalized learning contributes to the growth of students' academic performance by 7-30%. Using AI to monitor educational processes can reduce the risk of expulsions by up to 15%. At the same time, the risks of reduced social interaction and possible ethical issues, such as the opacity of algorithms and the risk of data leakage, have been identified. Artificial intelligence has significant potential for optimizing educational processes, provided ethical standards are met. Technological solutions must be combined with a socially oriented approach, particularly through integrating hybrid learning models. It is recommended that Ukraine use Kazakhstan's experience in centralizing solutions, investing in analytical tools, and training teachers.

What neural networks and how a urologist can utilize in his routine tasks: practical tips

Artificial intelligence (AI) is becoming an integral part of modern medicine, including urology. This article examines key approaches to implementing AI in diagnostics, treatment, and patient management. The focus is on the potential of neural networks, such as convolutional and recurrent networks, for medical image analysis, outcome prediction, and automation of routine tasks. Particular attention is given to the application of deep learning algorithms in identifying and segmenting urological pathologies in ultrasound, CT, and MRI images. Successful examples of AI use in diagnosing prostate and bladder cancer, predicting complication risks, and developing personalized therapeutic strategies are discussed. The advantages and limitations of AI in urological practice are explored, including the need for high-quality data for model training, challenges in algorithm interpretation, and ethical considerations. The article provides practical recommendations for urologists on integrating AI into clinical activities, emphasizing its role in improving healthcare quality and enhancing decision-making accuracy. The article serves as a practical guide for urologists seeking to integrate AI into clinical practice, emphasizing the importance of combining AI technologies with clinical expertise to optimize the quality of medical care. On the threshold of a new era in urology, those who are the first to master artificial intelligence will not only transform approaches to treatment but also shape the future of the profession.

Artificial intelligence in higher education institutions: review of innovations, opportunities and challenges

Artificial intelligence is revolutionizing industries including institutions of higher learning as it enhances teaching and learning processes, streamline administrative tasks and drive innovations. Despite the unprecedented opportunities, AI tools if not used correctly, can be challenging in education institutions. The purpose of this study was to comprehensively review the AI innovations, opportunities and challenges associated with the use of AI in higher Education of learning. A systematic literature review methodology was adopted and used to locate and select existing studies, analyze and synthesize the evidence to arrive at clear conclusion about the current debate in the area of study. Following the PRISMA, the study analyzed a total of 54 documents that met the inclusion and exclusion criteria set for selection of the documents. The review unveiled many opportunities including enhanced research capabilities, automation of administrative tasks among others. Artificial Intelligence tools are found to refine and streamline the administrative tasks in different units in higher institutions of learning. The challenges include ethical concerns, integrity issues and data fabrication issues. With the challenges notwithstanding, the benefits of Artificial Intelligence cannot be over emphasized. Artificial intelligence remains a powerful tool for research, automation of administrative tasked, personalized learning, inclusivity and accessibility of educational content for all. Emphasis should be put in regulatory frameworks detailing how such tools can be used while maintaining the level of ethical standards required.

COMPUTATIONALLY EFFICIENT DEEP FEDERATED LEARNING WITH OPTIMIZED FEATURE SELECTION FOR IOT BOTNET DETECTION

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Visual learning model for behavioral cloning in gaming: Towards human-like ai systems

Behavioral cloning is a transformative paradigm in artificial intelligence, enabling systems to emulate human behaviors in complex domains such as gaming, robotics, and autonomous systems. This whitepaper presents a novel visual learning model designed to learn strategic and dynamic behaviors by analyzing gameplay footage. By employing sequential data processing and advanced temporal modeling, the architecture bridges human actions with actionable artificial intelligence (AI) strategies. The paper delves into the intricacies of model architecture, training methodologies, and evaluation metrics, offering a robust framework for real-time, context-aware decision-making. Key applications span gaming bots, collaborative artificial intelligence (AI) in robotics, and task automation systems. The proposed framework addresses critical challenges in synchronization, resource management, and adaptability, paving the way for generalized AI systems.

Evaluating blockchain’s impact on financial reporting: opportunities and threats in fintech’s input, processing, and output phases

Purpose The current study aims to investigate the effects of opportunities and threats of using blockchain on the input, processing and output components of the financial reporting process within the fintech landscape. Design/methodology/approach This study administered a questionnaire in Iran to 121 university lecturers in accounting and auditing, independent auditors, financial managers and internal auditors to better understand the effect of blockchain on financial reporting. The responses were analysed using SPSS and Smart PLS Software. Findings This study demonstrates how blockchain technology can improve the financial reporting process in the fintech industry by providing opportunities for remote labour, improved accountant roles and task automation. Threats include the requirements for blockchain expertise, standardisation, security issues and decreased flexibility. Limited R&D resources pose problems for small businesses. The main advantages of outputs are continuous, timely financial reporting and comparability; the risks associated with customised reports and regulatory difficulties in managing non-financial and financial data are the main disadvantages. The results show that all indicators of opportunities to use blockchain positively and significantly affect financial reporting opportunities within the fintech context. In addition, all the indicators of the threats of using blockchain have a positive and significant effect on the threats of financial reporting in the fintech context. Originality/value The present study is designed to meet the needs of a blockchain-based financial reporting system in the fintech context. Rapid growth and transformation into an advanced digital system has increased the importance of understanding the effects of the opportunities and threats of applying blockchain technology.

Transparent systems, opaque results: a study on automation compliance and task performance

In two experiments, we examine how features of an imperfect automated decision aid influence compliance with the aid in a simplified, simulated nautical collision avoidance task. Experiment 1 examined the impact of providing transparency in the pre-task instructions regarding which attributes of the task that the aid uses to provide its recommendations. Results showed that transparency here positively influenced compliance with the aid, leading to better task performance. Experiment 2 manipulated transparency via confidence estimates presented alongside the aid’s recommendations. There were no benefits from this form of transparency. In Experiment 2, lower compliance with the aid’s recommendations was found on more difficult collision problems, via a mediating loss of aid reliability and therefore trust. This runs contrary to the hypothesis that harder problems to solve ought to make participants more, rather than less dependent on the aid. Both experiments produced relatively low correlations between trust and compliance. The findings have important implications for the effectiveness of different kinds of transparency implementations, as well as providing a model/framework for understanding how generic factors such as automation reliability and problem difficulty influence both compliance and trust.

An Investigation Towards an Efficient Algorithm for the Automation of Stocking and Disposal Tasks in a Convenience Store Using a Robotic Arm System

Robotic systems have been introduced to convenience stores to fulfill several menial tasks. One of those tasks is stocking, disposing, and arranging the product in the item rack. The general approach is to use a robotic arm to do pick-and-place tasks with the product. However, to solve this task, an appropriate algorithm is needed. In this study, we propose building an efficient algorithm for automating stocking, disposal, and arranging tasks with a robotic arm. In order to build it, a set of cost functions can be derived from the goal of stocking, disposal, and arranging processes in convenience stores. The derived cost function can then be used as a basis for creating a new algorithm that can drive the logical decisions of the system. A method to find temporary spaces within the item rack is also implemented in this algorithm. To find the most efficient form for the developed algorithm, several algorithms based on the cost function are built and tested on a simulated system of convenience store conditions. From the experiment, it is shown that, with the cost function created, using the greedy search algorithm performs the best compared to the other methods.

Використання штучного інтелекту при вивченні дисциплін циклу DevOps

The article examines the pressing issue of utilizing artificial intelligence (AI) technologies in the context of teaching DevOps-related disciplines in higher education institutions. Given the rapid growth of the IT industry and the increasing demand for professionals with DevOps skills, enhancing the efficiency of the educational process in this field is clearly crucial. AI offers new opportunities to address key educational challenges, including personalized learning, automation of routine tasks, creation of adaptive learning materials, as well as ensuring interactivity and accessibility of educational resources. The paper proposes approaches to integrating AI tools into the educational process to optimize learning, improve students’ understanding of DevOps principles such as continuous integration (CI), continuous delivery (CD), process automation, containerization, system monitoring, and security. Particular attention is given to the development of interactive platforms for laboratory work, the use of virtual environments for simulating real-life DevOps scenarios, and the implementation of chatbots and automated assessment systems to support students during their studies. The article analyzes the impact of AI technologies on learning outcomes, considering both quantitative and qualitative indicators, including improved practical skills, reduced task completion time, and increased student engagement in studying DevOps-related disciplines. An analysis of existing practices in implementing AI in technical education highlights the main advantages and challenges that educational institutions may encounter. The paper outlines prospects for further AI integration into DevOps teaching, including combining AI with modern cloud services, advancing cognitive technologies capable of natural language understanding, and adopting innovative approaches to organizing the educational environment. The study’s results indicate significant potential for AI to enhance the quality of education and prepare highly skilled specialists in the field of DevOps.

Analysis of Machine Learning Security Blockchain Based IoT Enabled Web Platform Applications

Traditional criminal reporting methods include issues with witness hesitancy, security flaws, and inefficiencies. Even with the introduction of web platforms and mobile applications, there are still issues with data security, transparency, and user anonymity. These include of avoiding unwanted access, preserving confidentiality, and protecting data integrity. This research suggests using smart contracts and blockchain technology to overcome these problems. Improving the anonymity, efficiency, and security of crime reporting procedures is the main goal. In order to guarantee data integrity, transparency, and decentralization, the system makes use of blockchain's decentralized and immutable properties. The reporting process will be streamlined by the automation of tasks like complaint validation and proof verification through smart contracts. The suggested solution aims to transform crime reporting by tackling issues with confidentiality, data integrity, and access control. This will foster transparency, accountability, and confidence.

Can ChatGPT Be Addictive? A Call to Examine the Shift from Support to Dependence in AI Conversational Large Language Models

Abstract The rapid rise of ChatGPT has introduced a transformative tool that enhances productivity, communication, and task automation across industries. However, concerns are emerging regarding the addictive potential of AI large language models. This paper explores how ChatGPT fosters dependency through key features such as personalised responses, emotional validation, and continuous engagement. By offering instant gratification and adaptive dialogue, ChatGPT may blur the line between AI and human interaction, creating pseudosocial bonds that can replace genuine human relationships. Additionally, its ability to streamline decision-making and boost productivity may lead to over-reliance, reducing users' critical thinking skills and contributing to compulsive usage patterns. These behavioural tendencies align with known features of addiction, such as increased tolerance and conflict with daily life priorities. This viewpoint paper highlights the need for further research into the psychological and social impacts of prolonged interaction with AI tools like ChatGPT.

Artificial Intelligence in ERP: Unlocking New Horizons in Supply Chain Forecasting and Resource Optimization

Purpose: This article aims to evaluate the potential of Artificial intelligence (Ai) in ERP systems to enhance Forecasting in supply chain and resource optimization. It explores how AI can improve forecast accuracy, automate routine tasks, and optimize resource allocation within ERP systems. By analyzing the benefits and challenges of AI integration, the document provides insights for organizations seeking to leverage AI for enhanced supply chain management. Methodology: The methodology employed in the document involves both qualitative and quantitative research techniques. Quantitative data, such as forecasting accuracy, inventory turnover, and cost, is gathered from real-time ERP system data logs to measure ERP performance before and after AI implementation. Qualitative data is collected through interviews with ERP system administrators and supply chain managers to gain insights on user experience and challenges faced in Supply chain function and during AI integration. The research also discusses the selection of suitable machine learning models and their implementation methodology, including data preprocessing, training, and testing phases. Performance metrics, such as Mean Absolute Percentage Error (MAPE), are used to assess the improvements achieved through AI integration. Findings: The study found that AI integration in ERP systems significantly improved forecasting accuracy by 20%. This was attributed to AI's ability to analyze vast amounts of data and identify patterns that traditional ERP systems cannot do without significant work. Inventory turnover ratio increased by 33%, indicating faster movement of stock and reduced holding costs. This was due to AI's improved demand forecasting and real-time inventory adjustments. Operational costs were reduced by 15% due to automation of routine tasks, optimized resource allocation, and minimized waste in production and logistics. Unique Contribution to Theory, Practice and Policy: The research supports existing literature and case studies, confirming AI's potential to revolutionize ERP systems and supply chain management. The findings support existing literature on the potential of AI in supply chain management, specifically in forecasting and resource optimization. The research demonstrates the tangible benefits of AI integration, such as improved forecasting accuracy, optimized resource allocation, and reduced operational costs. The discussion on potential challenges, such as data security and algorithmic bias, helps organizations anticipate and address these issues proactively. The findings can inform government policies and industry regulations related to AI adoption in ERP systems and supply chain management. The emphasis on addressing algorithmic bias and data security concerns encourages responsible and ethical AI implementation. The research highlights the transformative potential of AI, encouraging businesses and policymakers to invest in AI-driven solutions for enhanced supply chain resilience and competitiveness.

Explaining Automated Vehicle Behavior at an Appropriate Abstraction Level and Timescale to Maintain Common Ground

Automation is becoming increasingly complex, playing a larger role in driving and expanding its operational design domain to dynamic urban roads. Explainable AI (XAI) research in computer science aims to craft explanations of automation that help people understand the behavior of complex algorithms. However, many XAI approaches rely on fixed-format explanations, which may not effectively support drivers with varying levels of automation knowledge and tasks with different timescales. Maintaining common ground is a multilevel process, in which individuals and automation must adjust communication format and abstraction based on knowledge and time constraints. We first draw on existing research to suggest that common ground is a shared understanding between drivers and automation that requires constant maintenance. We applied the abstraction hierarchy (AH) modeling method, which describes complex systems across multiple abstraction levels to match drivers’ cognitive capacity. We modified it to translate vehicle and traffic data into multilevel explanations of automation behavior. We expanded the model into the abstraction–decomposition space, naming it the Driver–Automation Teaming model, designed to generate explanations that account for task timescale. With this modified model, we developed three human–machine interface concepts to demonstrate how it can improve XAI’s support for driver–automation collaboration.

Challenges and Opportunities in Cytopathology Artificial Intelligence.

Artificial Intelligence (AI) has the potential to revolutionize cytopathology by enhancing diagnostic accuracy, efficiency, and accessibility. However, the implementation of AI in this field presents significant challenges and opportunities. This review paper explores the current landscape of AI applications in cytopathology, highlighting the critical challenges, including data quality and availability, algorithm development, integration and standardization, and clinical validation. We discuss challenges such as the limitation of only one optical section and z-stack scanning, the complexities associated with acquiring high-quality labeled data, the intricacies of developing robust and generalizable AI models, and the difficulties in integrating AI tools into existing laboratory workflows. The review also identifies substantial opportunities that AI brings to cytopathology. These include the potential for improved diagnostic accuracy through enhanced detection capabilities and consistent, reproducible results, which can reduce observer variability. AI-driven automation of routine tasks can significantly increase efficiency, allowing cytopathologists to focus on more complex analyses. Furthermore, AI can serve as a valuable educational tool, augmenting the training of cytopathologists and facilitating global health initiatives by making high-quality diagnostics accessible in resource-limited settings. The review underscores the importance of addressing these challenges to harness the full potential of AI in cytopathology, ultimately improving patient care and outcomes.

ШТУЧНИЙ ІНТЕЛЕКТ У СУЧАСНІЙ ОСВІТІ: ПЕРСПЕКТИВИ ЗАСТОСУВАННЯ ТА ВИКЛИКИ

The article examines the prospects for the application of artificial intelligence (AI) technologies in modern education, identifies the main directions of their implementation, key advantages and challenges. It is noted that the implementation of AI is an important component of the digital transformation of education, which involves the personalization of the educational process, automation of routine tasks of teachers, and expanding access to high-quality educational services. Particular attention is paid to the problems of digital inequality, insufficient training of teachers, as well as the lack of a clear regulatory framework for regulating the use of AI in the educational environment. The paper analyzes the experience of integrating AI into the educational process in Ukraine and abroad, in particular successful examples of the use of adaptive learning systems, automated assessment, intelligent assistants and proctoring systems. It is established that personalized learning contributes to taking into account the individual needs of students, but requires an ethical approach to the protection of personal data. Automation of assessment allows for transparency and efficiency of feedback, but has limitations in creative tasks. Particular attention is paid to the ethical risks associated with the use of AI, in particular in the area of data confidentiality, as well as the importance of developing teacher training programs for the effective use of technology. The creation of a national strategy for integrating AI into education, which includes the development of infrastructure, ensuring digital literacy and the formation of standards for the use of technology, is recommended. The article emphasizes that the effective implementation of AI can significantly improve the quality of education, create a more inclusive and adaptive educational environment, and promote the development of academic integrity. At the same time, the success of the implementation depends on an integrated approach that takes into account technical, ethical and organizational aspects. Innovative technologies, in particular AI, have the potential to become an important tool for transforming the educational process, contributing to increasing its efficiency, adaptability and flexibility. The use of such technologies will make education more accessible, aimed at meeting the needs of both society and each individual student.

Optimus: Interactive Informational Chatbot

Introduction: Optimus is an interactive chatbot developed on the GPT-4 Turbo framework, which offers intelligent responses and task automation. It uses NLP combined with in-code functionalities for Google, YouTube, and Pinterest searches, along with Zapier AI-based automation to interact with third-party applications like email, Google Docs, WhatsApp, and Google Drive. It utilizes prompt engineering and request routing to provide precise and context-aware assistance. By adopting a structured back-end and well-planned development of the front-end, it aims to enhance AI-driven digital interactions and automation. Objectives: Optimus is intended to provide a better user experience by combining multimedia retrieval with NLP, going beyond the limitations of text-based chatbots. With Google, YouTube, and Pinterest, it utilizes richer, more contextualized responses to enhance knowledge retention for education, healthcare, and content generation. Optimus also automates tasks through Zapier AI, allowing frictionless third-party connections. Methods: Optimus is a modular chatbot using GPT-4 Turbo for natural language processing, multimedia search, and automation. It executes user commands by text pre-processing, entity extraction, intent detection, and dialogue management. Optimus fetches real-time images and videos from Google, YouTube, and Pinterest and enriches user experience with feature extraction and content filtering. Connected to third-party platforms through Zapier, it automates processes such as email, Google Docs management, and file uploads. Though a complete frontend is under consideration, a preview version permits simple interaction. As a closed system with security limitations, Optimus does not yet have memory storage and privacy policies. Python-based with efficient multimedia retrieval, it plans to extend to frontend and cloud deployment, transforming chatbot interactions by using AI-based conversations and multimedia integration. Results: Optimus exhibits accurate text retrieval, bringing on-time accurate answers. Image retrieval improves visual content searching on Google and Pinterest. YouTube integration gives informative videos, improving multimedia accessibility. Email automation through Zapier simplifies communication based on intent recognition and template pre-set values. Conclusions: Optimus is a plug-and-play chatbot with NLP, multimedia retrieval, and automation capabilities for seamless digital interaction. Its NLP component is developed using GPT-4 Turbo and processes text through entity recognition, intent classification, and dialogue management. Optimus fetches current images and videos from Google, YouTube, and Pinterest, filtering content for relevance. It automates functions such as email processing, Google Doc management, and file upload using Zapier. Though an entire UI is in development, a preview version enables simple interaction. As a closed secure system, it has no memory and privacy policies. Written in Python, it applies code-based integrations in smooth media access and task automation. With backend structure and future frontend growth, cloud deployment, and multimedia processing, Optimus is intended to improve AI chatbot interactions with rich visual media.

Sosialisasi Peran Profesi Akuntan untuk Lulusan Akuntansi Pada Era Digital 5.0

Technological developments in the Digital Era 5.0 have significantly changed the landscape of the accounting profession. Artificial intelligence (AI), big data, blockchain, and the Internet of Things (IoT) are the main factors influencing the transformation in the world of accounting. Accountants are no longer just playing the role of recorders of financial transactions, but are also required to have in-depth data analysis skills and an understanding of new technologies that support efficiency and accuracy in financial management. The biggest challenge faced is the automation of various tasks that were previously done manually, which can reduce the need for conventional accountants. However, on the other hand, this development also opens up opportunities for accountants to increase their role in financial analysis, business planning, and technology-based risk management. This socialization activity aims to provide an understanding to accounting students and high school students regarding the role and challenges of the accounting profession in the digital era. Through a community service program in the form of a Focus Group Discussion (FGD), participants were given insight into the importance of adapting to technological developments and strategies that can be applied to stay relevant in the world of work. The results of this activity showed that the students involved gained a better understanding of the digital transformation in the accounting profession and the importance of developing technology-based skills. Thus, it is expected that future accountants can improve their competitiveness and be ready to face changes in the increasingly digital and complex business world.