1. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot in the Education Sector of Afghanistan
Fakhruddin Noori
Abstract
This paper explores the potential benefits, limitations, and risks associated with the implementation of GPT-4, a state-of-the-art AI chatbot, in the educational landscape of Afghanistan. Given the country’s unique socio-economic challenges and educational needs, GPT-4 presents both promising opportunities and significant concerns. The benefits of deploying GPT-4 in the Afghan education system are multi-faceted. It offers scalable, accessible, and cost-effective learning support, which is particularly valuable in remote and underserved areas where qualified teachers are scarce. However, the application of GPT-4 is not without its limitations. Given Afghanistan’s linguistic diversity, language barriers pose a significant challenge, as do cultural sensitivities that require careful customization of the chatbot’s responses. Furthermore, the AI’s reliance on internet connectivity and digital infrastructure, which are limited in many parts of Afghanistan, could hinder its widespread adoption. The risks associated with using GPT-4 in Afghan education include the potential for reinforcing biases in the training data, which may inadvertently propagate stereotypes or misinformation. So, the problem with data privacy and the ethical use of student information is a major challenge. Additionally, over-reliance on AI could sabotage the development of critical thinking skills and human teacher-student interactions. In conclusion, while GPT-4 offers substantial advantages for enhancing educational accessibility and quality in Afghanistan, carefully considering its limitations and proactively managing its risks is essential to ensure its effective and ethical deployment. Future research and pilot projects are necessary to tailor GPT-4’s implementation to Afghanistan’s specific educational context and to maximize its positive impact on the learning outcomes of Afghan students.
Keywords: GPT-4, AI Chatbot, Benefits, Limits, Risks, Education, Afghanistan
About the Author
Fakhruddin Noori, Assistant Professor, Department of Information Technology, Faculty of Computer Science, Kardan University, Kabul, Afghanistan, f.noori@kardan.edu.af
2. PImpact of Artificial Intelligence on Education: An Evaluation of the Use of Artificial Intelligence in the Higher Education Sector of Afghanistan
Abdullah Emran
This study explores the awareness, knowledge, use and effects of Artificial Intelligence among university students, teachers and administrators for learning, teaching and administrative purposes. The world is witnessing the culmination of artificial intelligence in every field of life, from education to economy and physics to engineering, to name a few. In the coming decades, most of the work will be done by machines and robots equipped with AI integrated into various fields that can potentially surpass human capabilities. With advanced artificial intelligence, whose intelligence level will be far greater than that of a human expert, war-ravaged countries like Afghanistan must be prepared for these developments. Without due consideration, AI could pose serious social, economic, political and security challenges. The study employs a mixed-method approach for a comprehensive understanding of the issue. The study found that most of the participants from the public and private universities in Afghanistan are aware of AI and have basic familiarity with AI tools to help them write through ChatGPT, learn new skills and use it as a search engine. Based on the findings of the study, it is proposed that authorities and the international community should play an active role in familiarizing workers from every field of work with artificial intelligence, learning its usages, adapting to the developments and promoting its productive and responsible use while taking measures to mitigate potential job displacements.
Keywords: Artificial Intelligence (AI), Awareness, University Education, AI Tools, AI Effects, Afghanistan
About the Author
Abdullah Emran, Assistant, Department of Research and Development, Kardan University, Kabul, Afghanistan,a.emran@kardan.edu.af
3. Recommender System Approach for Selection of Appropriate Subjects in Learning Management System (LMS) to Improve the Student’s Academic Performance
Fazal Maula Safi
Abstract
Most universities in Afghanistan today use the credit system, in which every semester, the students select subjects based on their authority of choice in the learning management system (LMS). However, sometimes, this process confuses the students with dozens of courses, which results in a substandard selection of subjects and bad performance at the end of the semester. To help the students in this regard, many techniques have been previously used to provide better results, which have somehow been achieved and need further development. This study explores how students can choose appropriate subjects based on their previous study results, using recommender system techniques to improve the performance of the students and to achieve the best results in their studies.
Keywords:Recommender System, Credit System, LMS
About the Author
Fazal Maula Safi, Assistant Professor, Department of Information Technology, Faculty of Computer Science, Kardan University, Kabul, Afghanistan, f.maula@kardan.edu.af
4. Using Artificial Intelligence (AI) to Boost Student Success: A Review
Farsheed Latifee, Mohammad Mukhlis Behsoodi, Abdul Jabar Momand
The integration of Artificial Intelligence (AI) and learning analytics in education has renewed efforts to enhance student success, especially in online and higher education environments. This paper presents a comprehensive review of recent research that applies AI-driven techniques to predict and improve student capacity and performance. Synthesizing the findings from multiple studies, the review examines the utilization of student interaction data, including engagement with learning resources, forums, quizzes, and collaborative tools, to develop predictive models for learning outcomes. Results indicate that leveraging diverse interaction-based features can achieve up to 75% predictive accuracies, underscoring these approaches’ potential to deepen understanding of online learning dynamics. Comparative studies on machine learning algorithms such as logistic regression and random forest also highlight their efficacy in predicting student persistence and success. Such predictive modelling enables early identification of at-risk students, facilitating targeted interventions to support academic achievement in higher education settings. The review also addresses broader implications, including the ethical considerations and educational challenges associated with AI integration in educational practices. As AI continues to evolve, its role in education expands to offer personalized learning experiences, optimize administrative tasks, and enhance overall educational efficiency. However, concerns regarding algorithmic bias, data privacy, and the equitable deployment of AI technologies underscore the need for careful implementation strategies and ongoing evaluation. This review paper emphasizes the transformative potential of AI and learning analytics in advancing student success while advocating for a balanced approach that mitigates risks and maximizes benefits in educational settings.
Keywords:: Artificial Intelligence (AI), Student Success, Learning Analytics, Education
About the Author
Farsheed Latifee, PDC Head, Spinghar Institute of Higher Education, Jalalabad, Nangarhar, Afghanistan, farsheed.latifee@gmail.com
Mohammad Mukhlis Behsoodi, VC. Academics, Spinghar Institute of Higher Education, Jalalabad, Nangarhar, Afghanistan
Abdul Jabar Momand, Founder, Akbar Momand Semi-Higher Education Institute, Jalalabad, Nangarhar, Afghanistan
5. Artificial Intelligence in Higher Education: Mitigating Risks, Embracing Opportunities, and Shaping the Future
Imranullah Akhtar
Abstract
This study aims to provide an in-depth analysis of the integration of artificial intelligence (AI) into higher education, focusing on the opportunities, risks, and strategies that universities can adopt. The goal is to utilize AI’s transformative power while addressing associated challenges effectively. This article also discusses the potential benefits, risks, and strategies for responsible AI implementation in higher education, emphasizing improving learning experiences and influencing the future. Researchers are investigating the use of AI in higher education from various perspectives, including potential threats to academic integrity and algorithmic bias, while evaluating its potential to improve student learning outcomes. Recommendations have been made to mitigate these hazards and promote the moral application of AI tools. This study examined the use of AI at universities in Jalalabad City, Afghanistan, by analyzing interviews and literature reviews. Fifty semi-structured interviews with professors and staff were conducted to collect data, and descriptive and content analysis methods were used to examine the qualitative data. This extensive study examines the potential effects of artificial intelligence on higher education. It examined many hazards, offered advice, and presented a strong ethical framework for its application and use in educational settings. According to previous research, artificial intelligence can drastically change the higher education sector. It also emphasizes the need to adopt a comprehensive strategy that fully exploits the benefits of AI while simultaneously overseeing its moral and practical applications. According to this study, different stakeholders in higher education universities, legislators, and educators must work together to develop innovative AI-driven learning resources, encourage innovation, and change teaching methods to maximize AI’s advantages while successfully managing any risks that may arise.
Keywords:: Artificial Intelligence, Higher Education, Challenges, Integration, Opportunities
About the Author
Imranullah Akhtar, Head of Professional Development Center, Lecturer in Law and Political Science, Alfalah University, Nangarhar, Afghanistan, imranullahakhtar@gmail.com
6. Using AI to Enhance Quality of Teaching and Learning Outcomes
Noorajan Atif
Abstract
This study aims to examine the role of AI, specifically generative AI (GenAI), in the quality enhancement of the teaching-learning process in higher education, mainly in classroom pedagogical practices, improving students’ learning outcomes, and personalizing learning. The researcher employed a descriptive research method to collect the data by reviewing the research articles and books and conducting a document analysis (universities’ policies on using AI). The study’s findings provide multi-faceted insights into using AI in teaching-learning. It reveals that optimizing the use of GenAI can support students to become more independent learners and critical thinkers, and it can also provide plenty of Drill-Practice opportunities for them to master the content, particularly for Afghan students who are believed to be more reliant on teachers and printed learning resources. On the other hand, the findings suggest that GenAI is an invaluable asset for teachers’ professional development in planning everyday pedagogical practices for their classrooms and generating materials and content. However, using GenAI can also pose significant challenges for teachers and institutions, particularly without an established code of conduct and implicit rules that students should abide by to avoid unauthorized activities. Plagiarism or academic dishonesty, misleading information, and misusing the platform to harm others are the key challenges of using GenAI in the teaching-learning process. Additionally, the study provides some valuable suggestions based on the findings.
Keywords:: Artificial intelligence, Teaching-learning Process, Personalized Learning, Students’ Outcomes
About the Author
Noorajan Atif, PhD (Education), Assistant Professor, Paktia University, Paktia, Afghanistan, Nooratif2@gmail.com
7. Exploring the Importance and Challenges of Artificial Intelligence in English Language Learning and Teaching in Afghanistan
Pervaiz Yaseeni
Abstract
Technology has an impact on the education system, making English language teaching and learning accessible to human beings worldwide. English is taught and learned through the language of Massive Open Online Courses (MOOCs) in developed countries. Artificial Intelligence (AI) is a new trend in the field of English language teaching, and AI-based tools are also integrated into the education systems of developing countries nowadays. This qualitative study aims to explore AI-based technologies practised in the field of English teaching in the EFL context. Moreover, it explores the procedure for executing AI-based tools in educational institutions in Afghanistan. This study was conducted through a systematic review of the literature. The articles were screened through keywords, and the targeted articles were reviewed and analyzed thematically. The study revealed that AI-based technologies support students and teachers to master English language skills. Smart teaching approaches and smart environments train creative and autonomous students. Grammarly is an AI-powered tool used to correct spelling and grammatical mistakes. ChatGPT is also a tool practised in scientific writing for summarizing and paraphrasing articles. Kahoot and Quizlet are experienced in assessing students’ performance in EFL. The paper argues that AI-powered technologies can help the government to educate rural area residents, and the use of the technologies can endorse the advancement of the education system in Afghanistan. Finally, the paper recommends that emerging AI-based tools with English subjects should start from school for a strong foundation.
Keywords:Artificial Intelligence, AI-based Tools, E-learning, English Language Teaching
About the Author
Pervaiz Yaseeni, Assistant Professor, Department of English Language and Literature, Nangarhar University, Nangarhar, Afghanistan, pervaizyaseeni20@gmail.com
8. Evaluation of Regression Models in Machine Learning for Price Forecasting: A Comprehensive Study
Wali Ullah Shinwari
Abstract
In data mining and artificial intelligence (AI), regression algorithms play a central role in predictive analytics. Predictive analytics, including real estate price forecasting, is of great value to countless stakeholders, including buyers, sellers, businesses, and government agencies, thereby facilitating informed decision-making processes. A comprehensive literature review has been conducted to analyze relevant attributes contributing to the forecasting process and identify optimal price forecasting models used in the existing literature. This study expands the understanding of regression modelling in the context of price forecasting, facilitating informed decision-making processes in various industries based on pricing strategy. The purpose of the study is to conduct a fair evaluation of regressor algorithms and to evaluate the effectiveness of the regression model for forecasting. Moreover, in this study, an attempt is made to evaluate the performance of four different machine-learning algorithms using different evaluation measures. This study examines the field of price forecasting in this area using several different regression models (A, B, C, and D) and two different data sets. Performance evaluations used for regression models, including the average of absolute values of all errors (MAE), the average of the squares of errors (MSE), and the square root of the average of squared errors (RMSE), are used to evaluate the prediction accuracy of each model. The experiments’ results highlighted the importance of choosing appropriate regression methods for robust predictive modelling across many fields. In particular, the results show that Model A consistently outperforms its counterparts on both datasets and demonstrates superior prediction accuracy.
Keywords:Regression Model, Machine Learning, Price Forecasting
About the Author
Wali Ullah Shinwari, Assistant Professor, Department of Information Technology, Faculty of Computer Science, Kardan University, Kabul, Afghanistan, w.shinwari.edu@kardan.edu.af
9. A Critical Analysis of AI's Sway over National Accounts, Using the Economic Circular 3D-Model: A Case of Assessing Afghanistan’s National Account (2002-2018).
Mohammad Ismail Fazly
Abstract
Artificial intelligence is one of the most challenging phenomena for labour in many societies of the modern age, but AI’s sway overproduction and the unemployment rate is crucial. This paper uses a theoretical framework (EC3D-Model) and an inductive research method. Triangulation is used to collect the data. The study found twenty-seven possible production procedures concerning the production process in real sectors of the economy. However, in the case of AI impact, fortunately, only two can have an adverse effect on employment opportunities.
Keywords:AI, National Accounts, Economic Circular 3D-Model, Afghanistan.
About the Author
Mohammad Ismail Fazly, Lecturer, Alfalah University, Faculty of Economic, Nangarhar-Afghanistan, ismailfazly4@gmail.com
10. Challenges and Opportunities for Artificial Intelligence in Afghanistan: An Exploration
Habibrahman Hakimi, Noor Aqa Hakimi
Abstract
Artificial intelligence (AI) technology is booming worldwide, creating wide-ranging changes in various areas of modern life. However, there are significant concerns about its development. Experts fear that AI technology could be used for dangerous purposes such as deepfakes, spreading misinformation, undermining trust in the media, and blackmail. It can also facilitate surveillance and invasion of privacy, threatening individual freedoms. Another major concern is job loss due to AI’s spread. This research article aims to understand the current state of AI in Afghanistan, highlighting the challenges blocking its development and the numerous opportunities it offers. Afghanistan faces significant problems in effectively adopting AI. Despite these challenges, emerging initiatives in the education, healthcare, and agriculture sectors aim to leverage AI for improved service delivery and operational efficiency. Additionally, this research emphasizes the importance of raising public awareness and enhancing the general population’s understanding of AI. By addressing these challenges and opportunities, Afghanistan can better navigate the complexities of AI adoption and its societal implications. This article discusses various methods to raise public awareness, emphasizing the positive applications of AI. The study identifies barriers such as limited infrastructure and lack of skilled professionals but notes emerging initiatives in education, healthcare, and agriculture utilizing AI for better services. Addressing these challenges requires strategic investments in AI research, education, and supportive government policies to ensure sustainable development. Furthermore, the study examines AI’s benefits in decision-making for development. Officials must develop strategies to address the outlined issues, such as enhancing recruitment tactics to increase the number of AI professionals.
Keywords:AI, Challenges, Opportunities, Public awareness.
About the Author
Habibrahman Hakimi, Department of Civil Engineering, Faculty of Engineering, Nangarhar University, Nangarhar, Afghanistan, Habibrahman1hakimi@gmail.com
Noor Aqa Hakimi, Department of Geographic Information System, Kabul Municipality, Kabul, Afghanistan
11. Leveraging Convolutional Neural Networks to Enhance Healthcare Diagnostics and Access in Afghanistan
Sayed Mortaza Kazemi
Abstract
There are many obstacles in Afghanistan’s healthcare system, such as restricted access to healthcare facilities, a lack of qualified healthcare professionals, and a high prevalence of diseases that can be prevented. The limited availability of AI-powered tools and advanced medical technologies makes it more difficult to diagnose health problems early and treat them effectively. This study investigates the potential applications of convolutional neural networks (CNNs) in Afghanistan to improve diagnostic performance and democratize access to high-quality medical care. The suggested method entails creating reliable CNN models that have been trained on substantial datasets of medical pictures, including ultrasounds and X-rays, in order to identify and categorize common illnesses, including cancer, TB, and pneumonia. After that, primary care clinics might implement these CNN-based diagnostic tools and incorporate them into patient community health worker-friendly mobile applications. With AI-powered diagnostics, frontline healthcare personnel can be equipped to detect diseases earlier, treat patients more quickly, and ultimately save lives. Along with discussing integrating AI-driven tools into the current healthcare infrastructure while addressing privacy and security concerns, the study also addresses ways to educate Afghan healthcare workers to use CNN technologies properly. This study shows how convolutional neural networks can appropriately address urgent healthcare issues in settings with limited resources in Afghanistan. The results may help build novel AI-powered solutions to improve health outcomes and increase underserved areas’ access to high-quality healthcare.
Keywords:Leveraging Convolutional Neural Networks, Healthcare Diagnostics, Afghanistan
About the Author
Sayed Mortaza Kazemi, Assistant Professor, Department of Information Technology, Faculty of Computer Science, Kardan University, Kabul, Afghanistan, m.kazemi@kardan.edu.af
12. Usage of Information Computing Technology, Data Mining, and ML (Machine Learning) to Reduce Child Mortality and Morbidity: Case Study Afghanistan
Ihsanullah Nazari
Abstract
Child mortality and morbidity are some of the challenges all over the world. Usually, most of these phenomena are preventable. The highest mortality rates are in developing countries. The approximately under-five mortality rate (Death per 1000 live births) is 57 in Afghanistan, which is a huge number in 2022. There are many challenges, for example, shortage of enough doctors, shortage of poor awareness, lack of standard health facilities, shortage of health management information systems in most of the health facilities, cultural issues, infrastructure and transportation problems, white area issues where health facilities are too far, climate issues where in some provinces for months they face snowfall and cannot access the health facilities, etc. In recent decades, information communication technology has significantly advanced in various fields, including medicine. This paper represents how to solve most child mortality and morbidity issues using information communication technology; herein, it proposes using machine learning algorithms and data mining techniques to forecast the hazard level of child-related issues.
Keywords: Information Computing Technology, Data Mining, Machine Learning, Child Mortality, Morbidity, Afghanistan
About the Author
Ihsanullah Nazari, Lecturer, Department of Information Technology, Faculty of Computer Science, Kardan University, Kabul, Afghanistan, i.nazari@kardan.edu.af
13. Advancing Cardiovascular Disease Network Analysis: A Comparative Study of Correlation-Based Graph Construction and Link Prediction
Zabihullah Burhani, Abolfazl Dibajib
Abstract
This article presents a new method in the analysis of cardiovascular diseases by making graphs based on statistical methods (correlation coefficients) and weighted link prediction algorithms. In this method, we converted the heart disease dataset into three undirected weighted networks by correlation methods such as Pearson, Spearman and a combination of both. Then, we applied weighted link prediction algorithms, such as weighted common neighbours (WCN), weighted preferred attachment (WPA), and weighted Jaccard coefficient (WJC), on these networks. The study found that the network based on Pearson’s correlation had an excellent performance, with the WPA algorithm achieving 100% AUC and 99.99% precision. Our network-based model outperformed existing methods and certainly provided advantages in capturing feature dependencies and nonlinear relationships. Although our study is promising, it also acknowledges limitations in dataset size and computational complexity. Our computational results show that this approach has significant potential to increase cardiovascular disease prediction and risk assessment. In conclusion, it can be emphasized that this approach can pave the way for more effective and personalized strategies for managing and preventing cardiovascular diseases.
Keywords:Cardiovascular Disease, Link Prediction, Network Analysis, Graph
About the Author
Zabihullah Burhani, Department of Computer Science, University of Takhar, Takhar, Afghanistan, zabihullahburhani@gmail.com
Abolfazl Dibajib, Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
14. AI-Enhanced Automated Reporting and Alert System for Road Accidents Using the YOLO Backbone Network
Muhammad Haleem, Ahmad Sohail Raoufi
Abstract
According to the increasing number of road accidents, as of the WHO’s annual report, more than 50 million casualties, including death cases of 1.4 million across the globe - is a significant rate. In the domain of health care, response to the issue gained the attention of researchers using advanced techniques for image processing. This study presents an enhanced AI reporting and alert system for road accident detection using the YOLO (You Only Look Once) backbone network. The system integrates YOLO’s real-time object detection capabilities with advanced irregularity detection techniques to accurately identify and classify road accidents from CCTV footage. Our approach employs a lightweight, efficient YOLOv5 model to process video frames, ensuring rapid and reliable detection even in resource-constrained environments. By combining YOLO’s high-speed object detection with backbone network and event classification algorithms, our solution minimizes false signals and enhances detection precision, including bi-directed traffic. Once an accident is detected, the system promptly generates alerts and notifies relevant authorities through e-mail notification as an automated mechanism to facilitate the timely emergency response. Comprehensive testing done on various CCTV demonstrates the system’s efficiency in various traffic scenarios. Our solution edges up road accident monitoring by providing a practical tool that reasonably reduces the response times and approaches towards saving lives in traffic setups.
Keywords:Backbone Network, Bi-Directed Traffic, Image Processing, YOLO, CCTV
About the Author
Muhammad Haleem, Assistant Professor, Department of Information Technology, Faculty of Computer Science, Kardan University, Kabul, Afghanistan, m.haleem@kardan.edu.af
Ahmad Sohail Raoufi, Department of Information Technology, Faculty of Computer Science, Kardan University, Kabul, Afghanistan
15. Potential of AI for Predictive Structural Damage Assessment: A Comprehensive Review within Afghanistan Context
Zabihullah Dalil Shinwari, Abdul Tawfiq Pouya, Sayed Dawod Karimi
Abstract
This article investigates the application of artificial intelligence (AI) in predicting structural damage within Afghanistan, addressing the unique challenges posed by seismic activity, extreme weather, and prolonged conflict. Traditional structural assessment methods, highly reliant on manual inspections, often lack efficiency and accuracy. AI technologies, including machine learning (ML) and deep learning (DL), offer significant improvements by analyzing large datasets from sensors, satellite imagery, and historical records. These technologies enable real-time monitoring and early detection of structural problems, thereby enhancing infrastructure safety and resilience. In Afghanistan, AI-driven predictive models are essential for managing and maintaining critical infrastructures such as bridges, roads, dams, power plants, and historical buildings. AI’s benefits include enhanced accuracy, cost efficiency, and improved safety, which is vital in a resource-constrained and high-risk environment. However, successful AI implementation faces challenges such as data quality, computational resource requirements, and system integration. Addressing these challenges requires a national AI strategy, enhanced data collection through IoT devices and satellite technology, investments in computational resources, and specialized training programs. The research highlights the importance of pilot projects and international collaboration to demonstrate AI’s effectiveness and facilitate knowledge exchange. Establishing robust legal and ethical frameworks is also crucial for responsible AI usage. Future trends suggest that advances in AI and integration with IoT will further improve structural health monitoring, making AI-driven predictive maintenance increasingly standard. By adopting these technologies, Afghanistan can achieve safer, more resilient, and sustainable infrastructure development, protecting lives and property. This article aims to inform policy-making and strategic planning, emphasizing AI’s critical role in enhancing infrastructure resilience in Afghanistan.
Keywords:AI, Machine Learning (ML), Deep Learning (DL), Predictive Structural, Afghanistan
About the Author
Zabihullah Dalil Shinwari, Lecturer, Department of Civil Engineering, Kardan University Kabul, Afghanistan, z.shinwari@kardan.edu.af
Abdul Tawfiq Pouya, Head, Department of Civil Engineering, Kardan University Kabul, Afghanistan, a.tawfiq@kardan.edu.af
Sayed Dawod Karimi, Lecturer, Department of Civil Engineering, Kardan University Kabul, Afghanistan, d.karimi@kardan.edu.af
16. A Smart Approach to Gas Cylinder Safety: AI-Enabled Leakage Detection System
Muhammad Tahir, Sabaoon Khan, Sohail Noori
Abstract
The frequency of domestic gas cylinder usage comes with an inherent risk of leakage, which can lead to dangerous accidents and potentially catastrophic events. Addressing this issue, we have developed an integrated safety system employing artificial intelligence (AI) to enhance household safety and reduce the risks associated with gas cylinder leaks. Our approach utilizes a network of sensors to monitor the gas cylinder, promptly detecting any leakage. Once detected, this information is relayed to a central device equipped with AI capabilities. This intelligent device assesses the severity of the leakage and executes a multi-faceted response strategy. It communicates an alert to the user’s mobile phone application, activates an audible alarm system, and initiates an automatic mechanism attempting to shut off the cylinder’s nozzle to prevent further gas escape. Our experimental results have shown that the system not only detects leaks with high accuracy but also effectively categorizes the severity of leakages, thereby allowing for timely and appropriate responses. Moreover, the automatic shutoff feature has demonstrated efficiency in minimizing the potential damage from gas leaks. This innovative system offers a promising solution to enhance household safety by using AI to detect and manage gas cylinder leaks, creating a safer environment for domestic energy usage.
Keywords:AI Safety, Gas Cylinder Safety, IoT Based Home Safety
About the Author
Muhammad Tahir, Assistant Professor, Department of Information Technology, Faculty of Computer Science, Kardan University, Kabul, Afghanistan, m.tahir@kardan.edu.af
Sabaoon Khan, Assistant Professor, Department of Engineering, Faculty of Engineering and Technology, Kardan University, Kabul, Afghanistan
Sohail Noori, Department of Information Technology, Faculty of Computer Science, Kardan University, Kabul, Afghanistan
17. Artificial Intelligence and Robotics
Ezatullah Ehsas
Abstract
Artificial intelligence (AI) and robotics are two essential and advanced branches of computer science and engineering that are studied and used separately and in combination. Artificial intelligence deals with designing and constructing computer systems and programs that can perform tasks that normally require human intelligence. AI is a new scientific field that has created significant transformations across all scientific and industrial fields, providing great conveniences for today’s societies. Robotics deals with the design, construction, and use of robots. Robots can operate independently or with human guidance. Today, robots have replaced humans in dangerous and complex work environments, reducing casualties. Additionally, in most working environments, the accuracy and speed of robots surpass that of humans. This article aims to investigate and analyze the integration of artificial intelligence in robotics to improve automation and enhance cooperation between humans and robots. With advancements in AI and robotics technology, the capabilities of these two fields have significantly increased, providing new opportunities for industrial, service, and home applications. This research shows that integrating AI with robotic systems improves performance, accuracy, and learning ability. Intelligent robots can perform more complex tasks independently or in cooperation with humans. This will increase efficiency, reduce costs, and improve workplace safety. In conclusion, it is pointed out that fully exploiting the potential of AI and robotics integration requires further development of advanced algorithms, improved sensors, and increased computing power. Additionally, establishing appropriate standards and regulations for safe and effective cooperation between humans and robots is necessary. This article can be a valuable resource for researchers and engineers in artificial intelligence and robotics.
Keywords:AI, Robotics, Performance, Accuracy, Learning.
About the Author
Ezatullah Ehsas, Student of Master’s Degree in Artificial Intelligence, Amirkabir Univeristy of Technology, Iran, ezatullah.ehsas31@gmail.com
18. Utilizing User and Item Distrust for Recommendation System Using Birank Algorithms
Khalilullah Akbari
Abstract
Researchers have widely studied the recommender system from various aspects. Due to the massive amount of data and availability of computational power, finding relevant items to recommend to users in order to increase engagement got the attention of researchers. Graph ranking algorithm is used widely in recommendation systems. BiRank algorithm is proposed to rank nodes in a bipartite graph in a way that is normalized row-wise and column-wise. In this paper, an attempt has been made to utilize this algorithm in order to solve the distrust issue in the system, such as the user not wanting to see more like that item or not wanting to see from that source of data or user. In order to solve this problem, the study initializes the Birank algorithm with a mask matrix where those distrusted items or users will be initialized with a large negative value so that they get the lowest rank and do not appear in the top recommended items. Based on the experiments, the study found that our model outperformed the previous model and utilized the distrust factor effectively in one model.
Keywords:User and Item Distrust, Recommendation System, Birank Algorithms
About the Author
Khalilullah Akbari, Assistant Professor, Department of Information Technology, Faculty of Computer Science, Kardan University, Kabul, Afghanistan, k.akbari@kardan.edu.af
19. Artificial Intelligence Integration and Implementation of Legal Practices by Using Load Balancing Scale: Opportunities and Challenges of Transforming Private Services.
Nasrullah
Abstract
Artificial intelligence (AI) must not be realized and understood as a new technology because it is already used and will be used in the future in public and private sectors. AI has emerged as an excellent tool for providing case-based frameworks across multiple industry platforms for different purposes. It holds great promise for its practicality use across several domains and virtual applications, making it a potentially valuable resource for government agencies, different societies and voluble forms. AI can enhance humanoid proficiency and highly available capabilities in critical sections and life-threatening situations. The article will advocate for “human knowledge in the loop” to be tested in a virtual cloud environment to test several strategies that combine social knowledge AI base policies to migrate the guarantee individualized legal results to ensure AI functions and features are complemented rather than replaced. This study explores the possible ways to find benefits and some limitations of AI in this regard. With simplified strategies, AI usage in the public sector emphasizes the potential source for greater productivity, availability, reliability, safety, and security. It reduces different requirements by analyzing all results with the pros and cons of using AI case-based design architecture in a virtual cloud environment in the public sector. Therefore, in this study, organizational theory will be considered a different tool for finding some analysis and graphs to finalize uncertainty challenges and maximize reliability with AI application deployment services. This research paper’s result will help us design better and understand how AI may transform public service delivery by stimulating new ideas and challenges and improving efficiency and reliability by considering organizational, ethical, and societal implications.
Keywords:Artificial Intelligence, Legal Practices, Load Balancing Scale, Opportunities, Challenges, Private Services.
About the Author
Nasrullah, Dean, Faculty of Computer Science, Khurasan University, Nangarhar, Afghanistan, Nasrullah1550@gmail.com
20. بررسی تاثیر هوش مصنوعی بر آموزش
پوهنیار سیدبدرالدین نصرت، نامزد پوهنیار اجمل هاشمی
چکیده
هدف تحقیق حاضر تاثیر هوش مصنوعی بر روند آموزش میباشد. این تحقیق بر اساس روش کیفی از نوع پدیدارشناسی انجام شده است. جامعه آماری این تحقیق را اساتید پوهنتون که عملا در زمنیه های متفاوت از هوش مصنوعی در فعالیت های اکادمیک خویش استفاده می کنند تشکیل میدهد، روش نمونه گیری هدفمند از نوع گوله برفی انتخاب خواهد شد. حجم نمونه گیری در روش تحقیق کیفی بر اساس اشباع نظری انتخاب می شوند. ابزار جمع آوری اطلاعات از طریق پرسشنامه نیمه ساختار یافته صورت گرفت، اطلاعات به دست آمده برحسب هفت مرحله دیکلمن مورد تحلیل قرار گرفت. یافته های متوقعه که می تواند منجر بهشناسایی مقوله های اصلی: از 1) تاثیرات کلی هوش مصنوعی بر آموزش، 2) فرصت یاری دهنده هوش مصنوعی بر آموزش و 3) چالش ها و نگرانی های هوش مصنوعی و تاثیر آن بر آموزش 4) آینده هوش مصنوعی در آموزش بدست خواهد آمد. باتوجهبه نتایج حاصله چنین نتیجه به دست خواهد آمد که ورود هوش مصنوعی بر حوزه آموزش می تواند مزایا و معیایب را به همراه داشته باشد.
کلید واژه:: تاثیر، هوش مصنوعی، آموزش
21. Investigating the Impact of Artificial Intelligence on Education
Seyed Badrudin Nasrat, Ajmal Hashemi
Abstract
This research aims to investigate the impact of artificial intelligence on the education sector. The statistical population of this research consists of university professors who use artificial intelligence in their academic activities in different contexts, and the purposeful snowball sampling method will be chosen. The sampling volume in the qualitative research method is chosen based on theoretical saturation. The data collection tool was done through a semi-structured questionnaire, and the obtained data was analyzed according to Dickleman’s seven stages. Expected findings that can lead to the identification of these four categories: 1) the general effects of artificial intelligence on education, 2) the opportunity to help artificial intelligence in education and 3) the challenges and concerns of artificial intelligence and its impact on education 4) the future of artificial intelligence in training. Based on the results, it can be concluded whether introducing artificial intelligence in education can bring advantages or disadvantages.
Keywords:Artificial Intelligence, Impact, Education.
About the Author
Seyed Badrudin Nasrat, Assistant Professor, Kabul Education University, Kabul, Afghanistan, badr.nasrat@keu.edu.af; badr.nasrat@gmail.com
Ajmal Hashemi, Lecturer, Kabul Education University, Kabul, Afghanistan
22. Explaining the Role of Artificial Intelligence (AI) in Entertainment, Creation of Content, and Attracting Audiences to the Cultural Committee of Al-Beroni University
Asadullah Aria, Mohammad Yahya Rahil, Naqibullah Bayan
Abstract
This study explores the multidimensional effects of artificial intelligence on entertainment, the creation of content, and attracting audiences to the cultural committee of Al-Beroni University students. This study used mixed-method research based on quantitative and qualitative research. The quantitative data was analyzed using SPSS software to highlight the themes related to the topic, while the qualitative data was evaluated using MAXQDA software based on grounded theory. This study’s findings indicate that using artificial intelligence in creating entertainment content has helped Al-Beroni University’s cultural committee attract more students. Finally, this research will add to the increasing body of knowledge on the role of artificial intelligence in creative processes by analyzing the impact of AI on content production and audience attraction.
Keywords:Artificial Intelligence (AI), Content Creation, Entertainment, Attracting Audiences and Al-Beroni University.
About the Author
Asadullah Aria, Associate Professor, School of Journalism and Communication, Al-Beroni University, Kapisa, Afghanistan, tahoraaria@yahoo.com
Mohammad Yahya Rahil, Associate Professor, School of Journalism and Communication, Kabul University, Afghanistan
Naqibullah Bayan, Assistant Professor, School of Journalism and Communication, Al-Beroni University, Kapisa, Afghanistan
23. نړیوال امنیت ته د مصنوعي ځیرکتیا ننګونې
عزیزاحمد فضلي
خلاص
مصنوعي ځیرکتیا (AI) د ډیجیټل دور یو له مخکښو ټیکنالوژیو څخه ده، چې د انسان د ژوند په بېلابېلو اړخونو پراخه اغېزه لري. یوه ساحه چې AI په کې مهم مګر متنازع رول لوبوي نړیوال امنیت دی، دا څېړنیزه مقاله په نړیوال امنیت باندې د AI منفي اغېزې څېړي او د دې ټیکنالوژۍ کارولو څخه رامینځته شوي ننګونې تحلیلوي. دا مقاله په نړیوال سخت امنیت د مصنوعي ځیرکتیا د منفي اغیزو په برخه کې د خود مختاره وسلو زیاتوالی، د سایبر ګواښونو او سمارټ بریدونو زیاتوالی او په نړیوال نرم امنیت د مصنوعي ځیرکتیا د منفي اغېزو په برخه کې د غلطو معلوماتو او پروپاګند پراخه خپرېدل، د محرمیت سرغړونې، د حساسو معلوماتو راټولول او د AI کارولو پورې اړوند اخلاقي او حقوقي مسلې په ګوته کوي. په پای کې، مقاله د مناسبو نړیوالو قوانینو او مقرراتو د جوړولو، د عامه پوهاوي د زیاتولو او د AI کارولو په اړه د نړیوالې همکارۍ پیاوړتیا باندې ټینګار کوي. یوازې د یوې هر اړخېزې او همغږې کړنلارې له لارې د AI له ګټو ګټه اخیستل کېدی شي او په ورته وخت کې په نړیوال امنیت د دې منفي اغېزو مخه نیول کیدی شي.
کیلي:مصنوعي ځیرکتیا، امنیت، نړیوال امنیت، سخت امنیت، نرم امنیت
About the Author
Azizullah Fazli, Director of Quality Assurance & Lecturer, Mili Institute of Higher Education, Kabul, Afghanistan, Azizahmadfazli200@gmail.com
24. Use of AI for Political Gains: Analyzing the Role of Deepfake and AI-generated Fake Images
Dr. Peerzada Tufail Ahmad and Ali Saeed
Abstract
Technological advancement changes how we live, operate and carry out day-to-day functions. The introduction of new technological innovations brings, on the one hand, opportunities and, on the other hand, challenges and threats as well. This has been the case with almost all technological innovations, including Artificial intelligence (AI). In contemporary times, we are witnessing the emergence and rise of AI, thereby affecting all the spheres of our lives. Although AI’s emergence could positively affect our lives by bringing numerous advantages, there are apprehensions about the misuse of AI and its threats. The potential dangers of AI misuse are a cause for concern and caution. AI affects not only the education, health, manufacturing, and economic sectors but also the political and social spheres. There have been cases, particularly in the political sphere, where political actors have misused AI against their opponents for political gains, particularly with AI-generated deepfake videos and fake images. This study is based on trend analysis and aims to analyze the effects of AI and its misuse by political actors to defame their opponents, suppress the opposition and critics, generate hate against their political opponents and build a narrative in their favour. This study underscores the need for research and analysis in AI, particularly in understanding the misuse of AI-generated deepfake videos and images for political gains, focusing on some cases in the US, Israel and India.
Keywords:AI, Deepfake, Fake images, Politics, Misuse, Political gain, Opponents.
About the Author
Dr. Peerzada, Tufail Ahmad, Coordinator, Department of Research & Development and Assistant Professor, MIR, Kardan University, Kabul, Afghanistan, t.ahmad@kardan.edu.af
Ali Saeed, Assistant Professor, MIR, Kardan University, Kabul, Afghanistan
