Journal of Information Technology, Cybersecurity, and Artificial Intelligence
Journal of Information Technology, Cybersecurity, and Artificial Intelligence (JITCAI) The Journal of Information Technology, Cybersecurity, and Artificial Intelligence (JITCAI) is an international, peer-reviewed, open-access journal dedicated to advancing knowledge in the interconnected fields of Information Technology, Cybersecurity, and Artificial Intelligence. JITCAI provides a platform for academics, researchers, and industry professionals to publish cutting-edge research, comprehensive reviews, case studies, and insightful short communications that address contemporary challenges and emerging trends in these dynamic disciplines. We aim to foster innovation and knowledge sharing by bridging the gap between theory and practical application while promoting interdisciplinary collaboration. The journal welcomes high-quality contributions that explore technological advancements, security solutions, AI innovations, and ethical considerations that shape the future of these fields. As a Gold Open Access journal, JITCAI makes all published articles accessible globally, supporting the dissemination of impactful research. With an affordable Article Processing Charge (APC) of $50, we aim to lower barriers for authors while maintaining rigorous editorial and peer-review standards. A waiver option is available for authors facing financial constraints, ensuring accessibility and inclusivity for all contributors. JITCAI publishes quarterly and accepts manuscripts ranging from 1000 to 5000 words, ensuring flexibility in addressing various academic contributions.
Enhancing Temperature and Rainfall Prediction Accuracy Through Deep Learning Frameworks
The information of accurate forecasting on temperature, rainfall is also very crucial for disaster preparedness, as well for climate management. Typical statistical and machine learning approaches have limited ability to capture nonlinear and spatiotemporally varying structure of climate fields. This research utilized recent state-of-the-art deep learning models to improve the prediction models for both temperature and rainfall. The hybrid Convolutional Neural Networks and Long Short-Term Memory (CNN–LSTM) method achieved the best results (R² = 0.98 for temperature, 0.91 for rainfall), outperforming those of Multiple Linear Regression (MLR) and Random Forest (RF) as a traditional model. The Physics-Informed Neural Network (PINN) model delivered physically consistent and stable predictions, especially under extreme weather such as heavy rainfall or heatwaves. Relative humidity, atmospheric pressure and sea surface temperature were found as most important predictors-base on feature importance analysis. The regional analysis remained that the coastal region performed best, whereas the hilly region with the high topographical complexity presented a relatively lower accuracy. In general, embedding deep learning into physical constraints ended up improving a lot both correctness and robustness of predictions. Further work should be carried out to improve interpretability, inclusiveness of data and transferability in space of such models with the ambition to build a more sustainable real-time weather forecasting system.
Copyright (c) 2025 Mohammad Sohel Kabir, Abdullah Al Naseeh Chowdhury, Mozibur Rahman, Md. Rasel, Md Muhasin Ali (Author) https://creativecommons.org/licenses/by/4.0
Hybrid Approach with a focus on Preprocessing Techniques for Detecting Phishing Websites
Phishing is regarded as a significant cybersecurity problem in the digital era, utilizing the fabrication of fraudulent websites to deceive users and expropriate their sensitive information, including passwords and financial data. The growing dependence on the internet has led to a marked increase in the frequency of these attacks, resulting in considerable financial losses for individuals and businesses alike. This underscores the pressing necessity for efficient strategies to counteract such assaults. This research aims to create a hybrid model for identifying phishing websites via URL analysis. The suggested model combines Convolutional Neural Networks (CNN) with Long Short-Term Memory networks (LSTM) and an Attention Mechanism to make predictions more accurate and uncover hidden patterns in the data. The model was trained on the "Url_Detection_Dataset" from the Kaggle platform, and its performance was assessed using precision, recall, and F1-score measures. The results showed that the hybrid model is better than traditional methods at telling apart real and harmful URLs, making it a useful tool in cybersecurity. The results provide a framework for subsequent research and promote the creation of more resilient, flexible, and effective solutions.
Copyright (c) 2025 Abeer Yahya Salawi (Author) https://creativecommons.org/licenses/by/4.0
MACHINE LEARNING (ML) TO EVALUATE GOVERNANCE, RISK, AND COMPLIANCE (GRC) RISKS ASSOCIATED WITH LARGE LANGUAGE MODELS (LLMs)
In today’s AI-driven digital world, Governance, Risk, and Compliance (GRC) has become vital for organizations as they leverage AI technologies to drive business success and resilience. GRC represents a strategic approach that helps organization using Large Language Models (LLMs) automation tasks and enhances customer service, while maintaining the regulatory complexity across various industries and regions. This paper explores a machine learning approach to evaluate Governance, Risk, and Compliance (GRC) risks associated with Large Language Models (LLMs). It utilizes Azure OpenAI Service logs to construct a representative dataset, with key features including response_time_ms, model_type, temperature, tokens_used, is_logged, data_sensitivity, compliance_flag, bias_score, and toxicity_score. These features are used to train a model that predicts GRC risk levels in LLM interactions, enabling organizations to improve efficiency, foster innovation, and deliver customer value, while maintaining compliance and regulatory requirements.
Artificial Intelligence, Machine Learning, Large Language Model, Governance Risk Compliance
Copyright (c) 2025 Upakar Bhatta (Author) https://creativecommons.org/licenses/by/4.0
Named Entity Recognition for Hindi Current Landscape and Emerging Trends
Named Entity Recognition (NER) plays a crucial role in Natural Language Processing (NLP) by automatically identifying and classifying entities such as names of people, places, organizations, dates, and numerical values within unstructured text. While NER has seen major advancements in resource-rich languages like English, building robust NER systems for Indian languages—particularly Hindi—remains a significant challenge. Hindi presents unique linguistic complexities such as rich morphology, free word order, the absence of capitalization cues, and widespread use of code-mixed text, which complicate the task further. Over the years, researchers have explored a wide range of approaches to address these challenges, starting with rule-based and statistical models and progressing to sophisticated deep learning and transformer-based techniques. Multilingual models like mBERT, IndicBERT, and MuRIL have shown promise in improving accuracy and generalizability for Hindi NER. This review offers an in-depth look at the current state of Hindi NER, including the available annotated datasets, computational models, and performance benchmarks. It highlights the gaps that persist, such as the scarcity of high-quality annotated data, difficulties in handling informal and domain-specific language, and limited adaptability across different text types. The paper also outlines future directions for research, emphasizing the need for low-resource learning strategies, domain adaptation, and better handling of noisy and code-mixed data. As Hindi continues to dominate communication in various digital spaces, advancing NER systems for this language is more relevant than ever.
Copyright (c) 2025 SHALINI SHARMA, Dr. Piyush Pratap Singh (Author) https://creativecommons.org/licenses/by/4.0
Digital Payment Solutions Drive Efficiency in Supply Chains of Bangladesh
This paper discusses how digital payment systems can enhance efficiency of the supply chain in Bangladesh based on the quantitative research method where its data was collected through 400 respondents, who included suppliers, distributors, wholesalers, and retailers. This paper applied standardized questionnaire to assess effects of digital payment in the speed of transaction, reduction of costs, transparency and trust, efficiency in general and perception by stakeholders. Descriptive analytics revealed that a strong agreement had been reached on the claim that digital payments accelerate transactions, improve transparency, and increase performance of the supply chain, though a moderate level of variability was noted between perceptions with regard to cost-cutting and adoption. Correlation and regression analysis of the hypothesis proved the statistically significant positive correlation between the use of digital payments and the principal efficiency indicators. These findings indicate that it is a strategic means of modernizing the supply chain in Bangladesh using digital payments, which is consistent with global trends of digital transformation. The research contributes to the theoretical bases and practical implementation as it provides empirical data about the benefits of implementing digital payments. It further highlights the need to have supportive policies, strong digital infrastructure, and advocacy campaigns to deal with the resistance to change, limited access, and unequal digital literacy levels. These are insights that can be used in the firms, politicians, and banks that desire to establish sustainable and competitive supply chain ecosystems.
Copyright (c) 2025 Sanjana Ahmed Chaity, Md. Ahsan Shoishob (Author) https://creativecommons.org/licenses/by/4.0
Machine Learning in Assessing the Impact of Climate Change on Water Resources
The water resources of Bangladesh are highly susceptible to climate change which is reflected in patterns of river flows, groundwater and water quality. This work uses a machine learning (ML) approach to quantify the changes induced in representative river basins under historical and future SSP2-4.5 and SSP5-8.5 scenarios. Historical climate monitoring also indicated significant regional warming, especially in the coastal region (+0.25 °C/decade) and growing spatial heterogeneities of rainfall. Future projections under SSP5-8.5 suggest dramatic changes, such as a +4.1 °C temperature increase and a 13.5% change in rainfall by the end of the century. Comparing different ML models for predicting streamflow, Long Short-Term Memory (LSTM) model proved to be the best, with an accuracy and the fewest errors. The increase in mean annual streamflow in the Ganges, Brahmaputra, and Meghna basins is substantial under the LSTM projections and the Meghna basin could witness as high as 21% increase under SSP5-8.5. These transformations align with a significant increase in the frequency of extreme floods, that might extend to yearly events before the century is over. Groundwater is estimated to decrease drastically, and water quality is likely to deteriorate, with coastal salinity increasing by almost 100%. Spatial vulnerability diagnosis indicates that the South-West and coastal zones are severely vulnerable due to both water scarcity and salinity encroachment. A region-specific adaptation plan for sustainable groundwater management needs to be developed, with an advanced plan for enhancing the resilience of Bangladesh’s groundwater resources under future climate scenarios.
Copyright (c) 2025 Kazi Rumanuzzaman, Mst Sanjida Alam, Akram Hossain, Sheikh Urvana Akter Mim, Md Maheadi Hasan (Author) https://creativecommons.org/licenses/by/4.0
This study examines the legal nature of industrial robots in light of the rapid progress in artificial intelligence technologies and the growing reliance on such systems in various aspects of life. It raises new legal and ethical questions about the extent to which industrial robots can be granted legal personality, and the forms and nature of such personality. The study discusses this concept as a legal framework that permits attributing certain rights or obligations to robots, depending on their use and degree of functional independence from humans. The importance of this discussion becomes evident with the emergence of advanced applications of industrial robots, such as self-driving cars, production-line robots, chatbots, and algorithms capable of making impactful decisions that may cause material or moral harm without direct human intervention. The study further investigates whether the legal system needs to adopt a special model of legal personality for these entities to enable accountability for their actions or the outcomes of their decisions. Using the descriptive-analytical approach, the study centered on the following question: What is the legal nature of industrial robots? The study concludes that granting robots a special or conditional legal personality may help fill the legislative gap concerning liability, particularly in cases where tracing the direct human actor proves difficult. The findings emphasize the necessity of establishing gradual and balanced legal frameworks through which industrial robots can be granted limited and well-regulated legal personality. Such frameworks must both account for technological advancement and ensure effective human oversight. The study also calls for international cooperation to formulate unified legal standards consistent with legal and human values.
Industrial robots, Artificial Intelligence, Self-driving cars, Chatbots, Production-line robots.
Copyright (c) 2025 Abdullah Theeb Mahmmoud, Huda hassan baraghethi, Hasan Ahmed Breijieh, Waheeb Abu-Ulbeh (Author) https://creativecommons.org/licenses/by/4.0
Use Artificial Intelligence in Autism Spectrum Disorder: a comprehensive review in assessment, intervention, and data collection
Autism Spectrum Disorder (ASD) presents varied challenges across assessment, intervention, and behavioural monitoring. With recent advancements in Artificial Intelligence (AI), introduced tools have emerged that support clinicians, educators, and families in understanding and managing ASD more effectively. Significant innovations include explainable AI, mobile applications, computer vision, and wearable devices, which establish promising potential to increase accuracy, accessibility, and consistency of care. This review produces findings from recent literature to examine how AI contributes to the ASD care process. It highlights the benefits of AI while noting ethical and practical considerations. Also addresses challenges in data security, privacy, and ethical AI deployment within ASD care settings.
Copyright (c) 2025 Muna Abualhoul (Author) https://creativecommons.org/licenses/by/4.0
Responsible AI for AI Sustainable Future: Governance, Ethics, and The Reality Behind the Promise
Artificial intelligence has emerged as a powerful force shaping global development, offering promising solutions across health, education, climate change, and governance. However, its rapid integration into critical sectors raises urgent questions about ethics, governance, and sustainability. This systematic review explores the promise and practice of responsible AI through the lens of three core objectives: the governance mechanisms guiding AI implementation, the ethical frameworks shaping its design, and the practical realities influencing its deployment across contexts. Drawing from sixty peer-reviewed articles published between 2017 and 2024, the review identifies strong global consensus on foundational principles such as fairness, accountability, and transparency. Nonetheless, a significant implementation gap persists, particularly in low-resource settings, where enforcement mechanisms and institutional readiness are often lacking. The findings also reveal that ethical commitments are frequently undermined by organizational constraints and commercial interests, leading to surface-level adherence without substantive change. Environmental sustainability, a critical dimension of responsible AI, remains underrepresented in current governance discussions despite mounting evidence of AI’s carbon footprint. This review contributes to the growing body of scholarship advocating for inclusive, enforceable, and context-sensitive approaches to responsible AI. It underscores the need for deeper engagement with the political, social, and environmental realities that shape AI’s impact on sustainable development. Ultimately, bridging the gap between AI’s ethical aspirations and real-world outcomes requires not only technical innovation but also strong institutional leadership, interdisciplinary collaboration, and meaningful stakeholder participation.
Copyright (c) 2025 Miracle Atianashie, Mark K. Kuffour, Bernard Kyiewu , Philipa Serwaa (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Artificial Intelligence on Enhancing Guest Experience of Hotel Industry in Kuala Lumpur, Malaysia
Utilizing the Technology Acceptance Model (TAM) as its theoretical backbone, this research examines AI integration into hotel guest experiences of Kuala Lumpur. The study explores relationships between PU, PEOU, Attitude Toward Using AI, and Guest Satisfaction (GUSA), investigating AUAI as a mediator and examining the moderating role of guest age, previous AI experience and trip purpose. Through the empirical survey of 630 hotel customers and analyzing the data with structure equation model (SEM), this study finds that PUSE and PEUS have significant impacts on AUAI and thus indirectly affect GUSA; meanwhile, PUSE and PEUS are directly influencing customer satisfaction simultaneously so that there are parallel direct and indirect effects. Adding to the TAM-strain in hospitality research, this result places post-adoption satisfaction as a primary outcome. Disparities between demographic and experience segments imply specificity in implementing AI. The findings have broader implications in both theoretical and practical ways: theoretically, it furthers the understanding of AI adoption within services context by incorporating technology acceptance with satisfaction outcomes; practically, it provides hoteliers and developers of AI technologies with practical steps toward creating solutions that are useful, easy to use, and constructs favorable guest attitudes insights valuable for strategically fast growing urban destinations such as Kuala Lumpur.
Attitude Towards Using Artificial Intelligence
Copyright (c) 2025 Shah Bin Taufiqur Rahman, Dr. Darian Low Eng Swee (Author) https://creativecommons.org/licenses/by/4.0
Implementation of Blockchain for Secure, Transparent, and Trustworthy Cross-Border Payments in Global Banking
This paper investigated the application of the blockchain as an innovative solution for secure, transparent and confident cross border payments in global banking. This research was prompted by legacy systems' high costs, slow processing, security concerns, and lack of transparency, which reduced efficiency and user trust. The purposes were to investigate if the blockchain could improve security, transparency, trust, efficiency, adoption intention and use satisfaction of international transactions. The study design was quantitative in nature, and a structured questionnaire was used to collect data via a sampling of banking professionals, fintech gurus, corporate clients and cross-border payers. Data were analyzed through descriptive statistics and frequency distribution to find out the perception of the respondents. The findings showed that the majority of respondents have experienced inefficiencies in the current systems and were confident blockchain will provide a remedy by offering tamper-proof records, real-time tracking of transactions, reduced cost, and faster processing. In addition, the results also showed high adoption intention and favorable satisfaction expectations, which means that blockchain has potential to reshape global banking by decreasing dependence on intermediaries and increasing confidence in institutions. The research showed that clarity of regulation, organizational readiness, and user awareness are key for effective implementation. In summary, we certify that blockchain technology has potential to revolutionize cross-border payments and improve the financial global integrations.
Copyright (c) 2025 Tanveer Ahmed Siddquee (Author) https://creativecommons.org/licenses/by/4.0
A Gray Image Quantum Encryption using GNEQR Representation
In the digital era, characterized by extensive online data exchange, information security has become a priority. While traditional encryption methods have proven effective in protecting data transfers, the advent of advanced quantum computing has increased susceptibility to security breaches. Quantum encryption provides a revolutionary solution to this problem by using quantum mechanics principles to establish algorithms that are impermeable to decryption. Using these quantum properties, cryptographic protocols are developed to provide superior security, unlike traditional encryption methods. The image plays an important role in transmitting information in all areas. Therefore, quantum image encryption methods are specifically designed to counter the potential risks posed by quantum computers, which can compromise conventional encryption protocols. This ensures the preservation of data security despite advances in quantum computing technology. In addition, quantum image encryption improves data transmission efficiency by establishing secure communication channels using quantum stats, thereby reducing the need for bandwidth and improving transmission speed. This paper proposes a new method of quantum encryption based on GNEQR representation and the modification of pixel values and positions in an image. After converting the image into a quantum form, we applied an algorithm to modify the values and positions of the pixels using a succession of quantum gates. We concluded this study with a statistical analysis showing the robustness of our quantum image encryption method.
Copyright (c) 2025 Achraf ZEMATE, Moulay Brahim Sedra (Author) https://creativecommons.org/licenses/by/4.0
Comprehensive Analysis and Detection of IoT Network Attacks Using Recon Host Discovery Traffic Dataset
The proliferation of Internet of Things (IoT) devices has introduced unparalleled interconnectivity and significant security challenges. Reconnaissance attacks, particularly Host Discovery, are often precursors to more severe cyber threats. In this study, we examine a labeled network traffic flow dataset to analyze patterns and identify key indicators of Recon Host Discovery attacks. Leveraging exploratory data analysis and feature correlation techniques, we uncover critical traffic behaviors, such as short flow durations and anomalous packet statistics, that distinguish benign from malicious activities. The findings lay the groundwork for developing robust detection mechanisms for IoT networks, emphasizing the importance of targeted feature selection and real-time analytics.
Copyright (c) 2025 Ahmed Al Zaidy (Author) https://creativecommons.org/licenses/by/4.0
Fair and Ethical Resume Screening: Enhancing ATS with JustScreen the ResumeScreeningApp
Abstract— In today's fast-paced job market, the efficiency and fairness of the resume screening process are paramount. "JustScreen" emerges as a cutting-edge solution leveraging advanced Natural Language Processing (NLP) to automate resume evaluation, thus eliminating biases and promoting merit- based candidate selection. This thesis explores JustScreen's innovative approach to integrating NLP and machine learning algorithms to enhance the recruitment workflow, ensuring a more streamlined, unbiased, and efficient candidate assessment process. The methodology involves several key components: data preprocessing, NLP information extraction, fairness metrics calculation, bias mitigation, and interpretability techniques. By utilizing frameworks such as spaCy for NLP tasks, JustScreen aims to overcome the challenges of traditional manual screening processes, improving both accuracy and fairness. This thesis explores the transition from developing a full Application Tracking System (ATS) to creating a powerful enhancement for existing ATS systems. The ResumeScreeningApp/ JustScreen integrates generative AI to provide comprehensive resume analysis, adding significant value to traditional ATS functionalities. Initial evaluations indicate a significant advancement in talent acquisition practices, promoting equal opportunities and reducing the impact of potentially discriminatory factors. This research signifies a transformative shift in recruitment, setting new standards for ethical and efficient hiring practices using Generative AI.
Artifical Intelligence, Human Resources Information Systems
Copyright (c) 2025 Gloribeth Navarro (Author) https://creativecommons.org/licenses/by/4.0
Cloud Computing: Evolution, Challenges, and Future Prospects
Cloud computing has redefined how individuals and organizations interact with computational resources. Providing scalable, flexible, and cost-efficient solutions has become a cornerstone of digital transformation in various sectors, including healthcare, education, finance, and artificial Intelligence. This paper examines the evolution of cloud computing, its architecture, service delivery models, benefits, challenges, and emerging trends. It also highlights critical aspects of cloud adoption and the implications of recent technological advancements. The study utilizes authentic sources to offer a holistic view of cloud computing's current and future trajectory.
Copyright (c) 2024 Safana alzide (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Cloud Security through Intrusion Detection: A Comprehensive Study Using the ISOT-CID Dataset
Cloud computing has revolutionized data management, offering unparalleled scalability, flexibility, and efficiency. However, its open and multi-tenant nature introduces significant security vulnerabilities, making it an attractive target for cyber threats. Intrusion Detection Systems (IDS) tailored for cloud environments are essential in mitigating these risks. Despite various IDS models, benchmarking datasets representing realistic cloud environments is a substantial limitation. This study utilizes the ISOT Cloud Intrusion Detection Benchmark Dataset (ISOT-CID), a publicly available dataset featuring a range of network and application layer attacks collected from a real production cloud environment. The research explores the dataset's structure, analyzes attack patterns, and evaluates IDS models' performance to provide actionable insights for enhancing cloud security. This work contributes to the field by presenting a systematic analysis of ISOT-CID, identifying effective IDS models, and proposing improvements for future cloud intrusion detection research.
Copyright (c) 2024 Safana Alzide (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Generative AI on Student Engagement and Ethics in Higher Education
The rapid adoption of Artificial Intelligence (AI) in higher education is reshaping students’ learning experiences, with tools such as ChatGPT, Grammarly, and Microsoft Copilot becoming integral to academic work. This study, informed by data from the Digital Education Council Global AI Student Survey 2024, examines the impact of AI on students, focusing on usage patterns, trust in AI-generated content, ethical awareness, and expectations for institutional support. Findings indicate that 86% of students use AI for various academic tasks, with a majority expressing concerns about trust, fairness, and over-reliance on AI. While students value AI’s benefits, only 5% are fully aware of institutional guidelines on AI use, and 72% desire more AI literacy courses, reflecting a significant need for comprehensive support in navigating AI responsibly. The study underscores the importance of clear ethical guidelines, faculty training, and student involvement in AI policy formation to foster responsible AI use and preserve academic integrity. These insights offer valuable guidance for educators and policymakers seeking to integrate AI ethically and effectively into higher education.
Copyright (c) 2024 Ahmed Al Zaidy (Author) https://creativecommons.org/licenses/by/4.0
This paper presents MageMemo, an innovative productivity tool that integrates gamification and fantasy storytelling to enhance user engagement in task management. Traditional productivity applications often struggle to sustain long-term user interest, decreasing effectiveness over time. MageMemo addresses this challenge by creating a captivating experience where users earn achievements, level up, and uncover an unfolding narrative as they complete real-life tasks. By combining principles of Human-Computer Interaction (HCI) with a compelling fantasy theme, MageMemo offers a novel approach to improving productivity and motivation. Outlined are the design methodology, technical implementation, and evaluation metrics employed in the development of MageMemo, providing insights into its progression and potential to revolutionize how people use task management applications.
Human-Computer Interaction, Swift, Gamification, Fantasy, User-Centric Design, Positivie Psychology
Copyright (c) 2024 Hunter Winghart, Dr. Ahmed Al Zaidy (Author) https://creativecommons.org/licenses/by/4.0
This article explores Florida's high rate of cybercrime and the efforts being made to combat it. Florida has the second-highest rate of cybercrime events in the United States, with significant financial losses. The most frequent dangers are call center fraud, ransomware, investment fraud, and business email compromise. Strong defense tactics, including multi-factor authentication, safe password policies, cybersecurity training, frequent software upgrades, network segmentation, and dependable backups, are emphasized throughout the text. It also emphasizes the importance of efficient incident response procedures, promoting teamwork, and using continuous monitoring to lessen cyber threats. The essay discusses the vital role that international collaborations and law enforcement play in the battle against cybercrime.
analysis of the high cybercrime rates in Florida, focusing on the financial impact, prevalent cyber threats, and strategic countermeasures
Copyright (c) 2024 Ahmed Al Zaidy (Author) https://creativecommons.org/licenses/by/4.0/
Digital Crimes and Digital Terrorism: The New Frontier of Threats in Cyberspace
This paper explores the rise of digital crimes and digital terrorism as emerging threats that exploit cyberspace to conduct illegal activities, impact society, and compromise individuals’ privacy and security. With a growing dependency on digital platforms, these crimes have become more complex, highlighting the need for strengthened security measures, regulatory frameworks, and coordinated global efforts to address these issues.
Exploring Cybernetics Students' Perceptions of AI in Education: A Comprehensive Analytical Study
This study investigates the multifaceted perceptions of cybernetics students toward integrating artificial intelligence (AI) in educational environments. This research uncovers the students' knowledge levels, preferred sources of information, perceived benefits, and significant students through a comprehensive analysis of survey data combined with recent scholarly insights. Detailed visual analyses underscore key trends, offering a clear perspective on how these students view the future of AI in their academic journey. The findings suggest that while students appreciate AI’s utiAI, they express valid concerns about job security and the potential reduction in human engagement. This study contributes critical insights for educators and policymakers aiming to align AI practices with student needs and expectations.