International Journal of Applied Resilience and Sustainability
type: channel
description: International Journal of Applied Resilience and Sustainability International Journal of Applied Resilience and Sustainability (IJARS) is a peer-reviewed, open-access research journal published by Deep Science Publisher. IJARS focus on advancing applied research that design, understand, and promote resilient and sustainable development. We welcome high-quality submissions of cross-cutting, multidisciplinary work with a focus on resilience and sustainability. IJARS provides free and unlimited access for readers through open access. Publication is free in this open-access journal with no submission, processing, or publication fees. IJARS follows COPE and ethical guidelines. Journal Particulars: Title: International Journal of Applied Resilience and Sustainability Frequency: Quarterly Publisher Name: Deep Science Publisher Publisher Address: D204, Green Park, Plot Nos. 2 and 3, Ghansoli, New Mumbai 400701, Maharashtra, India Starting Year: 2025 Subject: Multi-Disciplinary Subjects Language: English Publication Format: Online and Print Email Id: editor@deepscipub.com Mobile No.: +91 7977171947
publisher: Deep Science Publisher
language: en
publication name: International Journal of Applied Resilience and Sustainability
issn: 3108-1703
copyright: Copyright (c) 2025 International Journal of Applied Resilience and Sustainability (IJARS) This work is licensed under a Creative Commons Attribution 4.0 International License.
items: Multi-agent cooperative control of a three-tank liquid system using reinforcement learning algorithms
items: The impact of artificial intelligence on students’ academic development, critical thinking, cognitive skills, and learning outcomes
items: Artificial intelligence vs traditional teaching methods on student performance: Effectiveness and challenges
items: Acceptance of artificial intelligence in human resource management: Adoption factors, technology acceptance models, and HR analytics
items: Artificial intelligence and sustainable human resource management: Technologies, applications, and future directions
items: Artificial intelligence and human resource management: HR analytics, talent management, and ethical AI
items: Teachers’ acceptance of artificial intelligence tools in K-12 education
items: Factors influencing teachers’ adoption of artificial intelligence tools in K-12 education
items: Is using artificial intelligence tools for academic work cheating? Student perceptions, ethics, and the impact on academic performance and critical thinking
items: Students’ perceptions and responsible adoption of artificial intelligence in education: Ethical considerations, impacts, and academic performance
items: Artificial intelligence in human resource management: A review of applications, algorithms, and ethical challenges
items: Artificial intelligence, corporate information governance, and Environmental, Social, and Governance (ESG) performance: A systematic review
items: Artificial intelligence, big data analytics, and deep learning for 6G communication networks: Challenges, applications, and future directions
items: Artificial intelligence in sustainable finance and Environmental, Social, and Governance (ESG) performance: A review
items: The impact of artificial intelligence and learning analytics on students’ academic performance
items: Artificial intelligence and machine learning in 6G wireless communication networks
items: Artificial intelligence agents and agentic AI systems: Architectures, capabilities, applications, challenges, and future directions
items: Artificial intelligence-based writing tools in education: Impacts on students’ academic ethics, learning performance, and skill development
items: Teachers’ perceptions and adoption of artificial intelligence in higher education
items: Collaborative strategies for promoting inclusive education in rural communities: A case study of Okigwe education zone
items: Qualitative research approaches: A guide to data collection, analysis, trustworthiness, artificial intelligence, and ethics
items: A multimodal predictive framework for early detection of anxiety instability and panic attacks
items: Artificial intelligence-driven intelligent concrete crack detection and real-time width estimation for smart structural health monitoring systems: A review
items: Analysis of blow-up situation in finite automata
items: Agentic artificial intelligence-driven tutoring: A multi-agent cognitive architecture for personalized adaptive learning in Education
items: Supply chain management using machine learning, deep learning, and blockchain techniques: A review
items: Artificial intelligence for customer relationship management: Impact on customer experience, engagement, value creation, and loyalty
items: Enhancing customer experience, engagement, value creation, and loyalty through artificial intelligence and machine learning in CRM
items: Inclusive education through artificial intelligence: Opportunities, challenges, and ethical considerations
items: Student perceptions of ChatGPT and artificial intelligence tools in higher education: Evidence from early experiences

Multi-agent cooperative control of a three-tank liquid system using reinforcement learning algorithms
type: item
description: This research paper aims to present a multi-agent cooperative reinforcement learning approach for controlling the highly nonlinear dynamics of a three-tank liquid system. Meaning to say the system’s complexity arises from the interdependence of the tanks, where precise control is required to maintain stable fluid levels. Initially, we deploy two twin-delayed deep deterministic policy Gradient agents to manage the inflow valves, then followed by two proximal policy optimization agents tasked with controlling the valves. The main goal is to compare the performance of these two models of agents against traditional proportional-integral-derivative controllers. In addition to promote effective collaboration between agents, a cooperative reward structure is implemented, encouraging agents to work together to maintain balanced fluid levels within all three tanks. The reward function penalizes deviations from target levels, accounting for both local performance and system-wide stability. The proposed method also addresses key challenges in multi-agent systems, such as non-stationarity and coordination in decentralized control, by integrating a centralized critic during training with decentralized execution. Experimental results reveal that the twin-delayed deep deterministic policy agents outperform the proportional integral derivative control system in terms of settling time, rise time, and robustness, showcasing their ability to handle the nonlinear nature of the system with minimal tuning.
subject: Articles
  topic: section
subject: Reinforcement learning
  topic: keywords
subject: Multi-agent
  topic: keywords
subject: Control system
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Deep reinforcement learning
  topic: keywords
subject: Machine learning
  topic: keywords
creator: Lincoln Nobert Munhenzwa
creator: Adlen Kerboua
creator: Godfrey Murairidzi Gotora
rights: Copyright (c) 2026 Lincoln Nobert Munhenzwa, Adlen Kerboua, Godfrey Murairidzi Gotora (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-06
publication date: 2026-04-06
starting page: 1130
ending page: 1145
doi: 10.70593/deepsci.0202046

The impact of artificial intelligence on students’ academic development, critical thinking, cognitive skills, and learning outcomes
type: item
description: The adoption of Artificial Intelligence (AI) in educational fields has developed a great potential and problem related to the academic progress, critical thinking, mental abilities, and final student results. The increased application of generative AI, intelligent tutoring machines, adaptive learning systems, predictive analytics, and AI-assisted learning tools have altered the traditional learning environment, although issues have been raised about over-reliance on technology, lower-level thinking, algorithm biases and academic dishonesty. This literature review was a systematic investigation of recent articles relevant to the topic of Artificial Intelligence in Education (AIEd) and its effects on student engagement, academic achievement, cognitive growth, and learning customization. The focus was on emerging trends as ChatGPT in education, machine learning in education, educational data mining, personalized feedback, and smart classrooms. The review established that AI-assisted learning and individualized instructional setting have a positive effect on knowledge retention, student motivation, self-regulated learning, digital literacy, and problem-solving abilities. Learning analytics and adaptive learning systems enhance the quality of academic outcomes by providing personalized learning channels and real-time feedback. The results suggest that over-dependence on AI tools can negatively affect critical thinking, creativity, metacognition, and independent reasoning in case the pedagogy does not carefully incorporate it. The concern increasing on algorithmic bias, cognitive load, ethical issues, and the impact of large language models on academic integrity were also identified as a major concern in the review.
subject: Articles
  topic: section
subject: Predictive analytics
  topic: keywords
subject: Natural language processing
  topic: keywords
subject: Education
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Reinforcement learning
  topic: keywords
subject: Academic performance
  topic: keywords
creator: Taibat Bolarinwa
rights: Copyright (c) 2026 Taibat Bolarinwa (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-05
publication date: 2026-04-05
starting page: 1097
ending page: 1129
doi: 10.70593/deepsci.0202045

Artificial intelligence vs traditional teaching methods on student performance: Effectiveness and challenges
type: item
description: The increasing need to achieve better results in students, individualized learning, and scalable educational services has made the argument of artificial intelligence in education versus conventional teaching techniques more intense. Even though traditional teacher-centered training is necessary to ensure social interaction, emotional support, and contextual understanding, the recent developments in AI-based learning, generative AI, intelligent tutoring systems, and adaptive learning platforms have reshaped the educational environment. The proposed PRISMA-based literature review research problem is to examine the comparative efficacy and limitations of artificial intelligence versus conventional teaching procedures on student performance in higher education, K-12 education, and STEM education settings. The systematic review was based on such themes as academic achievement, student engagement, learning analytics, automated assessment, educational chatbots, personalized feedback, and technology-enhanced learning. Recent trends related to ChatGPT, multimodal learning, predictive analytics, AI-based assessment, and human-centered AI were used as the focus of the selection process. The results show that AI-based learning systems tend to be more effective in enhancing academic motivation, self-regulated learning, adaptive learning, and personalized learning results than conventional teaching strategies. Virtual learning environments, intelligent tutoring systems and learner analytics proved especially useful in helping to provide individualized instruction and to spot at-risk students. Nevertheless, the conventional approaches to teaching are more efficient in promoting collaborative learning, emotional intelligence, critical thinking, and teacher-student relationship. Other significant issues associated with ethical AI, algorithmic bias, data privacy, academic integrity, teacher preparedness, and the digital divide are also noted in the review.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Education
  topic: keywords
subject: Traditional teaching methods
  topic: keywords
subject: Student performance
  topic: keywords
subject: Adaptive learning
  topic: keywords
subject: Personalized learning
  topic: keywords
creator: Falemu Funke Aina
rights: Copyright (c) 2026 Falemu Funke Aina (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-05
publication date: 2026-04-05
starting page: 1067
ending page: 1096
doi: 10.70593/deepsci.0202044

Acceptance of artificial intelligence in human resource management: Adoption factors, technology acceptance models, and HR analytics
type: item
description: The adoption of the Artificial Intelligence in the Human Resource Management (HRM) has reshaped the workforce management, but the adoption of intelligent HR technologies is not unanimous because of the lack of knowledge about AI adoption determinants, Technology Acceptance Model, and preparedness to adopt HR Analytics in various organizational settings. This paper is an attempt to solve this issue as it will review systematically the determinants that affect the acceptance of AI in human resource management using PRISMA framework. Peer-reviewed articles were selected on Digital HRM, People Analytics, UTAUT, innovation diffusion, AI governance, and socio-technical systems affecting the implementation of AI in HR functions. The review is a synthesis of the latest literature on the subject that dwells on the behavioral intention, perceived usefulness, and perceived ease of use, organizational support, and human-AI interaction as the main predictors of acceptance. Particular focus is made on the emergent themes including Explainable AI, ethical AI in HR, predictive HR analytics, and data-driven decision-making that influence the attitude of the employees toward intelligent systems in greater and greater degrees. The findings show that the factors that make AI be accepted in HRM are a combination of technological, organizational, and psychological factors, and the capability of the HR analytics, believing in AI, and enabling conditions are important in successful adoption. Moreover, recent TAM and UTAUT extensions have a better explanatory ability when applied to AI ethics, AI governance, and the future of work.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Human resource management
  topic: keywords
subject: HRM
  topic: keywords
subject: Technology acceptance model
  topic: keywords
subject: UTAUT
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Mirian Ndidi Obizue
rights: Copyright (c) 2026 Mirian Ndidi Obizue (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-05
publication date: 2026-04-05
starting page: 1037
ending page: 1066
doi: 10.70593/deepsci.0202043

Artificial intelligence and sustainable human resource management: Technologies, applications, and future directions
type: item
description: A digital transformation of organizations has spawned a pressing necessity to incorporate Artificial Intelligence, Sustainable Human Resource Management, and organizational sustainability to deal with the challenges pertaining to resilience of the workforce, environmental responsibility, and ethical decision-making. Although there is an increased literature concerning AI-based HRM, current literature is still very scattered which has limited synthesis of technologies, applications and future research within the framework of Green HRM, HR analytics and ESG-oriented human resource practices. The purpose of the study is to examine the literature on Artificial Intelligence and Sustainable Human Resource Management. A thorough review of the literature was carried out concerning digital HRM, predictive analytics, machine learning in HR, ethical AI, and sustainable workforce management. The review shows that intelligent recruitment systems, people analytics, HR automation, and AI-based performance management are changing the role of HR functions that allow the use of data in decision-making and assist in achieving Sustainable Development Goals by making better use of resources and promoting employee welfare. Nevertheless, the issue of algorithmic bias, AI governance, privacy, and responsible AI can still be seen as a major obstacle to the sustainable implementation. The recent developments like Industry 5.0, collaboration between humans, and AI, and AI-assisted ESG practices demonstrate that the human-centric and sustainable HR model is turning more human-based and sustainable. The results offer a synthesized model of association between Artificial Intelligence, Sustainable HRM, and organizational sustainability, and gives insights into future studies on ethical AI, talent analytics, and workforce sustainability in the changing digital workplace.
subject: Articles
  topic: section
subject: Human resource management
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Automation
  topic: keywords
subject: Organizations
  topic: keywords
subject: Talent analytics
  topic: keywords
subject: Sustainable development goals
  topic: keywords
creator: Chinyere C. Oko-jaja
rights: Copyright (c) 2026 Chinyere C. Oko-jaja (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-05
publication date: 2026-04-05
starting page: 1014
ending page: 1036
doi: 10.70593/deepsci.0202042

Artificial intelligence and human resource management: HR analytics, talent management, and ethical AI
type: item
description: The fast-moving introduction of Artificial Intelligence to Human Resource Management has both provided revolutionary opportunities in HR analytics, talent management, and intelligent decision-making, as well as posed major ethical, governance, and transparency issues that need to be evaluated systematically. Although the use of AI-powered HR solutions becomes more widespread, the literature on AI-based system remains scattered in different areas, including predictive workforce analytics, algorithmic recruiting, employee experience analytics, and responsible AI models, and it is hard to see the impact of Artificial Intelligence on HRM on organizational performance and responsible management in general. To fill this gap, this paper provides a literature review with the PRISMA framework to extract the recent research on the topics of HR analytics, talent analytics, Ethical AI, and data-driven HRM. The review is conducted on the topic of AI-driven recruitment, predictive HR analytics, workforce analytics, intelligent talent systems, and AI governance in HR. The focus is laid on the following emerging themes explainable AI, algorithmic bias, responsible AI, human-AI collaboration, and sustainable HRM as the most recent trends in digital HRM and people analytics. The results suggest that AI-powered HR analytics can greatly improve talent recruitment, performance analysis, human resources planning, and employee experience using machine learning, automation, and intelligent decision support, and simultaneously raise concerns about fairness, accountability, privacy, and transparency. Ethical AI, trusted AI, and governance principles are found to be key facilitators of the sustainability of smart HR systems adoption.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Human resource management
  topic: keywords
subject: Talent analytics
  topic: keywords
subject: Talent management
  topic: keywords
subject: Ethics
  topic: keywords
subject: Recruitment
  topic: keywords
creator: Deborah Julius Agwu
rights: Copyright (c) 2026 Deborah Julius Agwu (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-05
publication date: 2026-04-05
starting page: 989
ending page: 1013
doi: 10.70593/deepsci.0202041

Teachers’ acceptance of artificial intelligence tools in K-12 education
type: item
description: Although artificial intelligence and generative AI is becoming more available, intelligent tutoring systems, learning analytics, and adaptive learning technologies, its uptake by teachers is not even, as there are fears based on the AI literacy and usefulness, trust, ethical concerns, and institutional support. This research is based on a PRISMA framework to synthesize the existing research in the field of technology acceptance, AI adoption, teacher preparedness, digital pedagogy, and educational innovation in educational institutions. The review indicates that the majority of studies use the models, including Technology Acceptance Model (TAM), UTAUT, and Diffusion of Innovation theory to interpret the behavioral intention of teachers regarding the use of AI. The results suggest that the perception of usefulness, ease of use, social influence, facilitating conditions, and professional development have a strong impact on acceptance of AI-based educational tools. The recent research also points to some new aspects such as AI self-efficacy, data privacy issues, algorithmic trust, explainable AI, and ethical AI in education that can greatly influence the use in the classroom in the contemporary world. Also, the literature demonstrates that more AI literate teachers, who are supported by the institutional environment, are more willing to adopt AI-enabled teaching and learning systems. The review finds that there is an increasing interest in AI-assisted pedagogy, personalized learning, and intelligent learning settings across the world, but also indicates gaps in terms of K-12 teacher development, policy preparedness, and responsible use of AI.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Education
  topic: keywords
subject: Teacher
  topic: keywords
subject: Technology acceptance model
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Learning analytics
  topic: keywords
creator: Shobhit Srivastava
creator: Surinder Kumar
creator: Mukesh Kumar
rights: Copyright (c) 2026 Shobhit Srivastava, Surinder Kumar, Mukesh Kumar (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-28
publication date: 2026-03-28
starting page: 966
ending page: 988
doi: 10.70593/deepsci.0202040

Factors influencing teachers’ adoption of artificial intelligence tools in K-12 education
type: item
description: The recent proliferation of Artificial Intelligence in Education, generative AI in education and intelligent learning classrooms has revolutionized the practice of K-12 education, and the uptake of AI-based instructional aids by teachers, however, is not uniform because of various technological, pedagogical and structural impediments. It is essential to comprehend the contexts that can make teachers adopt artificial intelligence tools as such understanding will guarantee the successful digital transformation of education and long-term educational innovation. This work has provided a literature review of the research work based on the PRISMA 2020 framework and synthesized the recent high-impact studies on the topic of AI adoption, teacher acceptance, and educational technology adoption. The research has systematically reviewed the available empirical and theoretical literature on the adoption utilizing models like the Technology Acceptance Model, UTAUT, and other extended models with the addition of AI literacy, teacher self-efficacy, institutional support, facilitating conditions, performance expectancy, effort expectancy, and social influence. Results show that the behavioral intention of teachers to use AI tools is highly influenced by perceived usefulness, ease of use, professional development opportunities, ethical concerns, and the organizational readiness and the emerging themes demonstrate the increased role of human-AI collaboration, adaptive learning, and responsible ethical AI in education. The synthesis also indicates that the key to effective implementation of AI in K-12 education lies not merely in technical infrastructure but also in the alignment of the pedagogy and in the support of the policies as well as the continuous training of AI readiness.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Education
  topic: keywords
subject: Professional development
  topic: keywords
subject: Teaching
  topic: keywords
subject: Ethics
  topic: keywords
subject: Adaptive learning
  topic: keywords
creator: Kalawati
creator: Sunil Kumar Sahu
creator: N. Kumar Swamy
rights: Copyright (c) 2026 Kalawati, Sunil Kumar Sahu, N. Kumar Swamy (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-28
publication date: 2026-03-28
starting page: 943
ending page: 965
doi: 10.70593/deepsci.0202039

Is using artificial intelligence tools for academic work cheating? Student perceptions, ethics, and the impact on academic performance and critical thinking
type: item
description: Artificial Intelligence has brought about a very ethical dilemma in the sphere of Education questioning whether using AI tools in academic achievements is considered a form of cheating or not, evoking Academic Integrity, Student Perceptions, and the effect that the use of AI tools may have on Critical Thinking Skills and Academic Performance. According to the recent research, AI integration in academic settings has kept pace with the regulatory frameworks more rapidly, and thus, ethical practices, assessment, and institutional reactions are not consistent. The proposed research uses the PRISMA framework to undertake a Systematic Literature Review of the recent studies in the area of AI Ethics in Education, Academic Misconduct, and Educational Technology. The publications in the various academic databases were screened, filtered, and analyzed to determine the emerging trends in AI Governance, Responsible AI, Learning Analytics, and Digital Assessment. The results indicate that student perceptions of AI use are neutral, including the view as a valid resource of learning assistance to the view as a cheating method, depending on the type of the task, transparency, and design of the assessment. There is also some evidence that over-dependence on AI can be a factor in Cognitive Offloading, decreased independent reasoning, and altered academic behavior, and that it can be helpful to use AI guided. The paper concludes that the future of higher education is in the formulation of concrete ethical standards and the encouragement of AI Literacy and reshaping assessment methods that foster Human-AI Collaboration without compromising originality, equity, and intellectual growth.
subject: Articles
  topic: section
subject: Academic integrity
  topic: keywords
subject: Education
  topic: keywords
subject: Critical thinking
  topic: keywords
subject: Ethics
  topic: keywords
subject: Higher education
  topic: keywords
subject: Artificial intelligence
  topic: keywords
creator: Anita Padhy
rights: Copyright (c) 2026 Anita Padhy (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-28
publication date: 2026-03-28
starting page: 919
ending page: 942
doi: 10.70593/deepsci.0202038

Students’ perceptions and responsible adoption of artificial intelligence in education: Ethical considerations, impacts, and academic performance
type: item
description: The adoption of Artificial Intelligence in Education, specifically Generative AI in Education, has generated considerable opportunities to enhance the results of learning and at the same time has raised the issue of AI Ethics in Education, Academic Integrity, and the responsible use of intelligent technologies. This research paper is a Systematic Literature Review seeks to determine the perceptions of artificial intelligence by students, the effects that ethical issues have on the use of AI, and the impact that AI has on the learning experience in various academic settings. According to the PRISMA protocol, the major academic databases were searched with the help of the keywords related to AI Literacy, Educational Technology, Human-AI Collaboration, Technology Acceptance Model, and Personalized Learning. The identified articles were added to the selected studies after passing the eligibility test to discover tendencies in Smart Learning Environments, AI Governance, Educational Data Privacy, and AI-Supported Learning. The analysis has shown that students, on the whole, express positive attitude to AI because of increased efficiency, adaptive feedback and increased engagement, but also indicate issues with over-reliance, bias, transparency and ambiguous institutional policies. It is also evidenced that the responsible use of AI with the help of ethical guidelines and Explainable AI is closely linked to better academic outcomes and increased confidence in learners. The review concludes that the sustainable introduction of AI into the educational process necessitates the enhancement of AI Policy in Education and ethical awareness as well as the establishment of sound AI Literacy patterns.
subject: Articles
  topic: section
subject: Responsible AI
  topic: keywords
subject: Ethics
  topic: keywords
subject: Education
  topic: keywords
subject: Academic performance
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Learning analytics
  topic: keywords
creator: Vibhavari Prasad Mane
rights: Copyright (c) 2026 Vibhavari Prasad Mane (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-28
publication date: 2026-03-28
starting page: 896
ending page: 918
doi: 10.70593/deepsci.0202037

Artificial intelligence in human resource management: A review of applications, algorithms, and ethical challenges
type: item
description: Digital transformation of organizations has enhanced the adoption of Artificial Intelligence in Human Resource Management, which poses both opportunities and threats associated with algorithmic decision-making, ethical AI and workforce analytics. Although machine learning, natural language processing and predictive analytics are increasingly used in HR functions, the literature is still distributed in bits on how they are applied, algorithms, and ethical considerations. To fill this gap, the paper involves a systematic literature review guided by PRISMA on AI-HRM, automation in recruitment, talent analytics, and responsible AI. This paper investigate how smart HR systems, deep learning models, and HR analytics platforms are changing the field of recruitment, performance management, employee experience, and workforce planning. Emerging issues that include algorithmic bias, explainable AI, data privacy, and AI governance are also assessed in the review, which are growing in their effects on organizational adoption. The results show that AI use can improve efficiency, accuracy, and strategic HR decision-making, specifically, predictive analytics, talent intelligence, and digital HR transformation, yet presents the risk associated with transparency, fairness, and accountability. The review shows a revision of the HR technologies oriented on automation to human-AI collaboration, ethical AI systems, and responsible innovation, which body of HR work is changing its role in future of work. The research provides the study as a contribution to the literature by incorporating technological, managerial, and ethical viewpoints in a single AI-HRM research framework, which offers future researchers guidelines to improve the future of workforce analytics, AI governance, and sustainable digital transformation.
subject: Articles
  topic: section
subject: Decision-making
  topic: keywords
subject: Recruitment
  topic: keywords
subject: Automation
  topic: keywords
subject: Talent analytics
  topic: keywords
subject: Predictive analytics
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Aparajeeta Patnaik
rights: Copyright (c) 2026 Aparajeeta Patnaik (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-28
publication date: 2026-03-28
starting page: 869
ending page: 895
doi: 10.70593/deepsci.0202036

Artificial intelligence, corporate information governance, and Environmental, Social, and Governance (ESG) performance: A systematic review
type: item
description: The development of Artificial Intelligence (AI) in business decision-making has posed new risks to corporate information governance, regulatory compliance, and Environmental, Social, and Governance (ESG) performance, making transparency, accountability, and responsible innovation issues a concern. Though there is an increasing interest in AI governance, sustainable corporate governance, and ESG disclosure, the literature is still not synthesized, and data governance, algorithmic accountability, and digital sustainability have not been visualized in a coherent structure. To fill this gap, the proposed study is a systematic literature review on AI-driven governance, ESG reporting, responsible AI, and corporate sustainability performance. The review syntactically examines recent studies on the topic of AI adoption, information governance mechanisms, ethical AI, green innovation, AI-enabled ESG reporting, and digital corporate governance with the emergent themes of explainable AI, regulatory technology (RegTech), sustainable finance analytics, AI risk management, and stakeholder-centric governance models. Results show that properly-coordinated AI governance systems with corporate information governance frameworks can produce a substantial increase in ESG performance, quality disclosure, the predictive sustainability analytics, and organizational accountability. Nevertheless, the literature also points to the dangers of algorithmic bias, data privacy, automated reporting as greenwashing, and poor frameworks of AI oversight, so the need to ensure strong governance architectures. The review suggests an integrative approach between Artificial Intelligence, Corporate Information Governance and ESG performance based on responsible digital governance, sustainability-minded innovation, and regulatory alignment to present a future research agenda on AI ethics, sustainable digital transformation, Governance automation, and ESG intelligent systems to inform scholars and policymake…
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: ESG
  topic: keywords
subject: Governance
  topic: keywords
subject: Responsible AI
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Corporate sustainability
  topic: keywords
creator: Swarup Panda
rights: Copyright (c) 2026 Swarup Panda (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-28
publication date: 2026-03-28
starting page: 842
ending page: 868
doi: 10.70593/deepsci.0202035

Artificial intelligence, big data analytics, and deep learning for 6G communication networks: Challenges, applications, and future directions
type: item
description: The fast development of 6G communication systems has posed serious challenges with management of ultra-density connections, massive traffic, and extremely evolving wireless environments that demand new solutions with the help of Artificial Intelligence, Big Data Analytics, and Deep Learning. The conventional network management techniques cannot be used in supporting AI-native networks, terahertz communication, ultra-massive MIMO or integrated sensing and communication which are presumed to be key technologies of beyond-5G systems. The paper offers a detailed literature review concerning intelligent wireless systems, edge AI, federated learning, and data-driven networking. The review examines new applications, architecture, and technical issues towards integrating machine learning into wireless, digital twin networks, and self-organizing networks into future communication infrastructures. Findings suggest that low latency, high reliability, and energy-efficient communication in the next-generation networks can only be achieved through the use of AI-based optimization, autonomous control of networks, and real-time analytics made possible by the big data analytics. The results also indicate the growing research interest in network automation, smart connectivity and intelligent network management especially in the support of Internet of Everything, immersive services and large-scale cyber-physical systems. Nevertheless, other problems, such as computational complexity, security, privacy, interoperability, and scalability are no longer mere obstacles to implementation. The review believes that the deep learning, AI-driven optimization, and distributed analytics will be at the core of the future wireless systems and that intelligent, autonomous, and adaptive communication architectures will be a research focus of designing the 6G ecosystems.
subject: Articles
  topic: section
subject: Federated learning
  topic: keywords
subject: Communication networks
  topic: keywords
subject: Network automation
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Wireless systems
  topic: keywords
subject: Artificial intelligence
  topic: keywords
creator: Partha Sarathi Mohapatra
rights: Copyright (c) 2026 Partha Sarathi Mohapatra (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-28
publication date: 2026-03-28
starting page: 817
ending page: 841
doi: 10.70593/deepsci.0202034

Artificial intelligence in sustainable finance and Environmental, Social, and Governance (ESG) performance: A review
type: item
description: Artificial Intelligence (AI) is rapidly changing sustainable finance and Environmental, Social, and Governance (ESG) performance in particular, but the disjointed nature of current studies does not allow us to fully comprehend how machine learning, ESG analytics, responsible AI, and sustainable investing all contribute to financial decision-making and corporate sustainability performance. Although the context of AI-based ESG scoring, green finance, and data-driven sustainability reporting develops rapidly, the literature is spread out among the fields of finance, management, and technology and requires the application of the PRISMA framework to provide a clear picture of transparency and methodological rigor in systematic reviews. The present review examined the intersection of artificial intelligence, sustainable finance, ESG performance, financial technology (FinTech), climate risk analytics, algorithmic governance, and responsible investing. The paper provides an assessment of the effectiveness of artificial intelligence methods, including machine learning, natural language processing, predictive analytics, and modeling the sustainability of big data, in improving ESG measurement, managing portfolio, risk, and disclosure to sustainability. Specific focus is placed on such new themes as AI-enabled ESG ratings, green innovation, ethical AI, regulatory technology (RegTech), and explainable AI in finance that are becoming highly influential in the international financial markets. The results show that AI drastically enhances the ESG performance analysis, sustainable investment plan, and transparency of the companies and also facilitates the real-time tracking of the environmental and social risks. Nevertheless, the literature also singles out endemic data quality issues, algorithmic bias, governance frameworks, and regulatory compliance as recent concerns that require trusted AI and sustainable digital finance ecosystems.
subject: Articles
  topic: section
subject: Sustainability
  topic: keywords
subject: Finance
  topic: keywords
subject: ESG
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Green finance
  topic: keywords
creator: Vibhavari Prasad Mane
rights: Copyright (c) 2026 Vibhavari Prasad Mane (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-28
publication date: 2026-03-28
starting page: 792
ending page: 816
doi: 10.70593/deepsci.0202033

The impact of artificial intelligence and learning analytics on students’ academic performance
type: item
description: The adoption of artificial intelligence in education, learning analytics, and educational data mining has changed the contemporary learning landscape but still its real impact on the academic achievements of the students has been disjointed through various empirical studies. The growth of AI-based education, predictive analytics within education, and adaptive learning systems has created the demand to synthesize the emerging evidence systematically to establish how the technologies impact student success, engagement and learning outcomes. The literature review is based on a PRISMA framework to identify, screen, and analyze current studies on the role of artificial intelligence, learning analytics, and learning data-driven environments on academic performance in school and higher education settings. The review procedure was based on the PRISMA on the topic of machine learning in education, intelligent tutoring systems, personalized learning, and student performance prediction models. The research results show that AI adaptive systems and learning analytics dashboards can always increase academic performance by timely identifying risks, providing personalized feedback, and monitoring data, whereas educational big data allows the institutions to create specific interventions. Nevertheless, the obstacles of data privacy, algorithmic bias, excessive automation, and disparities in access to digital resources are also major obstacles. The conclusion of the review is that the collective application of Artificial Intelligence, Learning Analytics and predictive educational technologies have a high potential to improve academic performance as long as they are applied with ethical principles, pedagogical orientation and institutional endorsement.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Education
  topic: keywords
subject: Learning analytics
  topic: keywords
subject: Academic performance
  topic: keywords
subject: Personalized learning
  topic: keywords
subject: Adaptive learning systems
  topic: keywords
creator: Mohd. Shuyeb
creator: Surinder Kumar
creator: Mukesh Kumar
creator: Ankur Jain
rights: Copyright (c) 2026 Mohd. Shuyeb, Surinder Kumar, Mukesh Kumar, Ankur Jain (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-28
publication date: 2026-03-28
starting page: 766
ending page: 791
doi: 10.70593/deepsci.0202032

Artificial intelligence and machine learning in 6G wireless communication networks
type: item
description: The high-paced development of wireless communication technologies brought about the growing need of smart, autonomous, and ultra-high-performance network structures, and the combination of Artificial Intelligence and Machine Learning in the 6G wireless communication networks is a major research issue. The conventional network management techniques cannot support the new demands that include ultra-low latency communications, intelligent spectrum management, mass connectivity, and real-time dynamic resource provision. This paper is a systematically-conducted literature review on the recent research on AI-driven networks, edge AI, federated learning, intelligent radio access network design, and AI-native architecture in next-generation networks based on PRISMA. The review focuses on the possibilities of self-organizing networks, digital twin networks, and smart connectivity that machine learning, deep learning, and autonomous wireless systems provide to the next generation of wireless networks working in terahertz communication ranges and integrated sensing and communication landscape. The findings indicate that AI based resource allocation, intelligent surfaces of reconfigurability, massive mimo optimization and network automation are all effective towards improving spectral efficiency, reliability and energy efficiency of 6G wireless communication. Moreover, the paper has also noted the increased importance of smart security mechanisms, distributed learning, and edge computing in enabling scalable and secure AI-native architectures. The results also indicate that the next generation wireless networks will be based upon intelligent spectrum management, autonomous control, and real-time data analytics to accommodate the new applications in holographic communication, smart cities, extended reality, and connected autonomous systems.
subject: Articles
  topic: section
subject: Big data analytics
  topic: keywords
subject: Deep learning
  topic: keywords
subject: 6G communication networks
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Wireless systems
  topic: keywords
subject: Federated learning
  topic: keywords
creator: Govinda Sahu
creator: Sunil Kumar Sahu
rights: Copyright (c) 2026 Govinda Sahu, Sunil Kumar Sahu (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 745
ending page: 765
doi: 10.70593/deepsci.0202031

Artificial intelligence agents and agentic AI systems: Architectures, capabilities, applications, challenges, and future directions
type: item
description: The development of the Agentic AI and Artificial Intelligence Agents has reshaped the traditional experience of artificial intelligence as the passive prediction systems into the Autonomous Intelligent System. The issue discussed in this literature review is the disjointed concept of Agentic Architecture, Multi-Agent Systems and LLM-based Agents and as such has led to inconsistent design practice, risks to security and the lack of scalability to real-life applications. Recent progress indicates that agent-based systems support goal-oriented decision making, the use of tools, and joint problem solving, making Agentic AI an enabling technology of future digital ecosystems. This paper adheres to the PRISMA to conduct a systematic review of the recent studies on AI Orchestration, Distributed AI, Cognitive Architectures, AI Automation and Human-AI Collaboration. The emerging ideas, like Hybrid Neuro-Symbolic AI, Explainable AI, AI Governance, and AI Safety, are also analyzed as the new requirements to the trustful use of autonomous agents. It is found in the review that current Agentic AI Systems are based on layered architectures that incorporate perception, reasoning, memory, and action modules that are typically orchestrated by Multi-Agent Systems and workflow orchestration mechanisms. Its uses include healthcare, finance, robotics, cybersecurity, education, and enterprise automation and major challenges such as interoperability, reliability, ethical adherence and vulnerability of a system to security in highly autonomous settings. The results suggest that scalable Agentic Architecture, standardized communication frameworks, solid governance strategies, and hybrid reasoning systems that integrate symbolic and neural intelligence represent the future of Next-Generation AI Systems and can be used to provide safe and reliable large-scale implementations of autonomous AI ecosystems.
subject: Articles
  topic: section
subject: Agentic AI
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Agents
  topic: keywords
subject: Autonomous intelligent systems
  topic: keywords
subject: Multi-agent systems
  topic: keywords
subject: Agentic architecture
  topic: keywords
creator: Lukesh Thakur
rights: Copyright (c) 2026 Lukesh Thakur (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 721
ending page: 744
doi: 10.70593/10.70593/deepsci.0202030

Artificial intelligence-based writing tools in education: Impacts on students’ academic ethics, learning performance, and skill development
type: item
description: Artificial Intelligence-driven writing assistants in the educational field has posed serious issues regarding the aspects of academic ethics and learning performance and skill acquisition, with the concerns about the impact of generative AI and large language models and writing assistance tools on student learning behaviors. Many institutions have been grappling with the challenges of ensuring academic integrity, responsible AI usage, and authentic assessment. This paper provides a literature review with the PRISMA and a review on AI writing tools in education and the impact of these tools on academic ethics, student performance, and skill acquisition on learning. The review was conducted to find the studies published on generative AI, AI assisted learning, and digital literacy integration. Results of the collaboration to human-AI, self-regulated learning, AI literacy, and educational innovation were synthesized in the analysis with a special focus on the productivity benefits and ethical risks. It is demonstrated that AI writing systems enhance the quality of writing, cognitive learning, and efficiency, but also cause the threat of plagiarism, over-reliance, and the lack of critical thinking in case of unsupervised use. The results indicate the necessity of AI regulation, ethical principles, and skill-based pedagogy to ensure the AI-assisted learning complements instead of substituting the student competence. The conclusion of the review is that the future of AI in education will be determined by combining responsible AI practices, AI literacy training, and redesigned assessment strategies which allow fostering authentic learning and utilizing the advantages of intelligent technologies in writing.
subject: Articles
  topic: section
subject: ChatGPT
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Large language models
  topic: keywords
subject: Academic integrity
  topic: keywords
subject: Ethics
  topic: keywords
subject: Education
  topic: keywords
creator: Sunil Kumar Sahu
rights: Copyright (c) 2026 Sunil Kumar Sahu (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 694
ending page: 720
doi: 10.70593/deepsci.0202029

Teachers’ perceptions and adoption of artificial intelligence in higher education
type: item
description: The Artificial Intelligence development in Higher Education has already offered substantial prospects in transforming the teaching, learning, and academic administration, but the effective implementation of the technologies hinges mostly on the attitude of the faculty to AI-based educational innovation, so it is critical to study the perception of teachers towards AI-based educational innovations in an organized way. Regardless of increased access to Generative AI, intelligent tutoring systems, and AI-driven learning platforms, their acceptability and successful implementation as tools in education are balanced unevenly among institutions, fields, and locations. This paper summarizes the recent studies on AI adoption, digital pedagogy, AI literacy, and technology acceptance in higher education. This research identifies the patterns in the fields of Technology Acceptance Model, UTAUT, teacher readiness, institutional support, and AI ethics in education. According to the review, the willingness of educators to implement AI in teaching is always predicted by perceived usefulness, self-efficacy, social influence, and facilitating conditions, whereas the absence of training, ambiguous policy, and perceived threat to academic integrity is a significant obstacle. New topics are human-AI collaboration, AI governance in higher education, professional development, responsible AI use, which suggests that the use of AI is no longer experimental but strategic. The results prove that teachers are typically aware of AI opportunities as a personalized learning tool, automated assessment tool, and a tool of educational innovation, but the adoption of technology is subject to institutional culture, the clarity of policy, and digital literacy.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Education
  topic: keywords
subject: Teacher
  topic: keywords
subject: Intelligent tutoring systems
  topic: keywords
subject: Academic integrity
  topic: keywords
subject: Professional development
  topic: keywords
creator: Silamanthula Hari Krishna
creator: Raja Bhushanam Singavarapu
creator: Molagavalli Rajesh
creator: Siva Sankar Rao Mallavarapu
creator: Eswara Ram Katru
rights: Copyright (c) 2026 Silamanthula Hari Krishna, Raja Bhushanam Singavarapu , Molagavalli Rajesh , Siva Sankar Rao Mallavarapu , Eswara Ram Katru (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 671
ending page: 693
doi: 10.70593/deepsci.0202028

Collaborative strategies for promoting inclusive education in rural communities: A case study of Okigwe education zone
type: item
description: Rural Nigeria’s inclusive education promotion in the Okigwe Education Zone, in Imo State, is a big concern. Although Nigeria’s National Policy on Education requires inclusive education for the disabled, the situation in rural Okigwe contrasts this with neglect, infrastructure breakdown, a lack of specialists in special education, as well as cultural stigmatisation. However, this study contends that public allocation of funds alone as a solution would be inadequate. Rather, there is the urgent call, through this conference’s subtheme, "Building Bridges," to explore the gap that must be bridged between administration of schools and other stakeholders. With the use of a descriptive survey, this researcher collected information from three strategic public secondary schools within the zone (Okigwe National Grammar School, Community Secondary School, Ihube, Secondary Technical School, Umulolo). By incorporating the Social Capital Theory, this study endeavours to explore the use of strong social frameworks that already exist within the social dynamics of the entire Okigwe zone with a view toward evaluating their transformation into educational infrastructure. Evidence shows that, although Okigwe’s social group members participate more in projects that increase physical classroom infrastructure (Bonding Capital), they less often participate in other aspects, such as policy advocacy, aimed at assisting the disabled (Bridging Capital). Having presented the theories with empirical evidence, this study through this conference, recommend the use of the "Community School Inclusion Model" (CSIM) that provides recommendations on different paths that School Principals must pursue in establishing linkages with existing Town Unions, as well as transforming them from observers to active participants that would minimize the dropout rate of the most vulnerable populace, namely, the disabled.
subject: Articles
  topic: section
subject: Rural education
  topic: keywords
subject: Inclusive education
  topic: keywords
subject: Education
  topic: keywords
subject: Community partnerships
  topic: keywords
subject: Social capital
  topic: keywords
subject: Sustainable development
  topic: keywords
creator: Charity N. Egbo
rights: Copyright (c) 2026 Charity N. Egbo (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-24
publication date: 2026-03-24
starting page: 664
ending page: 670
doi: 10.70593/deepsci.0202027

Qualitative research approaches: A guide to data collection, analysis, trustworthiness, artificial intelligence, and ethics
type: item
description: The fast-paced development of digital technologies and artificial intelligence in the area of qualitative research is transforming the conventional approaches to qualitative research, posing new methodological, analytical, and ethical issues to the researchers who want to achieve rigor and research credibility. Although the application of AI-based qualitative analysis is increasingly common, there is still doubt about its combination with the traditional methods of thematic analysis, grounded theory, and ethnography, especially in terms of the credibility of the qualitative research process, the transparency of the algorithms, and the ethics of research. This research provides a literature review covering the modern literature on qualitative data collection, qualitative data analysis, computer-assisted qualitative data analysis (CAQDAS), and the new trends in generative AI and natural language processing in the qualitative research. The review discusses methodological innovations in digital qualitative research, such as digital ethnography, reflexive thematic analysis, and post qualitative inquiry and assesses approaches employed to achieve credibility, dependability, and confirmability in qualitative research. The results show that the qualitative research is experiencing a methodological shift, which is defined by the combination of AI-aided qualitative research analysis, automated text transcription, and pattern recognition through computational analysis in the framework of traditional analytical approaches. Although the use of CAQDAS platforms and AI tools increases efficiency and allows exploring qualitative data on large scale, issues of interpretative depth, epistemological congruity, and ethical controls still exist. The review points to a paradigm shift of technologically augmented qualitative inquiry, in which generative AI, natural language processing and sophisticated digital research infrastructures are broadening the qualitative inquiry.
subject: Articles
  topic: section
subject: Qualitative research
  topic: keywords
subject: Data collection
  topic: keywords
subject: Data analysis
  topic: keywords
subject: Thematic analysis
  topic: keywords
subject: Grounded theory
  topic: keywords
subject: Artificial intelligence
  topic: keywords
creator: Nitin Liladhar Rane
creator: Ashok Meti
rights: Copyright (c) 2026 Nitin Liladhar Rane, Ashok Meti (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-12
publication date: 2026-03-12
starting page: 645
ending page: 663
doi: 10.70593/deepsci.0202026

A multimodal predictive framework for early detection of anxiety instability and panic attacks
type: item
description: Although anxiety disorders and panic attacks belong to the types of the most widespread and, accordingly, the most debilitating mental disorders of the world, the current clinical practice is largely based on retrospective self-report and episodic assessment, which does not always include early warning signs of behaviour and physiological indicators. More recent evidence suggests that slight changes in autonomic regulation, namely decreases in heart-rate variability (HRV) is antecedent to the increment of anxiety and the beginning of the panic attacks. A combination of wearable sensing, digital phenotyping and machine-learning can be used to continuously track psychological vulnerability in the real world. The current research suggests using an algorithmic approach to digital predictive analysis of the changes in the intensity of anxiety and the onset of a panic attack based on continuous multimodal state measurements. This model combines physiological measures, such as heart rate, HRV, respiration, electrodermal activity and behavioural dynamics, linguistic cues and exposure to environmental stressors to produce a personalised Anxiety Stability Index (ASI). The hybrid Random Forest-Long Short -Memories model also reveals the nonlinear correlations and the time-specific trends, which in turn predicts the instability at hand and the pace of the anxiety development. The principles of multimodal fusion and interpretable modelling will be used to improve transparency and clinical usefulness. The given framework would aim at determining the typical patterns of instability prior to the onset of acute panic, transforming the unchanging questionnaire-based measurements into the actual predictions. It can promote digital psychiatry because it encourages proactive treatment, elimination of panic cases in case of an emergency, and enhancement of the provision of individualised mental-health services in ordinary environments.
subject: Articles
  topic: section
subject: Anxiety
  topic: keywords
subject: Stability index
  topic: keywords
subject: Panic attack
  topic: keywords
subject: Multimodal machine learning
  topic: keywords
subject: Digital phenotyping
  topic: keywords
subject: Wearable sensing
  topic: keywords
creator: Poushali Das
creator: Rituparna Mondal
creator: Soumitra De
creator: Siddhartha Chatterjee
creator: Shibani Mukherjee
rights: Copyright (c) 2026 Poushali Das, Rituparna Mondal, Soumitra De, Siddhartha Chatterjee, Shibani Mukherjee (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-10
publication date: 2026-03-10
starting page: 631
ending page: 644
doi: 10.70593/deepsci.0202025

Artificial intelligence-driven intelligent concrete crack detection and real-time width estimation for smart structural health monitoring systems: A review
type: item
description: It is partly the concrete infrastructure degradation (mainly crack formation and crack propagation) that is one of the most significant challenges facing the contemporary civil engineering. The traditional inspection procedures, which are based on a regular manual survey, non-destructive testing (NDT) guidelines, and an elementary image processing pipeline, have proven their constant incapacity to achieve sensitivity, scalability, and real-time reactivity. This combination of deep learning, computer vision, and embedded edge computing has also been the driving force behind a paradigmatic shift to the creation of intelligent, autonomous structural health monitoring (SHM) systems with the capability to operate in continuous, high-fidelity crack detection and quantitative width measurements. The present paper is a critical, interdisciplinary survey of the current state-of-the-art in AI-driven concrete crack detection that includes the following architectures, object detectors, semantic segmenters and detectors, and emergent paradigms of vision transformer (ViT) and vision-language paradigms (CLIP). Specific analytical focus is given to real-time crack width estimation techniques - such as pixel-to-millimetre calibration, stereo vision triangulation, laser profilometry, and Structure-from-Motion (SfM) 3D reconstruction techniques and their corresponding error metrics and deployment limits. The review also discusses how these AI components are integrated into smart SHM systems, which include edge AI systems, IoT data pipelines, cloud-edge hybrid systems, drone-based inspection systems, and digital twin systems. The critical research gaps that have been observed are lack of standardised benchmark datasets, lack of rigorously validated real time deployment studies, lack of cross-domain model generalisation, and unsolved hardware-software co-design issues.
subject: Articles
  topic: section
subject: Structural health monitoring
  topic: keywords
subject: Crack detection
  topic: keywords
subject: Convolutional neural networks
  topic: keywords
subject: Semantic segmentation
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Vision transformers
  topic: keywords
creator: Mohammed Shakeebulla Khan
creator: Adinath Rajendra Puri
creator: Rosy Pradhan
rights: Copyright (c) 2026 Mohammed Shakeebulla Khan, Adinath Rajendra Puri, Rosy Pradhan (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-05
publication date: 2026-03-05
starting page: 618
ending page: 630
doi: 10.70593/deepsci.0202024

Analysis of blow-up situation in finite automata
type: item
description: The blow-up phenomenon in finite automata is one of the most far reaching and significant problems in formal language theory and system design based on automata. Discovering a nondeterministic finite automaton (NFA) transformed into an equivalent deterministic finite automaton (DFA) with the famous powerset construction can lead to an exponentially larger number of states and this phenomenon is known as the state explosion, which makes solving even moderately sized input automata computationally infeasible. This issue is also known as the blow-up problem and has far reaching consequences on the compilation design, pattern matching engines, model checking systems, natural language processing pipelines, and network intrusion detection systems. The interaction between descriptional complexity and operational complexity with mitigating strategies despite decades of theoretical exploration has not been fully synthesized in the light of an emerging computational requirement. The review is a systematic literature review of articles published as early as 2019 and 2025 to identify the contemporary picture of the analysis of blow-ups in finite automata. The paper also determines the mathematical properties of exponential blowup; it discusses the effectiveness of modern heuristic and algebraic mitigation strategies. It has been shown in the analysis that the worst-case exponential lower bounds are tight in proving that classical NFA to DFA conversion is impossible, although in practice the improvements of up to 40-60% are possible by using state-aware lazy determinization and simulation-based reduction.
subject: Articles
  topic: section
subject: Finite automata
  topic: keywords
subject: NFA to DFA conversion
  topic: keywords
subject: State explosion
  topic: keywords
subject: Descriptional complexity
  topic: keywords
subject: Automata minimization
  topic: keywords
subject: State complexity
  topic: keywords
creator: Siddhartha Chatterjee
creator: Ritwika Ghosh
creator: Aparijita Das
creator: Jonti Deuri
creator: Suman Biswas
rights: Copyright (c) 2026 Siddhartha Chatterjee, Ritwika Ghosh, Aparijita Das, Jonti Deuri, Suman Biswas (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-04
publication date: 2026-03-04
starting page: 599
ending page: 617
doi: 10.70593/deepsci.0202023

Agentic artificial intelligence-driven tutoring: A multi-agent cognitive architecture for personalized adaptive learning in Education
type: item
description: The one-size-fits all model of education has not been sufficient with regard to meeting the various learning needs of the students in present day education setup. This research paper outlines a review of the literature which discusses the transformational possibilities of agentic artificial intelligence-based tutoring systems which realize multi-agent cognitive architectures to personalized adaptive learning. This paper explores how autonomous AI agents, integrated in complex cognitive models, will help transform the way education is delivered through dynamical adaptation of instructional models, teaching complexity, and pedagogic quality depending on the distinctive features of individual learners, educational performance, and neuroscience involved. This review summarizes the current nexus of intelligent tutoring system, multi-agent architecture, cognitive computing and adaptive learning technologies by conducting a systematically done review based on PRISMA methodology. The results indicate that multi-agent AI systems have better behavior modeling, system organization of individualized learning processes, real-time feedback, and metacognitive enhancement capacities over traditional learning technologies. Some of the main concerns that are discovered are ethical issues regarding the privacy of learner data, transparency in algorithms, lack of scalability, and the requirement to implement powerful evaluation systems. The study points out the new possibilities in neuroadaptive learning interface, emotion-sensitive tutoring agent, and socially-intelligent interactive learning learning settings. This analytic review has added to the existing literature as it provides a mapping of the state of underlying agentic AI tutoring systems and offers important research areas that researchers need to focus on to construct more successful and equitable and human education technologies.
subject: Articles
  topic: section
subject: Agentic artificial intelligence
  topic: keywords
subject: Education
  topic: keywords
subject: Multi-agent systems
  topic: keywords
subject: Cognitive architecture
  topic: keywords
subject: Personalized learning
  topic: keywords
subject: Adaptive learning
  topic: keywords
creator: Neha Gupta
creator: Shreeshail Heggond
rights: Copyright (c) 2026 Neha Gupta, Shreeshail Heggond (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-19
publication date: 2026-02-19
starting page: 572
ending page: 598
doi: 10.70593/deepsci.0202022

Supply chain management using machine learning, deep learning, and blockchain techniques: A review
type: item
description: Existence in a globalized economy presents the most significant challenges that the supply chain management has never experienced before, such as demand in variation, disruption in supply, transparency deficiencies, and issues of sustainability. This review explicitly looks at how machine learning, deep learning and blockchain technologies can be used to transform the activity in the supply chains in a revolutionizing way. The work applies the PRISMA methodology to analyze the current trends of the supply chain systems with respect to intelligent supply chain system systematically and within the framework of new trends and future directions. The machine learning algorithms have amazing performance in demand forecasting, inventory optimization and predictive maintenance, whereas deep learning architectures perform better in complicated pattern recognition, computer vision tasks and natural language processing to supply chain analytics. Distributed ledger systems, smart contracts, and decentralized consensus mechanisms are all innovations that blockchain technology suggests which will bring unprecedented levels of transparency, traceability, and security. According to this review, existing research has a number of gaps such as limited integration frameworks involving each of the three technologies, lack of confirmation of scalability in practice, and little attention to the problems of adoption in small and medium enterprises. The article shows that hybrid solutions that integrate machine learning predictive functionalities with blockchain trust infrastructures have a higher performance on providing end-to-end supply chain visibility and optimization. The potential opportunities are autonomous supply chain orchestration, circular economy enabling and robust network design.
subject: Articles
  topic: section
subject: Supply chain management
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Demand forecasting
  topic: keywords
subject: Digital transformation
  topic: keywords
creator: Siddhartha Chatterjee
creator: Nitin Liladhar Rane
rights: Copyright (c) 2026 Siddhartha Chatterjee , Nitin Liladhar Rane (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-19
publication date: 2026-02-19
starting page: 537
ending page: 571
doi: 10.70593/deepsci.0202021

Artificial intelligence for customer relationship management: Impact on customer experience, engagement, value creation, and loyalty
type: item
description: The integration of artificial intelligence in Customer Relationship Management systems is providing a paradigm shift on how organizations approach customer relationships and bring long term value. The emergence of AI-powered CRM tools has brought capabilities in terms of personalization, predictive analytics, and automated customer service, which the entire concept of their contribution to customer experience, value-forming and loyalty development has not completely developed yet. The literature review is based on the PRISMA approach, which aims to structure the existing studies on AI-based CRM systems, their application in various industries, technological applications, and objective customer relations based on their effects. By critically assessing the existing research in the field, this review has revealed important gaps in the conceptualization of the psychological processes of desensitizing the mind to AI-enhanced customer experiences, the ethical aspect of automated personalization, and the sustainability of AI-mediated customer relationships in the long term. The results show that the integration of AI in CRM plays a significant role in its ability to predict customer data, hyper-personalization at scales, and operational efficiency, and, at the same time, raises the issue of data privacy, bias in algorithms, and reduced human touchpoints. The review will add to the knowledge base because it summarizes new trends, sets the frames of implementation, evaluates the comparative performance of different AI methods, and offers a further direction in research which covers the technological, organizational, and social aspects of AI-driven customer relationship management in the ever-digitized market environment.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Customer relationship management
  topic: keywords
subject: Customer experience
  topic: keywords
subject: Customer loyalty
  topic: keywords
subject: Value creation
  topic: keywords
subject: Predictive analytics
  topic: keywords
creator: Birupaksha Biswas
creator: Jayesh Rane
creator: Nitin Liladhar Rane
rights: Copyright (c) 2026 Birupaksha Biswas , Jayesh Rane , Nitin Liladhar Rane (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-19
publication date: 2026-02-19
starting page: 503
ending page: 536
doi: 10.70593/deepsci.0202020

Enhancing customer experience, engagement, value creation, and loyalty through artificial intelligence and machine learning in CRM
type: item
description: The digital interactions have undergone an exponential trend that has fundamentally shifted the customer relationship management (CRM) systems, and advanced technological interventions are required to handle the more intricate expectations and behavior of the customer. The conventional CRM strategies are unable to handle large volumes of unstructured customer information, forecast subtle behavioral patterns, and provide personalized experiences at a large scale to generate lower customer satisfaction and loyalty. This is a broad literature review on the impacts of machine learning (ML) and deep learning (DL) in revolutionizing CRM practices in increasing customer experience, generating sustainable value, engaging, and gaining long-term loyalty. This paper is a systematic review conducted by relying on the PRISMA methodology to gather and analyze the current research. As it has been shown in the review, ML and DL technologies such as natural language processing, recommendation systems, predictive analytics, and sentiment analysis began to have impressive advantages in terms of the accuracy of customer segmentation, the ability to predict churn, lifetime value estimation, and personalization strategies. More so, incorporation of generative AI, federated learning, and explainable AI models is the next innovation in the development of transparent, privacy and ethically sound CRM systems. The study reveals that there are important gaps in the existing literature about the concepts of real-time emotion recognition, cross-channel behavior synthesis and sustainable AI implementations, and proposes the opportunities in edge computing integration, quantum machine learning applications, and neuromorphic computing to optimize CRM.
subject: Articles
  topic: section
subject: Customer relationship management
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Predictive analytics
  topic: keywords
subject: Customer loyalty
  topic: keywords
subject: Artificial intelligence
  topic: keywords
creator: Saundarya Biswas
creator: Nitin Liladhar Rane
creator: Jayesh Rane
rights: Copyright (c) 2026 Saundarya Biswas , Nitin Liladhar Rane, Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-19
publication date: 2026-02-19
starting page: 473
ending page: 502
doi: 10.70593/deepsci.0202019

Inclusive education through artificial intelligence: Opportunities, challenges, and ethical considerations
type: item
description: The introduction of artificial intelligence into the educational system is the most promising in terms of realizing inclusive education, but there are still a lot of problems and ethical issues. This project is rather critical as it focuses on eliminating the issue of educational inequity when millions of learners with disabilities and varying learning requirements still struggle with the opportunities of obtaining quality education despite the technological achievements. It has functioned through an in-depth mixed-methods study utilizing the information on learning organizations in various countries that have deployed AI-driven inclusive education frameworks by 2023-2025. The research technique involved using structural equation modeling, machine learning classification algorithm, and hierarchical regression analysis in determining the effectiveness of AI technologies in inclusive education, their accessibility and ethical considerations. The findings show that adaptive learning systems based on AI can achieve vastly greater learning outcomes among learners with disabilities, and the impact size amounts to between 0.68 and 1.24 in various categories of disabled people. Nonetheless, in spite of the above, the analysis also shows worrying trends regarding bias in the algorithms, 34.7% of AI systems in the analysis demonstrated statistically significant discrimination based on certain demographic group. The paper establishes that there are five moral aspects of concern, namely, data privacy breaches, insufficient levels of algorithmic transparency, lack of digital accessibility, lack of teacher control, and socioeconomic inequalities in access to AI.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Inclusive education
  topic: keywords
subject: Algorithmic bias
  topic: keywords
subject: Equity
  topic: keywords
subject: Universal design
  topic: keywords
subject: Ethics
  topic: keywords
creator: Ojo Amos Adewale
creator: Nitin Liladhar Rane
creator: Martina Oluchi Ogbonna
creator: Jayesh Rane
rights: Copyright (c) 2026 Ojo Amos Adewale , Nitin Liladhar Rane , Martina Oluchi Ogbonna , Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-19
publication date: 2026-02-19
starting page: 455
ending page: 472
doi: 10.70593/deepsci.0202018

Student perceptions of ChatGPT and artificial intelligence tools in higher education: Evidence from early experiences
type: item
description: Due to the growth of generative artificial intelligence and the advancement of ChatGPT in particular, there are unprecedented debates about its use and use in the landscape of higher education. The extensive use of these technologies has not been accompanied with empirical research studies on student perceptions, attitudes, and instructional design significance, which forms a significant knowledge gap that hampers cognizant policy formulation and designing of instruction. This research holds its place as it deals with this gap. In this study, undergraduate and postgraduate students from various institutions in four continents were involved in the study. The outcomes showed that positive attitudes to ChatGPT adoption were significantly predicted by the perceived usefulness (β = 0.436, p < 0.001) and perceived ease of use (β = 0.328, p < 0.001), and the influence of ethical concerns on this relationship was negative (β = -0.187, p < 0.01). Surprisingly, students also showed advanced knowledge regarding the correct application of AI when they were asked about the legitimate application of AI in brainstorming and organizing research with 73.2% of the students acknowledging the valid application in these fields but in a context of assessment, they showed concerns about wanting AI to directly write the answers. The results add to the technology acceptance theory by generalizing Technology Acceptance Model (TAM) to the generative AI setting, as well as, offer practical implications to educators and policy makers engaged in the process of implementing the use of artificial intelligence in educational institutions.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Higher education
  topic: keywords
subject: Student perceptions
  topic: keywords
subject: Technology acceptance model
  topic: keywords
subject: Academic integrity
  topic: keywords
creator: Dimple Ravindra Patil
creator: Nitin Liladhar Rane
creator: Obizue Mirian Ndidi
creator: Jayesh Rane
rights: Copyright (c) 2026 Dimple Ravindra Patil , Nitin Liladhar Rane , Obizue Mirian Ndidi , Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-19
publication date: 2026-02-19
starting page: 446
ending page: 454
doi: 10.70593/deepsci.0202017