International Journal of Applied Resilience and Sustainability
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
Copyright (c) 2025 International Journal of Applied Resilience and Sustainability (IJARS) This work is licensed under a Creative Commons Attribution 4.0 International License.
Agentic artificial intelligence-driven tutoring: A multi-agent cognitive architecture for personalized adaptive learning in Education
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.
Copyright (c) 2026 Neha Gupta, Shreeshail Heggond (Author) https://creativecommons.org/licenses/by/4.0
Supply chain management using machine learning, deep learning, and blockchain techniques: A review
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.
Copyright (c) 2026 Siddhartha Chatterjee , Nitin Liladhar Rane (Author) https://creativecommons.org/licenses/by/4.0
Artificial intelligence for customer relationship management: Impact on customer experience, engagement, value creation, and loyalty
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.
Copyright (c) 2026 Birupaksha Biswas , Jayesh Rane , Nitin Liladhar Rane (Author) https://creativecommons.org/licenses/by/4.0
Enhancing customer experience, engagement, value creation, and loyalty through artificial intelligence and machine learning in CRM
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.
Copyright (c) 2026 Saundarya Biswas , Nitin Liladhar Rane, Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
Inclusive education through artificial intelligence: Opportunities, challenges, and ethical considerations
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.
Copyright (c) 2026 Ojo Amos Adewale , Nitin Liladhar Rane , Martina Oluchi Ogbonna , Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
Student perceptions of ChatGPT and artificial intelligence tools in higher education: Evidence from early experiences
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.
Copyright (c) 2026 Dimple Ravindra Patil , Nitin Liladhar Rane , Obizue Mirian Ndidi , Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
Impact of generative artificial intelligence in education: Opportunities, challenges, and strategies
Due to the exponential growth of generative artificial intelligence (GenAI) technologies, especially large language models, like ChatGPT, Claude, and Gemini, unprecedented shifts in education have occurred in nearly every part of the world. Although adoption rates have risen exponentially. It has been demonstrated that a fifth of these technologies bear extensive and diverse implications on the efficiency of teaching and learning, academic honesty, and the attainment of learning outcomes. The present study concerns three core issues: to begin with, the empirical models used to assess the GenAI, and its difference effects in educational settings are nonexistent; secondly, there is a lack of insight into the implementation-based strategies to maintain the balance between innovation and academic integrity; and finally, the policies within the measurement of equitable access and ethical practices lack empirical justification. A mixed-methods design with educational institutions based in various countries, as well as qualitative, GenAI implementation frameworks was used. Evidence shows that GenAI integration is significantly associated with better personalized learning improvements (β = 0.67, p < 0.001) and increased pedagogical efficiency (42% of administrative tasks were reduced), but at the same time, it is associated with significant issues, such as academic dishonesty (88% of students used GenAI to complete assessments) and equity (equity). The analysis has identified five key success factors in the success of GenAI implementation that are extensive faculty training programs, strong ethical guidelines, dynamic assessment practices, technology support, and alignment among policies.
Copyright (c) 2026 Shreeshail Heggond , Nitin Liladhar Rane (Author) https://creativecommons.org/licenses/by/4.0
Qualitative research using artificial intelligence: Methods, techniques, challenges, and future directions
The introduction of artificial intelligence into qualitative research is a massive revolution in the extraction, interpretation, and analysis of non-numerical data by researchers. Although the traditional approaches of qualitative research are deeply informative and focused, they have the major problems such as lengthy analytic procedures, researcher biasness, absence of scaling and varying interpretation. This is a literature review that covers the way the AI technologies are transforming the qualitative research methods of various fields. The article examines the new AI-related methods such as natural language processing, machine learning algorithms, deep learning models, and network architectures that support automated coding, thematic analysis, sentiments, and pattern recognition of qualitative data. This study points out the essential applications of PRISMA-based systematic analysis that include interview transcription, focus group analysis, ethnographic observation, narrative inquiry, and case study research. The review shows that the opportunities provided by the integration of AI are unprecedented, providing improved chances to be more accurate, efficient, and scalable, but researchers are faced with significant challenges concerning the bias in the algorithms and interpretive validity, ethics, data privacy, and maintenance of human contextual awareness. The results confirm that the use of hybrid ways to include AI capacities with human skills results in better results than automated ones or conventional manual methods.
Copyright (c) 2026 Dimple Ravindra Patil , Nitin Liladhar Rane , Obizue Mirian Ndidi, Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
Business intelligence systems integrating artificial intelligence, big data analytics, machine learning, internet of things, and blockchain
The rise of digital transformation has posed challenges on organizations like never before due to the exponential growth, which has created the need to have a competitive edge that is obtained through using data in decision-making. The conventional business intelligence solutions have problems in managing the enormous amount, speed, and diversity of data produced by business organizations today leading to the provision of delayed insights and loss of opportunities. This literature review provides an analysis of the intersection of Artificial Intelligence, Big Data Analytics, Machine Learning, Internet of Things, and Blockchain technologies in transforming the business intelligence systems. The presented research applies a systematic review based on the PRISMA methodology in order to explore the opportunity of these new technologies to work together in order to enhance predictive analytics, automate business processes, guarantee information integrity, and generate business value never experienced before. Exploring a wide range of implementation applications in optimization of supply chains, customer experience improvement, fraud detection, and strategic planning, the review evaluates the implementation structures, technical architecture models and algorithm types. Among the core conclusions, it is possible to note that integrated AI-ML-Big Data environments will provide better predictive quality, IoT allows acquiring real-time operational intelligence, and Blockchain guarantees data provenance and trust. Nevertheless, there remain serious challenges such as quality of data, complexities of integrating the data, skills gap, ethical considerations and regulation compliance.
Copyright (c) 2026 Nitin Liladhar Rane, Obizue Emmanuel Chika , Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
Pedagogy with generative artificial intelligence: Opportunities and challenges in education
Students using ChatGPT demonstrated significantly superior performance on AI-assisted assignments (p < 0.001), reporting benefits including time efficiency, personalized learning experiences, and enhanced conceptual understanding. However, these advantages came with notable trade-offs: the AI group scored lower on proctored examinations (p < 0.01), suggesting potential overreliance that may hinder independent learning capabilities. Student perceptions revealed this tension, with 71% acknowledging AI dependence for task completion while 58% expressed concerns about compromised learning depth. The research identifies critical risks accompanying AI integration: concerns about content accuracy, plagiarism vulnerabilities, ethical dilemmas, and the potential erosion of deep learning. Traditional assessment methods, particularly take-home assignments, proved inadequate in AI-enabled environments, highlighting the need for fundamental pedagogical restructuring. The authors advocate for comprehensive educational reform encompassing redesigned assessments that prioritize genuine understanding over AI-generated responses, institutional policy frameworks governing ethical AI use, and systematic AI literacy development among students and educators. The study emphasizes that generative AI's transformative potential, including personalized tutoring, immediate feedback, and content generation efficiency can only be realized through deliberate pedagogical innovation. The research calls for longitudinal studies examining sustained learning outcomes, discipline-specific AI applications, and development of evidence-based guidelines balancing educational enhancement with academic integrity preservation. Ultimately, the study positions generative AI as a "double-edged sword" requiring proactive adaptation from educational stakeholders to ensure it serves as a pedagogical ally rather than undermining educational foundations.
Copyright (c) 2026 Ojo Amos Adewale , Jayesh Rane , Martina Oluchi Ogbonna , Nitin Liladhar Rane (Author) https://creativecommons.org/licenses/by/4.0
Artificial intelligence, machine learning, deep learning, and big data for circular economy, resilience, and sustainable development
The increasing environmental destruction, resource exploitation, and the climate change issues require the major revolutionary change in how the societies generate, consume, and govern resources. The conventional models of the linear economy have failed to deal with the intricate sustainability issues of the twenty first century. This review of the literature provides how Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Big Data analytics help to facilitate Circular Economy (CE) principles, improve resilience, and benefit Sustainable Development Goals (SDGs). The review includes a variety of applications in the domain of waste management optimization, resources efficiency improvement, supply chain circularity, predictive maintenance systems, environmental monitoring. The highlights of the findings indicate that AI-based solutions have the potential to decrease waste by 30-45, improve resource use by 25-40, and improve the accuracy of the decisions in the circular systems by up to 60. High-yield learning algorithms along with Internet of Things (IoT) sensors allow monitoring and adaptive control of the processes of the circular economy in real time. Nevertheless, there are still a lot of challenges, such as data quality, bias in algorithms, AI models consumption, and the lack of implementation in developing economies. The review can point to research gaps relating to critical areas of cross-sectoral integration, the use of AI ethically, and solutions scalability to small and medium enterprises. The results prove that the systematic application of smart technologies can spur the desire to build sustainable, resilient, and circular economic frameworks and provide for various SDGs at the same time.
Copyright (c) 2026 Birupaksha Biswas , Nitin Liladhar Rane, Suhena Sarkar (Author) https://creativecommons.org/licenses/by/4.0
Generative artificial intelligence-driven adaptive learning for sustainable, personalized, and resilient education systems
The educational field of the globe is under a tremendous pressure of providing equity, model individualized, and sustained learning solutions to various student groups. The conventional education designs find it hard to support individual learning pace, style as well as needs and with scalability and efficiency of resources. This literature review explores the disruptive power of generative artificial intelligence to transform adaptive learning systems to establish more sustainable, customised and resilient educational systems. The review of the existing literature summarizes the latest studies on how generative AI technologies, such as large language models, multimodal AI systems, and intelligent tutoring agents, are transforming the mechanisms of delivering education. Systematic review to determine how AI can be integrated in adaptive learning platforms through the PRISMA methodology, this review presents the applications of generative AI in the context of K-12 education, higher education, professional development, and lifelong learning. The findings indicate a great progress in the real time content creation and the personalized feedback system, automated evaluation systems and curriculum modification through intelligence. The most important conclusions are that, based on the generative AI-controlled systems, learning performance is improved in the form of adjustable difficult levels, generation of contextual content, and customized learning processes, as well as in the context of environmental sustainability through minimized physical resource use. The review establishes such crucial issues as algorithmic bias, data privacy problems, digital equity differences, and complexities in pedagogical integration. The opportunities that are in the offing include hybrid human-AI instruction frameworks, emotion cognizant adaptive frameworks, and blockchain incorporated credentials validation.
Copyright (c) 2026 Manjunath Munenakoppa, Nitin Liladhar Rane, Jayesh Rane, Shreeshail Heggond (Author) https://creativecommons.org/licenses/by/4.0
Explainable and ethical artificial intelligence in customer relationship management: Improving customer experience, value creation, and loyalty
The fast development of the artificial intelligence into the customer relationship management systems has offered the businesses unexampled possibilities to improve customers experiences yet posing serious questions of transparency, fairness, and ethical decision-making. The given comprehensive literature review discusses the intersection of explainable AI and ethical considerations in the realm of CRM ecosystems, which involves the core issue of technological progress and responsibility and trust. The paper utilizes PRISMA to ensure a systematic interpretation of the latest trends in research to determine how the organizations can use interpretable machine learning models, transparent algorithm processes and value-oriented AI systems to enhance customer relations without losing ethics. Important results include explainable AI methods like LIME, SHAP, and attention mechanisms as they have a high impact on customer confidence and response to ethical governance standards. The study establishes a direct relationship between operational transparency of AI-driven personalization, AI-driven recommendation systems, and predictive analytics and customer loyalty, lifetime value. Nonetheless, more issues remain in the process of introducing real-time explainability and controlling the bias of algorithms, adhering to the requirements of data privacy, and building culturally adaptable moral systems in a wide variety of markets. This review states the existence of important gaps in longitudinal research studies about the long-term effectiveness of explainable AI in customer retention, limited literature has been investigated on hybrid human-AI collaborative models in CRM, and there is a lack of research to explore quantum computing as a privacy-preserving customer analytics tool.
Copyright (c) 2026 Ankur Chaudhary, Nitin Liladhar Rane, Ritesh Rastogi, Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
Artificial intelligence for enhancing learning and motivation among education faculty students
The rapid development of Artificial Intelligent (AI) has triggered change of thoughts about conventional pedagogical patterns in educational institutions especially in relation to student involvement and educational motivation. Even though there has been significant investment in educational technology infrastructure, the modern-day education faculty programs are still grappling with a deteriorating student motivation, poor personal learning activities and a lack of readiness to work in a technology-based teaching capacity. The research filling the critical gap that is presented in this study is the empirical effectiveness of AI-based intervention targeted at education faculty students in particular, as they are in a unique position to impact the future of the educational practices. Results were obtained through a mixed-methods research design that studied undergraduate and graduate education students and three semesters using an integrated AI learning ecosystem based on adaptive learning platforms, intelligent tutoring systems, feedback mechanisms based on natural language processing, and elements of gamification. The study used structural equation modeling, hierarchical linear regression, and thematic analysis as part of the evaluation of cognitive, affective, and behavioral aspects of learning engagement. Findings showed statistically significant differences with many outcome measures of biochemical results, such as a gain in intrinsic motivation scores of 34.7, a gain in learning persistence measures of 41.2, and an increase in academic performance indicators of 28.9. In addition, qualitative results also showed paradigm changes in self-efficacy perceptions and attitude to the use of technology amongst students.
Copyright (c) 2026 Idowu Johnson Mosaku, Obizue Mirian Ndidi, Nitin Liladhar Rane, Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
Why teachers do or don't rely on artificial intelligence in education: Impact, trust, and adoption factors
The enhancement of Artificial Intelligence (AI) technologies in educational institutions has both presented opportunities and challenges to the teaching professionals on a scale never been witnessed before. In spite of the rapid rate of development of AI-driven educational technologies, their acceptance among teachers is not evenly spread, and there are complicated factors behind it that define their decision-making patterns. This paper fills in this critical gap on the necessity to comprehend why teachers accept or reject the use of AI by considering the interaction of trust machinery with the perceived impact and determinants of adoption in the teaching environment. We also conducted a survey of high education teachers working in various institutional contexts, with the help of complex mixed-methodology approaches to study the connections between the variables in the paradigm of technology acceptance, organizational support systems, and AI adoption behaviors. The analysis helps to discover that teacher confidence in AI-based educational technology acts as the main mediator between perceived usefulness and real adoption, and psychological safety and institutional support are high levels of moderation. Findings have indicated that 5 of the six core constructs, meaning perceived usefulness, ease of use, trust in AI systems, institutional infrastructure, and professional self-efficacy, explain 68.3 percent of adoption variance. More so, the research has discovered some of the key obstacles such as complex technology, ethical issues covered with data privacy and the fear of being displaced professionally. These results add to the theoretical improvement of the Technology Acceptance Model used in the educational settings as well as offering practical implications to the policymakers and developers of educational technologies aimed at achieving successful incorporation of AI meaningfully into the teaching processes.
Copyright (c) 2026 Swapnil Malipatil, Jayesh Rane, Shrishail Chiniwalar, Swati Dhurve, Nitin Liladhar Rane (Author) https://creativecommons.org/licenses/by/4.0
Evaluating teachers’ perceptions of artificial intelligence tools in education: Opportunities and challenges
The active growth of the emergence of generative artificial intelligence (GenAI) technology in education has presented both opportunities and challenges never before seen that require a systematic study. Although AI has been widely integrated in K-12 and academic institutions, there is scarce empirical data on how teachers perceive the implementation, thus, establishing a critical gap in the knowledge about the feasibility of the implementation and the technological revolution in education. The study is a mixed-methods study that examine the perceptions of K-12 and higher education teachers regarding AI tools on various dimensions, such as perceived usefulness, easy to use, pedagogical integration, ethical issues, and professional development requirements. Based on structural equation modeling, confirmatory factor analysis, hierarchical regression analysis, this study creates and confirms a multidimensional AI Perception Framework taking into consideration constructions of Technology Acceptance Model including education-specific variables. An analysis of survey data shows that, although 73.8 of the teachers confirm that AI has the potential to transform the teaching process, only 45.2 of them actively use AI tools in education, which differ dramatically depending on the subject area, grade level, and given school resources. The statistical tool proves that behavioral intention is significantly predicted by perceived ease of use (β =0.426, p <0.001), but ethical concerns moderate this association (β = -0.283, p=0.001). The results present three main opportunities, including the improvement of administrative efficiency, the role of personalized learning, and the establishment of innovative assessments; and four fateful issues, such as a lack of personal growth, information privacy, a threat of algorithmic bias, and implementation equity discrimination.
Copyright (c) 2026 Shreeshail Heggond, Nitin Liladhar Rane, Manjunath Munenakoppa, Ramesh Baragani (Author) https://creativecommons.org/licenses/by/4.0
Ethics, bias, and fairness challenges in artificial intelligence and machine learning
The fast development of artificial intelligence and machine learning technologies in the area of crucial social activities has raised the level of concern about ethical interests, algorithmic discrimination, and justice in automated decision-making procedures. This literature review demonstrates that the ethical issues of AI development, bias reduction, and equity systems are complex phenomena, and the development of AI should be performed responsibly. The problem statement focuses on the increasing gap between technological improvement and moral responsibility, where the AI systems reproduce the discriminating trends, violate the rights to privacy, and deliver obscure judgments to influence basic human rights. Based on the PRISMA approach to conducting a systematic literature review, the research is a synthesis of the existing knowledge about the sources of bias in machine learning pipelines, metrics of fairness and trade-offs, ethical frameworks that govern AI, and new regulatory environments. The findings indicate that there remain ongoing problems with the attainment of algorithmic fairness across intersectional groups, between fairness definitions, in terms of black box transparency, and accountability mechanisms of AI-related harm. The crucial shortcomings that have been found are the low standardization of fairness measures, lack of equal representation in training data sets, and the lack of interdisciplinary interaction and emerging regulatory frameworks that are not keeping pace with technological advancement. The proposed review article will add to the overall perspective of any challenges that are present nowadays, practical mitigation measures and future research advancements that would be useful in creating reliable AI systems that meet human values and societal expectations.
Copyright (c) 2026 Ritesh Rastogi, Nitin Liladhar Rane, Ankur Chaudhary, Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
Responsible agentic artificial intelligence governance: Risk, safety, and ethical challenges in autonomous systems
With the development of the systems of artificial intelligence as non-communicative and non-cognitive assistive tools, to autonomous agents independent of human intention rendering decisions and carrying out tasks, society experiences the challenges that it never experienced before in the issues of responsibility of their creation and implementation. The swift decentralization of agentic AI systems into essential sectors such as healthcare, financial systems, transportation, and defense has lagged behind the formulation of sufficient governance strategies, among other aspects of excessively high risks and safety controls and ethical accountability. These systems are autonomous and have the ability to exhibit new emergent behavior and multi-step thinking, which have prompted new safety issues not properly handled by the traditional AI governance frameworks. This literature review is designed in accordance with the PRISMA approach to analyze the existing literature, theories, and new tendencies in the responsible agentic AI regulation in a systematic manner. The discussion has identified that there are severe issues to define effective oversight systems of autonomous AI agents such as checking goal congruency, tracking on emergent capabilities, engineering against negative instrumental actions, and accountability in multi-agent systems. The prevailing systems of governance are characterized by disintegration in the various regulatory systems and there is little coordination between the technical safety actions and the policy actions. The findings indicate that there is a drastic response to adaptive governance constructs able to adjust to the quickly changing AI functions, combination of technical safety protocols with ethics, and formation of international coordination systems.
Copyright (c) 2026 Birupaksha Biswas, Suhena Sarkar (Author) https://creativecommons.org/licenses/by/4.0
Green education for sustainable development goals: Environmental, social, and economic resilience
Although the issue of sustainability has been acknowledged by many, the structural systems of education world over have not been able to resolve the issue of incorporating green education principles that are sensitive enough to tackle issues of environmental degradation, social inequity as well as economic instability. The modeling of the modern educational systems still tends to separate the aspect of sustainability and make it a single topic not a paradigm shift towards a comprehensive resilience in all three pillars of sustainable development. This literature review attempts to explore the changing frontiers of green education and its important role in attainment of Sustainable Development Goals with the major focus on developing environmental stewardship, social equity, and economic viability. The discussion demonstrates that there are five significant pedagogical strategies, which include experiential learning ecosystems, digital sustainability platforms, community-based collaborative models, systems thinking frameworks, and transformative action competence development. The new trends are the use of artificial intelligence to achieve personalized sustainability education, gamification of environmental education, virtual reality to experience an ecological environment, blockchain to monitor sustainability credentials, and citizen science programs that would make the transition between formal and informal education. Green education has shown quantifiable behavior change effects, policy effects, innovation capability, and community stability on implementation of green education using holistic, context-sensitive strategies.
Copyright (c) 2026 Anita Padhy, Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
Enhancing adaptive and sustainable resilience through artificial intelligence, machine learning, internet of things, big data analytics, and blockchain
Contemporary organizations and society are challenged unlike ever before by climate change, cybersecurity, and supply chain earthquakes, pandemics, and infrastructure breakdowns and require innovative solutions to create resilient systems. This literature review discusses the way in which the coming together of artificial intelligence, machine learning, internet of things, big data analytics, and blockchain technologies in unison can increase resilience in various areas. The research problem revolves around the ways these emerging technologies can be used hand in hand with each other and result in the following outcomes: predictive capabilities, adaptive response, distributed trust mechanism, and real time intelligence which makes systems resilient. This review integrates the existing studies on the topics of smart cities, healthcare system, supply chain management, disaster response, the protection of critical infrastructure, and financial services, following the PRISMA approach. It has been found that cohesive technology solutions show a higher level of resilience performance relative to isolated ones, especially increased situational awareness and automated deep decision-making, decentralized governance, and procedural accountability. Important observations suggest that predictive analytics powered by AI with the use of IoT sensor networks make a reaction to risks in advance possible, whereas blockchain holds the data integrity and its credibility in distributed systems. But there have remained considerable gaps on the realms of standardization, interoperability issues, ethical concern, energy consumption as well as scalability drawbacks.
Copyright (c) 2026 Sylvester Enomah, Obizue Mirian Ndidi, Nitin Liladhar Rane, Jayesh Rane (Author) https://creativecommons.org/licenses/by/4.0
Agentic artificial intelligence in education: A review on personalized adaptive learning through autonomous tutoring systems
The accelerated development of artificial intelligence has triggered disruptive shifts in the educational settings of all sizes, but conventional learning models remain into the inability of providing really personalized, adaptive experiences in large scale. This literature analysis focuses on how agentic artificial intelligence has come to existence in education and specifically a personalized adaptive learning autonomous tutoring system. The problem statement deals with the life-and-death situation of standardized education provision in comparison to the needs of individual learners that the current technologies have addressed half-heartedly. This review synthesizes the current literature on autonomous AI agents that are intended to act as intelligent tutors and discuss their architectures, decision-making processes as well as adaptive learning processes through systematic analysis using the PRISMA methodology. The paper explores the way in which these systems utilize reinforcement learning, natural language processing, affective computing and cognitive modeling as a means of providing dynamic, learner-focused educational experiences. Findings indicate that the research made a major step forward in terms of multi agent tutoring models, real-time knowledge tracking, emotion sensitive pedagogy, and optimal sequencing of the curriculum. According to the review, such persistent issues are ethical issues, algorithmic-based decision-making transparency, data privacy, and equitable access. Results indicate that the agentic AI tutoring systems can have significant potential in overcoming learning gaps, differentiation of educational requirements, and business model of personalized training. Nevertheless, there is significant implementation impediments on technological infrastructure, integration of teachers and regulatory frameworks.
Copyright (c) 2026 Ashok Meti (Author) https://creativecommons.org/licenses/by/4.0
Artificial intelligence-driven green education for sustainable development goals: A review
The intersection of environmental education and artificial intelligence signifies a paradigmatical paradigm shift towards attainment of the United Nations Sustainable Development Goals, and specifically in SDG 4 (Quality Education) and SDG 13 (Climate Action). This is despite the fact that despite considerable growths in technology, the traditional methods used to dispense education are challenged by the need to be able to offer scalable, personalized and beneficial green education that will communicate environmental awareness and sustainability practices. With this overall literature review, the PRISMA methodology will be used to use and analyze emerging trends, applications, and innovations in AI-driven green education systematically. The research considers the innovation of machine learning algorithms, natural language processing, computer vision, and intelligent tutoring systems in the context of environmental literacy, climate change education, and sustainability awareness in the different educational settings. With the thorough study of modern literature, this review provides insight into such key uses as adaptive learning systems, ecological simulations providing virtual reality, AI-based calculators of carbon footprint, and automated environmental monitoring systems. The results indicate that the opportunities exist in the personalized sustainability courses, integration of real-time environmental data, and gamification of learning, whereas such aspects as digital equity, algorithm prejudice, and privacy, as well as technological infrastructure in developing countries, are problematic. The review is a synthesis of the AI methods and teaching and learning processes and application in respect to the sustainability concepts that can be put into practical use by ensuring that teachers, policymakers, technologists, and researchers dedicated to enhancing green education with the use of intelligent systems can use it to embrace a sustainable future.
Copyright (c) 2026 Ankur Chaudhary (Author) https://creativecommons.org/licenses/by/4.0
Artificial intelligence-based digital twin framework for circular economy optimization in healthcare waste management
Health care systems have grown to produce ever growing amounts of garbage, which further strain the environment, costs of running the system, and regulatory demands especially in the post pandemic period. The traditional methods of managing linear waste are also inefficient and do not well comply with a circular economy. The paper introduces an artificial intelligence-based, scientifically supported digital twin system that aims at maximizing the effectiveness of healthcare waste management through the application of real time sensing and analytics based on predictive algorithms and through the use of multi objective optimization that is integrated into the circle economy paradigm. The framework virtualizes the streams of waste materials in the hospital through Internet of Things based monitoring and implements the machine learning models to forecast, assess, and analyze waste and provide an optimization engine that balances the goals of both economy and environmental objectives within the regulatory constraints. Statistical results showed that there were significant and significant increases in all the key indicators. The average recycling rate rose by 20.0 to 50.7 percent which is the 30.7 percentage point, and the sample t testing group has verified that the difference is significant at p < 0.001, with a statistical power of 0.99. Statistically significant findings prove that the value of artificial intelligence facilitated digital twins can be used to provide the measurable, reproducible, and scalable benefits in terms of circularity, cost-effectiveness, and regulatory compliance in the management of healthcare waste and are justified in their integration into a data driven model of a sustainable approach to operating the hospital.
Copyright (c) 2026 Birupaksha Biswas, Joseph Ozigis Akomodi (Author)
Green artificial intelligence for sustainable and resilient development: A review
The blistering development of the technologies of artificial intelligence, though a transformative one has resulted in the growing environmental crisis due to the exponential increase in computational, energy demand, and carbon emissions. The AI systems made to date are characterized by massive power demands which provide pressure on the energy systems of the world and add to climate change, which is an inherent contradiction between technology development and the environment. This review of the literature explores 542 current articles to analyze the new paradigm of Green Artificial Intelligence as a group to make AI innovation and environmental stewardship align through energy-saving algorithms, green computing methods, and climate-aware implementation models. Our methodological approach was based on the PRISMA system, as we searched databases and filtered 1,052 records and finally settled on 534 articles, which directly relate to the topic of AI and sustainability. In our analysis, it is possible to note a tremendous advancement of energy-saving machine learning algorithms, green data centers, the integration of renewable energy usage, and artificial intelligence usage to monitor the environment and take climate action. Nevertheless, there exist decisive loopholes in the standardized measurement systems, lifecycle assessment approaches, harmonization of policies and fair access of green AI innovations in developing economies. The contribution of this review is the observation of seven major areas of implementation, the assessment of the emergent methods of energy optimization, issues of implementation and a hierarchical research agenda.
Copyright (c) 2026 Dimple Ravindra Patil, Nitin Liladhar Rane, Obizue Mirian Ndidi, Jayesh Rane (Author)
Predicting psychological resilience and mental health from multimodal wearable sensor data using graph neural networks
Early diagnosis of mental vulnerability is still a problem since most clinical evaluations are based on the subjective aspects of evaluation like self-report and not on the objective physiological indications. The idea of psychological resilience which is a fundamental protective factor against stress related disorders has been connected to the autonomic regulation and daily behavioral patterns, but the objective method of calculating on such a scale is yet to be established. This paper presents a multimodal graph neural network, which combines the wearable derived physiological signals to forecast trait level resilience and stress as well as state level. The ongoing heart activity, electrodermal activity, and motion data of adult participants were gathered and matched with resilient score proven validated scores. Participant specific graphs were created whereby physiological modalities were the nodes and the inter signal dependencies were the edges. Statistical analysis showed that there were significant physiological disparities that were related to resilience. High resilience group members had much better values of root mean square of successive differences and physical activity per day (mean difference on 1000 extra steps per day, p = 0.003). The proposed graph neural network (GNN) performed well in classification tasks in terms of area under the receiver operating characteristic curve (0.80 +- 0.02) to differentiate high and low resilience, which was significantly better than that of logistic regression (0.60), random forest (0.65), and long short-term memory models (0.72), with the difference being supported by DeLong test (p < 0.01). Graph based learning also offers added benefits of discrimination and physiologically explainable output and thus can facilitate its future as a scalable and objective digital mental health monitoring and early risk stratification tool.
Copyright (c) 2026 Birupaksha Biswas, Suhena Sarkar (Author)
Evaluating human health impacts of emerging environmental contaminants using artificial intelligence
The growing number of new environmental pollutants is an immense burden on human health risk assessment where rate of introducing chemicals and its detection in the environment is out-running the traditional toxicological tools of assessment. Artificial intelligence methods of data collection provide an opportunity to fill this gap by combining heterogeneous information that applies to hazard and exposure. In paper, a designed analytical framework of artificial intelligence was created to evaluate and rank the possible human health hazards of the representative emerging contaminants of the chosen artificial intelligence through combined physicochemical descriptors, toxicological bioassay indicators, and exposure related parameters. Several simple machine learning models such as logistic regression, random forest, gradient boosting and deep neural networks were trained and assessed through repeated cross validation to offer statistical soundness and to reduce overfitting. Accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve were used to measure model performance. The ensemble random forest model had shown excellent and statistically significant performance with an average classification accuracy of about 85 percent, recall of more than 0.85 with high-risk contaminants and an area under curve of near 0.90 with each fold of validation showing strong discriminating capacity. Explainable artificial intelligence analyses indicated that least amount of lipophilicity, environmental persistence, signs of endocrine related biological activity, and the intensity of use or production were the most important contributors to risk classification and together, they explained most of the model explaining power. Comprehensively, these results can be used to understand that statistically sound artificial intelligence models can be successful in the recognition and rank of potential human health interest emerging contaminants.
Copyright (c) 2026 Joseph Ozigis Akomodi, Birupaksha Biswas (Author)
Artificial intelligence-driven cybersecurity for resilient and sustainable business in Industry 5.0
The Industry 5.0 paradigm indicates the transition to human-centered, sustainable, and resilient Industry manufacturing ecosystems in need of artificial intelligence, cyber-physical systems, and collaborative robotics. But this hyper-networked industrial environment brings cybersecurity risks in a scale never previously witnessed globally, and these risks place continuity of operations, integrity of data, and sustainability of the business at stake. The conventional security structures used in the Industry 4.0 cannot protect the complex, dynamic, and multidimensional cyber threats to the Industry 5.0 structures. The proposed research is a new cybersecurity framework that has been developed based on artificial intelligence to be deployed in Industry 5.0 specifically and will embrace adaptive deep learning algorithms, federated learning models, and quantum-resistant cryptography protocols. Our hybrid methodology involved the use of Convolutional Neural Networks along with Long Short-Term Memory networks and Generative Adversarial Networks which are used to identify, forecast and counter attack advanced persistent threats in real time industries. Through statistical analysis, it was found that our AI-based framework had 98.73 percent accuracy in detecting threats with a false positive rate of 0.89 per cent, meaning it was 34.2 per cent better than the current state-of-the-art methods. More so, the framework showed 97.4 resiliency to adversarial attacks and a 67.3-time reduction to detect threats which is much less than traditional intrusion detection systems. The applied implementation led to the rate of security incidents decreasing by 43.8 per cent and improving business continuity indicators by 52.6 per cent which was a direct contributor to sustainable operating practices. The novelty of this research is its theoretically based and empirically-validated AI-based context of cybersecurity architecture that fills crucial gaps in the literature of Industry 5.0 security and offers anyone with actionable suggestions on how to develop resilient and sustainable digital industrial ecosystems.
Copyright (c) 2026 Swapnil Malipatil, Jayesh Rane, Sibaram Prasad Panda, Nitin Liladhar Rane (Author)
Enhancing ESG performance and business sustainability through artificial intelligence: A review
Artificial intelligence and sustainability are one of the most radical paradigms of modern business work, but the challenges of its effective implementation into an Environmental, Social, and Governance framework remain with the organizations. The underlying cause of this challenge is the disjointed strategies in implementation, a lack of knowledge of the multifaceted possibilities of AI, and failure to have adequate structures to measure the results of sustainability. The following comprehensive literature review effectively evaluates artificial intelligence to improve the ESG performance and business sustainability in various industries and applicability. The review integrates the existing information on AI-based sustainability solutions, including machine learning algorithms that forecast data and predictive analytics, Internet of Things integration that controls the real-time monitoring, blockchain to ensure the transparent management of supply chains and digital twins to optimize the use of resources through a systematic analysis of 738 peer-reviewed articles based on PRISMA methodology. The results show that AI technologies can greatly enhance the precision of environmental monitoring to up 85 percent, operational carbon footprint to 30-40 percent, supply chain clarity, and governance systems through automated compliance systems. Although, continued issues can be seen in negative data quality, costs of implementation, ethical issues, and specialized expertise. The reviewed article advances the subject due to its systematic taxonomy of AI applications in sustainability, critical success factors of its use, and a prof submission of an integrated framework relating AI capabilities to ESG outputs. The analysis provides the development of explainable AI to ESG reporting, federated learning to privacy-preserving sustainability analytics, and quantum computing to solve complex optimization issues in a circular economy models as the emerging trends.
Copyright (c) 2026 Ashok Meti, Nitin Liladhar Rane (Author)
Sustainable supply chain management through a digital twin-enabled federated deep reinforcement learning framework
The need to have sustainable supply chain management demands the decision frameworks being jointly optimal in the economic performance and environmental impact without violating data decentralization across the stakeholders. The proposed study suggests multiple-level (federated) deep reinforcement learning (FedDRL) to the optimization of supply chains to unite inventory management, production strategy, and transportation decisions in conditions of an explicit carbon-emission penalty. The supply chain entities also train a local deep reinforcement learning agent on local operational data, and periodically, the aggregation of the parameters is done via federated averaging to create a global policy without collecting all the data in a central location. The reward is a joint-coding of overall operational cost and emissions through a sustainability weight l which can be tuned allowing the economic-environmental trade-off space to be steered. The framework was tested using large stochastic simulations of a variety of demand scenarios and studied with regards to Order-Up-To heuristics, unreinforced non-federated DRL, and centralized reinforcement learning baselines. Repeated-measures comparisons were used to provide a statistical significance with an expression of means of a significantly lower total cost and variance of the proposed approach compared to baselines (p < 0.01). A monotonic and statistically significant change in the results of cost-emission analysis with different l values was found, which indicated the stability of Pareto-efficiency. Altogether, the findings indicate that federated reinforcement learning is capable of providing almost centralized performance, high operational efficiency, and governability sustainability, which is a scalable and privacy-controlling solution to data-driven supply chain optimization.
Copyright (c) 2026 Santanu Acharyya, Suhena Sarkar, Bappaditya Biswas, Birupaksha Biswas, Prithwijit Banerjee (Author)
Explainable artificial intelligence for sustainable business performance: Integrating ESG metrics into AI adoption models
The rapid nature of the incorporation of artificial intelligence systems into the functioning of organizations has exposed a paradox that is critical in that when the algorithms of machine learning remain veiled the stakeholders cannot trust them and consequently hinder the sustainable transformation of business operations. Although there have been significant improvements in the capabilities of AI, their black-box character poses significant obstacles to their implementation in sustainability-oriented businesses, especially in the systems of environmental, social, and governance measurements and optimization. This is filled in the present research, where an integrated framework is developed and empirically supported to combine the explainable artificial intelligence principles with extensive ESG metrics to improve the performance of organizations in terms of sustainability. We used a mixed-methods design by gathering longitudinal evidence of multinationals in various sectors of the industry, spanning a specific period, using a structural equation model, hierarchical regression model, and machine learning classification algorithm as a form of analyzing the mediating mechanisms through which XAI transparency has effect on ESG performance outcomes. We realize that organizational sustainability performance scores on organizations practicing XAI-enabled systems of ESG monitoring are significantly much higher, with transparency systems that mediate the connection between AI adoption and ESG performances. Particularly, XAI implementation has been associated with a 23.7 percent increase in composite ESG scores with the environment performance responding the most. The study is theoretically useful in terms of expanding the scope of stakeholder theory and resource-based view theories to include algorithmic transparency as a strategic business resource and offers practitioners with practical models of how explainable AI technologies can be incorporated into sustainability management systems in order to generate quantifiable change in corporate environmental and social results.
Copyright (c) 2026 Anita Padhy, Nitin Liladhar Rane, Jayesh Rane (Author)