Introduction: Artificial intelligence (AI), when applied in e-learning, transforms in the concept of blended learning. Artificial intelligence integrated into the learning environment has helped the learners to be more engaging and interactive with their mode of learning. The research work aims to analyze the trend of the usage of Artificial Intelligence in the context of an e-learning environment and also intends to incorporate the trends of its usage and apply the same to create a successful learning environment. The utility of conducting the research work is to analyze the use and application of Artificial Intelligence incorporated through the use of data analytics and natural language processing.Method: The study was undertaken as a thorough review with emphasis placed on successful implementation of artificial intelligence in hybrid higher education, and its success can be demonstrated through its effects or implications in curriculum development and performance in general.Results: Based on the research study, the study shows that the pursuit of personalized instruction through heightening the level of processing various administrative issues and assisting teachers in delivering quality training, the use of AI technology shall be vital within the realm of hybrid education.Discussion: This particular research covers challenges and ethics surrounding biases within algorithms, data privacy, and the need for transparency in AI-driven learning. This particular article also manifests the efficacy of AI technology in offering a personalized learning environment, administrative efficacy, and teaching efficacy.Conclusion: The future will involve applying AI technologies in the domain of hybrid education. Hence, it is essential for all stakeholders to ensure that development with regard to AI is in line with the learning process and establishes a fair learning environment for all individuals.
Virendra Gomase, Suhas B. Dhande, P. Natu· International journal of com...· 0 citations
The advent of Industry 4.0 has changed manufacturing systems due to utilizing new digital technologies, including AI (Artificial Intelligence), IoT (Internet of Things), cloud computing, big data analysis, and automation. One of the technologies developed in this domain is Digital Twin (DT), which is an effective method that facilitates the establishment of virtual models of physical manufacturing systems in real time. Nevertheless, conventional digital twins are only geared towards monitoring and visualization and lack the ability to make autonomous decisions. The merger of AI with Digital Twin technology allows for the intelligent prediction, optimization, and flexible control of manufacturing processes.The research paper presents a Digital Twin architecture powered by AI for improving production processes in manufacturing environments with Industry 4.0 technology. The architecture integrates IoT-enabled data collection, machine learning algorithms, forecasting technologies, simulating, and intelligent decision-making levels with the aim of enhancing production efficiency, eliminating downtime, improving usage of resources, and increasing quality of products. The paper discusses various components of the selected architecture as well as its operational processes and application in industries. It also outlines challenges that can arise while implementing the suggested architecture in manufacturing environments and possible directions for future research within the area of AI-powered Digital Twin technology.
Virendra Gomase, Suhas B. Dhande, P. Natu et al.· Journal of Intelligent Decis...· 0 citations
Business Intelligence systems powered by Artificial Intelligence (AI) have demonstrated to be an innovative approach towards transforming organizational intelligence into valuable information for strategic decisions, maximum efficiency of operations, and constant innovations within organizations. Conventional Business Intelligence systems heavily utilize descriptive analytics based on historical reporting methods and dashboard-based visualizations, and as such, they feature limited predictive and prescriptive analytics capabilities. In this scenario, the current paper aims at providing the full conceptual framework of AI-based Business Intelligence, encompassing the various elements of the system, namely, data management, AI data analytics, automated insight generation, intelligent decision making, and innovation within the organization.This framework is based on machine learning, deep learning, natural language processing, predictive analytics, and anomaly detection technologies that help process structured and unstructured data from various sources including enterprise systems, customer databases, IoT devices, market information systems, and social media platforms. AI-based analytics can help in identifying trends that manifest themselves in business processes, performance problems, risks, and opportunities as well as in providing recommendations for decision making in real time.The framework promotes and boosts innovations through enhanced strategic planning, a focus on customers’ needs in product development, optimizations of operations including processes, and adaptability of business models. The findings indicate that business intelligence based on AI technology can help improve the quality of decisions in addition to fostering higher operational efficiency, agility of organizations, and securing compliance with the demands of modern competition. Nevertheless, one should keep in mind that challenges related to data quality, systems integration, data security, explainability, governance, and adequate readiness of organizations might hinder the implementation of this framework.The results highlight the need for AI to perform the role of an intelligent decision-making support tool which enhances human expertise without replacing it. Further studies should investigate the use of AI across various industries, use of Explainable Artificial Intelligence (XAI), Generative AI, AI governance frameworks and the validation of ideas proposed through machine learning experiments and organizational case studies. The suggested project gives a framework for companies in their attempts to achieve intelligent, flexible and innovation-oriented digital transformation using the AI-driven systems of Business Intelligence.
Virendra Gomase, Suhas B. Dhande, P. Natu et al.· Journal of Intelligent Decis...· 0 citations
AI has increasingly found its place in Management Information Systems (MIS), allowing organizations to process a huge amount of information and analyze it to find trends, predict business results, and enable decision-making. Yet, most AI technologies function as almost "black boxes," generating results without explaining the underlying logic behind them. This may lead to concerns about accountability, fairness, and ownership of decisions, hampering trust in the technology and limiting its acceptance in the company. Explainable Artificial Intelligence (XAI) solves these challenges through the provision of clear and intelligible accounts of the predictions and recommendations made by AI. This study investigates the significance of XAI in improving the executives’ decision-making process with greater transparency, trust, accountability, and the human–AI collaboration. By applying a conceptual and literature research methodology, the paper proposes a new MIS framework using XAI, where quality of data, transparency of model, relevance of the explanation, and human supervision advance the executives’ degree of trust and decision-making quality.The study claims that XAI must be regarded not only as a technical tool but as an organisational capability enabling managers to assess AI suggestions critically and act on them wisely. The research establishes the significance of user-centric explanations, governance systems, ongoing monitoring, and human responsibility. Findings indicate that explainable AI can boost managers’ confidence and enhance the quality, speed, and defensibility of strategic choices if the provided explanations are accurate, meaningful, easy to comprehend, and correlating with the goals of the organisation.
Virendra Gomase, Suhas B. Dhande, P. Natu· International journal of com...· 0 citations
Artificial Intelligence (AI) driven Decision Support Systems (DSS) help transform the field of strategic management through rapid, accurate and data-driven decision-making in increasingly complicated business environments. This article is centered on a comprehensive machine learning approach for AI-enabled strategic decision support involving all aspects of data acquisition, predictive analytics models, intelligent recommendation systems and collaboration between humans and AI. The framework suggested consists in the integration of data coming from the enterprise, customer relationship management systems, financial databases and market intelligence services and results obtained through machine learning technologies. The process of decision-making involves both the utilization of recommendation systems to develop possible strategies based on analytical results and human involvement ensuring that the outcome is ethical, clear and considerate. The research shows that AI-enabled DSS helps to enhance decision-making quality, operational efficiency, competitive edge, innovation capabilities, and organizational agility as a result of continuous learning. In addition, the paper argues that explainable AI, data governance, organizational preparedness, and responsible AI governance are important factors for successful implementation. Despite numerous advantages available, there are some challenges connected with data quality, transparency of algorithms, data confidentiality, safety, and managerial acceptance that should also be taken into account. The conceptual framework proposed emphasizes a focus on effective cooperation of sophisticated AI technologies with rational judgments that assist decision-makers while staying accountable and trustworthy. The results imply that AI-enabled decision support systems can be essential for businesses striving to adopt digitalization, ensure sustainability, and maintain long-term competitiveness in the context of intelligent organizations. Further studies should investigate target industries, Explainable AI (XAI), ethical frameworks of governance, and the integration of new technologies such as Digital Twins, Generative AI, and autonomous decision-making systems.
Virendra Gomase, Suhas B. Dhande, P. Natu· International journal of com...· 0 citations
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