1. Introduction

The advent of digital transformation is profoundly reshaping the educational landscape, influencing the very approaches to pedagogical revolution [1]. This shift has led to the evolution of integrated learning methodologies through the establishment of cross-arranged collaborative networks [2], fundamentally altering the character of traditional learning environments [3]. A significant outcome of these advancements is the emergence of new opportunities for personalized learning. These opportunities are characterized by an effective focus on collaborative foundations and streamlined access to essential information resources [4]. To fully capitalize on these possibilities, there is a pressing need to design comprehensive models capable of addressing the current challenges hindering progress within the educational innovation ecosystem [5].

Digital transformation empowers educational institutions to enhance their capabilities [6] by facilitating the advancement of hybrid and remote teaching methods, enabling the application of data analytics, and fostering borderless collaboration [7]. In essence, virtual classrooms and smart platforms are creating a seamless learning experience that effectively merges face-to-face and distance learning through scalable solutions [8]. This integration ultimately provides valuable benefits for both students and teachers [9]. Despite these significant developments, traditional educational sectors continue to grapple with considerable challenges [10]. A substantial number of learners face difficulties in accessing quality education due to a combination of socioeconomic factors, geographical limitations, and infrastructure constraints [11]. Furthermore, rigid classroom settings often restrict opportunities for individualized training or pragmatic collaborative engagement [12]. Businesses, too, encounter difficulties in leveraging these technologies to support resource sharing and facilitate engaging, interactive activities [13,14]. To address these multifaceted needs, a proposed architecture, VR–DRL, offers a solution by merging real-time interactions with virtual classrooms and digital resource sharing [15]. This framework empowers professors and students to engage with alternative 3D-enabled learning environments [16], which include interactive sessions, personalized content delivery, and effective communication [17].” IT driven changes enables business network-based value creation to become feasible and valuable business model” [18].

2. Research Problem

Conventional educational practices are not without their significant limitations, presenting a range of challenges that impede effective learning and instruction. These constraints are multifaceted, encompassing issues such as the scarcity of high-quality learning materials, which can directly impact the depth and breadth of student understanding. Furthermore, traditional learning environments can sometimes be characterized by rigid and even hostile settings, potentially stifling creativity, engagement, and individual student needs. Perhaps most critically, education faces complicated logistical problems that arise from physical or geographical boundaries, limiting access for many learners and creating disparities in educational opportunities. Addressing these inherent issues necessitates the development of a scalable and dynamic architectural approach. Such an approach is crucial not only for overcoming existing barriers but also for potentially modifying instructional methods in ways that can significantly enhance overall student performance and learning outcomes.

Related work

Digital transformation models are crucial for universities seeking to enhance flexibility and competitiveness. These models integrate institutional strategies with global trends, current technologies, and societal demands, offering tools for informed decision-making. They help educational institutions embrace digital transformation, improve student experiences, and foster ethical and environmentally responsible conduct. Solutions range from leveraging artificial intelligence for library needs to managing long-term digital innovation. Ultimately, these frameworks enable universities to adapt to the digital age, ensuring they remain relevant and effective.

Evolutionary Digital Strategy Model (EDSM)

This approach proposes a qualitative model where digital transformation is the primary catalyst for universities to gain a competitive advantage. It integrates principles of strategic management with evolutionary learning systems to navigate significant shifts in the higher education sector. The model serves as a decision support system, helping universities adapt to the rapidly changing global educational landscape shaped by digital transformation and other global events. Its aim is to empower institutions to enhance and manage student experiences, expectations, and strategic initiatives effectively.

Sustainable Digital Transformation Blueprint (SDTB)

This research employs a unique qualitative grounded theory approach to investigate the impact of sustainable digital transformation on universities. It utilizes open, axial, and selective coding techniques to thoroughly analyze and demonstrate the connection between sustainable digital transformation and higher education. The ultimate aim is to create a conceptual model that assists the education sector in seamlessly blending sustainability, green technology, and actionable strategies. This approach provides universities with a clear roadmap for planning, expanding, and implementing digital transformation effectively, all while preventing resource burnout.

Systematic Digital Transformation Framework (SDTF)

The proposed approach examines digital transformation through a thematic analysis of frameworks that define and guide effective transformation within learning organizations, emphasizing the need for a methodical, strategic approach [20]. It advocates for a transformative mindset that extends beyond isolated initiatives, urging education leaders and policymakers to leverage technology as an enabler for navigating challenging situations. This approach highlights the influence of economic, political, social, and technical trends on decision-making in both primary and secondary education systems.

Digital Transformation and Divides Framework (DTDF)

This approach examines how COVID-19 influenced the digital transition in higher education, with a specific focus on its effects on the foundational education of younger generations [21]. Through comprehensive research, it investigates emerging digital divides, identifies obstacles to change, and analyzes how educational institutions are adapting to a fully computerized environment. The method advocates for further research to refine information management, bolster students' digital skills, and ensure universities are well-matched with a digitalized future and the demands of the contemporary workforce.

eLearning Ethical Framework for Digital Transformation (EFDT)

This approach outlines a reference structure for integrating eLearning strategies into traditional face-to-face higher education institutions [22]. It prioritizes the acceptance and control of systems and procedures to ensure the ethical use of data in academic and learning analytics. The approach emphasizes selecting technologies that promote a more inclusive, participatory, and human-centered university environment.

Digital Transformation for Smart Innovation Systems (DTSIS)

This methodology investigates how digital transformation influences innovation policies in cities and regions, emphasizing place-based, bottom-up strategies [23]. It introduces cyber-physical systems of invention, which leverage digital companions and foster collaboration across physical, social, and digital environments. The approach guides decision-making by integrating software-driven insights with advanced data analytics. A case study of the OnlineS3 project illustrates how digital tools can operationalize and enhance smart specialization methods, improving scalability and providing dynamic, diverse information to better respond to the complexities of innovation systems.

Artificial Intelligence Library Services Framework (AI-LSF)

This approach introduces the Artificial Intelligence Library Services Innovative Conceptual Framework (AI-LSICF), designed to explore how creative, alternative learning opportunities can reshape university libraries [24]. Through a qualitative content analysis of existing literature, the paper investigates how adopting artificial intelligence can drive digital transformation and service innovation within libraries. The framework encourages library professionals to leverage AI for more effective service delivery, boosting competitiveness and helping them adapt to current educational demands by integrating.

Digital University Readiness Framework (DURF)

This approach evaluates the digital transformation preparedness of higher education institutions through a quantitative survey-based study conducted at Hanoi University of Science and Technology, Vietnam [25]. The research focuses on four key dimensions of a digital university model: education programs, learners, training services, and governance. This study introduces a readiness framework with criteria for assessing the availability of digital transformation processes. It aims to serve as a reference for other institutions as they embark on their own digital transformation journeys within the context of Industry 4.0.

Table 1. The Comparison of Exiting Methods

Table 1. Proposed models offer strategic guidance for implementing digital transformation in education. These systems are built upon four core pillars: creativity, inclusivity, sustainability, and preparedness. They encompass adaptive solutions like ethical eLearning, evolutionary learning, AI-driven services, and readiness evaluations to foster institutional growth. Ultimately, these strategies aim to help schools manage online student experiences, encourage innovation, and bridge digital divides within the dynamic and unpredictable educational landscape.

3. Methodologies Employed

Integrating technology into the classroom significantly enhances learning opportunities, engagement, and student achievement. Key advancements driving this educational progress include virtual and augmented reality, data analytic, and personalized learning environments. These technologies are instrumental in creating dynamic learning environments, increasing accessibility, and enabling effective teaching. Ultimately, this fosters global innovation and leads to improved outcomes for students.

Contribution 1: Innovative VR-DRL Framework for Education

The VR-DRL framework combines virtual reality environments with real-time collaboration and resource-sharing. This technology creates flexible, engaging, and immersive learning opportunities for both educators and students.

Figure 1. Architecture of Digital Learning Framework

Figure 1 illustrates how the intentional integration of technological advancements drives educational progress. A robust digital infrastructure is paramount, serving as the foundation for leaders to develop and execute their strategies. Learning systems and smart platforms are designed to broaden educational access through resource-sharing centers and innovative teaching strategies.

Current creative projects include virtual and augmented reality, along with personalized education, all enabled by technological developments. Data analytics provides continuous evaluation and feedback, constantly improving planning. Furthermore, specific implementation approaches for teacher and student development guarantee successful task accomplishment. The results of this paradigm are already evident in today's educational environments, manifesting as increased engagement, digital literacy, and a cooperative society, outcomes that have been steadily evolving.

(1)

Using dynamic weights and interaction parameters to optimize the sharing of resources and pupil input for enhanced personalized learning. Aligning the internet with various student demands is made possible by scalability and adaptability for equation 1.

(2)

In the VR-DRL framework, the equation 2 represents the dynamic mapping of VR resources to metrics for student interaction and dispersed resources This guarantees a virtual classroom that is both optimized and responsive, meeting the requirements of both individuals and groups to get better results.

(3)

Inside the VR-DRL framework, the equation 3 displays the adaptive scalability of user participation and work interaction It simulates fluid personalizing and knowledge synchronize processes that adapt to changes in user requirements and allocation of resources. This equation guarantees that the system is flexible and optimized with the user in mind.

(4)

This VR-DRL equation 4 represents the control of feedback-driven actions in the system. It takes into consideration network latency and connects energy weighting with virtual involvement scaling This optimizes resources and guarantees smooth cooperation, which in turn decreases wait times and improves the quality of online and distant learning.

Figure 2. Key Drivers of Learning in VR Environments

Figure 2 illustrates the dynamic interplay between learner factors and technological aspects that ultimately shape learning outcomes in virtual reality (VR). Key technological elements, such as VR time and space and technical availability, directly influence learner characteristics like comprehension, imagination, and interaction. These individual learner qualities are interconnected and collectively contribute to the overall learning effect. The figure underscores the importance of both robust technological resources and active student engagement to optimize educational experiences within VR environments.

(5)

In the VR-DRL framework, the equation displays the distribution of adaptive resources according to the interaction quality It stimulates the equilibrium between knowledge synchronization and the system's responsiveness taking into account the extra requirements for resources The framework will adapt to user involvement and material delivery in real time, creating an inclusive and unified digital learning environment.

(6)

The cycle of feedback for resources, changes in the VR-DRL system is shown by the equation. It links the processes of resource allocation and workload planning all the while taking into consideration system restrictions . Efficiency and flexibility in user-centric learning are ensured by the framework's capacity to constantly improve tactics for engagement and allocation of resources.

(7)

In the VR-DRL model, the equation represents the essential dynamics of the allocation of resources and participation. The equilibrium between knowledge usage and input from users scaling in light of system restrictions Allocating resources effectively, engaging students adaptively, and minimizing inefficiencies all contribute to a smooth and inclusive learning environment.

(8)

The feedback-driven processes in the VR-DRL framework are represented by equation 8which their combined variation Improving learning efficiency is achieved by coordinating resource selection and adaptive use of electricity Efficient and long-lasting virtual learning environments are made possible by the framework's ability to change feedback cycles and resource allocation dynamically.

Contribution 2: Enhanced Student Engagement and Performance

The VR-DRL framework significantly improves learning outcomes and fosters active engagement by incorporating interactive elements and customized learning pathways. Empirical data show notable increases in student participation, performance, and satisfaction when compared to traditional remote learning environments.

Figure 3. Key Pillars of Data-Driven Educational Success

Figure 3 outlines six core elements for enhancing education through cooperative approaches and data analytics. It highlights the optimal outcomes achievable from consistent data analysis, vigilant student performance tracking, and ample group learning opportunities. A future-focused curriculum is emphasized for fostering innovation, while stronger teacher-parent connections are shown to boost engagement. The potential for time savings is also presented, streamlining administrative and teaching procedures and paving the way for comprehensive educational development. This all-encompassing structure, ideally suited for modern classrooms, demonstrates how each component contributes to increased teacher efficiency and improved student performance.

(9)

The VR-DRL framework's scalability of the virtual resources and allocation of workloads are represented by the equation 9. This model illustrates the process of optimizing knowledge syncing and interaction between users' metrics to match resource demands. As a result, virtual classrooms can better accommodate a wide range of student demands, leading to tailored educational experiences.

(10)

Equation 10 explains how the VR-DRL system changes its resource allocation in response to user input and interactions. To enhance the user experience , it represents the balance between system synchronicity and dynamic burden adjustments This allows for an adaptable and responsive online classroom by constantly improving the management of resources and user engagement.

(11)

The VR-DRL framework's cycle of feedback and adaptive education parameters are optimized using the given equation. It simulates the synchronization of system stimulus adjustments and dynamic information integration to enhance the results of virtual learning. That way, learning processes may be fine-tuned indefinitely, making them more responsive and personalized in equation 11.

(12)

The VR-DRL framework's cycle of feedback and learning to adapt parameters are optimized using the given equation 12. It stimulates the synchronization of system stimulus adjustments and dynamic knowledge translation. This makes sure that learning tactics are being fine-tuned all the time, which makes the digital environment more responsive and personalized.

Figure 4. Process of Digital Transformation in Education with dynamic remote learning

Figure 4 presents a comprehensive strategy for successful digital transformation, emphasizing key interconnected elements. It begins with data analytics-driven investments across three crucial areas: technology, human development, and process improvements. These combined initiatives are designed to enhance the student experience by fostering innovation, streamlining operations, and generating new revenue streams. For organizations to thrive in today's digital economy, they must effectively leverage these linked components. Ultimately, collaborative efforts are essential in establishing a robust, statistically informed organizational framework.

(13)

The VR-DRL framework may be represented by equation 13, which describes the adaptation of input from users and project interactions It shows how real-time learning experiences are optimized for distributing resources and dynamic involvement by users With this, leads to more adaptability and longer periods of engagement in the online classroom.

(14)

The equation 14 represents the way online learning factors are changed in response to input and the allocation of resources. Taking constraints into account, it associates modifications to weight with the relationship between systems This permits the adaptability necessary for a responsive and adaptable virtual learning environment, maximizes user engagement, and guarantees effective use of resources.

(15)

To guarantee the effective allocation of resources and adaptive user involvement in the VR-DRL framework, the optimization for system feedback is represented by equation 15, It explains the control of system restrictions and the balancing of interaction grading and dynamic customer reaction scaling As a result, the system's performance and user engagement are both enhanced by an adaptive learning environment.

(16)

Equation 16, illustrates the VR-DRL framework's system feedback and resource allocation interact with It takes into consideration the limitations of the system and models the connection between user participation and changes to operation This allows for responsive and individualized learning in virtual environments by distributing resources efficiently and facilitating dynamic user interactions.

Contribution 3: Scalable and Inclusive Learning Solutions

This proposed framework aims to boost educational accessibility by reducing infrastructural and geographical barriers, thereby reaching students in diverse environments. It champions inclusive education by ensuring equitable access to high-quality learning resources and collaborative opportunities.

Figure 5. Proposed method of VR-DRL framework

Figure 5 illustrates a sophisticated educational ecosystem that integrates virtual reality (VR) to deliver tailored learning experiences. The input layer collects various elements, including digital content, VR tools, Learning Management Systems (LMS), and user devices, all geared towards initiating engagement. The processing layer facilitates interactive learning through data-driven learning insights, dynamic material distribution, and virtual classroom environments. The output layer delivers key results such as increased student interaction and customized learning paths. A continuous feedback system ensures ongoing refinement through evaluations and user comments. Furthermore, supporting elements like cloud storage and teacher training enhance both scalability and efficiency within this VR-powered learning environment

(17)

In the VR-DRL architecture, the feedback-driven interface is modeled by the equation 17, with an emphasis on the responsiveness of the system. This stands for how dynamic learning processes are aided by integrating knowledge scaling of user interaction and adaptations to workload This makes sure the system can adjust to user inputs that the resource needs, which improves engagement and makes sure the learning results are optimized.

(18)

Equation 18 represents the VR-DRL framework's description of the relationship between system modifications and continuous feedback To maximize user engagement, it demonstrates the balance between system synchronized and knowledge absorption By learning environment is both responsive and adaptable, with well-managed resources and user-specific experiences.

(19)

Within the VR-DRL paradigm, equation 19 represents the relationship between user engagement and resource management, denoted as . To guarantee seamless operation and customized learning experiences, it reflects the optimization of the allocation of resources and feedback from learners . The technology is designed to react to the requirements and input of learners via this dynamic interaction, making virtual classrooms more flexible and engaging.

(20)

Within the VR-DRL system, equation 20 depicts the dynamic response and allocation of resources. To improve engagement, it records the optimal interaction among users system adaptations and more education factors By guaranteeing adaptable reactions to user behavior, paves the way for tailored learning experiences. Technological innovations like virtual reality (VR), data analytics, and dynamic learning systems are transforming education by significantly boosting student engagement and performance. These solutions promote scalability, creativity, and teamwork, leading to more inclusive, accessible, and efficient learning environments. Furthermore, data-driven agile approaches foster continuous development, which in turn enhances operational effectiveness and improves learning opportunities for both educators and students alike.

4. Result and Discussion

The digital revolution in education is shattering traditional learning boundaries through innovative platforms and creative concepts. The integration of virtual reality (VR) environments with the VR-DRL architecture significantly enhances access, interaction, and collaboration. This comprehensive strategy directly addresses the challenges of resource sharing, geographical limitations, and rigid educational paradigms by fostering highly interactive and flexible learning environments.

Table 2. Simulation Environment

Analysis of Student Performance

Figure 6. Analysis of student performance

The VR-DRL framework demonstrably and positively impacts student performance, boasting an impressive 94.33% improvement rate as shown in Figure 6. This significant increase can be attributed, in part, to the framework's interactive virtual worlds and tailored learning experiences that cater to diverse student needs. By leveraging real-time feedback, readily available resources, and collaborative technologies, students can more effectively grasp complex concepts, leading to deeper understanding and enhanced academic achievement.

(21)

The VR-DRL framework's an ongoing process of learning adaption is represented by the equation 21. The sentence explains how the system responds to changes in the allocation of resources and interaction with users to improve the performance of learning. This guarantees that the system may change to meet the demands of its users and make the analysis of student performance.

Analysis of student Engagement

Figure 7. Analysis of student Engagement

The adoption of the VR-DRL architecture has led to a remarkable 92.78% increase in student participation, as depicted in Figure 7. This surge can be attributed to the architecture's immersive virtual worlds, which captivate students, and its real-time group projects, which actively encourage involvement. Furthermore, dynamic content distribution and gamified components enhance students' desire to remain engaged, ultimately boosting their enjoyment and the overall efficiency of their learning environment.

(22)

The equation 22 depicts the ever-changing relationship between the VR-DRL framework's handling of resources and user engagement. The sentence explains the process of optimizing system adaptations and the input of users to improve learning experiences. Efficient allocation of resources is made possible by the creation of a customized and adaptable by the analysis of student engagement.

Analysis of Accessibility

Figure 8. Analysis of Accessibility

The VR-DRL framework significantly boosts accessibility by an impressive 97.25%, as shown in Figure 8. It effectively breaks down both geographical and infrastructural barriers, ensuring learners everywhere have fair access to premium educational materials. By creating immersive virtual reality environments, this framework guarantees that learning opportunities are available to everyone, regardless of their location or socioeconomic status. This eliminates the need for physical classrooms and allows seamless participation for remote students.

(23)

The VR-DRL framework's relationship between user involvement and resource allocation is modeled by the equation 23. This demonstrates how the learning experience is optimized by the balance of system modifications and user feedback. This keeps the system agile and adaptable to user actions, which creates a more engaging by the analysis of accessibility.

Analysis of resource sharing

Figure 9. Analysis of resource sharing

The architecture significantly boosts resource sharing with a notable 91.89% improvement, as shown in Figure 9. This enables both instructors and students to easily access, share, and use digital educational materials. Virtual libraries and cloud-based storage facilitate efficient knowledge sharing and collaborative learning by providing prompt access to key resources and enhancing cooperation.

(24)

The VR-DRL architecture incorporates user interaction and dynamic resource management, as shown by the equation 24. It demonstrates the coordination of system replies and user involvement optimize the allocation of resources for customized learning. This equation guarantees an adaptable system that changes according to student input and resource requirements, by the analysis of resource sharing.

Analysis of flexibility

Figure 10. Analysis of flexibility

The VR-DRL system significantly enhances learning flexibility by 95.67%, as illustrated in Figure 10. This allows students to choose from various learning rates, schedules, and preferences. They can study course materials at their own pace, personalize their educational experiences to suit their individual needs, and utilize on-demand learning sessions. This adaptability promotes a more customized approach to instruction while maintaining high levels of engagement and performance.

(25)

The VR-DRL system incorporates user involvement and dynamic resource allocation , as seen in the equation 25. It stimulates the equilibrium between system changes and user feedback to maximize achievements in learning. In this way, the VR-DRL solution will continue to evolve in response to user actions, allowing us to create a more tailored by the analysis of flexibility.

This paper introduces the VR-DRL framework as a tool for modernizing education and thereby enhancing student performance, participation, and accessibility. Emphasizing interactive virtual environments and real-time cooperation, the design increases opportunities for flexible learning and resource sharing. Empirical results confirm the effectiveness of the concept in substituting inclusive learning environments with modern teaching approaches.

Table 3. Comparison of existing method and proposed method

5. Conclusions

The research underscores the transformational potential of digital technologies in education, particularly through the VR-DRL paradigm. By integrating virtual reality environments with dynamic resource-sharing technologies and real-time collaboration tools, VR-DRL effectively addresses key challenges in both traditional and remote learning systems.

Analysis reveals remarkable improvements: student performance increased by 94.33%, engagement by 92.78%, accessibility by 97.25%, resource sharing by 91.89%, and learning flexibility by 95.67%. Students gain from interactive and immersive learning opportunities that boost knowledge and motivation, while instructors benefit from scalable tools for effective education. This framework ensures inclusive learning by providing equitable access to high-quality education for students everywhere, overcoming institutional and spatial constraints.

These results emphasize the critical need to integrate technology-driven solutions with instructional strategies to create dynamic learning environments. The VR-DRL framework, by fostering uniqueness, collaboration, and accessibility, serves as a fundamental step in reimagining educational methodologies. This approach not only modernizes education but also offers a more engaging and efficient learning experience for every student.

ORCID: 0009-0009-7677-273X

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data Availability

The data used to support the findings of this study will be made available online on request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Ethical Statement

The authors ’research type did not suite the criteria to produce ethical statement.