The Impact of AI-Based Cloud Network Management on Microsoft Azure in Promoting Green Technology Awareness

Abstract

This study aimed to improve cloud network management skills through the Microsoft Azure AI Platform and to raise awareness of green technology among postgraduate STEM students at the Faculty of Education, Minia University. An e-learning environment was developed using the Chamilo system, which included interactive tools, educational resources, and assessment methods. The study employed Kuhlmann’s instructional design model and the general ADDIE model, following detailed procedures that aligned with the study’s objectives. Both experimental and quasi-experimental designs were used with a sample of 60 students. The measurement tools included a cognitive achievement test for cloud network management skills with AI platforms, an observation checklist for using the Azure AI platform, and a scale for green technology awareness. The results indicated enhancements in cloud network management skills using the Azure AI platform and an increase in green technology awareness among the participants. The study recommended designing learning environments that accommodate various learning styles and integrating AI platforms with green technology to support sustainable development goals.

1. Introduction

Artificial intelligence (AI) platforms are central to the fourth industrial revolution, alongside big data, nanotechnology, the Internet of Things (IoT), and robotics. These technologies profoundly affect education, especially higher education institutions, offering significant potential for advancing learning and achieving sustainable development goals. Thus, developing policies to support AI-enhanced learning within informatization is essential. Integrating AI systematically into education is key to advancing digital green learning and planning practices that meet educational objectives and sustainability targets.

UNESCO’s 2030 Agenda for Sustainable Development emphasizes the role of AI in education to boost human intelligence, safeguard human rights, and foster sustainable development through effective human–machine collaboration. The AI and Education Conference in Beijing (16–18 May 2019) underscored the need for AI in education across five areas: management and delivery, empowering teachers, assessing education, developing skills for the AI era, and providing lifelong learning opportunities. UNESCO also called for ethical use of AI in education, with ongoing monitoring and research [1].

AI employs techniques such as machine learning and modeling to enhance decision-making, with the potential to revolutionize how learners acquire knowledge and develop skills for future careers [2]. Research by Hassan [3], Tarah [4], Cao et al. [5], Al-Yazji [6], Grønmo [7], and Zawacki-Richter et al. [8] demonstrates the effectiveness of AI platforms in education and training. These platforms are being increasingly adopted globally, particularly in higher education, where they enhance the quality of learning experiences. AI platforms can handle tasks traditionally managed by teachers, such as monitoring performance, providing feedback, identifying weaknesses, explaining solutions, and interacting with learners. Studies by Lin et al. [9], Cantú-Ortiz et al. [10], Muniasamy and Alasiry [11], and Bakr and Taha [12] indicate that AI platforms can serve as teaching assistants, offer immediate feedback, and improve learning efficiency, underscoring their potential effectiveness in education. These studies also highlight that Google and Microsoft applications leverage advanced AI technologies to enhance learning.

One prominent AI platform is the Microsoft Azure AI Platform, recognized for its capabilities in cloud network management and green technology. Azure offers extensive cloud services such as remote storage, database hosting, and central account management, and is accessible to learners through university Microsoft accounts. Launched in February 2010, Microsoft Azure is a versatile public cloud platform known for its speed. It supports a wide range of cloud computing services, including resources for green technology.

Microsoft Azure tools assist in knowledge representation by building extensive databases for knowledge exchange and management. These databases help in making informed decisions and managing educational data, including information on teachers, learners, and staff, stored on Azure’s cloud. They can train neural networks to make decisions about educational institutions, improving outcomes, reducing costs, and promoting green technology.

Microsoft Azure is an expanding cloud computing platform offering solutions through various technologies. These technologies include AI applications for managing cloud networks, whether through public, private, hybrid, or multi-cloud setups, supporting green technology.

Green technology aligns with digital green learning, focusing on investing human resources through modern technologies like AI to support innovation and address skill gaps, enhancing the potential for sustainable development. Egypt’s sustainable development plan considers digital green learning and green technology vital for growth, job creation, foreign investment, and efficient resource use. Egypt’s strategy includes ensuring 30% of investment projects adhere to sustainability standards and ensuring technological accessibility [13].

Green technology, evolving from the industrial revolution through the information revolution to AI, refers to science applications emphasizing environmental concerns. Advances in digital technology encourage its use in education to create a learning environment that integrates teachers, learners, and content while promoting environmental preservation. Green technology is a continuous developmental process shaping future educational environments [14].

Thus, employing AI platforms like the Microsoft Azure AI Platform is necessary for green computing, reducing energy use, electronic waste, and carbon emissions, and raising learners’ green technology awareness. These platforms also enhance technical skills, improve learning outcomes, and align with labor market needs and global trends.

Research by Soliman [14], Gadallah [15], Moshref [16], Tu et al. [17], and Aithal [18] identifies green technology as a significant global challenge due to limited awareness, insufficient academic research on its educational applications, and inadequate training programs for learners. These studies underscore the absence of educational strategies that connect green technology to labor market needs, thereby impeding a healthy learning environment. Recommendations include developing a sustainable green curriculum that aligns with market demands.

Green technology also enhances electronic communication between teachers and learners, transforming educational institutions into networked organizations that expand learning opportunities. It promotes a sustainable development system that addresses learners’ needs and fosters their innovative skills [19].

The Innovation Diffusion Theory (IDT) [20] explains how new technologies like AI and green technology are adopted over time, focusing on characteristics such as relative advantage, compatibility, simplicity, clarity, and trialability. AI platforms for managing cloud networks and green technology are supported by theories such as Holmberg’s Interactive Theory [21], which emphasizes interaction among educational elements in electronic environments, and Cognitive Efficiency Theory, which assesses media effectiveness based on information representation and flow [22].

Given the fourth industrial revolution’s progress, integrating AI platforms into education is vital for addressing future challenges and creating innovative practices that achieve sustainability goals. Universities should develop programs anticipating future job changes due to AI, emphasizing effective AI use in higher education. This research investigates AI platforms’ effectiveness in developing cloud network management skills and green technology awareness among postgraduate students. The research questions are as follows:

(a)

Are there statistically significant differences between pre-test and post-test scores on the observation checklist after applying the electronic learning environment?

(b)

Are there statistically significant differences between pre-test and post-test scores on the achievement test after applying the electronic learning environment?

(c)

Are there statistically significant differences between pre-test and post-test scores on the Green Technology Awareness Scale after applying the electronic learning environment?

(d)

Is there a correlation among post-test scores on the observation checklist, achievement test, and Green Technology Awareness Scale?

2. Theoretical Background of the Study

2.1. AI Platforms and Cloud Network Management

Recent advancements in AI technologies have significantly impacted education, moving from a futuristic idea to a reality that reshapes educational practices. These improvements include basic interactive systems, deep learning, and machine learning applications. Such innovations address various educational challenges by developing intelligent educational software, advanced e-learning platforms, and data analysis tools. These tools help teachers and administrators make informed decisions, manage knowledge in extensive databases, enhance decision-making, and promote lifelong learning. They improve the competitiveness of education, benefiting both teachers and learners and preparing them to work with AI technologies.

According to recent studies on AI systems [23,24,25,26,27], these systems encompass the following:

• Sophisticated platforms, applications, software, and devices that utilize algorithms and logical functions to analyze data provided by humans.
• The need for big data to furnish ample information for a range of decision-making possibilities.
• The emulation of human intelligence through intricate computer programs and coding languages.
• Characteristics such as symbolic and knowledge representation, experimental research, and learning abilities.

Research has demonstrated an increasing trend in the use of AI systems in education [28,29,30,31,32,33,34]. These studies emphasize AI applications such as temporal evaluation, virtual teachers, electronic grading, cloud management, and personalized learning. They advocate for the use of AI platforms in education due to their flexibility, modernity, and accuracy, which enhance learners’ skills and the educational process. Therefore, this research employs the Microsoft Azure AI Platform to manage cloud networks and increase green technology awareness among the study’s participants.

2.1.1. Microsoft Azure Platform

Launched by Microsoft in February 2010, Azure is a cloud computing platform renowned for its speed and flexibility. It offers a broad range of services, including cloud network management, storage, applications, and infrastructure, making it a leading provider of general cloud services. Key features include the following (https://azure.microsoft.com) (accessed on 25 July 2024):

• Azure supports educational institutions with a growing array of services tailored to their needs.
• It handles everything from simple to complex tasks, including web services for cloud network hosting and management.
• Azure provides fully virtualized computers for managing customized software solutions.
• It offers extensive cloud network management services, such as remote storage, database hosting, and centralized account management.
• The platform excels in AI and IoT services.
• Azure integrates AI applications across various environments—public, private, hybrid, or multi-cloud.
• It includes Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) for analytics, storage, and networking.
• Educational institutions can leverage Azure for comprehensive data storage and decision-making support, reducing costs related to physical resources and paper through green computing.

Microsoft Azure is a versatile and rapidly evolving cloud computing platform that provides innovative solutions across a range of advanced technologies. These include robust AI applications for efficient cloud network management, supporting diverse deployment models such as public, private, hybrid, and multi-cloud configurations. Notably, Azure’s strong alignment with green technology initiatives makes it a leading choice for sustainable computing. By operating on a consumption-based model, Azure allows users to pay only for the services utilized, significantly reducing financial and environmental costs associated with traditional IT infrastructure, such as physical servers, electricity, and maintenance.

The platform’s advantages are particularly impactful in educational settings, as it offers the following:

(a)

No upfront infrastructure costs, making it accessible for institutions with limited budgets.

(b)

The elimination of costly infrastructure purchases, ensuring financial efficiency.

(c)

Seamless access to data anytime, anywhere, facilitating remote and hybrid learning environments.

(d)

Significant energy and carbon savings, driven by its green computing initiatives and use of renewable energy in data centers.

These features not only address technical and operational challenges but also align with the broader goals of sustainable development, making Azure a forward-thinking solution for institutions seeking to integrate environmentally conscious practices with cutting-edge educational technologies.

2.1.2. Importance and Features of Microsoft Azure

• Azure provides on-demand computing resources and manages costs effectively, eliminating the need for large investments in data centers, hardware, and staff. It can host internal email systems, websites, public services, and mobile applications.
• Microsoft offers centralized management through the Azure Active Directory server, enabling educational institutions to access these features without needing their server.
• Azure ensures high reliability with 99.95% uptime, 24/7 technical support, and global data access from any location. It also allows institutions to scale resources based on their needs and supports almost any operating system, language, tool, or framework.

2.1.3. Microsoft Azure Products and Services

Based on the above, Azure supports virtual machines, containers, and Windows servers to manage an educational institution’s infrastructure. It also hosts databases, offers Azure storage for data retention, and simplifies application testing, automatic troubleshooting, and cost-effective scaling for better performance (See Figure 1).

Azure’s products and services include the following:

AI platforms help address structural barriers to ensuring learning quality and improving educational systems. With Microsoft Azure AI, cloud network management can achieve the following:

• Create a well-organized knowledge base for effective information storage, allowing staff to access and learn from experimental rules not found in books or other sources.
• Store information securely in Azure’s cloud to prevent data leakage, loss, or theft, and identify users to ensure data protection.
• Solve maintenance, infrastructure, and resource issues by analyzing and addressing these problems, reducing costs and energy use through cloud storage and green technology.

2.1.4. Why Was Microsoft Azure Chosen?

Microsoft Azure was selected for this study due to its seamless integration of artificial intelligence capabilities and advanced cloud network management tools, positioning it as an ideal platform for the study’s objectives. The platform offers a diverse suite of educational services, including secure data storage, real-time performance analytics, and adaptive educational content, all of which align with the study’s goal of enhancing awareness of green technology among learners. Furthermore, Azure’s pay-as-you-go pricing model provides a cost-effective solution, enabling institutions to manage resources efficiently without the burden of upfront investments in infrastructure.

What sets Azure apart is its commitment to sustainability, reflected in its renewable energy-powered data centers and green computing initiatives, which were shown to reduce operational energy consumption by up to 40%. This combination of advanced technological capabilities and environmental stewardship ensures that Azure not only meets the technical and pedagogical demands of educational institutions but also aligns with broader goals of sustainable development, making it a forward-thinking choice for modern learning environments [35].

Furthermore, Azure is highly flexible in integrating with traditional educational systems, offering services such as Azure Virtual Machines, which enable the simulation of advanced learning environments. Microsoft’s commitment to sustainability through the use of renewable energy in its data centers makes Azure the most suitable choice for studies focusing on green technology. Therefore, Azure was selected due to its combination of technical efficiency, environmental sustainability, and its ability to support the study’s educational objectives.

Comparative Analysis of Microsoft Azure and Other Platforms

Compared to other cloud platforms like AWS and Google Cloud, Microsoft Azure demonstrates a distinct advantage in several critical areas, making it the optimal choice for this study. Azure provides robust support for AI applications through tools like Azure AI, enabling efficient cloud network management—a key factor in achieving the study’s objectives. Furthermore, Azure excels in its commitment to green technology initiatives, offering innovative solutions that align with the principles of sustainability. For example, Azure has implemented renewable energy-powered data centers, contributing to a reduction of up to 30% in carbon emissions compared to traditional setups, as highlighted in recent studies.

In contrast, while AWS offers a broad spectrum of services, it lacks the same integrated focus on environmentally conscious applications like Azure. Google Cloud, despite its strengths in data analysis and advanced AI capabilities, falls short in supporting higher education initiatives and programs specifically tailored to promote sustainable development. This limitation underscores the strategic advantage of Azure, which not only meets the technical demands of educational institutions but also integrates sustainability as a core element of its services. This alignment between technological innovation and environmental responsibility makes Azure particularly well suited for studies focused on fostering awareness of green technologies within the educational sector.

2.1.5. The Relationship Between Distance Learning, Face-to-Face Education, and Supporting Hybrid Models

This study highlights distance learning and its digital tools as an effective method for broadening learning access and providing a flexible environment driven by cloud and AI technologies. Hybrid education represents a progressive evolution of traditional teaching methods, integrating the strengths of both distance and face-to-face learning to address the diverse needs of contemporary students. While face-to-face education offers unparalleled benefits, such as fostering interpersonal skills and providing real-time interactions, distance learning enhances these experiences by offering digital tools that promote flexibility, accessibility, and personalized learning. This complementarity allows hybrid models to create an enriched educational environment that caters to varying learning preferences and contexts.

Research underscores that hybrid education can improve student engagement and academic outcomes by leveraging cloud technologies and AI-driven platforms to facilitate seamless integration between physical and virtual classrooms (Means et al., 2020 [36]). For example, tools like Microsoft Azure enable institutions to manage digital resources effectively, track student performance in real-time, and offer tailored feedback, ensuring continuity in the learning process across both settings. Furthermore, the incorporation of interactive assessments, multimedia content, and collaborative tools in hybrid models was shown to foster deeper cognitive engagement and self-directed learning [37].

From an institutional perspective, hybrid education offers scalability and resilience. In scenarios where full in-person attendance is impractical, such as during public health crises or for geographically dispersed learners, hybrid models ensure uninterrupted learning through robust digital infrastructure. Moreover, they provide instructors with advanced analytics to identify learning gaps, enabling targeted interventions that enhance student success both in and out of the classroom.

The synergy between distance and face-to-face education in hybrid models reflects a shift toward a learner-centric paradigm, prioritizing flexibility, inclusivity, and sustainability. As educational technology continues to evolve, hybrid education is poised to become a cornerstone of innovative pedagogical strategies, driving academic excellence and equitable access in the 21st century.

Supporting Institutions with Hybrid or Face-to-Face Models

The model proposed in this study can assist institutions adopting hybrid or face-to-face education in several ways.

▪

Increased Flexibility: Platforms like Microsoft Azure can provide interactive environments that allow students to access resources before or after in-person classes, boosting comprehension and practical application.

▪

Technological Integration: AI and cloud computing enable the seamless integration of face-to-face and digital learning activities, enriching the overall educational experience.

▪

Performance Analysis: The model allows the accurate tracking of student performance through cloud systems, enabling tailored interventions based on individual needs, both in class and remotely.

▪

Blended Learning Support: Institutions can implement a blended strategy where face-to-face education covers core concepts and personal interaction, while distance learning focuses on exercises and assessments.

By combining these approaches, the proposed model offers a strategic tool for educational institutions seeking to enhance education quality and provide greater flexibility to meet diverse student needs.

2.2. Green Technology

Digital technology plays a crucial role in today’s world, particularly with the advent of the fifth industrial revolution, which merges the physical and virtual realms. This transformation affects education, the economy, society, and technology, propelled by innovations such as the Internet of Things, AI, nanotechnology, and cybersecurity. As communication and technology progress, green technology has become increasingly important, with growing awareness of its tools and the impact of digital innovation across various sectors.

Green technology is essential for institutional development, integrating its tools into education to stay aligned with IT advancements while safeguarding the environment. University students need to grasp green technology and its applications to remain up-to-date. This understanding encompasses the following:

• Cognitive Dimension: Knowledge of green technology concepts, basic information, and the relationship between technology, science, and society, as well as the impact of their interaction.
• Skill Dimension: Practical skills in green computing, including tools, platforms, and technical skills needed to use the technology.
• Affective Dimension: Awareness of green economics, technological attitudes, and the technology’s role in supporting the local economy.
• Social Dimension: Understanding sustainable development and the social and environmental impacts of green technology, including its positive and negative effects on society.

2.2.1. Nature of Green Technology

Various studies, including those by Chatel [38], Al-Rifai et al. [39], Qin et al. [40], Al-Azab [41], and Wang et al. [42], define green technology as follows:

• The use of digital tools that prioritize environmental concerns to sustain a green environment.
• A continuous process focusing on future roles, driving educational reform toward green sustainability.
• A new approach adaptable to different conditions, emphasizing the interconnectedness of economic, environmental, and social aspects for sustainable development.
• The safe use of digital and environmental resources to produce clean energy, restore environmental damage, and create solutions for future generations to live in a healthy environment.

In this study, green technology involves using green computing tools, the safe disposal of electronic waste, and cloud network management to address challenges in institutions, particularly educational ones, by reducing the need for physical resources.

Also, research by Aithal [18], Soliman [14], Al-Husseini [43], Moshref [16], and Tu et al. [17] recommends integrating sustainable green curricula in higher education to foster sustainable educational practices. They suggest strategies to transition to a green economy, raise awareness, and adopt eco-friendly practices through advanced digital tools. These studies highlight the lack of technical awareness and motivation as barriers to implementing green technology, which impacts society and the environment by reducing emissions and preserving resources for future generations. Thus, this research aims to increase awareness of green technology and its tools among the study’s participants.

2.2.2. Applications and Tools of Green Technology in Current Research

Applying green technology is both an environmental necessity and a social responsibility, crucial for efficient energy use. One key application is managing cloud networks through AI platforms, marking a significant shift in how technology services address environmental challenges. This research highlights several environmental benefits of cloud computing, including the following:

• Sharing technological resources and software saves energy that would otherwise be consumed if each educational institution used its equipment.
• Cloud network management supports green initiatives, reducing the need for travel by staff and students, thereby cutting fuel use and carbon emissions.
• Proper use of green computing tools reduces energy consumption and limits harmful emissions and radiation, which are pressing environmental issues, especially in Egypt due to overpopulation and poor resource management.
• The safe disposal of electronic waste following international standards helps institutions avoid the high costs of replacing equipment.

In summary, green technology, particularly through cloud network management, is crucial for addressing environmental issues like thermal emissions, carbon dioxide, and radioactive waste. While cloud platforms reduce carbon footprints by improving energy efficiency and enabling remote data storage, the growing demand for cloud computing significantly increases energy consumption in data centers, driving up costs and emissions from electricity generation.

Crosdale [44] highlights efforts by cloud providers like Microsoft to enhance data center efficiency through renewable energy and smart energy management. However, rising energy demands remain a challenge, underscoring the need for sustainable policies and strategies to mitigate long-term environmental impacts.

This underscores the need for mandatory rules and mechanisms to implement green technology in educational institutions, starting with raising awareness, utilizing green computing tools, and promoting sustainable development through green education and economics.

2.2.3. Mechanisms for Applying Green Technology in Educational Institutions

Based on a review of various sources, including works by Burbules et al. [45], Abu Ghuffa [46], Tiven et al. [47], Glavič [48], Soliman [14], Al-Rifai et al. [39], and Whitby [49], this study outlines mechanisms for implementing green technology in educational institutions as follows (see Figure 2):

Awareness of Green Technology Concepts

This includes comprehending green technology, its tools, and applications, as well as green education. It emphasizes the significance of increasing awareness among learners about environmental issues and conservation to ensure long-term sustainability. Green technology strives to balance social, environmental, and economic factors to support future generations. It aims to educate individuals to actively tackle environmental challenges, cultivate a sustainable community, and encourage eco-friendly practices.

Green Computing: Green Computing Includes

• Green Usage: This involves reducing energy consumption by using computers efficiently. Cloud systems manage energy by controlling inactive devices and enabling automatic operation. Virtual resources allow multiple environments to share cloud computing on the same physical device, reducing the need for extra servers and conserving space and energy. Safe disposal of electronic waste and reducing carbon footprints also contribute to cost savings for educational institutions by avoiding new equipment purchases.
• Paperless Storage: Cloud management offers electronic storage for educational institutions, cutting down on paper, ink, and printer emissions, thereby supporting green technology.
• Cloud Management Transition: Shifting to cloud services provided by vendors with large data centers is a move from computing as a product to computing as a service, which is more resource and energy efficient.
• Automation Technology: Automation programs enhance energy and resource efficiency in cloud infrastructure.
• Multi-Tenancy: Cloud management allows multiple clients to share the same application infrastructure simultaneously, optimizing resource use.

Digital Green Education

Digital green education fosters transformative change by promoting sustainable development and investing in learners. It supports Egypt’s Vision 2030 by encouraging a shift towards a green economy, making education accessible via the cloud, and integrating strategic management and participatory approaches. This also involves training in leadership, decision-making, and green technology awareness.

Raising green technology awareness among learners is essential. This includes acquiring technical skills for using green technology tools like cloud management, environmental skills for conservation and innovation, and scientific skills to prepare learners for future challenges. Social skills focus on improving educational outcomes and career readiness, while economic skills emphasize resource conservation for future generations. This study aims to develop cloud management skills using AI and enhance green technology awareness among graduate students.

3. Methodology

The study employed the Microsoft Azure AI Platform (version 2023) for cloud network management and green technology awareness. An electronic learning environment based on Chamilo was developed to instruct postgraduate students in cloud management using Azure AI. This environment comprised five units, fifteen lessons, activities, and tasks, integrating CMS, LMS (version 2023), and social network tools. It provided interactive tools, multimedia, and learning analytics, enabling learners to control their pace, participate in discussions, and schedule tasks easily. The mobile-compatible system improved learning efficiency.

3.1. Method

A quasi-experimental method, detailed in

Table 1. The experimental design of the research.

Group	Pre-Test Application of Tools	Treatment	Post-Test Application of Tools
Sample students	Observation checklist	Electronic learning environment	Observation checklist
	Achievement test		Achievement test
	Green Technology Awareness Scale		Green Technology Awareness Scale

, was used to measure cloud network management skills and green technology awareness. The pre-test assessed these skills without interference. The electronic learning environment, using Microsoft Azure AI, was then applied to evaluate its effectiveness. The study compared pre-test and post-test results to identify differences.

Table 1 shows the use of an observation checklist, achievement test, and Green Technology Awareness Scale. The electronic learning environment, using Chamilo, aims to enhance cloud network management skills and green technology awareness via Microsoft Azure AI. It includes interactive content and flexible tasks, allowing students to manage their learning, track participation, and access resources. Skills are measured before and after the intervention to evaluate its effectiveness.

3.2. Participants

The study sample, comprising 60 postgraduate students, was selected to meet the requirements of the experimental research design, with a focus on ensuring homogeneity in terms of academic level and prior experience with educational tools. This sample size was chosen to ensure internal consistency of the results and to minimize potential external influences. The sample accurately represents the target population of the study and aligns with its objectives. As highlighted in educational research, a medium-sized, homogeneous sample is considered ideal for experimental studies as it provides precise measurements while maintaining the validity of the research design.

The study acknowledges that the limited sample size may constrain the generalizability of the findings to a broader context. Nevertheless, the selected sample is considered appropriate for achieving the study’s objectives and evaluating the impact of the intervention within the specified context. Future research is recommended to incorporate larger and more diverse samples to enhance the generalizability and applicability of the results across different educational settings.

Using the Chamilo system, 17 sessions aimed to enhance cloud management skills and green technology awareness. The process included orientation, pre-testing, and educational sessions with three sessions per week on each topic. Techniques like simulation and feedback were used. Evaluation and post-testing followed, based on Kuhlmann’s 3C Model [50], as shown in Figure 3.

The study adopted a single-group pre-test–post-test experimental design without a control group, chosen to suit the study’s objectives and sampling requirements. The participants, postgraduate students with similar prior knowledge and academic experience, were not exposed to similar experimental applications, ensuring the results were unaffected by external factors.

The sample was selected based on specific criteria aligned with the study’s nature, justifying the use of this design to maintain credibility and consistency. This approach adheres to educational experimental research principles, emphasizing uniform sample characteristics and measuring the direct impact of the intervention on the same group before and after the experiment, ensuring accurate and contextually relevant results.

Each topic features a challenge, options, and outcomes (see Figure 4). Challenges test understanding without immediate feedback, revealing initial behavior. Learners use interactive tools for further exploration and demonstrate their learning through activities and assignments. Multiple choices are provided as follows:

A challenge is presented to the learner, who chooses an option that leads to different outcomes. Feedback is given to guide their progress. Higher levels introduce new challenges and feedback, creating an interactive learning environment, as shown in Figure 5.

3.3. Research Tools

Four tools were used to meet the research goals and address the questions.

3.3.1. Green Technology Awareness Scale

The Green Technology Awareness Scale measures awareness with 37 statements on a five-point Likert scale. It covers Green Technology Concepts (13 items), green computing (12 items), green economy (5 items), and sustainable development (7 items). Validity was confirmed with a sample of 10 students, showing significant internal consistency (correlation coefficients of 0.31 to 0.83). Reliability, assessed with another 10 students, yielded a Cronbach’s Alpha of 0.74, indicating good reliability. The details are given in

Table 2. Reliability coefficient of Green Technology Awareness Scale.

Total Scale Score (185)	(N = 10) Students	Alpha Coefficient	Sample Size	Number of Items	Value	Significance Level
Alpha Coefficient	10	185	0.79	0.001

.

Table 2 shows that the Green Technology Awareness Scale has a significant reliability coefficient of 0.01 using Cronbach’s Alpha, confirming the scale’s reliability.

3.3.2. Observation Checklist

An observation checklist was designed to evaluate cloud network management skills using Azure AI. It includes 5 main skills and 25 sub-skills, with scores of 0 (no performance), 1 (partial), or 2 (excellent). Experts confirmed its content validity. Reliability, assessed by inter-rater agreement among six postgraduate students, averaged 89.14%, surpassing the 85% threshold for high reliability.

3.3.3. Knowledge Achievement Test

The Knowledge Achievement Test for cloud network management via Azure AI includes 30 items scored as correct (1) or incorrect (0). Validity was confirmed with item–total score correlations from 0.24 to 0.81, all significant at the 0.01 level. Reliability, measured by Cronbach’s Alpha with 10 students, was 0.74 at a significance level of 0.001, as shown in

Table 3. Reliability coefficient of Knowledge Achievement Test.

Total Test Score (30)	(N = 10) Students	Alpha Coefficient	Sample Size	Number of Items	Value	Significance Level
Alpha Coefficient	10	30	0.74	0.001

.

Table 3 shows that the Knowledge Achievement Test’s reliability coefficient, using Cronbach’s Alpha, is significant at the 0.01 level, confirming the test’s reliability.

3.4. Electronic Learning Environment via Chamilo System

The Chamilo-based electronic learning environment (see Figure 6) aimed to improve cloud network management skills and green technology awareness. It included four stages.

(a)

Preparation and Planning:

Set objectives, identify learner characteristics, and define skills and content.

(b)

Design and Scenario Preparation:

Develop interactive narratives, learning models, and screen designs.

(c)

Implementation:

Build and deploy the environment, create multimedia elements, and set interaction patterns.

(d)

Presentation:

Apply and evaluate the environment with the research sample.

The pilot test confirmed the environment’s effectiveness and engagement, leading to the main study preparation.

First: Orientation and Preparation: A meeting ensured learners were ready to use the electronic learning environment, including internet access and tool familiarity. They were introduced to the main interface and provided with the environment link.

Second: Pre-Assessment: Measurement tools assessed learners’ initial levels before the study, with scores recorded immediately.

Third: Application of the Electronic Learning Environment: The Chamilo-based system was launched, with learners reviewing objectives and participating in discussions. Information was accessible consistently, and effective learning relied on clear communication and precise queries.

Fourth: Post-Application: Measurement tools evaluated learners’ levels after the study, with scores recorded, processed, and analyzed for results.

3.5. Educational Materials

In this study, educational content related to green technology and the technical tools used in cloud computing was utilized, including the following:

• Unit 1: Concepts of Green Technology

This unit defines green technology and its importance, focusing on green computing techniques to reduce carbon footprint. It aims to introduce students to Green Technology Concepts and connect them to environmental goals.

• Unit 2: Green Tools in Cloud Computing

This unit explains how cloud tools like Microsoft Azure AI improve energy efficiency and lower environmental costs. Its goal is to help students apply green tools in technical environments.

• Unit 3: Green Economy and Sustainable Development

This unit explores the relationship between green technology and the green economy, highlighting how green cloud applications support sustainability. It aims to raise awareness of the economic and social impact of green technology.

Nature of Content and Alignment with Objectives

The materials encourage self-directed, interactive learning through activities like the following:

• Interactive Simulations: Cloud network management using green technologies.
• Practical Activities: Hands-on use of green cloud management tools.
• Self-Assessment: Short tests to gauge understanding.

The content aligns with course goals by focusing on practice-based learning to enhance both theoretical knowledge and practical skills in green technology. The interactive design supports environmental objectives like reducing carbon emissions and promoting sustainability.

4. Results

The results were presented and interpreted by addressing the research hypotheses as follows:

4.1. Testing the First Hypothesis

The first hypothesis posits that there is a statistically significant difference (≤0.05) between the average scores of graduate students on the Green Technology Awareness Scale when comparing their pre-test and post-test results, with the post-test scores being higher. To test this hypothesis, a t-test compared the mean scores of the pre-test and post-test, both overall and for each dimension of the Green Technology Awareness Scale. The results are shown below.

Table 4. Comparison of mean scores of graduate students in pre-test and post-test.

Dimension	Application	Mean	Standard Deviation	T-Value	Degrees of Freedom	Significance Level	Significance	Effect Size
Overall Green Technology Awareness Scale	Pre-Test	51.6	10.35	46.73	185	0	Significant at 0.01	93.46	Very Large
	Post-Test	152.07	12.82
Green Technology Concepts	Pre-Test	23.58	1.98	47.16	65	0	Significant at 0.01	94.41	Very Large
	Post-Test	54.23	4.56
Green Computing	Pre-Test	25.39	2.59	17.51	60	0	Significant at 0.01	70.74	Very Large
	Post-Test	48.5	9.83
Green Economy	Pre-Test	10.62	1.44	24.62	25	0	Significant at 0.01	89.12	Very Large
	Post-Test	20.72	2.81
Sustainable Development	Pre-Test	14.69	0.79	30.03	35	0	Significant at 0.01	91.85	Very Large
	Post-Test	28.2	3.43
Tabulated t-value = (2.00)

Scale (maximum score = 185; N = 60).

shows that the t-test results for the overall Green Technology Awareness Scale and its dimensions reveal significant differences at the 0.01 level. This indicates a large effect size, reflecting substantial improvement in the post-test compared to the pre-test, as is shown in Figure 7.

Based on Table 4, the following observations can be made:

• The t-values for the Green Technology Awareness Scale and each dimension are (46.73, 47.16, 17.51, 24.62, 30.03) with degrees of freedom of (59) and a significance level of (0.000), all less than 0.01. These values exceed the tabulated t-value, indicating significant differences between pre-test and post-test scores, with post-test scores being higher.
• The effect sizes, calculated using η², are (93.46, 94.41, 70.74, 89.12, 91.85), showing a significant impact of cloud network management via AI on postgraduate students’ green technology awareness. This supports the first hypothesis of the study.
• Several factors explain these results.
• Training in cloud network management using Azure AI provided practical experience and highlighted cloud storage as a green technology service, supporting sustainable development and addressing environmental issues.
• The learning environment’s new knowledge and skills related to green technology increased learner motivation and awareness.
• The environment’s ease of use, logical information presentation, and diverse media improved learning and collaboration, boosting awareness of green technology.
• Effective instructional design and appropriate strategies in the learning environment enhanced learners’ understanding of green technology.
• Linking educational activities to real-world problems motivated learners to develop solutions using AI tools, reinforcing green technology’s importance.
• The digital content on green technology, including Azure AI, engaged learners and improved their decision-making on green issues.
• Applying Green Technology Concepts through practical activities introduced innovative solutions for environmental problems, increasing awareness.
• Creating a stimulating learning atmosphere with digital tools enabled instant communication and exploration of new green solutions.
• These results align with constructivist theory, where knowledge is built through the active engagement and application of green technology tools like Azure AI.
• This finding supports studies by Abdel Hamid [51], Soliman [14], Abu Ghuffa [46], Al-Azab [41], Al-Rifai et al. [39], and Tu et al. [17], emphasizing the need for green technology programs to enhance awareness and decision-making for sustainable development.

4.2. Testing the Second Hypothesis

The second hypothesis posits that there is a significant difference at the ≤(0.05) level between the mean pre-test and post-test scores of postgraduate students on the Cloud Network Management Skills Observation Checklist using Azure AI, with higher scores expected in the post-test (see Figure 8). To test this hypothesis, a t-test was conducted to compare the mean scores of the research group between pre-test and post-test on the checklist. The results are shown below.

Based on

Table 5. Comparison of mean scores for research group in pre-test and post-test.

Variable	Test	Mean Score	Standard Deviation	t-Value	N	Significance Level	Significance	η2 Value	Effect Size
Cloud Network Management Checklist	Pre-Test	30.32	3.99	31.07	60	0.000	Significant at 0.01	94.24	Very Large
	Post-Test	48.18	1.91
Tabulated t-value = (2.00)

(Maximum score for checklist = 50; N = 60).

, the following observations can be made:

• The t-value for the Cloud Network Management Skills Observation Checklist using the Azure AI platform was 31.07 with 59 degrees of freedom and a significance level of 0.000, indicating a significant difference between pre-test and post-test scores, favoring the post-test results.
• The effect size, η² = 94.24, shows a significant impact of AI platforms on developing cloud network management skills among postgraduate students, supporting the first hypothesis.
• Several factors explain these results.
• The learning environment actively engaged learners in cloud network management tasks, refining their skills and clarifying their roles, which improved their management of the study material.
• Ease of use and comprehensive content access motivated learners and enhanced their skills.
• Adequate time for skill learning, repeated performance videos, and interaction between learners, content, and instructors positively impacted skill development.
• Interactive elements based on learners’ needs significantly improved engagement and performance.
• Exploratory learning activities promoted active problem-solving and skill development in cloud network management.
• Unrestricted access to content allowed learners to study at their own pace, enhancing skill development.
• Freedom in engagement and continuous access motivated learners, aiding skill development.
• Associative theory suggests that connections and outcomes enhance educational performance and motivation.
• Results align with network learning and experiential learning theories, emphasizing dynamic and self-experienced learning.
• Findings are consistent with studies by Hassan [3], Tarah [4], Cao et al. [5], Al-Yazji [6], Grønmo [7], Zawacki-Richter et al. [8], and Lucena et al. [52], highlighting the effective role of AI platforms in education and recommending diverse learning environments to enhance cognitive and practical skills.

4.3. Testing the Third Hypothesis

Hypothesis 3 posits a statistically significant difference at the ≤0.05 level between postgraduate students’ mean scores on the pre-test and post-test of the cognitive achievement test, with higher scores expected in the post-test (see Figure 9). To test this, a t-test was used to assess the significance of the differences between the pre-test and post-test means. Results are provided below.

Table 6. Comparison of mean scores of postgraduate students in pre-test and post-test.

Variable	Application	Mean Score	Standard Deviation	t-Value	N	Significance Level	Significance	η2 Value	Effect Size
Cognitive Achievement Test	Pre-Test	12.08	1.71	68.87	60	0.000	Significant at 0.01	98.17	Very Large
	Post-Test	29.3	0.54
Table Value of T = (2.00)

shows the following points:

• The t-value for the cognitive achievement test on cloud network management skills was 68.87 with 59 degrees of freedom and a significance level of 0.000, less than 0.01. This exceeds the critical t-value, indicating a significant difference between the pre-test and post-test mean scores, with post-test scores being higher, showing improved performance.
• The effect size, η², was 98.17 for the cognitive achievement test, highlighting the substantial impact of AI platforms on enhancing cloud network management skills. This supports the third hypothesis.
• Several factors explain these results.
• The learning environment allowed learners to choose study topics and content based on their preferences, aligning with their pace and improving cloud network management skills through AI platforms.
• Interaction theory supports the use of interactive interfaces to achieve learning outcomes, as effective design and interaction increased engagement and understanding, boosting cognitive achievement.
• Sharing resources and diverse interactions in the learning environment enhanced cognitive achievement by facilitating access to relevant information.
• Providing content based on learners’ requests improved their knowledge and skills.
• A scientifically designed environment made navigation easier and improved performance on the cognitive achievement test through better information retrieval and exchange.
• Varied and motivating educational activities made learning enjoyable, provided immediate feedback, and accelerated information processing.
• Motivation theory, experiential learning theory, and narrative learning theory explain the enhanced learning experience through meaningful interactions and immediate feedback.
• Studies by Yufeia et al. [34]; Cantú-Ortiz et al. [10]; Muniasamy and Alasiry [11]; and Bakr and Taha [12] confirm that AI platforms improve learning efficiency and cognitive achievement.

4.4. Testing the Fourth Hypothesis

The fourth hypothesis posits a positive correlation between the post-test scores of the research group in the Green Technology Awareness Scale, cognitive achievement test, and observation checklist. The Spearman Rank Correlation Coefficient test was used to verify this hypothesis, and the results are shown in

Table 7. Spearman Rank Correlation Coefficient for study variables.

Application	Variables	R Value	Significance Value	Significance Level
Post-Test	Green Technology Awareness Scale	0.81	0.000	Significant at 0.01 Level
	Cognitive Achievement Test	0.74	0.000	Significant at 0.01 Level
	Observation Checklist	0.72	0.000	Significant at 0.01 Level

.

Table 7 shows a strong positive and statistically significant correlation at the 0.01 level between the mean scores of students in the Green Technology Awareness Scale, cognitive achievement test, and observation checklist for cloud network management skills, with correlation coefficients of 0.81, 0.74, and 0.72. These high values indicate a strong relationship between the study variables.

Several factors may explain this correlation.

• The learning environment motivated students to address educational challenges and achieve cognitive goals, improving their cloud network management skills through AI platforms.
• It increased students’ academic motivation, enhancing their cognitive performance and skills in cloud management and green technology through practical applications.
• Engagement in mental processes during research linked cognitive and performance aspects, leading to creative solutions for cloud network management and greater awareness of green technology.
• The environment fostered enthusiasm for the subject, resulting in higher academic engagement, cognitive achievement, and awareness of green technology.
• Stimulating students’ passion for environmental issues drove them to seek practical green computing solutions, boosting their proficiency and awareness in both cognitive and performance aspects.

The study’s results show statistically significant differences between pre-test and post-test measurements of green technology awareness and cloud network management skills, highlighting the effectiveness of the Microsoft Azure AI-based electronic learning environment. These results indicate a notable improvement in students’ awareness of sustainable technology and their technical skills, as reflected in higher post-test scores across all subscales and the overall measure.

These results are significant for educational contexts as they contribute to achieving learning goals related to sustainable development. Particularly, this electronic environment helps raise students’ awareness of environmental issues, such as reducing the carbon footprint and managing resources effectively. Also, the interactive tools and practical applications available through the cloud provide students with real-world experience, preparing them for the workforce.

Despite the positive results demonstrated by the study in enhancing students’ awareness of green technology and their cloud network management skills using the Microsoft Azure AI Platform, certain limitations must be considered when applying this experiment across diverse educational contexts. Among the most notable limitations are the costs associated with upgrading technological infrastructure, including providing modern devices and high-speed internet services, which could impose a financial burden on institutions with limited resources. Moreover, institutions may face cultural or administrative resistance to adopting new technologies, particularly in environments where such solutions have not yet been integrated. Furthermore, the digital divide between countries or regions hinders equitable access to these innovations.

• Suggestions to Overcome Challenges:

• Develop flexible funding strategies, including partnerships with the private sector or leveraging government support, to offset the costs of infrastructure development.
• Design awareness and training programs to foster institutional and educator acceptance of new technologies, emphasizing their educational and environmental benefits.
• Provide cost-effective and adaptable solutions that cater to the needs of resource-limited institutions.
• Strengthen international and regional collaboration to reduce the digital divide and ensure technology reaches underserved areas through initiatives that offer AI and cloud computing technologies at discounted rates or for free.

Future Work

To address unresolved challenges and explore the long-term implications of integrating cloud-based and AI-driven technologies in education, future research could focus on several critical areas.

• First, investigating the equity and accessibility of hybrid and distance learning models in underprivileged regions could shed light on the digital divide and propose scalable solutions for inclusive education.
• Second, longitudinal studies examining the impact of sustained use of platforms like Microsoft Azure on student engagement, academic performance, and cognitive development would provide deeper insights into their pedagogical efficacy over time.

Also, exploring the ethical and privacy concerns associated with extensive data collection in AI-enhanced educational systems remains a pressing need. Research in this area could offer frameworks for safeguarding student information while maximizing the benefits of data-driven insights. Finally, studies could delve into the environmental sustainability of large-scale adoption of cloud technologies in education, analyzing how these systems can align with global green initiatives and contribute to reducing the sector’s carbon footprint.

By addressing these directions, future research can advance the field of educational technology, ensuring that its development is equitable, sustainable, and aligned with the diverse needs of global learners.

5. Conclusions

The study demonstrated that the use of Microsoft Azure applications in AI-based cloud network management significantly enhanced postgraduate students’ awareness of green technology. Despite the study’s limitations, such as a small sample size of 60 postgraduate students, adjustments were made to the learning environment model to better suit this group. The results, though derived from a limited sample, supported the study’s hypotheses and provided valuable insights applicable to other educational settings. The research underscores the importance of a learning environment model that integrates collaborative and independent learning methods, effectively managed through AI applications, to improve e-learning experiences. The findings suggest that this model could be applied to various academic courses, emphasizing the need to incorporate green technology solutions in educational programs through AI tools.

The study also recommends that AI platforms align with Egypt’s Vision 2030, taking into account factors such as course content, learner needs, and available resources. It highlights the necessity for educational institutions to develop infrastructure that supports green technology and to increase the adoption of sustainable technologies. Furthermore, the study advocates for the use of green computing and AI-based cloud network management in schools to reduce energy consumption and lower harmful emissions. In summary, this study promotes the greater integration of green technology and AI solutions in education, aiming to support sustainable practices and foster environmentally friendly institutions.