BusinessDay
REGIS ANUKWUOJI
June 9, 2025
The Founder and Chancellor of Maduka University, Samuel Maduka Onyishi, has called on Nigerian universities to adopt a more targeted approach to entrepreneurship education in order to produce job creators rather than job seekers.
Onyishi made the call while delivering the second Academic Lecture of the Faculty of Health Sciences and Technology at the University of Nigeria, Enugu Campus. His presentation was titled ‘Entrepreneurship for Human Development and Economic Progression: A Call on Nigerian Universities.’
He observed that the complexities of modern times and the rapidly evolving business environment have rendered the traditional “ivory tower” model of universities increasingly obsolete, stressing that in today’s expanding knowledge economy, universities no longer hold a monopoly on intellectual output.

“Rising global competition means our universities are under increasing pressure to contribute directly to economic development — hence the need for an entrepreneurial shift,” he stated.
According to him, the goal is to equip universities with the capacity to identify and create opportunities, foster innovation, encourage teamwork, embrace calculated risk-taking, and effectively respond to emerging challenges — hallmarks of the entrepreneurial mindset.

He argued that strategic entrepreneurship education is crucial to reducing widespread unemployment and poverty in Nigeria. According to him, Nigerian universities must go beyond theory and adopt a more practical and structured approach to entrepreneurship training.
Describing this as “targeted entrepreneurial education,” Onyishi proposed a comprehensive model that includes: entrepreneurship education, financial literacy, vocational and skill acquisition training, leadership development, time management, legal education, ICT literacy, life skills and healthy living, business intelligence, family planning, cooperative education.
He emphasized the need for universities to encourage students to form cooperative societies, investment clubs, and partnerships, adding that holiday internships are highly beneficial to students’ development.

“There is a need to set up incubation centres to hone business skills. School farms and agricultural business training programmes would go a long way in ensuring food security. We must all produce what we eat. This is the foundation of self-sufficiency, which is a key index of economic progression,” Onyishi stated.
He further stressed the importance of university-industry collaboration, especially in light of emerging technological disruptions.

“Artificial intelligence is already displacing workers. Our universities must begin to align with new work patterns to produce graduates who are relevant in a rapidly changing, AI-driven world,” he warned.
While expressing optimism about the growing potential of medical entrepreneurship in Nigeria, Onyishi decried the paradox of a nation with over 48 universities offering medical education, more than 400 nursing schools, and various medical technology institutions, yet still spending over $2.39 billion on medical tourism in 2024 alone.
“Even more is spent on importing medical supplies and consumables. These figures underscore the untapped potential in the medical entrepreneurship space,” he noted.
Onyishi identified the critical elements needed to unlock this potential which include professionals with integrity, long-term investors, various equity models (including sweat equity and consultancy royalties), and strong corporate governance.
Challenging academics and professionals in the audience, he remarked, “Nigeria has 63 federal, 63 state, and 149 private universities. Our number of professors grows daily, yet our institutions have not catalysed rapid national development. Perhaps, we are still trapped in the tradition of the ivory tower. It’s time to reimagine and reconstruct the university of the future — and the way forward is entrepreneurial. We must begin by integrating local initiatives, especially in the medical and allied sectors.”

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Written by: Andrew B. Raupp / @stemceo
Author’s Note: This article serves as an addendum to my Forbes 2017 analysis, “A Decentralized Internet Will Preserve Innovation In STEM Education”.
Web3 presents novel opportunities for brands to engage with their audiences and establish a unique online identity within a decentralized ecosystem. In the context of science, technology, engineering and mathematics (STEM) education, Web3 technology can serve as a vital link between educational institutions, learners, and decentralized resources. This analysis delves into the concept of Web3, its functionality, and how STEM stakeholders can potentially benefit from it.
As the internet has evolved, we have witnessed three distinct phases that have shaped our digital experiences. Each phase has brought unique advancements and challenges, transforming the way we interact online and redefining the possibilities of the digital realm.
Web 1.0: The Static Web
Web 1.0 marked the dawn of the internet age. In this initial phase, the primary focus was on sharing information through static web pages. Content was largely one-directional, with users consuming information without much ability to interact or contribute. Websites served as digital brochures, displaying text and images with limited functionality.
Web 2.0: The Interactive Web
The emergence of Web 2.0, revolutionized the online landscape by fostering collaboration and user-generated content. In this phase, the internet became more dynamic, enabling users to actively engage with websites, create and share content, and participate in online communities. Social media platforms, blogs, wikis, web apps and other interactive services flourished during this era, allowing users to connect, communicate, and collaborate on a global scale.
Web 3.0: The Semantic Web
As we transition to Web 3.0, we are witnessing a new wave of innovation that seeks to empower users and enable greater control over their online experiences. Web 3.0 aims to create a more intelligent and interconnected digital ecosystem, leveraging advanced technologies such as the metaverse (AR & VR), artificial intelligence, machine learning, and blockchain to enable semantic understanding and interoperable networks.
Web3: The Decentralized Web
The fundamental concept of Web3, introduced by Gavin Wood, co-founder of Ethereum, in 2014, is to challenge the centralization and supremacy of a few Web2 titans such as Facebook, Amazon, or Google, by establishing a decentralized iteration of the internet. By leveraging blockchain technologies, decentralized storage, and self-sovereign identity in a communal environment, Web3 intends to reclaim data ownership from the Web2 behemoths and return it to the users. This empowers individuals to control who has access to their data and their identity.
Web 3.0 & Web3: Controversy
In an era marked by the rise of both the decentralized web, or Web3, and the semantic web, or Web 3.0, a paradigm shift in the digital world is imminent. Both of these models aim to rectify the perceived flaws of the existing internet structure. However, their approaches and focal points are decidedly distinct.
Web 3.0 or the semantic web is principally concerned with efficiency and intelligence. It seeks to revolutionize the web by linking and reutilizing data across various websites, thereby enhancing the internet’s utility and accessibility. On the other hand, Web3 places a premium on empowerment and security, striving to return control over data and identity to individual users.
Interestingly, these differing emphases are reflected in the divergent technologies each employs. Web3 harnesses the power of blockchain technology, which is renowned for its decentralized and immutable nature. This makes data stored on Web3 difficult to alter, as it is distributed across numerous locations. Conversely, Web 3.0 utilizes data interchange technologies, such as RDF, SPARQL, OWL, and SKOS. This allows data within Web 3.0 to be more flexible and easy to modify.
Despite their differing focal points and technologies, both Web3 and Web 3.0 share a common ideal: the desire to place user data under the control of the user. The semantic web achieves this by storing data in a Solid pod, a centralized location. Web3, however, takes a decentralized approach. Users’ crypto wallets do not store data, but rather hold keys to assets located across the blockchain.
For those seeking a concise comparison of these two web models, one could distill it down to this: Web3 is decentralized, while Web 3.0 is semantic or linked. This statement encapsulates the key distinctions, with Web3’s decentralization offering empowerment and security, and Web 3.0’s semantic structure providing efficiency and interconnectedness. However, the nuances and complexities of these two models deserve more than a passing glance or a quick departure to avoid follow-up questions. The future of the web may very well rest on understanding and harnessing these differences to create a more secure, efficient, and user-centric internet.
Subsequently, there is a growing demand and sense of urgency to break free from the constraints of centralized authorities to promote user autonomy. The introduction of Web3 domains, decentralized applications (dApps), and blockchain-based platforms exemplifies this shift, providing users with unparalleled control, security, and privacy.
The evolution from Web 1.0 to Web3 has brought significant advancements to the digital world. From static web pages to interactive platforms and, finally, to intelligent and decentralized networks, the internet continues to reshape our lives and redefine the possibilities of online experiences. As we embrace the era of Web3, we can anticipate further transformations that will empower users, foster innovation, and create a more equitable and interconnected digital landscape.
To better understand the decentralized web, it is crucial to first examine the concept of decentralization. In the current digital landscape, the infrastructure supporting popular websites and online platforms is predominantly owned by corporations and, to a certain extent, regulated by governmental policies. This centralized model emerged as the most straightforward method of constructing network infrastructure — entities invest in server installation and software development to create online platforms that users can access, either through paid subscriptions or for free, provided they adhere to the platform’s rules.
The decentralized web, however, presents a radical departure from this centralized paradigm. In this new model, network infrastructure is not owned or controlled by a single entity, but rather distributed across multiple nodes, ensuring that no single authority has absolute control over the system. This decentralization fosters a more democratic digital environment, where users have greater autonomy and influence over the platforms they engage with.
By embracing the decentralized web, we are moving towards a more transparent digital ecosystem, in which the power dynamics shift from the hands of a few corporations and governments to the hands of individual users. The decentralized web holds the potential to reshape not only the way we interact online but also the way we approach various fields, including STEM education, by fostering innovation, collaboration, and accessibility on a global scale.
Web3 domains, or NFT domains, are a groundbreaking type of internet domain that function similarly to domain name systems (DNS) but are constructed using smart contracts and blockchain technology. Governed by decentralized domain registrars, Web3 domain names can be owned and controlled by individuals or organizations rather than centralized entities.
These domains are poised to play a pivotal role in Web3, the decentralized web, providing the foundation for decentralized apps (dApps), decentralized autonomous organizations (DAOs), non-fungible tokens (NFTs), and decentralized finance (DeFi) projects. With regards to STEM education, Web3 domains can facilitate the creation of human-readable names for decentralized websites and services, streamlining user navigation within the decentralized web.
Though Web3 domains and traditional domains both function as digital identities for websites and online content, they exhibit several key differences:
Web3 domains are not just digital addresses for websites; they represent a revolutionary shift in how interactions occur within the digital world. They offer a range of practical applications that can transform the methods of conducting transactions, communicating, developing content, and navigating the web. Here are some examples:
Web3 domains can help bridge the gap between learners, educators, and decentralized platforms, fostering a more interactive and engaging learning experience. By embracing this innovative technology, educational institutions can future-proof their digital presence and position themselves at the forefront of the Web3 revolution.
As the world increasingly embraces Web3 technology, the importance of Web3 domains in STEM education is set to grow. By investing in Web3 domains, educational institutions can not only ensure a strong digital presence but also pave the way for a new generation of STEM leaders equipped to navigate an interconnected, decentralized world.
Empowering STEM Educators with Web3 Domains
Web3 domains can empower educators by providing them with new avenues for professional development and collaboration. Decentralized platforms can facilitate the exchange of best practices, lesson plans, and teaching strategies among educators worldwide. This can lead to a more informed and skilled educator workforce, which can, in turn, have a positive impact on student outcomes in STEM subjects.
Moreover, Web3 domains can support the growth of education-themed decentralized applications (dApps) designed to enhance teaching and learning in STEM education. By embracing these decentralized tools, educators can streamline their work, improve student engagement, and promote a deeper understanding of complex STEM concepts.
Enhancing Access to Quality STEM Resources
One of the significant challenges in STEM education is providing equal access to quality resources for learners worldwide. Web3 domains can address this issue by enabling the seamless sharing of educational materials and tools on decentralized platforms. Educational institutions can use Web3 domains to store and distribute educational content, ensuring that students can access high-quality resources irrespective of their location or socioeconomic background.
In addition, Web3 domains can help promote the development and adoption of open educational resources (OERs) in the STEM fields. By hosting OERs on decentralized platforms, institutions can reduce the costs associated with traditional resources and promote the collaborative creation and updating of educational materials.
Incorporating Web3 Domains into the Curriculum
Educational institutions should consider integrating Web3 domains into their curricula, exploring innovative ways to utilize this technology in the learning process. For instance, institutions could create decentralized learning platforms using Web3 domains, facilitating a more personalized and immersive learning experience for students.
Furthermore, educators could develop Web3 domain-based projects and assignments that encourage students to interact with decentralized resources, providing them with valuable hands-on experience. By incorporating Web3 domains into STEM education, institutions can not only prepare students for a rapidly changing world but also equip them with the skills and knowledge needed to excel in the STEM fields.
Building a Global STEM Community with Web3 Domains
Web3 domains have the potential to connect students, educators, and researchers worldwide, creating a global community of STEM learners. This global network can foster cross-cultural collaboration and drive innovation in STEM education by enabling the exchange of ideas, resources, and expertise.
Educational institutions can leverage Web3 domains to create decentralized platforms that facilitate global communication and collaboration, providing students with opportunities to work on international projects and gain exposure to diverse perspectives. By embracing the power of Web3 domains, institutions can break down barriers and forge a more inclusive and interconnected STEM community.
Ensuring Data Privacy and Security in STEM Education
Data privacy and security are crucial concerns in the digital age, especially in the context of education. Web3 domains can offer an additional layer of security and data privacy for educational institutions, ensuring that sensitive information is not compromised. Decentralized platforms can store data securely using blockchain technology, which provides an immutable record of transactions, preventing unauthorized access or tampering.
By adopting Web3 domains, educational institutions can demonstrate their commitment to protecting students’ and educators’ data, thereby fostering trust and confidence among stakeholders.
Web3 and its corresponding technologies represent a significant shift in the digital landscape, offering unparalleled opportunities to revolutionize STEM education. By integrating Web3 domains into their curricula and fostering a culture of innovation, educational institutions can prepare the next generation of STEM leaders for a decentralized, interconnected world.
The Web3 revolution offers an opportunity to empower all pedagogical and andragogical stakeholders redefining STEM education for generations to come. As society stands on the cusp of this transformative moment, the question remains: are we ready to seize the opportunity and chart a new course for the future of STEM education?
Andrew B. Raupp is the Founder / Executive Director @stemdotorg. “Democratizing science, technology, engineering and math (STEM) education through sound policy & practice…Applying STEM to better understand it”
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Search Guardian News
By : Guardian Nigeria
Date: 10 Jun 2025
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Russia has announced plans to significantly scale up its educational engagement with Nigeria and other African countries, with a long-term target of hosting 500,000 international students in Russian universities. This was disclosed by the Russian Ambassador to Nigeria, Andrey Podelyshev, during a press briefing held in Abuja on Monday evening.
Ambassador Podelyshev revealed that Russian President Vladimir Putin has set a clear objective to boost the number of foreign students in Russia’s higher education system as part of a broader diplomatic and developmental initiative. Currently, Russia hosts around 32,000 students from Africa, including approximately 2,000 from Nigeria — a figure the Russian government aims to substantially increase.
“In line with the president’s objective, the quota will grow every year,” Podelyshev stated. “For 2025, 220 Nigerian students have already been awarded scholarships to study in Russia.”
The ambassador confirmed that these scholarships, approved in 2024, are set to cover the 2025 academic session, with preparations already in motion for the students’ arrival by September.
Addressing recent changes in Nigerian policy, which suspended government-funded transport and accommodation for students on foreign scholarships, Podelyshev explained that Russia has introduced a new grant structure. The revised package is designed to provide full support — including tuition, living expenses, and travel costs — for qualified students selected under the scholarship scheme.

However, Podelyshev was clear that Russia’s vision extends beyond scholarships. He described education as a strategic instrument in fostering long-term partnerships with Nigeria, particularly in key areas like energy, metallurgy, and industrial development.
“For example, if Russia is involved in rebuilding a metallurgical plant in Ajaokuta or establishing nuclear plants, we will need Nigerian professionals trained in Russia to implement these projects,” he said.
To ensure such education leads to tangible benefits for both countries, Podelyshev emphasized the importance of integrating academic training with bilateral economic projects. He pointed to the role of the Intergovernmental Commission on Economic, Scientific, and Technical Cooperation as a mechanism for aligning education with joint development goals.
In response to concerns about brain drain — the loss of skilled professionals who study abroad and do not return — Podelyshev advocated for project-based training models. These, he said, would give Nigerian students a clear reason to return home.
“If students know they are being trained for specific national projects that require their expertise upon return, they will have stronger incentives to come back,” he argued.

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© 2025 GUARDIAN Newspapers. ALL RIGHTS RESERVED
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Scientific Reports volume 15, Article number: 15845 (2025) Cite this article
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This study focuses on the application of virtual reality (VR) technology in English language teaching (ELT), and discusses the effect of combining VR with machine translation (MT) technology. VR technology is introduced into the study to provide innovative teaching methods for English teachers and create an immersive learning environment for students. Based on the latest development of deep learning (DL), a new MT model is proposed in this study, and it is successfully integrated with VR technology to optimize the quality of ELT. The experimental results show that the translation accuracy of the MT model designed in this study reaches 98.5%, the F1 score is stable at around 93%, and the semantic information recall rate is as high as 92%, all of which are better than the traditional model. In the preliminary test, the comparative experiment of 40 English majors further verified the effectiveness of the model in improving translation efficiency and quality. This study shows the great potential of the integration of VR and MT technology in ELT, and also proves its advantages by using experimental data, which provides technical support for ELT and provides reference for future practice.
The dawn of the era of big data has broadened people’s access to knowledge. In this context, the rise of artificial intelligence (AI) has brought great convenience to daily life and provided tangible technical support. VR, as a computer-generated immersive experience, combines human–computer interface, sensing technology and multimedia technology to create an interactive three-dimensional environment¹. With the rapid development of technology, VR has become an innovative tool in education, especially in English teaching. VR technology provides an immersive learning environment for students and promotes the interactivity of language learning. In order to achieve a wider and more efficient application, it is particularly important to combine VR with machine translation (MT) technology. This combination not only solves the language barrier problem, but also further enriches the teaching content and enhances students’ learning experience.
In English language teaching, educators face many challenges. Traditional teaching methods lack authentic contexts, making it difficult for students to apply what they have learned in the classroom to real-life situations. Meanwhile, low student participation, cultural differences, and limited educational resources are also common issues. Exploring new teaching methods and technological means is particularly important to address these challenges. The combination of DL and VR technology provides new possibilities for ELT. By constructing MT models based on DL and VR, an immersive learning environment can be created, providing real-time translation support, thereby stimulating students’ interest in learning and improving their language proficiency. When VR is integrated with DL-based MT applications, it creates a comprehensive language learning environment for students². Through VR, students can experience the culture and life of different countries, while simultaneously obtaining relevant language information in real-time through MT³. This immersive and interactive learning method not only stimulates students’ interest in learning but also significantly improves their learning efficiency. Although the research on VR and MT technology has made some progress at present, there is still a clear gap to effectively integrate these two technologies into ELT. On the one hand, most researches only focus on the single application of VR or MT, and lack of systematic exploration on their integration. On the other hand, existing research often ignores how VR and MT technologies can enhance students’ language learning experience in practical teaching, especially in cross-cultural communication scenarios. In addition, there are few studies on adjusting the combination of VR and MT technology to meet different learning needs according to individual differences of students.
The integration of AI into college English classes is of revolutionary significance. It helps teachers to develop more elaborate and cutting-edge teaching plans, thus improving the quality of ELT. Students also benefit from it, and have a deeper understanding of English concepts, increased participation and a completely new way of learning⁴. By visualizing abstract learning content and combining formal and informal learning methods, AI can also enhance learners’ sense of presence, intuition and attention. Language learning needs a favorable environment and atmosphere, and continuous practice is the key to making progress, which may not be easy for many China students to learn English⁵. The use of VR can create a virtual atmosphere for situational teaching, increase the proportion of students’ training in language learning, and promote the internalization of knowledge into ability. In addition, the application of VR can make English knowledge more three-dimensional and reduce students’ learning difficulty to some extent. With the development of DL, neural network is used to map the source language to the target language translation model, that is, the neural machine translation (NMT) model, which significantly improves the quality of machine translation. These models surpass the traditional statistical machine translation methods in performance and have become the mainstream methods in the industry and academia⁶. The main purpose of this study is to explore the innovative application of VR and MT technology in English teaching, and to solve the language barriers and interaction problems in current teaching. By combining these two technologies, we can improve the teaching quality and provide students with a better learning experience. The innovations include:
This paper integrates VR, MT technology and ELT, subverts the traditional teaching methods and realizes interdisciplinary integration and innovation. It provides students with a richer and more varied learning experience.
Using VR, students can experience the culture and life of different countries in simulated scenes and enhance their interest in English.
The introduction of MT technology, especially the MT model based on DL, has improved students’ English application ability by providing real-time, accurate and context-aware translation support. This goes beyond the traditional MT system, and uses the power of DL to better understand and generate natural language.
By combining VR and MT, teachers can provide personalized teaching according to the individual differences of students. This integration provides a more adaptable and interactive learning environment.
This article begins by introducing the background and significance of the research. It then explores the application of VR and MT technology in ELT. Following that, a detailed introduction to the DL-based MT model integrating VR is provided, and its effectiveness and feasibility are verified through experiments. Finally, in the conclusion section, the main findings and contributions of this article are summarized, and future research directions and suggestions are outlined.
Various languages exhibit distinct word orders, voices, and writing conventions, posing challenges for MT such as inverted word sequences, excessive vagueness, and limited translation precision. Consequently, improving MT quality has become a prominent research focus. The integration of VR/AR technology in language studies presents diverse case studies and rich content, covering vocabulary comprehension to sentence translation and even game design.
Zhang introduced a relative position encoding approach to address the limitations of absolute encoding in expressing positions within attention mechanisms, However, it doesn’t strengthen the attention module’s inherent positional encoding⁷. Wang et al. developed a language-learning VR game that helps learners with accurate word spelling⁸. Hui proposed a neural network-driven intelligent translation assessment technique to tackle semantic obscurity in MT outputs, enhancing both the dynamic assessment of MT quality and translation speed^9,10. Mallek et al. emphasized the importance of integrating educational theory and VR technology to enhance educational experience, which is consistent with the innovative teaching methods discussed in this study¹¹. Al Shloul et al. emphasized the role of activity-based learning in improving students’ performance. The integration of VR technology in ELT can further enhance this effect¹². Shah et al. pointed out that combining educational theory with VR can improve the teaching effect of engineering education and VR laboratory¹³.
Cai et al. developed learning software that transforms the campus into a virtual English realm, fostering student engagement with characters, objects, and media to hone English proficiency and campus understanding¹⁴. Wu et al. combined linguistic big data with language rules to further advance MT¹⁵. Wong et al. recommended a neural MT framework rooted in recurrent neural networks¹⁶, while Wang et al. proposed an attention-mechanism-based neural MT model¹⁷. Shahzad et al. summarized large-scale language models and their problems in learning environment. This is related to the integration of MT technology in ELT¹⁸.
Liang et al. proposed forth an enhanced neural MT approach leveraging generative adversarial networks to address the scarcity of training data for MT models¹⁹. This method’s uniqueness lies in its corpus expansion through data augmentation. Maimaiti built a Chinese-English MT model using kernel ridge regression technology to elevate translation quality, The approach views the translation process as a mapping between source and target language strings, utilizing these mappings for higher accuracy translations²⁰. Jia et al. devised bidirectional attention for language comprehension tasks, but each branch employs independent parameters, resulting in a bulky model that is unable to grasp local semantics effectively^21,22.
Although the above research has achieved important results in its field, there are limitations in the applicability of integrating VR and MT technologies into ELT. For example, Zhang’s research puts forward the relative position coding method, but it is not effective when dealing with complex language structure and context understanding. The language learning VR game developed by Wang et al. is helpful to vocabulary spelling, but it does not fully cultivate students’ overall language ability. Hui’s intelligent translation assessment technology has improved the quality and speed of translation, but it has not fully considered the real-time and interactivity in the actual teaching environment. In addition, most studies do not pay attention to students’ individual differences and learning needs, which are very critical in practical teaching and application of technology. On this basis, this paper puts forward an innovative method combining VR technology with DL-based MT technology. The goal of this improvement is to improve the accuracy of translation, and also to create a brand-new and immersive interactive learning experience for students. With the help of the unique advantages of VR technology and the powerful ability of DL, this paper hopes to bring changes to ELT, break through the limitations of traditional teaching and promote the development of ELT in a more efficient and interactive direction.
VR is a highly integrated entity that combines various disciplines such as contemporary simulation, human–computer interaction, and sensing. Its primary focus is on constructing a three-dimensional environment to offer users a more immersive experience. The application of VR to build a virtual learning environment and create problem-solving scenarios has significantly enhanced the learning enthusiasm and initiative of students²³. VR can concretize and visualize abstract language knowledge points by constructing a realistic and natural language environment, enabling students to access, understand, and apply language knowledge through various perceptual channels. Teachers can also use VR to set language difficulty levels in virtual scenes and configure corresponding brackets at different levels to assist students in making different choices based on their learning needs. Simultaneously, they can help students choose language difficulty levels according to their own knowledge level, facilitating varied levels of learning.
Traditional foreign language teaching classrooms cannot meet the specific “work scenario” teaching practice conditions required for specialized ELT, while VR can build virtual simulation learning environments to provide technical support for the transformation from public English to specialized ELT²⁴. Within the virtual learning environment, teachers offer students a platform and opportunities to freely express their opinions and fully showcase themselves²⁵. Teachers can transform flat language knowledge into three-dimensional real-time interactive scenes, thereby attracting students, improving teaching efficiency, expanding the scope of speech act teaching, and making teaching situations closer to real scenes. In a virtual language environment, students can communicate and interact with the created virtual characters and virtual teachers, making them feel that they are in a real language communication environment. This continuously exercises their English speaking ability and enhances the attractiveness of ELT activities, thereby improving their English learning efficiency.
MT is an emerging technology that integrates and utilizes computer technology and AI technology. The online translation systems or software commonly used on the internet currently belong to the MT system. The principle of MT is that users select example sentences with similar sentence structures to the source language from the corpus, and then convert the source language into the target language²⁶. MT is an intelligent product with high-tech content that uses computers to translate (convert) literature from one language to another. Its main function is to reduce communication barriers caused by differences, making people’s lives and work more convenient²⁷. The current online MT results still exhibit certain shortcomings, especially in the use of servers for the contrastive learning of data from different languages in the full-text scope. This approach aims to obtain grammar and text-related rules between languages, leading to low MT efficiency and accuracy. The prevailing MT technology at present is neural MT technology based on artificial neural networks (ANNs). Figure 1 illustrates the simplest schematic diagram of the ANN structure. A key feature of ANNs is their ability to learn from data, meaning they can automatically acquire optimal weight parameters based on training data.
ANN structure.
Applying VR technology and MT technology to English language teaching can create a more realistic, vivid and interactive learning environment. In this environment, students can have a dialogue with virtual characters, and MT technology can translate the dialogue content into students’ mother tongue or target language in real time, thus helping students better understand and master the English language. Theoretically, the combination of VR and MT has obvious advantages. First of all, the immersive learning environment provided by VR technology can stimulate students’ interest and motivation in learning, so that they can participate in language learning more actively. Secondly, the real-time translation function of MT technology can help students overcome language barriers and understand and master English faster. Finally, this combination can also promote students’ autonomous learning ability and let them explore and discover language rules independently in simulated real scenes.
Anwar et al. studied the subjective quality of experience (QoE) of 360-degree VR video. The research results can be used to improve the ELT experience based on VR²⁸. The QoE challenges and standard requirements in immersive media consumption discussed by Anwar et al. are also applicable to the VR environment in ELT²⁹. From the perspective of technical practice, some key problems need to be solved to realize the combination of VR and MT. The first is data integration, that is, how to effectively integrate interactive data in VR environment with MT model to achieve accurate real-time translation. Secondly, the interface design problem, how to design an intuitive and easy-to-use interface, so that students can easily learn and communicate in the VR environment. Finally, the performance optimization problem, how to ensure that the system can still maintain efficient and stable operation under a large number of interactive data and complex translation tasks.
In order to improve the accuracy of Chinese English MT, the first step is to convert the language to be translated into computer language, that is, to obtain the word vector of each word, extract the contextual information of the current word, and achieve accurate translation. Select word (w_{ji}) within the probability distribution (pleft( {w_{ji} |z_{ji} ,varphi } right)), where the polynomial probability distribution under (topicz_{ji}) is (p), and the Dirichlet distribution of parameter (alpha) is represented by (Dirleft( alpha right)). The probability density function can be obtained as follows:
In the formula, (theta = left( {theta_{1} ,theta_{2} , cdots ,theta_{k} } right),alpha = left( {alpha_{1} ,alpha_{2} , cdots ,alpha_{k} } right),Gamma left( cdot right)) and (Multinomialleft( theta right)) respectively represent the Gamma function and the polynomial distribution when the parameter is (theta). Therefore, the conversion formula for the probability density function is:
Analyzing the aforementioned process, it can be concluded that mining topic information in text first requires determining the proportion of different topics in each document within the document set. Based on probability, specific topics are then sampled from the topic distribution, and the polynomial distribution of the corresponding word list for that topic is sampled to obtain specific word results.
Due to the differences in word order and sentence length between Chinese and English, it is easy to produce word order during translation, which in turn leads to poor MT results. Therefore, a BiLSTM network is used to build the encoder. Using the BiLSTM network to encode the original translated sentences can effectively remember the node information of each memory unit, while avoiding the phenomenon of gradient vanishing during the encoding process of long sentences, captures more semantics, and alleviates the issue of word reordering.
Given the input sequence (x = left{ {x_{1} , cdots ,x_{n} } right}) on the source language side and the input sequence (y = left{ {y_{1} , cdots ,y_{m} } right}) on the target language side, where (x_{i}) and (y_{j}) represent the (i) th word on the source language side and the (j) th word on the target language side, respectively, and (n) and (m) represent the lengths of sequences (x) and (y). The main objective of the neural MT model is to maximize the maximum likelihood estimation function (Pleft( {y|x} right)). Among them, (Pleft( {y|x} right)) calculation is shown in Eq. (6).
Use (X) to represent the source language space and (Y) to represent the target language space. Take (X) as the input sample and use the MT model to map the input sample (X) to (Y). Using the conditional probability distribution (P_{theta } left( {y|x} right)) to represent the MT model, where (theta) represents the model parameters. There are parallel corpora with (N) sentence pairs in standard supervised training, represented by (B = left( {x^{left( n right)} ,y^{left( n right)} } right)) and (n = 1,2, cdots ,N). The MT model obtained by maximizing parallel corpus likelihood learning is as follows:
Maximizing the likelihood of monolingual data samples is a widely adopted technique for handling such data in machine learning, especially in the context of semi-supervised MT. This approach aims to optimize the utilization of available monolingual data, thereby enhancing the performance of the MT system.
The attention mechanism is commonly employed to calculate the similarity between source language words and target end words. Typically, various options exist for computing the alignment coefficients of attention mechanisms, including dot product, concatenation, etc. Drawing from the insights of deep self-attention networks, this article also utilizes a dot product approach, employing a query-key-value structure:
Among them, (Attentionleft( { cdot , cdot , cdot } right)) corresponds to the function of attention mechanism, and (q,k,v) represents the input query, key, and value, respectively.
In order to evaluate the translation performance of the model, this article uses size insensitive BLEU as the assessment indicator of the system’s translation quality. The BLEU script is an internationally recognized MT assessment script, that gauges the effectiveness of the translated text by comparing the similarity ratio of (n) tuples between the translated text and the reference translation, known as the (N – gram) matching rule. BLEU has incorporated corrections in its calculation, and the correction formula is as follows:
Among them, (Countleft( {n – gram} right)) is the number of occurrences of the (n) phrase in the MT translation, (Count_{clip} left( {n – gram} right)) is the minimum value taken between the occurrence of the (n) phrase in the (Countleft( {n – gram} right)) and reference translations, (C) is the translation translation, (C^{prime}) is the reference translation, and in calculation, (n – gram) is generally set to (4 – gram) at the highest.
In DL field, Convolutional Neural Network (CNN) shows excellent feature extraction ability. CNN plays an important role in image recognition, video analysis, drug discovery, board games and many other applications³⁰. It is worth noting that specially designed CNN variants also appear in the field of natural language processing (NLP). When dealing with sequence data or tasks that need context understanding, traditional neural networks face challenges in effectively capturing long-distance dependencies due to structural limitations.
In this case, the recurrent neural network (RNN) intervenes to establish continuity with the previous text, effectively integrate the context information, and enable the network to predict the following text according to the previous details³¹. When combined with VR, this ability can help to better understand and predict the user interaction in the virtual environment and enhance the immersive language learning experience³². RNN has encountered a “long-term dependence problem” in practical application: with the increase of network layers or the extension of sequence length, the gradient gradually decreases in the process of back propagation, which leads to the slow update of network weights. Coping with this challenge is very important for optimizing the integration of RNN and VR in advanced language teaching applications.
In order to overcome this limitation, Long-term and Short-term Memory Network (LSTM) was introduced as an improved version of RNN. On the basis of RNN, LSTM adds three key gating mechanisms (input gate, forgetting gate and output gate) and a cell state. These structures ensure the persistence and selective forgetting of information in the hidden layer, so that neurons can make more effective use of previous information. Figure 2 shows the structural differences between CNN and LSTM models. Among them, CNN mainly extracts local features through convolution layer and pooling layer, while LSTM maintains the continuity of sequence information through gating mechanism.
CNN and LSTM model structures.
In our research, the neural network architecture adopted is DL architecture based on self-attention mechanism-Transformer model. This model is especially suitable for dealing with sequence-to-sequence tasks, such as machine translation. The encoder is responsible for encoding the input sequence into a set of representation vectors rich in context information. These vectors capture the global dependencies in the sequence through the self-attention mechanism. The decoder is based on these representation vectors and the context of the generated output sequence.
The training data mainly comes from the open bilingual corpus, which contains a lot of parallel texts, that is, the corresponding sentences in the source language and the target language. In order to ensure the effect of model training, the data is strictly preprocessed: the cleaning process includes removing repeated sentences, non-standard characters, noise data and irrelevant marks. Word segmentation involves decomposing sentences into word or sub-word units to meet the processing needs of the model. The alignment step ensures that the sentences in the source language and the target language correspond semantically correctly.
Back propagation algorithm and gradient descent optimization strategy are adopted in model training. The model iterates over the training data and gradually learns the mapping relationship from the source language to the target language. Adam optimizer is used to accelerate the convergence process, and dropout technology is introduced to prevent over-fitting.
Taking one-dimensional convolution as an example, assuming the input is a one-dimensional array, its convolution kernel must also be a one-dimensional array. The one-dimensional continuous convolution formula is shown in (1), and the one-dimensional discrete convolution formula is shown in (2); (xleft( k right)) is the input of the convolutional layer mentioned earlier, (hleft( k right)) is the convolutional kernel, and (yleft( k right)) is the output of the convolutional layer:
In the formula, (hleft( k right),xleft( k right)) can be regarded as a variable function for performing convolution operations, (p) is an integral variable, (k) represents the number of digits moved, and (*) represents the convolution operation. Taking one-dimensional discrete convolution as an example, (P) is the upper limit of the convolution.
LSTM contains a storage unit that will update the information it stores as needed. The LSTM unit at time (t) consists of a series of transformation functions, including input function (left( {i_{t} } right)), forget function (left( {f_{t} } right)), output function (left( {o_{t} } right)), memory function (left( {c_{t} } right)), and hidden state function (left( {h_{t} } right)). The expressions for each function are as follows.
Among them, (Theta) represents bitwise multiplication, (b) represents offset vector, and (z_{t}) represents state update function.
In this study, five corpora, namely NUCLE, Supervised, CoNLL, JFLEG and Lang-8, were selected as test data sets. Among them, NUCLE contains 1200 articles, Supervised has 5000 articles, CoNLL has 200 articles, JFLEG has 2000 articles, and Lang-8 has 8000 articles. Their total sentence pairs are 50000, 80000, 3000, 510000 and 210885 respectively. These articles cover six fields: sports, military, economy, education, science and technology and society, which ensures the diversity and universality of data.
In the preprocessing step, firstly, news source details, special symbols and repeated sentences in sentence titles are removed, so as to reduce corpus noise. Then, features are extracted from the preprocessed corpus, and new dependency syntax and part-of-speech tagging techniques are used. To do this, mark the word before the comma as “comma” and the rest as “NULL”. Then, all commas are removed, and all features and labels are converted into the format needed for model training. CRF +  + Toolkit is used to train the Conditional Random Field (CRF) model to further optimize the feature representation.
Software environment: Based on Python programming language, the experiment builds and trains MT model with the help of deep learning frameworks such as TensorFlow and PyTorch. At the same time, NLTK and spaCy are used for text preprocessing and feature extraction.
Hardware configuration: The experiment is run on a server equipped with NVIDIA GTX 1080 Ti GPU, Intel i7 CPU and 32 GB RAM, which ensures high efficiency and speed of model training.
VR environment integration: In this study, the Oculus Rift VR platform and the corresponding controller are used to provide an immersive interactive experience. Customize and develop VR scenes related to teaching content through Unity 3D, such as simulating classrooms and laboratories. After many user tests, the interactivity and user experience are optimized. Under certain VR conditions, the real-time translation accuracy of MT model is tested, and its response speed and translation quality are assessed by simulating real dialogue scenes.
Forty third-year English majors, aged between 19 and 23, with an average age of 21.2, were recruited in the experiment. All participants have passed CET-4 and have no previous experience in VR. Participants were randomly divided into experimental group (20 people) and control group (20 people) according to their language proficiency test scores. The sex ratio of the two groups (experimental group: 8 males and 12 females; Control group: 7 males and 13 females) and English proficiency (the average score of CET-4 in experimental group is 523; There was no significant difference (p > 0.05) in the control group (518 points).
Students in the experimental group wear VR equipment, enter a customized VR learning environment, interact with virtual characters, and realize real-time translation and communication with the help of MT model. The control group used traditional teaching methods, including classroom study, homework and paper textbooks. The experiment lasted for 4 weeks. The experimental group had VR interaction three times a week (45 min each time), while the control group had traditional classroom learning for the same duration.
Students’ learning data and feedback were collected during the experiment. The results of statistical analysis show that the students in the experimental group have improved in participation and academic performance to varying degrees. This shows that the combination of deep learning and VR technology not only improves students’ interest and learning motivation, but also enhances their language ability. Table 1 presents the key comparative information between the experimental group and the control group in detail, including participants’ characteristics, teaching tools, teaching scenarios, intervention duration and assessment methods.
Figure 3 shows the comparison of translation accuracy between the model in this study and the traditional model in various corpora. These corpora represent various language styles, fields and themes, which ensure a comprehensive assessment of the model performance. The results show that the translation accuracy of this model is over 98%, which is better than the traditional model with an accuracy lower than 97%. The high accuracy achieved by our research model can be attributed to the optimization of its deep learning architecture and the customized training for specific corpora. This accuracy is especially important for professional translation scenarios, such as legal and medical documents.
Comparison of translation accuracy between different models.
Figure 4 shows the F1 score changes of the two models during the iterative training. The results show that with the increase of iteration times, the F1 scores of the two models are on the rise, but the F1 scores of the model designed in this study can always be higher than 90% and stable at around 93%, while the F1 scores of the traditional model never exceed 0.9. This research model shows higher performance stability in the iterative training process, and its F1 score keeps at a high level all the time. Although the F1 scores of both models increase with the increase of iteration times, this research model can reach a high performance level in a few iterations. This helps to shorten the training time.
Comparison of F1 values.
Figure 5 shows the comparison of semantic information recall rates between the two models when translating sentences in different corpora. The results show that the semantic information recall rate of this research model is over 90%, which is significantly higher than that of the traditional model. The high recall rate of semantic information shows that this research model can better understand and retain the semantic information of the original sentence in the translation process. The advantages of this research model will be particularly obvious in scenes that need to convey semantic information accurately, such as international business and diplomacy.
Comparison of recall rates.
In order to verify the effectiveness of this model in English teaching, 40 third-year English majors were tested. These students, ranging in age from 19 to 23, come from different language backgrounds and use different mother tongues. They all have a certain level of English, because they have taken courses for English majors. They were equally divided into group A and group B, with 20 students in each group. In each group, students numbered 1–10 are instructed to use the method described in this paper for translation, while students numbered 11–20 do not use this method. The test document is a 578-word English technical document. The final result is assessed based on two dimensions: translation quality and translation efficiency. BLEU score is used to measure translation quality and translation time is used to measure translation efficiency. As shown in Table 2, compared with Group B, the translation efficiency and quality of Group A are obviously improved, which shows that the method designed in this paper can effectively improve students’ translation skills.
Through in-depth analysis of the comparison results of the feature matching degree of sentence topic words between the two models in Fig. 6 on different corpora, we can clearly see that when using the proposed model for translation, the feature matching degree of sentence topic words is as high as 90% or more. In Table 3, this highlights the excellent ability of the model in capturing and preserving the core features of the original topic words. In contrast, traditional models have a feature matching degree of less than 85% in the translation process, indicating certain limitations in handling complex language structures and semantic relationships. This significant difference not only proves the superiority of the model in translation tasks, but also provides strong support for its widespread application in high-precision translation demand scenarios. Whether in professional fields such as legal documents and medical literature, or in important occasions of cross-cultural communication and information transmission such as international business and diplomacy, the model proposed in this article is expected to significantly improve the quality and accuracy of translation, and contribute significantly to the progress and development of related fields.
Comparison of feature matching degree.
It is not difficult to find from Table 4 that the model proposed in this paper is superior to other advanced models in translation accuracy, F1 score and semantic information recall. This shows that this model can better capture and retain the semantic information of sentences while maintaining the accuracy of translation.
It is not difficult to find from Table 4 that the model proposed in this paper is significantly higher than other models in feature matching, which shows that it can better retain the core features of the original sentence in the translation process. The translation speed and memory occupation of this model are also at a high level, but considering its superior performance, these are acceptable.
Table 5 shows the learning progress of students with different learning backgrounds in VR + MT environment. The results show that intermediate learners have achieved the greatest learning progress and the highest learning efficiency in VR + MT environment.
Intermediate learners may be more adapted to VR + MT, a new learning method, and can make full use of its advantages to improve their English. VR + MT environment provides personalized learning paths for students with different learning backgrounds, which helps to meet the different needs of students. In the future, we can further study how to optimize the VR + MT environment to better adapt to students with different learning backgrounds.
The experimental results of this study strongly prove that it is feasible to integrate VR and MT technology into ELT. These achievements reflect the possibility of technology integration, and also show significant educational advantages. The machine translation model designed in this study has a translation accuracy as high as 98.5%, far exceeding the traditional model. Such a high accuracy is crucial to ensure the completeness and correctness of the translated content. The F1 score is stable at about 93%, which further proves that the model is robust and reliable. The high F1 score shows that the model performs well in accuracy and recall, which is an important index to evaluate the overall quality of machine translation. The semantic information recall rate of 92% also shows that the model can accurately capture and retain the core meaning of the original text.
A comparative experiment involving 40 English majors further verifies the effectiveness of the model in improving translation efficiency and quality. Students who use VR + MT method get higher BLEU score and shorter translation time than those who don’t use this method. The average translation time of group A students using this tool is 0:48:39, and the BLEU score is 78.83. The average translation time of group B students who also used this tool was 0:51:18, and the BLEU score was 81.36. These data clearly show that VR + MT method is helpful to achieve faster and more accurate translation, which is very important to improve students’ language ability.
In addition, using the model proposed in this study, the feature matching degree of sentence subject words is over 90%. Such a high feature matching rate highlights that the model can accurately capture and retain the core features of the original sentence, which is of great significance to maintaining the contextual coherence of the translated text. In contrast, the feature matching degree of the traditional model is less than 85%, which shows that it has limitations in dealing with complex language structures and semantic relationships.
This study puts forward several strategies on how to effectively integrate VR and MT into English teaching practice. On the one hand, teachers can use VR to create an immersive learning environment, simulate real scenes, and improve students’ participation and learning motivation. VR enables students to interact with virtual characters, experience different cultural situations and greatly enrich the language learning experience. On the other hand, the integration of machine translation technology can provide real-time translation support, help students overcome language barriers and communicate more effectively, which is especially beneficial to students who have difficulties in learning a second language and communicating in real time.
Research shows that intermediate learners may benefit the most from VR + MT environment. Intermediate learners show the greatest learning progress and the highest learning efficiency. This discovery means that VR and MT technology may be effective in narrowing the gap between junior and advanced learners, and can provide customized learning experiences to meet their specific needs.
Theoretically, this study adds new contents to the related literature on the application of VR and MT in the field of education. By demonstrating the feasibility and effectiveness of integrating technology into English teaching, it lays a foundation for future research in this field. From a practical point of view, the research results provide valuable insights for educators and policy makers who expect to improve the quality and efficiency of language teaching. The VR + MT method proposed in this study can be used as an example to develop innovative teaching methods and improve students’ learning results with the help of cutting-edge technology.
However, while enjoying the convenience brought by VR and MT technologies, we must also face up to data privacy and security issues. These technologies will involve the collection and processing of a large number of sensitive user data in the process of use. Once these data are leaked, abused or attacked by security, it may bring serious losses and troubles to users.
In order to meet these challenges, a series of strict security measures must be taken. First, the collected user data should be encrypted. Secondly, strict access control mechanism should be established. At the same time, it is necessary to establish and improve the relevant legal and policy framework, and clarify the use norms and accountability mechanisms of technology.
User education is also an indispensable part. Educators should use various ways to raise users’ awareness of data protection, so that they can consciously protect their privacy in the process of using technology.
Although this study has achieved some promising results, there are still several limitations. The model has limitations in storage space, test data and reasoning time, and needs to be further optimized. At the same time, there are relatively few experimental data, and more extensive tests are needed to fully verify the performance of the model.
Future work can focus on the following directions. On the one hand, it plans to integrate more advanced deep learning technology, expand bilingual corpus and improve the accuracy of machine translation. On the other hand, we will study ways to optimize the VR + MT environment to better adapt to students with different learning backgrounds. In addition, it is also intended to explore the application of this integrated technology in teaching scenarios of languages other than English.
This study explores the application of VR in ELT and shows its great potential in teaching reform. The introduction of VR has built an immersive English learning environment for college students and promoted students’ free learning experience. This study demonstrates the unique advantages of VR technology in promoting ELT reform. VR also shows great potential in improving the overall teaching effect and reducing the teaching cost.
The results show that the quality of college English translation teaching has been improved by organically combining human intervention with machine advantages. The innovative MT model based on DL proposed in this study achieves 98.5% translation accuracy, and the F1 score is stable at around 93%, and it shows high stability in practical application. This trend indicates that the informationization of English translation teaching in colleges and universities will steadily move towards a higher level.
However, this study also has some limitations. Limited by storage space, test data and reasoning time, the proposed model still needs further optimization. Future research can focus on improving the accuracy of MT and expanding and enhancing bilingual corpus in various ways.
The datasets used and/or analyzed during the current study are available from the corresponding author Lynette P. Rue on reasonable request via e-mail philipwuhu@163.com.
Deep learning
Machine translation
Virtual reality
English language teaching
Quality of experience
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Anhui Province Philosophy and Social Science Planning Youth Project: Research on the Inheritance and Translation of Anhui Folk Literature Intangible Cultural Heritage, AHSKQ2020D190.
School of Foreign Studies, Anhui Polytechnic University, Wuhu, 241000, China
Tao Su
Education Program Supervision in Mathematics in the Department of Education, Trece Martirez City, 4109, Cavite, Philippines
Lynette P. Rue
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Tao Su: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, funding acquisition. Lynette P. Rue: writing—review and editing, visualization, supervision, project administration.
Correspondence to Lynette P. Rue.
The authors declare no competing interests.
The studies involving human participants were reviewed and approved by School of Foreign Studies, Anhui Polytechnic University Ethics Committee (Approval Number: 2022.6520002). The participants provided their written informed consent to participate in this study. All methods were performed in accordance with relevant guidelines and regulations.
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Su, T., Rue, L.P. The analysis of English language teaching with machine translation based on virtual reality technology. Sci Rep 15, 15845 (2025). https://doi.org/10.1038/s41598-025-00592-4
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Received: 03 October 2024
Accepted: 29 April 2025
Published: 06 May 2025
DOI: https://doi.org/10.1038/s41598-025-00592-4
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