On the afternoon of May 28, 2026, the "Embarking on a New Digital Journey through Industry-Education Integration — Launch and Signing Ceremony of the Industry-Education Community for Data Annotation and Intelligent Data Governance" was held at the Yangpu Yungongchang Tower in Shanghai. The event was hosted by Grouphorse Group and supported by Shanghai Huo  chuangyun Information Technology Co., Ltd.

Corporate representatives from the fields of artificial intelligence, data services, and intelligent language services, together with delegates from industrial parks and universities, gathered to witness the official launch of this industry-education integration platform dedicated to serving the development of high-quality datasets, the high-quality growth of the data annotation industry, and the cultivation of digital talent.

At the event, speeches were delivered by Gu Danting, Business Director of Public Service Sector at Volcanic Engine; Yan Weiqi, General Manager of Shanghai Huo chuangyun Information Technology Co., Ltd.; and Tang Xing, Founder of Grouphorse Group. Zhang Li, Chairperson of Grouphorse Group, presented an overview of the industrial ecosystem and the development plan for the community.

Representatives from enterprises including Shanghai Maidashu Data Technology Co., Ltd., EGO Group, Shanghai Tangui Intelligent Technology Development Co., Ltd., and Shanghai Shuzhe Data Co., Ltd., as well as representatives from Fudan University, Tongji University, Shanghai International Studies University, Tianjin Foreign Studies University, and Shanghai Ocean University, delivered speeches successively.

Subsequently, the Industry-Education Community for Data Annotation and Intelligent Data Governance was officially launched, accompanied by a collaborative signing ceremony and the unveiling ceremony of the "Grouphorse Data Annotation Industry College at Zhengzhou University of Science and Technology."

In her speech, Gu Danting noted that artificial intelligence is transitioning from technological innovation to widespread industrial application. High-quality data serves as the "source of living water" for the evolution of large models, while data annotation and data governance form the core foundation for AI implementation. Volcanic Engine will leverage its capabilities in large models, data governance, and AI cloud-native infrastructure to support the community in bridging technology infrastructure, industrial scenarios, university resources, and talent cultivation.

Gu Danting, Business Director of the Public Service Sector at Volcanic Engine

In his speech, Yan Weiqi noted that this event marked the first ecosystem activity held since the launch of the Volcanic Engine AI Agent Industrial Park on March 2, 2026. The Volcanic Engine Industrial Park is jointly established through in-depth collaboration between Yangpu District and Volcanic Engine, with its primary operations undertaken by Huochuangyun, a subsidiary of Yangpu Science and Technology Innovation Group. The park is positioned around two core functions: serving as the "birthplace" of the data annotation industry and as a "testing ground" for AI industry innovation.

Looking ahead, Yan Weiqi stated that Huochuangyun will continue to improve its support systems across policy, space, collaboration, talent, and services, providing more efficient and practical service guarantees for resident enterprises and partner universities.

Mr.Yan Weiqi, General Manager of Shanghai Huo chuangyun Information Technology Co., Ltd.

In his video address, Tang Xing, Founder of Grouphorse Group, noted that the country has been intensively rolling out policies around data elements, high-quality data, and "AI+education," signaling that data annotation and intelligent data governance are now facing significant development opportunities. By launching this community, Grouphorse Group aims to organize university students and transform them into the data annotation and data governance talent needed in the AI era; bring real-world enterprise tasks into universities, forming a closed loop of "real-world scenarios—real-world training—real-world delivery"; and gradually establish annotation standards, quality control benchmarks, talent evaluation criteria, and project management systems, thereby advancing the field from fragmented labor toward standardized training, organized delivery, and platform-based operations.

Tang Xing stated that Grouphorse Group has long served the United Nations and numerous international organizations, and as a member of the United Nations University's Global AI Network, it continues to focus on issues such as responsible AI, data governance, digital trust, and global governance, having accumulated international resources and quality management expertise in language services, international communication, global governance, and AI-related fields. Leveraging its long-established methodologies in task decomposition, personnel allocation, process quality inspection, and result verification, Grouphorse Group is now transferring its quality management experience from the language services sector to data annotation and intelligent data governance scenarios, providing process-oriented support for high-quality data delivery.

The launch of this Industry-Education Community for Data Annotation and Intelligent Data Governance represents a significant step in Grouphorse Group's AI-driven transformation, as well as a systematic exploration to connect university talent resources, enterprise real-world scenarios, park industrial ecosystems, and international capabilities. As the founding initiator and the secretariat unit of the community, Grouphorse Group will be responsible for daily coordination, member services, project matchmaking, and outcome consolidation, driving the formation of sustained collaborative mechanisms among universities, enterprises, parks, and industry resources.

Tang Xing emphasized that the secretariat not only undertakes daily coordination duties but also plays a pivotal organizational role in project matchmaking, standard consolidation, quality management, and commercialization of outcomes. The value of the community lies in bringing together otherwise fragmented university talent, enterprise scenarios, expert resources, and quality control processes to form a schedulable, trainable, and deliverable industrial collaboration mechanism, enabling key issues such as "where projects come from, where talent is located, how quality is assured, and how outcomes are delivered" to be resolved in a closed-loop manner within a single framework.

Currently, the AI industry is rapidly shifting from model capability competition to scenario deployment competition. Whether it is large model training, agent development, multimodal applications, or vertical scenario deployments in law, education, healthcare, manufacturing, finance, and international communication, high-quality data underpins them all. Data annotation is no longer merely simple data processing; it is becoming a critical foundational link connecting data resources, industry knowledge, model training, and application delivery.

In recent years, the country has been consistently releasing policy signals around data elements, high-quality datasets, the data annotation industry, and "AI+education." From promoting the high-quality development of the data annotation industry to intensively implementing the "AI+" initiative, and from advancing the systematic rollout of "AI+education," high-quality data, digital talent cultivation, and industry-education integration are becoming vital pillars for the development of the AI industry. The launch of the Industry-Education Community for Data Annotation and Intelligent Data Governance is a concrete response to this industrial trend and policy direction.

Against this backdrop, Grouphorse Group has been steadily advancing its business transformation around artificial intelligence, data governance, and intelligent language services, while accelerating its presence in the AI Agent industrial ecosystem in Yangpu, Shanghai. Drawing on its quality management experience from long-term service to international organizations, its nationwide university collaboration network, and its multilingual semantic processing capabilities, Grouphorse Group has gradually developed the organizational foundation, process awareness, and delivery capacity for high-quality data services.

In the field of AI and data governance, Grouphorse Group's strategic initiatives have consistently attracted attention from relevant stakeholders, and the Group has hosted visits and exchanges with national ministries, local governments, and industry partners. The launch of this community in Yangpu, Shanghai, also marks the entry of Grouphorse Group's industry-education integration strategy around high-quality data services into a substantive implementation phase.

Mr.Tang Xing, Founder of Grouphorse Group

In her presentation on the development of the community, Zhang Li, Chairperson of Grouphorse Group, outlined that the community will be built around three key principles: "real-world scenarios, collaborative co-construction, and long-term value." Going forward, the community will bring real industry scenarios from enterprises into universities, and jointly carry out curriculum development, project-based training, competency assessment, and outcome commercialization with higher education institutions, gradually forming a complete closed loop of "curriculum—training—capability—employment."

Zhang Li noted that as artificial intelligence evolves from generalization to industry-specific and precision applications, what the industry truly needs is no longer just basic data processors, but interdisciplinary talent equipped with language comprehension, semantic judgment, industry-specific knowledge, and quality governance capabilities. For schools of foreign languages, translation studies, and liberal arts institutions, this presents both a challenge and a significant opportunity. The community aims to help universities transform their existing disciplinary strengths into new, identifiable, trainable, and deliverable competencies for the AI era.

According to the introduction, the community will initially adopt a development approach of "standardized framework with tiered implementation," featuring three types of partnership models: member units, inaugural co-building units, and demonstration base co-building units. Member units are primarily for early-stage participation, information sharing, and resource matchmaking; inaugural co-building units will focus on importing curriculum modules, faculty development programs, and real-world project training; demonstration base co-building units will work with partner institutions based on their unique characteristics to promote customized training programs, multimodal project integration, on-campus kick-off events, and thematic forums, gradually forming distinctive bases with regional influence and industry demonstration impact.

Ms. Zhang Li, Chairperson of Grouphorse Group

University representatives also shared their perspectives on topics including disciplinary development, talent cultivation in the AI era, transformation of foreign language education, data annotation training, and industry applications. These included Wang Yanqiang, Assistant Dean of the College of Foreign Languages and Literature and Secretary of the Translation Department Party Branch at Fudan University; Xu Wensheng, Professor and Doctoral Supervisor at the School of Foreign Languages of Tongji University; Cao Shujing, Deputy Secretary of the Party Committee of the College of English at Shanghai International Studies University; Pan Yingna, Head of the Data Department of the AI Translation Laboratory at Tianjin Foreign Studies University; and Cao Shouqi, Secretary of the Party Committee of the College of Information Sciences at Shanghai Ocean University.

Wang Yanqiang, Assistant Dean of the College of Foreign Languages and Literature and Secretary of the Translation Department Party Branch at Fudan University, observed that artificial intelligence has not rendered foreign language and translation studies obsolete. On the contrary, it is prompting universities to rethink the place of language, knowledge, and human judgment in the society of the future. On the surface, data annotation is about labeling data, but at a deeper level, it is about helping machines understand the world. For a machine to comprehend the meaning of a sentence, the tone of a text, a cultural context, a legal rule, or a value orientation, it depends on fine-grained human judgment of language, semantics, context, and rules—precisely the kind of capability that foreign language and translation studies have long cultivated. The advent of the AI era does not signal the departure of liberal arts graduates; rather, it marks their re-entry with reorganized competencies.

Mr. Wang Yanqiang, Assistant Dean of the College of Foreign Languages and Literature and Secretary of the Party Branch of the Translation Department, Fudan University

Xu Wensheng, Professor and Doctoral Supervisor at the School of Foreign Languages of Tongji University, noted that in a century-old institution renowned for its strengths in engineering and technology, dialogue between foreign languages and engineering or information disciplines is not a cross-disciplinary barrier but a daily reality. Training in translation technology, corpus linguistics, and computer-assisted translation aligns closely with the requirements of data annotation, including "multilingual semantic precision, interpretable rules, and auditable processes." He believes that the School of Foreign Languages of Tongji University can participate in the community's development by leveraging three key assets: the foundation of "technical foreign languages," the integrated "technology + humanities" strengths, and the extensive industry-university network of the Yangtze River Delta region. This would translate disciplinary training into a deployable, integrable, and actionable resource system. Genuine industry-education integration, he emphasized, should not stop at the ceremonial unveiling of partnerships. Rather, it must involve bringing real projects into the classroom, ensuring that outputs flow back to industry, and using feedback to continuously refine and improve training programs.

Professor Xu Wensheng, Doctoral Supervisor at the School of Foreign Languages, Tongji University

Cao Shujing, Deputy Secretary of the Party Committee of the College of English at Shanghai International Studies University, offered her perspective from the angles of student employment and talent cultivation. She pointed out that many foreign language students today are not lacking in learning ability or overall competence, but rather in access to emerging industries. Many universities are not indifferent to practical training, but lack authentic, stable, and sustainable project scenarios. Many enterprises are not unwilling to tap into university talent, but lack an intermediary mechanism to translate industrial needs into teaching tasks, training modules, and job opportunities. The Industry-Education Community for Data Annotation and Intelligent Data Governance, she noted, is precisely the bridge that connects these gaps. Through the community, universities can bring real industrial demands into the classroom, transform real enterprise projects into training modules, and convert student learning outcomes into demonstrable, assessable, and employable credentials.

Ms. Cao Shujing, Deputy Secretary of the Party Committee of the College of English, Shanghai International Studies University

Pan Yingna, Head of the Data Department of the AI Translation Laboratory at Tianjin Foreign Studies University, shared in her remarks that leveraging the university's multidisciplinary expert resources in multiple languages, the relevant team at Tianjin Foreign Studies University has accumulated practical experience in areas such as large model training, multilingual speech data collection and annotation, and video corpus processing in recent years. The team has developed a data supply capability covering "text—audio—visual" content and has piloted a three-tier collaborative mechanism in some projects, comprising "enterprise quality control (QC) + student project management + student annotation teams."

She noted that Tianjin Foreign Studies University has been engaged in ongoing collaboration with Grouphorse Group on data annotation training and real-world project tasks, with multiple cohorts totaling over 100 students participating in corpus processing, semantic judgment, rule recognition, quality review, and project coordination. This case demonstrates that data annotation and intelligent data governance can be effectively integrated into university settings, provide authentic training for students, genuinely serve enterprise needs, and generate sustainable outcomes in industry-education integration.

Ms. Pan Yingna, Head of the Data Department at the AI Translation Laboratory, Tianjin Foreign Studies University

Cao Shouqi, Secretary of the Party Committee of the College of Information Sciences at Shanghai Ocean University, offered his perspective from a science and engineering faculty standpoint. He noted that in the era of large models, if high-quality data is the "fuel" and algorithms are the "engine," then data annotation and governance serve as the "refinery" and "filter" that ensure the fuel is pure and the engine runs efficiently. The College of Information Sciences at Shanghai Ocean University has long been engaged in marine big data, smart fisheries, and AI applications, and has come to understand that whether dealing with marine monitoring data or complex multilingual text data, the core challenges are the same: without high-quality annotation, there can be no trustworthy intelligence; without interdisciplinary rules, there can be no sustainable governance. The College will leverage its technical strengths to serve as a technologically capable supporter that "understands semantics," a "human-centered" practitioner of data governance, and a "practical" connector for industry-education integration.

Mr. Cao Shouqi, Secretary of the Party Committee of the College of Information Sciences, Shanghai Ocean University

The attending university representatives generally agreed that artificial intelligence is reshaping the talent cultivation models in higher education. Universities should not treat AI merely as a technical course; rather, they should embed AI across multidisciplinary scenarios—including language, law, management, engineering, information sciences, marine studies, and international communication—using real-world projects to enhance students' comprehensive competitiveness for future industries. The more complex the technological system, the greater the need for people who understand language, rules, responsibility, and industry contexts.

Corporate representatives, speaking from perspectives of industry demand, project scenarios, data services, and intelligent applications, emphasized that the value of the community lies in integrating fragmented enterprise needs, university talent, and park resources to create a more stable collaborative mechanism. Going forward, member enterprises within the community will, in alignment with their respective business scenarios, continue to facilitate demand matchmaking in data annotation, corpus development, data governance, agent training, and industry applications, providing more concrete project entry points for universities to participate in AI industry-education integration.

Mr.Jiang Liyang, Vice President of EGO Intelligence

Ms. Gao Yijun, Vice President of Operations, Shanghai Maidashu Data Technology Co., Ltd.

Ms. Yang Yue'e, General Manager of Shanghai Tangui Intelligent Technology Development Co., Ltd.

Ms. Wang Xiaojing, General Manager of the Marketing Department, Shanghai Shuzhe Data Co., Ltd.

At the event, the first cohort of partner representatives from enterprises and universities jointly participated in the launch and signing ceremony of the Industry-Education Community for Data Annotation and Intelligent Data Governance.

Corporate partners included: EGO Group Co., Ltd., Shanghai Maidashu Data Technology Co., Ltd., Shanghai Tangui Intelligent Technology Development Co., Ltd., Shanghai Shuzhe Data Technology Co., Ltd., Suzhou Heshuju Information Technology Co., Ltd., Shanghai Zhihong Technology Development Co., Ltd., Shanghai Kusa Technology Co., Ltd., and Shanghai Yuliang Engine Network Technology Co., Ltd.

University partners included: Fudan University, Tongji University, Shanghai International Studies University, Tianjin Foreign Studies University, Shanghai Ocean University, Shanghai University of Finance and Economics, Shanghai University, China University of Geosciences (Beijing), Ningbo University, Southwestern University of Finance and Economics, University of Shanghai for Science and Technology, Shanghai Normal University, Shanghai University of Sport, Shanghai University of International Business and Economics, East China Jiaotong University, Shanghai Maritime University, Suzhou University of Science and Technology, Shanghai Institute of Technology, Shanghai Lixin University of Accounting and Finance, Nanjing Institute of Technology, Shanghai Customs College, Zhejiang International Studies University, Suzhou Institute of Technology, Yancheng Institute of Technology, Shanghai Dianji University, Shanghai Electronic Information College, Sanda University, Xianda College of Economics and Humanities of Shanghai International Studies University, Zhejiang Yuexiu University of Foreign Languages, Tianhua College of Shanghai Normal University, Zijin College of Nanjing University of Science and Technology, Tianfu College of Southwestern University of Finance and Economics, Zhengzhou University of Science and Technology, Shanghai Zhongqiao Vocational and Technical University, and Nanchang Vocational University of Science and Technology.

The parties will advance collaboration at different levels, including member units, inaugural co-building units, and demonstration base co-building units. Notably, Zhengzhou University of Science and Technology and Grouphorse Group jointly held the unveiling ceremony for the "Grouphorse Data Annotation Industry College at Zhengzhou University of Science and Technology," marking a transition of the community initiative from platform-level advocacy to project-level implementation.

Signing Ceremony for the First Cohort of Partners of the Industry-Education Community for Data Annotation and Intelligent Data Governance

Industry-Education Community for Data Annotation and Intelligent Data Governance — Launch Ceremony

Grouphorse Data Annotation Industry College at Zhengzhou University of Science and Technology — Unveiling Ceremony

Looking ahead, the collaboration will be driven by real project implementation as the key lever, with a focus on enhancing student capabilities and delivering tangible outcomes for enterprise scenarios. The goal is to explore a collaborative model in which "universities provide the talent base, enterprises supply real-world scenarios, parks offer the industrial ecosystem, and the community serves as the organizational mechanism."

Unlike traditional industry-education integration platforms, the Industry-Education Community for Data Annotation and Intelligent Data Governance places greater emphasis on scenario-driven and delivery-oriented approaches. On the one hand, the community aims to bring real enterprise tasks into universities, preventing industry-education integration from remaining at the level of ceremonial honor or abstract concepts. On the other hand, through project-based training, tiered competency assessment, quality review mechanisms, and case study accumulation, the community will help universities translate students' professional, linguistic, and AI application capabilities into industry-recognizable employability and delivery skills.

The community also plans to actively expand its international cooperation efforts. Leveraging Grouphorse Group's accumulated resources in international organization services, intelligent language services, global governance issues, and international communication, the community will explore specialized areas such as cross-lingual corpus development, international regulatory text annotation, responsible AI, and data governance for Chinese enterprises going global, fostering broader connections between Shanghai Yangpu's AI industrial ecosystem and universities, enterprises, and institutions both domestically and internationally.

From language services to intelligent data, from university training to industrial delivery, and from local parks to international collaboration, the launch of the Industry-Education Community for Data Annotation and Intelligent Data Governance marks a new practical phase in Grouphorse Group's AI transformation. For Grouphorse Group, this is not merely a business upgrade, but also a future-oriented organizational mobilization: in an era where AI is redrawing industry boundaries, enterprises equipped with university networks, international resources, language capabilities, and scenario-organizing capacity are well-positioned to find their place in the new track of high-quality data and intelligent governance.

The event was also supported by Shanghai Yangpu Technology Investment Development Co., Ltd., which provided the venue. As a platform for curriculum co-development, project-based training, competency assessment, and employment transition support, the community will adhere to the principles of open co-construction, pragmatic collaboration, and steady advancement. It welcomes more universities, enterprises, research institutions, and industry organizations to join in building a new ecosystem for data annotation and intelligent data governance for the AI era.

Starting from the AI Agent industrial ecosystem in Yangpu, Shanghai, the community will further aggregate university-industry resources, application scenarios, and innovation factors, positioning high-quality data as a critical pillar for AI industry development, while also offering new pathways for talent cultivation, youth employment, and enterprise intelligent transformation.