May 22 – tcworld China

“Translation” for In-Cabin Experience Goes Beyond Translation: Testing in Automotive Content Localization

2026年5月16日 • 0 Comment

Automotive HMI localization goes far beyond simple text translation. This session will focus on the unique challenges faced by Chinese automakers going global: when Chinese is the source language, how can we avoid cycle delays and semantic deviations caused by “double translation”? We will break down the entire process of automotive HMI localization testing and introduce various testing scenarios. We will also present real-world “pain points” across different languages in UI layout, RTL adaptation, and voice recognition. Furthermore, we will analyze the fundamental differences between automotive HMI localization and traditional app localization from five dimensions: safety, interaction, ecosystem, and beyond. The goal is to help the audience build a complete understanding — shifting from a “firefighting” approach to translation toward a “systematic” testing framework.

Internationalization of UI Design and AI Assistance

2026年5月15日 • 0 Comment

This session discusses the necessity of addressing internationalization in UI design, the key aspects that need to be considered when designing for internationalization, and how to adopt internationalization design strategies to minimize future localization issues. It also explores how to leverage AI tools and techniques to support and enhance internationalization design efforts.

From a Documentation Team to Pioneers in AI Transformation

2026年4月10日 • 0 Comment

As a documentation team, we are often the “fall guys” — taking the blame while having little voice. How can we find a way to break the deadlock? By restructuring the manual development process through structural transformation and AI-powered content creation. Supporting localization and global business, we enhance the core value of the team. Through rich media transformation, we increase the team’s output value and customer satisfaction. What plans and explorations have we made at different stages? Let us share our team’s transformation journey, in the hope of providing some inspiration for other teams still exploring their way.

Analyzing NVIDIA’s Documentation Ecosystem: How Domestic AI Chips Can Bridge the Gap

2026年4月10日 • 0 Comment

NVIDIA’s technical documentation system is at the heart of its CUDA ecosystem dominance, seamlessly connecting products with developers. In this talk, we’ll break down the architecture, writing logic, tools, and publishing systems behind NVIDIA’s official docs — and show how standardized, modular, scenario-based documentation lowers developer barriers and amplifies technical authority. We’ll then compare this with the current state of domestic AI chip documentation, identify critical gaps beyond hardware performance, and explore actionable pathways for China’s chip ecosystem to catch up and break through.

Humanizing Technical Information: Smart Strategies for Error Code Content Management

2026年4月10日 • 0 Comment

Error messages are a critical touchpoint for software-to-user communication. The quality of their expression directly impacts problem-solving efficiency and user satisfaction. However, traditional error message management suffers from systemic issues: developers write using technical jargon that users find difficult to understand; content is scattered throughout the code without unified management; multilingual translation quality is inconsistent, with terminology discrepancies; and users face complex, inefficient paths to find and access information. These pain points fundamentally reflect the absence of a technical content management system.

This talk presents a systematic methodology covering the complete lifecycle of error messages:

• Content Design: Establish user-centered information architecture and writing standards, translating technical language into understandable and actionable user language

• Intelligent Production: Leverage AI to automatically identify error sources in code, intelligently generating user-friendly expressions that comply with technical writing standards based on context

• Professional Localization: Build domain-specific terminology databases and translation memories to ensure accuracy and consistency of multilingual content

• Efficient Delivery: Enable natural language queries and personalized diagnostic recommendations through an intelligent knowledge base

This methodology has been validated through practical implementation and can provide technical communication teams with actionable insights for their intelligent transformation.

BISTU Technical Communication University-Industry Collaboration Case — Practices Based on the “University-Industry Collaboration Guide for Technical Communication”

2026年4月10日 • 0 Comment

Under the guidance of the group standard “University-Industry Collaboration Guide for Technical Communication,” Beijing Information Science & Technology University (BISTU) has achieved encouraging progress in the following areas:

1) Educational Collaboration
BISTU has established the nation’s first micro-major in Technical Communication, open to undergraduate students across all disciplines. In terms of curriculum development, technical communication courses at the university are offered at four levels: undergraduate programs in Translation, MTI (Master of Translation and Interpreting) students, university-wide elective courses, and the micro-major program. Industry mentors are invited to co-develop courses and laboratories.

2) Research Collaboration
The university undertakes commissioned research projects from enterprises.

3) Value-Added Collaboration
The university participates in the development of industry standards and carries out consulting activities.

4) Management Collaboration
The university hosts industry exchange forums, building a platform for dialogue between the industry and the education sector.

The collaboration between the technical communication industry and universities still faces certain challenges, which this presentation will also address.

From Manual to Automated: A Practical Introduction to the Model Context Protocol for Technical Writers (screen recording)

2026年3月10日 • 0 Comment

Technical writers spend hours on repetitive tasks like extracting information from bug tracking systems, reformatting content across platforms, and keeping documentation synchronized with multiple systems. The Model Context Protocol (MCP) is an open standard for connecting AI assistants to enterprise tools, and it offers a practical solution.

This session introduces MCP through a real-world example: automating release notes generation. We’ll explore what MCP is, how it works, and why technical communicators should care about it. Using a working demonstration, we’ll examine the key decisions involved in implementing MCP-based automation: choosing between existing servers versus building custom ones, balancing automation with human review, and measuring actual time savings versus effort invested.

Attendees will leave understanding how to evaluate MCP for their own repetitive tasks, where to find ready-to-use MCP servers, and practical first steps for getting started with AI-powered documentation automation.

Reinventing and Evolving: The New Paradigm of Content Strategy Driven by AI

2026年2月10日 • 0 Comment

The rise of generative AI is fundamentally redefining the boundaries and value of content strategy. While the traditional “plan → create → distribute → manage” content loop remains a useful framework, its core has undergone profound changes: shifting from “document creation” to building an “intelligent content ecosystem” and moving from serving “human readers” to accommodating both “machine understanding and invocation.” This isn’t merely an addition to existing content strategies; it represents a paradigm-level transformation.

This session explores the construction of an Intelligent Content Ecosystem, where knowledge continuously gains value through human-AI collaboration. It highlights the value leap for content teams—transitioning from information communicators to strategic decision-support partners. For practitioners, mastering this new logic means evolving from executors into architects of enterprise knowledge infrastructure.

When AI Makes Everything Faster: Why Has Decision-Making Become the Greatest Bottleneck?

2026年2月10日 • 0 Comment

AI is dramatically expanding the external world: more information, faster analysis, more options. Yet a counterintuitive outcome is emerging: why is decision-making becoming harder — and scarier — with AI?

When AI can present multiple “seemingly reasonable” answers at once, traditional decision-making approaches that rely on external analysis, data, and models begin to break down. External systems can no longer make the real trade-offs for us. This signals a critical shift: the rise of AI is moving decision-making from “looking outward for answers” back to “looking inward for judgment.” In other words, AI is not the adversary of decision-making — it is finally returning the ultimate responsibility of choice back to the human. When AI optimizes the external dimension to its limit, it pushes us to evolve our decision-making role: from being analysts of external information and environments to becoming Chief Decision Officers aligned with our inner values, purpose, and meaning.

What does a Chief Decision Officer do? When options approach infinity and efficiency is high, what truly determines outcomes is no longer how much information we possess, but clarity on:
What are you choosing for?
Which direction are you willing to pay the price for?
Behind your hesitation — is it rational weighing, or internal avoidance?

This topic focuses on the real bottlenecks decision-makers face in the AI era, exploring why decision difficulty is shifting from the technical level to the human level — cognition, psychology, and identity — and introduces the perspective of Purpose-Driven Decision-Making. It helps audiences understand: when AI perfects the external dimension, what truly cannot be avoided is the upgrade of human internal decision-making capability. Decision-making must return to a “whole person” to complete the final judgment. It is not just about external information — it is about integrated human judgment in complex environments.

What the audience will take away is not more tools or models, but a clearer decision-making lens: how to make the critical trade-offs that only a “human” can make — even with AI’s support.

From Tacit Knowledge to Tangible Assets: Digital Transformation of Validation Work Powered by the SECI Model

2026年2月10日 • 0 Comment

Manufacturing enterprises engaged in validation work have long faced the challenge of tacit knowledge walking out the door with experienced personnel, while paper-based models fail to support knowledge reuse. This presentation uses the digital transformation of pharmaceutical equipment validation as a case study to illustrate how the SECI knowledge conversion model can be applied to systematically consolidate dispersed validation experience into reusable explicit assets, thereby building a knowledge management system that covers the entire validation lifecycle.

The presentation will present a complete methodological pathway from current-state diagnosis, system architecture design, to knowledge operation. It will focus on analyzing the mapping of the four SECI stages onto validation scenarios, as well as the construction framework of a four-dimensional knowledge graph encompassing Know-What, Know-How, Know-Why, and Know-Whom. Through cross-industry comparisons, the presentation will demonstrate how validation work in sectors such as pharmaceuticals, medical devices, and precision manufacturing can adapt these approaches.

Attendees will gain a knowledge conversion methodology for digitally transforming validation work, understand key implementation steps, system integration strategies, and risk management essentials. They will also master a reusable framework for capturing validation knowledge assets, providing a practical transformation pathway for technical communication and knowledge management practices.

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