The emergence of AI presents both a crisis and an opportunity for interpretation and translation education.
In an era where collaboration between technology and humans is essential, this paper presents the competencies that interpreters and translators must possess and the educational methods that instructors should design.
Artificial Intelligence Encyclopedia.
You can find the artificial intelligence knowledge you need at aiseries.oopy.io.

Like the Pakte Group, the EMT translation competency model presents five core competency areas: language and culture, translation, technical skills, personal and interpersonal skills, and service delivery.
First, linguistic and cultural competence includes the ability to understand language variations, cultural elements, values, and references, and to write according to the cultural conventions of the culture in which the target text is used.
Translation competence encompasses strategic, methodological, and thematic skills for translation.
Technical skills include the ability to use IT applications, search engine corpora, CAT tools, file management, web technologies, data literacy, and workflow management tools.
Personal and interpersonal skills and service delivery capabilities are what differentiate us at Pakte Group, and include time management, teamwork, lifelong learning, and meeting deadlines.
--- From "01_Traditional Interpretation and Translation Skills"

Papago is a translation service launched by the Korean portal company Naver in 2017, and is an NMT engine optimized for Korean-based language pairs.
'Papago' means parrot in Esperanto, and as its name suggests, it aims to provide AI translation that imitates and speaks various languages.
Papago has adopted pure neural network translation since its launch, and has touted its strengths in context-sensitive translation through machine learning in core language pairs such as Korean↔English/Chinese/Japanese.
It has a translation algorithm that reflects Korean linguistic characteristics such as word order, particles, and vocabulary usage, making it particularly strong in translating everyday conversational or colloquial language.
The user interface is also simple and optimized for mobile use, making it highly accessible for learners and general users.
Additionally, Naver continuously improves quality by learning from the vast web text data and user feedback it possesses.
--- From "03_AI-based Translation Tools and Their Use"

The development of three key technologies has made this remarkable transformation possible.
First, the performance of mobile processors has improved tremendously.
The latest chips, such as Apple's A17 Pro and Qualcomm's Snapdragon 8 Gen 3, now boast PC-level computing power and even feature dedicated AI NPUs, making them capable of handling complex AI tasks.
With over 12GB of RAM and 1TB of storage, it's now possible to run even the largest language models on a smartphone.
Finally, thanks to 5G networks, connectivity to cloud AI services has also become faster.
--- From "06_Interpreting in the AI ​​Era"

A comprehensive review of recent research suggests that translation and interpretation education models utilizing generative AI can be broadly categorized into three types.
The first is AI-assisted learning, which partially utilizes AI tools in existing classes. Typical examples include comparing and editing AI translation results, searching for terms, and storing them in memory.
The second is AI-centric, which uses AI as a primary content creation tool.
This includes prompt design, text generation, and role-play script creation.
The third is AI integration, which redesigns the entire curriculum by incorporating AI.
This includes real-time feedback, self-learning scenarios, and the use of AI tutors.

--- From "09_Redesign of Interpretation and Translation Education Curriculum"