INTRODUCTION TO ARTIFICIAL INTELLIGENCE FOR TRANSLATORS AND INTERPRETERS - 2025/6

Module Overview

This module introduces students to the fundamentals of Artificial Intelligence (AI) and its applications in the field of translation and interpreting. The course will cover a wide range of topics, from the basic concepts of AI to more advanced areas and techniques including machine learning, large language models (LLMs) and LLM leveraging and customisation of automatic speech recognition (ASR) engines. Students will be taught different prompting techniques which allows them to interact with LLMs like ChatGPT, so they can develop advanced problem-solving skills.

Students will tackle AI-related tasks that are relevant in the fields of translation and interpreting, such as machine translation, customisation of ASR engines and the use of machine assistance in tasks requiring creativity skills (e.g. transcreation). They will also explore the ethical implications of AI and the potential impact of AI on the future of the language industry.

Through a combination of theoretical lectures and practical exercises, students will gain hands-on experience with AI tools and techniques. They will learn how to use AI to improve their own translation and interpreting skills, as well as how to develop innovative AI-powered solutions for the language industry.

Practical sessions will give participants hands-on experience in LLM prompting and using other AI-powered tools individually and in teams. The practical sessions will greatly enhance students¿ problem-solving skills, resourcefulness and their ability to identify problems, suggest alternative solutions and evaluate the outcomes of original methods used.

This module is suitable for students with a variety of backgrounds, including those with no prior knowledge of AI. It is particularly relevant to students interested in translation, interpreting, language technology, and computational linguistics.

Module provider

Literature & Languages

Module Leader

GOING Henrietta (Educatn Offc)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

Indicative module content:
- Introduction to artificial intelligence (AI) and natural language processing (NLP)
- Introduction to large language models and prompting techniques
- Examples of how to use AI and NLP in tasks related to translation and interpreting
- Hands on exercise on how to tune an automatic speech engine
- Automatic processing of multilingual texts

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:
¿ knowledge of the important concepts and paradigms of artificial intelligence.
¿ ability to analyse and design solutions to problems from translation technology using data-driven approaches
¿ knowledge of how to use artificial intelligence to accomplish tasks related to translation and interpreting



Thus, the summative assessment for this module consists of:


¿ Portfolio of Solutions to the Exercise and Reflective Comments on the Solutions (60%)
Students will be given homework every two weeks and will be asked to prepare a portfolio with their answers to the homework. In some cases, students will be asked to write "small essays" (200 - 250 words) explaining how they used some tools, whilst in other cases they will need to provide a short explanation of on a topic related to the ones discussed in the class. The portfolios can be seen as a diary of the practical activities covered in this module. The solutions are expected to indicate any problems that the students encountered and how they solved them. In order to pass, the students will have to submit all the homework. For the homework given in the first eight weeks, the solutions will be discussed in class and the students will have the chance to update their portfolios with reflective analysis of their initial solutions. All the pieces of homework given during the semester will have to be included in the final portfolio and the marking will focus on both how the solution was achieved and on the reflective analysis. The portfolios will be due at the end of the semester.

¿ Short essay (1000-1200) on a topic given in the class (40%)
Students will have to submit an essay on one of the more theoretical topics covered in the first half of the semester. The essay will be due at the middle of the semester.

Formative assessment

Formative assessment will focus on student participation and class discussions throughout the module. The solutions for the homework given in the first eight weeks will be discussed in the class.

The students will be able to discuss their solutions to the homework before submitting the portfolio.


Feedback

Students will be provided with detailed written feedback following assignment submission. Verbal feedback will also occur in class and individual appointments if required.

Module aims

• The module aims to: provide students with a thorough understanding of the basic and intermediate concepts from artificial intelligence and machine learning
• give students hands-on experience in how to tackle problems from translation and interpreting using artificial intelligence
• develop students' skills to understand the strengths and limitations of using artificial intelligence
• offer students opportunities to actively use large language models and different prompting techniques in order to effectively address language-specific tasks

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to provide students with problem-solving skills and a good understanding of artificial intelligence with emphasis on how it can be used to tackle problems from translation. By the end of the module, students will feel confident of their ability to use AI-based tools that allow them to accomplish useful goals related to translation and interpreting technologies. This is in line with our masters programmes overall aims of enhancing students¿ background in technologies for translation and interpreting, as well as enhancing their employability and resourcefulness and resilience.

The learning and teaching methods include:

¿ Seminars and workshops will be interspersed with opportunities for group and whole class discussions

¿ Contact hours will be complemented with materials and activities for guided study posted on SurreyLearn

¿ Self-study

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: TRAM511

Other information

Surrey's Curriculum Framework is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills and capabilities in the following areas: This module involves practical problem-solving skills that teach a student how to reason about and solve unseen problems. The main focus of the module is on problems related to translation and interpreting, but the AI-related skills developed as a result of successfully completing the module can prove useful for everyday problems enhancing their Resilience and Resourcefulness. The design of the module will encourage active participation, peer support and reflective engagement. Students will be encouraged to reflect on their own performance on the basis of formative feedback they receive. The students are expected to engage with online materials and a variety of software libraries. They will learn how and where to access AI-based tools, evaluate their relevance to the problem at hand and how to use them to solve their problems. Successful completion of the coursework requires persistence to engage in the process of trial and error that is needed to explore potential solutions. Overall, this module will enhance students¿ Digital Capabilities. This module will improve students¿ Employability by diversifying the employment avenues available to them. The technical and problem-solving AI-related skills acquired in this module, will prepare the students to take jobs in companies that employ advanced technologies in the translation process. Even if the students choose to be freelance translators, they will be able to accept a wider range of jobs. The module will also enable students to apply for jobs in data science.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.