• Inactive for Year: 2025/26

• Module Leader(s): Mrs Phoebe Yu
• Lecturer: Dr Michael Jin, Ms Michelle Deeter, Dr Ya-Yun Chen

• Owning School: Modern Languages
• Teaching Location: Newcastle City Campus

Semesters

Your programme is made up of credits, the total differs on programme to programme.

Aims

This module equips students with a critical and practical understanding of how artificial intelligence (AI) and digital tools are reshaping the interpreting profession. As interpreting increasingly integrates technology across various modes — simultaneous, consecutive, liaison, and remote — this module explores the evolving landscape of interpreter workflows, tools, and environments.

Students will:

- Develop an advanced understanding of current academic theories and professional practices related to AI and digital technologies in interpreting.
- Gain hands-on experience with a range of interpreting technologies, including computer-assisted interpreting (CAI) tools, speech recognition systems, and remote interpreting platforms.
- Learn to critically evaluate the benefits, limitations, and ethical implications of using AI and digital tools in diverse interpreting scenarios, such as video-mediated and hybrid settings.
- Acquire the skills to select and apply appropriate technologies to support pre-task preparation, in-task performance, and post-task reflection.
- Reflect on the impact of digital transformation on interpreter cognition, professional identity, and continuous professional development.

This module encourages students to become adaptive, tech-literate professionals capable of navigating and shaping the future of interpreting in increasingly digital and AI-augmented environments.

Outline Of Syllabus

This module is taught in the form of lectures and seminars.

The lectures aim to give research-based introduction to the influence of technology on interpreting. Specifically, they look at different degrees of technology involvement in interpreting, including technology-supported interpreting (for example virtual booth in RSI), technology-mediated interpreting (remote or distance interpreting), and artificial intelligence in interpreting (technology replacing interpreters). The lectures will integrate academic theories (interpreting studies) and industrial knowledge via relevant case studies. These will be led by the lecturers but will involve consolidation of learning via group work and class discussion. The existing and new knowledge will be integrated, demonstrated, and assessed in the final assignment.

The seminars focus on the application of knowledge gained from the lecture sessions. The seminars will feature practical exercises, where students experience various modes of AI and technology-integrated interpreting, experiment with different interpreting tools (individually or in teams), evaluate the strength and weakness of the technology, and identify and compare skills unique to specific interpreting modes via technology. The existing and new knowledge will serve as a foundation upon which new skills will be forged and demonstrated in formative group work and portfolio assessment.

Teaching Methods

Teaching Activities

Teaching Rationale And Relationship

The teaching methods adopted in this module are a combination of lectures and seminars. The former is used to help students acquire relevant knowledge while the latter helps students assimilate relevant knowledge through discussion and some hands-on experience in designed scenarios and simulated contexts of interpreting.

In the lectures, students will gain advanced knowledge about how technology is used in interpreting. This will be delivered through the presentation of various case studies, which will be discussed in relation to both interpreting studies theories and professional contexts. This knowledge will be further consolidated through small group activities and class discussions.

In the seminars, students will engage in various scenarios designed to contextualise the knowledge gained from lectures. They will develop skills to perform interpreting with various interpreting tools/software and under the influence of different degrees of technology involvement. Students are expected to work in small groups and share their findings in class in order to further enrich their learning. Lecturers will provide feedback on group activities and further consolidate student learning by relating the activities back to theory and industrial practice.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment

Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Assessment Rationale And Relationship

The summative assessment for this module comprises two components: a group presentation and a portfolio assessment.

a) The 20-minute group presentations (20% of module mark) will be centred around the evaluation of one particular piece of interpreting technology. Students will be expected to give a short demonstration using technology followed by a discussion on the benefits and challenges of working with the selected piece of technology as an interpreter.

b) The portfolio assessment (80% of module mark) aims to capture the students' experiential journey by encouraging them to reflect on the use of digital tools in their interpreting practice throughout the year. It is expected that the reflection will be informed by the various professional and theoretical aspects explored throughout the module.

The formative mock conference will provide a platform for interpreting practice and peer/staff feedback on the students' performance and integration of AI and other digital tools in their practice.

Reading Lists

• CHN8040's Reading List

Timetable

• Timetable Website: www.ncl.ac.uk/timetable/
• CHN8040's Timetable