GENERATIVE AI FOR STUDY AND WORK - 2026/7

Module Overview

This module introduces students to generative AI, with a specific focus on higher education and early-career workplaces. Students will explore the inner workings of generative AI to enable them to make critical decisions about whether, when and how to use AI tools. It develops students¿ critical awareness, helping them develop independence and confidence, strengthen their learning and preserve and develop their own voice and authenticity. Students will explore how to use generative AI ethically and responsibly, as discussed through different lenses such as copyright, environmental factors or workers¿ rights. They will also learn to plan and document their working processes, with a specific focus on AI use.
The module also focuses on careers, including how AI can support employer research, interview preparation, and the development of CVs and cover letters whilst maintaining authenticity and accuracy. Throughout the module, emphasis is placed on reflective practice, with students analysing and evaluating their own and others¿ AI-related choices.

Module provider

SAHCI School Admin

Module Leader

DIPPOLD Doris (Lit & Langs)

Number of Credits: 15

ECTS Credits: 7.5

Framework: Global Graduate Award

Module cap (Maximum number of students): 30

Module Availability

Semester 2

Prerequisites / Co-requisites

none

Module content

Indicative module content includes:

- Generative AI basics: data, model training, prompting, hallucinations, bias, privacy, copyright, environmental factors
- Generative AI & criticality: planning, monitoring, evaluating
- Prompting as communicative practice: prompt structure & different types of prompts
- Critical language awareness for generative AI
- Generative AI for academic practice: assessment & key academic skills
- Gen AI for careers: CVs, cover letters, etc.

Assessment pattern

Alternative Assessment

Students complete the same assessment.

Assessment Strategy

Assessment 1: Students write a 300 word blog post about a concept, important figure or invention from their subject area. In addition, they write a 1000 word reflection on the role of generative AI in the production of this blog post, reflecting on the factors discussed in class. Only the reflection will be marked, but the blog post must be submitted.

Assessment 2: Students interview a friend, family member or colleague about their use of generative AI for work purposes and reflect on the outcomes of the interview in light of the module content, in form of a short recorded presentation.

Module aims

• develop foundational understanding of generative AI, including key concepts such as training, prompting, hallucinations, bias, privacy and copyright, and how these shape responsible use in university study and early career workplaces.
• plan, document and justify AI related choices for academic and professional tasks, demonstrating awareness of ethical considerations, risks and institutional expectations.
• use language strategically when interacting with generative AI, treating prompting as a communicative practice and applying critical language awareness to both prompts and outputs.
• integrate generative AI thoughtfully, ethically and responsibly into academic and careers activities, so that AI supports learning, independence and employability without jeopardising students¿ own judgement, voice, expertise and authenticity.
• use generative AI selectively and effectively in careers contexts, e.g. to analyse job adverts, prepare for interviews, and prepare CVs and cover letters
• reflect critically on their personal and others¿ AI-related practices

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to help students apply theoretical concepts in a practical manner and discuss their ideas with others. The module is taught primarily in seminar-style, with lecture-type input by the tutor which is followed by practical examples and exercises. Students will be encouraged to discuss self-generated or collected examples. The learning and teaching methods include:
Weekly lectures: These will introduce the topics which will be developed in the seminars but will also include some partner and teamwork activities to help students understand and apply these concepts.
Guided learning: Students' in-class learning is supported by a structured programme of guided learning activities to do at home. Students do practical analysis exercises, read relevant literature, and find examples which can be further discussed in class. Content provided for each weekly session include the lecture slides for the sessions as well as the Panopto-recorded lecture-elements for each class. The captured content aims to develop understanding of key concepts and theories. Students are expected to engage with the captured content to deepen their engagement with the materials and complete the guided learning exercises.

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: LINL001

Other information

n/a

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 2026/7 academic year.