LS2CAI: Communicating with Artificial Intelligence

Module code: LS2CAI

Module provider: English Language and Applied Linguistics; School of Humanities

Credits: 20

ECTS credits: 10

Level: 5

When you’ll be taught: Semester 1

Module convenor: Professor Rodney Jones, email: r.h.jones@reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2026/7

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 26 March 2026

Overview

Module aims and purpose

In this module students will learn the technical and linguistics principles behind generative AI and how to effectively use it to produce effective communication products. It will begin with a focus on the history of Natural Language Processing (NLP) and Human-Computer Interaction (HCI) and the ways generative AI tools produce and ‘understand’ language and multimodal communication. Particular attention will be paid to the ways humans and AI tools ‘co-create’ language and communication, the ‘language ideologies’ promoted by generative AI, and the impact of generative AI on human language, social relationships and creativity. There will also be a focus on the growing role of AI in multilingual and intercultural communication, as well as in multimodal communication (e.g. voice, image, gesture).   Students will learn about different use cases for generative AI in various professions from business to the creative industries and debate about the ethical dimensions of using generative AI tools for different purposes. The bulk of the module will focus on equipping students with the practical tools to use AI to produce effective communication products (e.g. reports, proposals, promotional campaigns and videos) including principles of ‘prompt engineering’, human-computer collaborative work, building and testing custom chatbots and AI agents, and ‘troubleshooting’ AI outputs. Finally, students will explore the societal impact and possible dangers associated with generative AI use, including stereotyping and biases, ‘hallucinations’ and misinformation, the business models of AI companies, and the environmental impacts of AI. Students will be assessed based on 1) a hands-on group project in which they use AI to design a communication campaign from brainstorming to implementation and 2) an individually authored policy paper in which they formulate and justify a policy for generative AI use for a particular business, professional or educational context. 

Module learning outcomes

By the end of the module, it is expected that students will be able to: 

1. Describe core concepts from theories of Communication, Natural Language Processing and Human-Computer Interaction to understand how generative AI systems produce language and multimodal content. 
2. Create a human–AI collaborative communication product using effective prompts and enquiry skills into interaction strategies, images, and/or audio generation tools and knowledge of genre, audience, and platform. 
3. Evaluate the communicative, social, ethical, racial, environmental, economic, sociopolitical and colonial implications of AI use and human–AI collaboration, with particular attention to language ideologies, bias, and intercultural communication. 

Module content

1. Foundations: What is ‘Communicating with AI’? 

• Historical context: NLP, Turing, Symbolic and Statistical approaches, linguistic debates 
• How AI ‘talks’ and ‘writes’: statistical language modelling and synthetic language. 
• Myths and realities of ‘AI conversation’ 
• ‘Reasoning’ and ‘explainability’  
• Sociotechnical imaginaries: what people think AI is, and why that matters. 

2. Theories of Communication and Sociolinguistic Perspectives on AI 

• AI and human communication models  
• Relevance of pragmatics, discourse analysis, and conversation analysis to AI-generated language. 
• Language ideologies in AI: what counts as ‘good’ language. 
• Enregisterment and voice: how AIs adopt, mimic, and invent registers. 
• Power and inequality: accent bias, register bias. 
• Stance and identity in AI interactions  
• LLMs and multilingualism 
• AI and intercultural communication  

3. Use cases 

• AI as collaborator, tool: co-author . 
• Sector-specific uses (law, medicine, education, creative industries) 
• Professional ethics and intellectual property 
• Communicating AI outputs to stakeholders (e.g., report writing, disclaimers) 
• AI in persuasive communication 

4. Practical Communication Skills  

• Interface affordances: how they shape ‘conversation’ 
• Prompt engineering as a new form of language use 
• Prompt engineering principles and practice 
• Collaborating with AI  
• Interactional ‘frames’  
• Troubleshooting AI outputs 
• Designing custom chatbots and agents 
• Using multimodal tools (e.g. image and video generators)  
• Automating workflows 

5. Societal impacts 

• The ‘alignment’ problem 
• AI and politics 
• Bias and stereotyping 
• The AI industry: Winners and losers 
• Environmental impacts 
• Artificial General Intelligence (AGI), ‘consciousness’ and ‘singularity’  

Structure

Teaching and learning methods

The module is delivered through interactive lectures and workshops in which content delivery is interspersed with group activities. Each lesson begins with a group discussion which students prepare for beforehand through reading and engaging in digital content. Students also communicate through a online collaborative spaces (message boards, wikis). Students will also work together in groups to create communication products and report to the instructor and the whole class on their processes and progress. Formative feedback is provided throughout, with opportunities to share work-in-progress and receive input from both peers and tutors. 

Study hours

At least 22 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): 10% of the total marks available for that piece of work will be deducted from the mark for each calendar day (or part thereof) following the deadline up to a total of three calendar days;
• where the piece of work is submitted up to three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in you Individual Learning Plan), the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan), no penalty shall be imposed;
• where the piece of work is submitted more than three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three calendar days of the deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): no penalty will be applied;
• where the piece of work is submitted more than three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): a grade of Fail will be awarded.

Where a piece of work is submitted late after a deadline which has been revised owing to an extension granted through the Assessment Adjustments policy and process (self-certified or otherwise), it will be subject to the maximum penalty (i.e., considered to be more than three calendar days late). This will also apply when such an extension is used in conjunction with a DAS-agreed extension as a reasonable adjustment.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Formative feedback will be provided through class discussion and opportunities to share work-in-progress (receiving input from both peers and tutors). 

Reassessment

Additional costs