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 learnÌýthe technical and linguistics principles behind generative AI andÌýhow to effectivelyÌýuseÌýitÌýto produce effective communicationÌýproducts. It will beginÌýwith a focus on the history of Natural Language ProcessingÌý(NLP) and Human-Computer InteractionÌý(HCI)ÌýandÌýthe ways generative AI toolsÌýproduce and ‘understand’ language and multimodal communication.ÌýParticular attentionÌýwill be paid to the waysÌýhumans and AI tools ‘co-create’Ìýlanguage andÌýcommunication, the ‘language ideologies’ promoted by generative AI,Ìýand theÌýimpact of generative AI on human language,ÌýsocialÌýrelationshipsÌýand creativity.ÌýThere will also be a focus on theÌýgrowingÌýrole of AI in multilingual and intercultural communication, as well asÌýin multimodalÌýcommunicationÌý(e.g. voice,Ìýimage,Ìýgesture).ÌýÌýÌýStudents will learn about different use cases for generative AI in various professions fromÌýbusiness to the creative industries and debate about the ethical dimensions of using generative AI tools for different purposes.ÌýThe bulk of the module will focus on equipping students with the practical tools toÌýuse AI toÌýproduce effective communication products (e.g. reports, proposals, promotional campaigns andÌývideos)Ìýincluding principles of ‘prompt engineering’,Ìýhuman-computer collaborative work,Ìýbuilding and testing custom chatbots and AI agents,Ìýand ‘troubleshooting’ AI outputs.ÌýFinally, students will exploreÌýthe societal impactÌýandÌýpossible dangersÌýassociated with generative AI use, including stereotyping and biases,Ìý‘hallucinations’Ìýand misinformation, theÌýbusiness models ofÌýAI companies,Ìýand the environmental impacts of AI.ÌýStudents will be assessed based onÌý1)Ìýa hands-on group project in which they use AI to design a communication campaign from brainstorming to implementationÌýand 2) an individually authored policy paper in which they formulate and justify a policy for generative AI useÌýfor a particular business, professional or educational context.Ìý

Module learning outcomes

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

1. ¶Ù±ð²õ³¦°ù¾±²ú±ðÌýcore concepts fromÌýtheories of Communication,ÌýNatural Language Processing and Human-Computer InteractionÌýto understandÌýhow generative AI systems produce language and multimodal content.Ìý
2. Create aÌýhuman–AI collaborative communication product using effective prompts andÌýenquiry skills intoÌýinteraction strategies, images, and/or audio generation toolsÌýand knowledgeÌýof genre, audience, and platform.Ìý
3. EvaluateÌýthe communicative, social, ethical,Ìýracial,Ìýenvironmental, economic,ÌýsociopoliticalÌýand colonial implicationsÌýofÌýAI use andÌýhuman–AI collaboration, with particular attention to language ideologies, bias, and intercultural communication.Ìý

Module content

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

• Historical context:ÌýNLP, Turing, Symbolic andÌýStatistical approaches, linguistic debatesÌý
• How AI ‘talks’Ìýand ‘writes’: statistical language modelling and synthetic language.Ìý
• Myths and realities of ‘AI conversation’Ìý
• ‘Reasoning’ and ‘explainability’ÌýÌý
• Sociotechnical imaginaries: what peopleÌýthinkÌýAI is, and why that matters.Ìý

2. Theories of CommunicationÌýand Sociolinguistic Perspectives on AIÌý

• AI and human communication modelsÌýÌý
• Relevance of pragmatics, discourse analysis, and conversation analysis to AI-generated language.Ìý
• Language ideologies in AI: what counts as ‘good’ language.Ìý
• EnregistermentÌýand voice: how AIs adopt, mimic,ÌýandÌýinvent registers.Ìý
• Power and inequality: accent bias, register bias.Ìý
• Stance and identity in AI interactionsÌýÌý
• LLMsÌýand multilingualismÌý
• AI and intercultural communicationÌýÌý

3. Use casesÌý

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

4. Practical Communication SkillsÌýÌý

• Interface affordances: how they shape ‘conversation’Ìý
• Prompt engineering as a new form of language useÌý
• Prompt engineeringÌýprinciples and practiceÌý
• CollaboratingÌýwith AIÌýÌý
• Interactional ‘frames’ÌýÌý
• TroubleshootingÌýAI outputsÌý
• Designing custom chatbots and agentsÌý
• Using multimodal tools (e.g. image and video generators)ÌýÌý
• Automating workflowsÌý

5. Societal impactsÌý

• The ‘alignment’ problemÌý
• AI and politicsÌý
• Bias and stereotypingÌý
• The AI industry: Winners and losersÌý
• Environmental impactsÌý
• ArtificialÌýGeneral Intelligence (AGI), ‘consciousness’ and ‘singularity’ÌýÌý

Structure

Teaching and learning methods

The module is delivered through interactive lecturesÌýand workshopsÌýin 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 throughÌýaÌýonline collaborative spaces (message boards, wikis).ÌýStudents will also work together in groups to createÌýcommunication products and report to the instructor and the whole class on their processes and progress.ÌýFormative feedback is provided throughout, with opportunities to share work-in-progress and receive input from both peers and tutors.Ìý

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: /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 andÌýopportunities to share work-in-progressÌý(receivingÌýinput from both peers and tutors).Ìý

Reassessment

Additional costs