CS1AA: Introduction to Applied AI

Module code: CS1AA

Module provider: Computer Science; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: 4

When you’ll be taught: Semester 2

Module convenor: Dr Lily Sun, email: lily.sun@reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

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

Module(s) excluded: IN TAKING THIS MODULE YOU CANNOT TAKE CS1AC (Compulsory)

Placement information: NA

Academic year: 2026/7

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 16 July 2026

Overview

Module aims and purpose

This module aims to provide students with an introduction to Artificial Intelligence (AI) with an emphasis on practical skills using Large Language Models (LLMs), document-based AI (Retrieval Augmented Generation), and agentic AI concepts. Students will explore how AI can assist in problem-solving, knowledge discovery and retrieval, text generation, and multi-step task execution without requiring programming skills. The module combines conceptual understanding of AI principles with hands-on exposure to web-based AI tools, emphasizes context engineering, LLM Orchestration, and critical and ethical evaluation of AI outputs. 

By integrating applied exercises and team-based projects, students develop the ability to design AI-assisted solutions, critically reflect on their effectiveness, and understand AI’s potential and limitations in real-world contexts. This approach prepares students for professional applications of AI technologies across diverse domains.

Module learning outcomes

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

1. Explain the fundamental concepts and capabilities of AI, including LLMs, and their practical applications.
2. Apply AI tools to perform tasks such as text generation, summarization, Q&A, and conversational AI using effective AI interaction design.
3. Critically assess the quality, reliability, and bias of AI-generated outputs in real-world contexts.
4. Collaborate to design, implement, and present AI-assisted solutions with critical reflection on their societal and ethical implications.

Module content

The module content combines lectures and lab-based, hands-on exercises to introduce students to AI applications. Topics include:

• Fundamentals of AI and types of AI applications
• Introduction to Large Language Models (LLMs) and their capabilities
• LLM Orchestration techniques for generating fit-for-purpose AI outputs
• Text generation, summarization, and paraphrasing using AI tools
• Single-turn and multi-turn question-answering systems
• Retrieval Augmented Generation (RAG) including reasoning-aware and multi-step information retrieval from user-provided documents
• AI applications in business, education, and society
• Team-based project: planning, implementing, and presenting an AI-assisted solution

Structure

Teaching and learning methods

The module consists of 2-hour lectures and 2-hour practical sessions each week. The lectures introduce students to theoretical concepts and real-world applications of AI, while the practical sessions focus on hands-on experimentation with web-based AI tools, including ChatGPT, DocGPT, and no-code AI platforms. Formative feedback during practical sessions supports skill development and ethical reflection. These sessions are further supplemented with a range of digital learning resources to support independent study. The summative assessment consists of a team-based project, allowing students to collaboratively design and implement an applied AI solution, and a final examination, which assesses conceptual understanding, practical reasoning, and critical evaluation of AI applications.

Study hours

At least 44 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.

Weekly lab exercises and prompt-engineering/LLM orchestration tasks, where students experiment with web-based AI tools and receive verbal or written feedback during practical sessions.

In-class discussions and short reflective activities on AI outputs and limitations, providing immediate feedback on understanding and critical thinking.

Project planning check-ins, allowing groups to receive formative feedback on their proposed AI solutions before final submission.

Reassessment

Additional costs