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Module

CSC8646 : Generative AI for Businesses

  • Offered for Year: 2025/26
  • Module Leader(s): Dr Stephen McGough
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus
Semesters

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

Semester 2 Credit Value: 10
ECTS Credits: 5.0
European Credit Transfer System

Aims

This module aims to provide foundations for generative AI in the context of business applications
including concepts and techniques of modern AI, introduction to generative AI, tools and
techniques of prompt engineering, applications and use cases of generative AI that are
transforming the modern world from the health sector to tech industry and finance.

Outline Of Syllabus

Selected topics are chosen from:

• Role of Generative AI in Modern Business Environments.
• Fundamentals of Generative AI including Neural networks, deep learning and Large Language.
• Models (LLMs).
• Principles of AI-Generated Content Creation.
• Generative AI Tools and Prompt Engineering.
• Utilising Generative AI in Business Scenarios.
• Generative AI applications including the software industry, finance, security, health, environmental,
engineering, and robotics.
• Automating workflows with Generative AI.
• Organisational Impacts and Strategic Integration.
• Ethical frameworks in AI-driven content generation.
• AI safety, regulations and policies.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture81:008:00Some lecture materials maybe pre-recorded. Lectures (in person). Some lectures may be delivered by industry practitioners.
Guided Independent StudyAssessment preparation and completion201:0020:00Main summative assignment.
Scheduled Learning And Teaching ActivitiesPractical101:0010:00Practicals (in person) 1 hour drop in practical per week.
Scheduled Learning And Teaching ActivitiesSmall group teaching41:004:00Group seminars discussing use cases, forming strategy for potential use cases.
Guided Independent StudyProject work261:0026:00Practical coursework and assignment preparation.
Guided Independent StudyIndependent study321:0032:00Independent study on course content.
Total100:00
Teaching Rationale And Relationship

The teaching methods combine traditional lectures with practical sessions so that students can explore the topics covered in both a theoretical and practical context. Lectures outline the underlying principles, algorithms and theory, while practical lab work encourages students to implement the algorithms using rea-world data, in terms of applying the methods to real world data examples.

Lecture material maybe be pre-recorded and students have the opportunity to watch the videos ahead of the
lecture. Lectures in person and where possible streamed live with recap will be available. Lecture follow up will consist of Q&A about the lecture material

Assessment Methods

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

Other Assessment
Description Semester When Set Percentage Comment
Report2M100Assessed coursework covering the module material.
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.

Description Semester When Set Comment
Prob solv exercises2MA set of short exercises conducted during the practical sessions.
Assessment Rationale And Relationship

The report tests the students’ ability to apply Generative AI techniques, using effective tools and methods to solve a real-world challenge.

Through the module students will complete a set of short exercises (formative assessment) conducted during the practical sessions. The student will be assessed on their understanding of generative approaches, prompt engineering as well as data processing skills, and the use of standard tools.

Reading Lists

Timetable