Module manager: Peizhi Shi
Email: p.shi@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2025/26
This module is not approved as an Elective
This module explores the growing role of Artificial Intelligence (AI) in business, with a particular focus on Natural Language Processing (NLP) and Large Language Models (LLMs). Students will develop a strong foundation in AI-driven methods, learning how to leverage cutting-edge tools and techniques to solve real-world business problems. Through a mix of lectures and hands-on workshops, they will gain practical experience in AI programming, deep learning, and prompt engineering while critically assessing the capabilities and limitations of modern AI models. By the end of the module, students will be well-equipped to apply AI solutions in various business contexts, from automation to decision support.
The module aims to:
- Provide a comprehensive introduction to AI, machine learning, and NLP in business applications.
- Develop practical skills in programming, data processing, and AI-driven text analysis.
- Explore various approaches to building, fine-tuning, and applying AI models for business use cases.
- Introduce best practices for designing effective AI-driven solutions, including prompt engineering and model evaluation.
- Encourage critical thinking about AI's impact, including ethical considerations and future trends.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
On successful completion of the module, students will be able to:
- Understand the principles of AI as applied to business.
- Use programming and AI tools to analyse data.
- Design and apply AI-driven solutions to real-world business problems.
- Critically evaluate AI model outputs and assess their effectiveness.
- Engage with emerging AI technologies and trends in business through practical application, case studies, and critical analysis.
On successful completion of the module students will have demonstrated the following skills learning outcomes:
- Problem-Solving & Analytical Skills: Develop analytical and creative approaches to solving AI-related business challenges.
- Commercial Awareness: Understand the role of AI in business operations, decision-making, and competitive advantage.
- IT Skills: Use Python, OpenAI API
- Digital creation, problem solving and innovation: Implement AI-driven solutions to automate and optimize business tasks.
- Systems Thinking: Understand the impact of AI solutions on different business systems.
- Decision-Making: Assess AI-driven business insights and develop data-driven recommendations.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 5 | 2 | 10 |
| Practical | 10 | 1.5 | 15 |
| Private study hours | 125 | ||
| Total Contact hours | 25 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Opportunities for Formative Feedback
The innovative content of this module includes interactive quizzes and polls designed to assess students' understanding of each concept in AI for business. These quizzes provide instantaneous written formative feedback, including explanations of correct and incorrect answers, clarification of key concepts, and guidance on common misconceptions. Additionally, students will receive formative feedback during practical sessions. Practical exercises with detailed explanations will be provided, and feedback will be delivered through verbal discussions with instructors in the classroom. Furthermore, this module includes a series of exercises for which solutions are not provided during the workshop. Instead, correct solutions and detailed explanations such as a breakdown of correct solutions, clarification of common errors, insights into the reasoning behind correct answers, and suggestions for improvement will be made available at the end of each week. This structured written formative feedback will help students track their progress and refine their understanding. The above formative feedback will be closely aligned with the assessment criteria, supporting students in enhancing their practical skills in AI applications and thereby improving their coursework performance.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | 3,000 | 100 |
| Total percentage (Assessment Coursework) | 100 | |
The resit for this module will be 100% by 3,000 word coursework. Indicative coursework tasks for the Applied AI in Business module could include a case study in which students design an AI-driven solution to a real-world business problem. This task would require students to apply deep learning and Large Language Models (LLMs) to analyse business data, propose an AI implementation strategy, and critically evaluate its effectiveness. Students' understanding of the principles of AI in business will be assessed through the business understanding component of the assessment. The ability to design and apply AI-driven solutions, engage with AI, and use programming and AI tools to analyse data will be assessed through the data understanding, preparation, and modelling components. The ability to critically evaluate AI model outputs and assess their effectiveness will be assessed through the evaluation component.
The reading list is available from the Library website
Last updated: 30/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team