| Mode(s) of Study | Code | CATS Credits | ECTSCredits | Framework | HECoSCode |
|---|---|---|---|---|---|
| Full-time blended Part-time blended |
MA71 | 15 | 7 | FHEQ - L7 | artificial intelligence |
Prerequisites and Co-requisites
Learning Outcomes
| Code | AttributesDeveloped | Outcomes |
|---|---|---|
| LO1 | Knowledge and Understanding | Demonstrate a comprehensive understanding of AI's transformative impact across diverse business sectors. |
| LO2 | Intellectual Skills | Analyse the strengths and limitations of AI applications, distinguishing areas of autonomous operation, human oversight, and constraints. |
| LO3 | Technical/Practical Skills | Design innovative models of human-AI collaboration, enhancing human capabilities while considering technological constraints. |
| LO4 | Technical/Practical Skills | Develop strategic plans for AI integration, addressing workflow integration, risk management, and organisational adaptability. |
| LO5 | Professional/Transferable Skills | Lead ethical decision-making processes related to AI, adhering to professional standards and fostering responsible innovation. |
Student Workload
The methods of teaching and learning for this module are based on LSI's Professional 15 teaching system, consisting of the following activities.
| Activity | TotalHours |
|---|---|
| Introductory lecture | 1.50 |
| Concept learning (knowledge graph) | 18.00 |
| AI formative assessment | 9.00 |
| Case Study Review | 9.00 |
| AI Roleplay | 13.50 |
| Workshop/Lab Sessions | 13.50 |
| Independent reading, exploration and practice | 61.50 |
| Summative assessment | 24.00 |
| Total: | 150.00 |
Teaching and Learning Methods
| Activity | Description |
|---|---|
| Introductory lecture | This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session. The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers. A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again. |
| Concept learning (knowledge graph) | Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS. This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed. |
| AI formative assessment | Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios. The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability. |
| Case Study Review | In this learning activity, students explore recent real-world case studies relevant to their course topic. The case studies will have been selected and curated by the module leader to represent up-to-date examples. They guide students through key details, contextual factors, and outcomes. This approach enhances students' understanding of current industry trends, challenges, and solutions, preparing them for real-world scenarios they may encounter in their future careers. The learning experienced will be augmented by AI (virtual private tutor) allowing the students to critically engage with the content and discuss the case studies. |
| AI Roleplay | AI Roleplay is an innovative educational approach that leverages artificial intelligence to create immersive, interactive learning experiences for university students. In this activity, students are presented with a professional challenge or scenario relevant to their course. They then engage in a simulated interaction with one or more AI-powered characters, each programmed to embody specific roles, personalities, and expertise. These AI characters respond dynamically to the student's inputs, creating a realistic and adaptive roleplay environment. Students can practice their communication skills, decision-making, problem-solving, and other professional competencies in a safe, low-stakes setting. After the session, the AI system provides detailed feedback on the student's performance, highlighting strengths and areas for improvement. This personalised guidance helps students refine their skills and gain confidence in handling real-world professional situations. |
| Workshop/Lab Sessions | Those studying in the blended learning mode will attend these 9 weekly classes (in person or remotely) during weeks 2 to 10. These sessions will complement the theory already studied during the preceding week (in our flipped-learning model), with discussions, analysis, practice or experience . They will be interactive and participatory, rather than one-way lectures. There will also be an opportunity for Q&A in every session. Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning. They may include hands-on project work, group discussions or debates, roleplay, simulation, case studies, presentations, and other learning activities and opportunities. These workshops present an opportunity to apply critical thinking and problem-solving skills. They also encourage collaboration and foster a sense of community among students. |
| Independent reading, exploration and practice | This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge. |
| Summative assessment | Summative assessments are used to evaluate student learning at the end of a module. These assessments can take many forms, including exams, papers, or presentations. Instructors can use summative assessments to measure whether students have achieved the learning outcomes for the module and provide them with a sense of their overall progress. Summative assessments can also be used to evaluate the effectiveness of the teaching methods used in the module. |
Assessment Patterns
| Weighting | Format | Outcomes assessed |
|---|---|---|
| 50 |
Invigilated Exam
This is a time-limited examination undertaken during the summative assessment period under the School’s remote invigilation conditions to support quality and academic integrity. The examination enables students to demonstrate achievement of the module learning outcomes, particularly in relation to knowledge and understanding, and where relevant professional and transferable skills. Questions may include problems, issues or dilemmas reflecting situations students could encounter in professional practice. Students are expected to synthesise and apply their learning to analyse the issues and reach sound, reasoned judgements. To support preparation, formative assessment activities, including quizzes, dialogues and an AI-augmented assignment, are built into the module. Students receive actionable feedback from AI, staff and peers to help improve future work and develop their ability to give constructive feedback to others. |
I LO2 K LO1 P LO5 |
| 50 |
Simulation and Role Playing Coursework
This coursework requires students to take on a defined professional role within a realistic scenario related to the module. Students must interpret the situation, apply relevant knowledge, exercise judgement and determine an appropriate response. The scenario may involve competing stakeholder interests and commercial, technical, organisational, legal, ethical or professional considerations. Students may also need to respond to new information or changing circumstances. The assessment focuses on application of knowledge, professional judgement, decision-making, adaptability and communication. In the virtual learning environment (VLE), students will complete formative activities that mirror simulation-based tasks involving organisations, projects, professional roles and workplace challenges. These activities will provide opportunities to practise decision-making and receive actionable feedback from AI, staff and peers. |
I LO2 K LO1 P LO5 T LO3 T LO4 |
References/Indicative Reading List
| Importance | ISBN | Description |
|---|---|---|
| Core Textbook | 9789815238211 | Majeed, M. Artificial Intelligence in Business Management. Bentham Science Publishers, 2024 |
| Core Textbook | 9781948198998 | Munoz, J. Mark, and Al Naqvi. Business strategy in the artificial intelligence economy. Business Expert Press, 2018. |
| Supplementary Reading | 9783319974354 | Akerkar, Rajendra. Artificial intelligence for business. Springer, 2019. |
| Supplementary Reading | 9781638356318 | Krunic, Veljko. Succeeding with AI: How to make AI work for your business. Simon and Schuster, 2020. |
| Supplementary Reading | 9781000409475 | Unhelkar, Bhuvan, and Tad Gonsalves. Artificial intelligence for business optimization: research and applications. CRC Press, 2021. |
| Supplementary Reading | 9781800563469 | Chojecki, Przemek. Artificial Intelligence Business: How you can profit from AI. Packt Publishing, 2020 |
| Supplementary Reading | 9783319772516 | Corea, Francesco. Applied artificial intelligence: Where AI can be used in business. Vol. 1. Springer International Publishing, 2019. |
| Supplementary Reading | 9781492036579 | Castrounis, Alex. AI for people and business: A framework for better human experiences and business success. O'Reilly Media, 2019. |
| Supplementary Reading | 9781119651802 | Anderson, Jason L., and Jeffrey L. Coveyduc. Artificial intelligence for business: A roadmap for getting started with AI. John Wiley & Sons, 2020. |
| Supplementary Reading | 9781800438828 | Syam, Niladri and Kaul, Rajeeve. Machine Learning and Artificial Intelligence in Marketing and Sales: Essential Reference for Practitioners and Data Scientists. Emerald Publishing, 2021 |
Related Programmes
| Programme | Term | Type | |
|---|---|---|---|
| 1 | MSc AI for Business Transformation | 1 | Core |
| 2 | MSc Data Science and Analytics | 1 | Core |
| 3 | MSc Digital Innovation and Entrepreneurship | 1 | Core |
| 4 | MSc AI and Machine Learning | 2 | Core |
| 5 | MSc Software Technical Leadership | 2 | Optional |
| 6 | MSc Digital Project Management | 1 | Core |
Module Review and Approval
| Version | Date | ReviewedBy | NextReview |
|---|---|---|---|
| 1.0 | dd MMMM yyyy | Dr Yuri Jiang | September 2027 |