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Talk to an Expert| Category | Assignment | Subject | Business |
|---|---|---|---|
| University | Arden University | Module Title | BUS7028 Fundamentals of Artificial Intelligence |
| Field | Details | |
|---|---|---|
| 1. | Module Title | Fundamentals of Artificial Intelligence |
| 2. | Module Code | BUS7028 |
| 3. | Module Leader | Dr. Feng Jiao |
| 4. | Level | 7 |
| 5. | Credits | 20 |
| 6. | Pre-requisite Module(s) | None |
| 7. | Subject Area | Business |
| 8. | Campus/Site | All BL centres, DL |
| 9. | Mode of Delivery | DL, BL, APP |
| 10. | Start Date | 30/10/21 |
| Section | Details |
|---|---|
| 11. Module Aims | Introduction In today's dynamic business environment, Artificial Intelligence (AI) is revolutionising how companies operate and make decisions. This module is tailored for senior managers, focusing on non-technical aspects of AI to enable informed decision-making and strategic planning. It will cover the ethical use of AI, its applications in business, and how AI integrates with cloud-based solutions.AimThe aim of this module is to provide senior managers with a fundamental understanding of AI. Alongside covering its strategic applications in business, it will also emphasise the importance of ethical considerations and the integration of AI with cloud technologies to drive innovation and efficiency. |
| 12. Reference points | This MDF was compiled in line with the QAA Business and Management Masters’ Benchmark Statement 2023 (category 1 degree), the QAA Quality Code (2018), FHEQ L7 and the standards set by the PSRBs detailed in section 13. |
| 13. Professional, Statutory & Regulatory Bodies (PSRB) | Outline approval for professional accreditation has been secured from:• Chartered Management Institute (CMI)• Chartered Institute of Logistics & Transport (MSc Supply Chain Management)• Association of Project Management (MSc Project Management)• Project Management Institute (MSc Project Management)• Chartered Institute of Marketing (MSc Strategic Marketing)• The Institute of Leadership (ILM) |
| Section | Details |
|---|---|
| 14. Module Learning Outcomes | |
| 15. Learning and Teaching Objectives |
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| The module LOs will be achieved via:1. Understanding the basics of AI and applying them via the use of case analysis, practical exercises, software appraisal and reflection (LO 1, 2, 3, 4, 5)2. Assessing and evaluating the impact of challenges associated with AI operations using case analysis, scenario planning and group exercises (LO2, 3,4)3. Exploring big data sets and AI using case analysis and software solutions (LO3, 4)4. Designing appropriate enterprise solutions to meet the challenges of the contemporary organisational environment including AI and big data analytics services (LO3, 4)5. Evaluation of flexible cloud storage models with dynamic scaling through emergent trends (LO2, 4, 5). | |
| 16. Module Content | |
| The module covers the following topic areas:• Artificial Intelligence fundamentals • History and evolution of AI • Types of AI • Identifying AI opportunities and use cases • AI Applications in Business • Data Management for AI • AI Implementation Strategies • Training and development for AI skills • Managing cultural shifts due to AI integration • Risk Management in AI• Ethics of Artificial Intelligence• Data, databases and analytics• Big Data storage and management• Cloud computing fundamentals, frameworks and applications• Solutions architecture and X-as-a-service • AI integration ethics, risks, mitigation and sustainability goals | |
| 17. Learning Activities | |
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The module will be delivered via a series on online activities conducted by the tutor. These might include:
General formative feedback will be provided throughout the module in the class (BL delivery) and in online sessions (DL delivery). Formative feedback will also be provided in discussion forums, online exercises and group activities.
Specific Formative Feedback
There will be one piece of individual assessment. Specific formative assessment opportunities will also be offered in relation to the summative assessment. The tutor will examine an outline draft shared via a link and provide detailed developmental feedback at least 2 weeks before summative submission.
Formative Assessment for School of Leadership and Management academic programmes
using this module in accordance with CIM, ILM requirements will have the following formative task:
Students will be able to create one-A4 page draft plan of their assignment- Presentation & Poster for tutor comments in week 6 or earlier if agreed by the tutor.
Formative Assessment for Apprenticeship students
Apprenticeship students will be able to submit a work in progress draft to the lecturer for a review for improvement during week 6.
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Section |
Details | |||
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18. Module Assessment |
Assessment Type | Weighting % | Fine Grade or Pass/Fail | Learning Outcomes Assessed |
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Component 1: PR Diagnostic Multimedia Presentation (Task 1–2) (Individual with speaker notes (Max 1000 words for speaker notes)) |
30% | FG | LO 1,2,5 | (Wk10) |
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Component 2: PSTR Individual "Work Product-Poster" (Task 3 – Task 4) (1000 words) |
70% | FG | LO 3,4,5 | (Wk10) |
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Assessment Description |
Component 1: Individual task, Presentation, maximum 1000 words, 30% Each Student will produce a PowerPoint file, equivalent to a 7-minute presentation, using text, audio, images, animations, or video including speaker notes. The presentation should specify a strategic organisational issue which can be solved by applying a cloud-based AI transformation in an organisation of choice or use a tutor provided case study (30%, maximum 1,000 words).Apprentice students must use their own organisation in this section.To ensure the best possible outcome, it is important for the student to choose an organisation that they are familiar with or have a keen interest in. This will enable them to produce a presentation that is both insightful and informative, highlighting the key strategic organisational issues faced by the organisation. |
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