5B064E - Applied Artificial Intelligence 01 Sep 2026 - 31 Jul 2032 | Version 0
Associated Module Information
| Module Code: | 5B064E | ||
|---|---|---|---|
| Module Title: | Applied Artificial Intelligence | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Built and Sustainable Environment | ||
| Faculty Sub Group: | Sustainable Environment | ||
| Module Leader: | Mabrouka Abuhmida | ||
| Module Team: | |||
| First Intended Intake: | SEP 2026 | Final Year of Intake: | |
| Date Closed: | |||
| Credit Value: | 30 | Credit Level: | 5 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100366 - computer science | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 0 |
|---|---|
| Valid From | 01 Sep 2026 |
| Valid To | 31 Jul 2032 |
Module Aims
To develop a conceptual and practical understanding of AI principles, models, and algorithms relevant to STEM disciplines.
To enable learners to apply AI tools and techniques to analyse, model, and solve authentic interdisciplinary problems.
To foster ethical awareness, innovation, and employability through engagement with real-world AI challenges that support sustainable and responsible technological development.
Content Summary
This module introduces students to the fundamental principles, techniques, and applications of Artificial Intelligence (AI) across scientific, engineering, and computing domains. Learners explore the concepts that enable machines to perceive, learn, reason, and act intelligently, including search algorithms, rule-based systems, and modern approaches such as machine learning and natural language processing. Through hands-on experimentation and group problem-solving, students apply AI techniques to authentic interdisciplinary challenges reflecting societal, environmental, or industrial contexts. Ethical, sustainable, and responsible AI development is emphasised throughout, helping students understand both the potential and limitations of intelligent systems.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Seminar | 20 |
| Groupwork | 15 |
| Guided Study | 20 |
| Problem/Challenge based learning | 120 |
| Practical Classes and Workshops | 55 |
| Formative Assessment | 10 |
| Summative Assessment | 60 |
| Total Hours Selected | 300 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | To be able to analyse and apply core AI concepts—including knowledge representation, search, and learning—to design intelligent solutions for defined STEM problems. |
| LO2 | To be able to collaborate to design and evaluate an AI-based prototype or model that addresses an interdisciplinary grand challenge, demonstrating ethical reasoning, teamwork, and professional communication |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Case study | Each student contributes a clearly defined written section of the use case proposal (e.g., problem analysis, AI technique selection, ethical considerations, branding and feasibility planning) and highlights their authored part. The submission includes: a consolidated group proposal, Description of the use case (background research, definition of the field, problem statement, and expected outputs) A division-of-work statement showing individual roles and proportional contributions Appendix: meeting logs evidencing collaboration and project management activity. | 0 | 2500 | 40 | No | 40 |
| Synchronous Onsite Oral Assessment | Group Presentation (Synchronous Onsite) | In this continuation, teams implement and evaluate their proposed AI solution. The final Group report submission includes A Final report describes their developed solution, evaluation plans, and results. Appendix: meeting logs evidencing collaboration and project management activity. A prototype demonstration in a presentation. | 15 | N/A | 60 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Case study | ✔ | ✘ | |
| Group Presentation (Synchronous Onsite) | ✘ | ✔ | |