| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
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Artificial Intelligence (AI) is redefining the way we live and work by enabling the design and development of automated processes that mimic human cognition and behaviour and provide deep and complex integration between information to respond autonomously. The ultimate goal of AI is to support human decision making and action with informed intelligent services. This course concerns critical and responsible design, development and evaluation of AI technologies with a focus on human-AI-interaction. The aim of this module is to provide students with a cross-disciplinary background and the advanced skills of utilising and critically evaluating the impact of Human-AI concepts and technologies within their ecosystems.
• Introduction to advanced automation (personalisation, adaptive systems, prediction/forecasting, cognitive services, qualitative analysis (visual and natural language processing), hybrid intelligence systems, black boxing)
• Intelligence, problem solving & decision making in humans and machines
• Designing interactions with applied artificial intelligence, machine learning (ML) & recommender systems
• AI interaction and experience design + development
• Human-AI benefits, victims & disasters
• Understandable / relatable AI
• Ethical & responsible AI
• Human-AI ecosystems & markets (case studies e.g. in autonomous agriculture, manufacturing, transportation, finance, healthcare, security, social media, gaming ...etc)
• To have a broad foundational understanding of types and techniques in AI/ML
• To be able to demonstrate good understanding of the potential use cases and benefits of artificial intelligence (AI) technologies
• To have a critical understanding of the ethical, social and legal implications of AI applications on human life and work
• To be able to understand appropriate design, development and research methods for human-AI interaction
• To be able to design and develop applied artificial intelligence / machine learning applications for given requirements
• To be able to critically assess potential benefits and possible negative effects of AI systems in situated use
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Structured Guided Learning | Lecture materials | 24 | 0:30 | 12:00 | Asynchronous online delivery (primarily video) of core concepts (flipped classroom learning material |
| Scheduled Learning And Teaching Activities | Lecture | 6 | 2:00 | 12:00 | content delivered PIP - Live discussion, small group activities, feedback & Q&A on asynch lectures |
| Guided Independent Study | Directed research and reading | 12 | 1:00 | 12:00 | Preparatory reading & practice for taught sessions (accompanies lecture materials blocks). |
| Guided Independent Study | Project work | 17 | 2:00 | 34:00 | Project implementation & structured discussion/ reflection on learning objectives of coursework |
| Scheduled Learning And Teaching Activities | Workshops | 6 | 2:00 | 12:00 | PIP– timetabled workshops . Practical aspects AI/ML interactive system developmnt |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 6 | 1:00 | 6:00 | Asynchronous online Supported work on applied project &writeup- video chat in lecturer contact hours |
| Guided Independent Study | Independent study | 6 | 2:00 | 12:00 | Background reading |
| Total | 100:00 |
The teaching methods provide a framework for the student to understand and investigate Human-AI applications for a given problem. Workshops with a synchronous and organised / student-group led component will deliver hands-on skills for employing a range of modern AI/ML methods based on common frameworks and exemplary data sets. The independent project work enables the student to immerse themselves in a research area to gain domain-related knowledge about applications of machine learning and artificial intelligence methods.
The format of resits will be determined by the Board of Examiners
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | M | 100 | Structured discussion / reflection on key learning objectives of coursework project + applied AI implementation and evaluation repor |
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Written exercise | 2 | M | Outline of project report. Feedback will be given prior to summative assessment |
The report examines the learners’ ability to critically reflect on the design and development of human-AI applications for given requirements and against a specific application use-case. This is akin to real-world AI/ML applications / solutions development, but on a smaller (model) scale. The assessment tests the students’ ability to use key frameworks to explore applications of data analytics and/or machine learning and/or artificial intelligence methods in realistic contexts and improve the students’ professional portfolio and employability. An AI-user testing component built into the assignment will support reflective discussion about how individual students have met the learning objectives of the module and how the principles of professional practice in data science were embedded in the student’s practical experience.
The formative assessment will provide students with feedback on their ideas for their emerging project report.
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Disclaimer: The information contained within the Module Catalogue relates to the 2022/23 academic year. In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described. Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2023/24 entry will be published here in early-April 2023. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.