Postgraduate

CSC8611 : Human-Artificial Intelligence (AI) Interaction & Futures

Semesters
Semester 2 Credit Value: 10
ECTS Credits: 5.0

Aims

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.

Outline Of Syllabus

•       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)

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture62:0012:00content delivered PIP - Live discussion, small group activities, feedback & Q&A on asynch lectures
Structured Guided LearningLecture materials240:3012:00Asynchronous online delivery (primarily video) of core concepts (flipped classroom learning material
Guided Independent StudyDirected research and reading121:0012:00Preparatory reading & practice for taught sessions (accompanies lecture materials blocks).
Scheduled Learning And Teaching ActivitiesWorkshops62:0012:00PIP– timetabled workshops . Practical aspects AI/ML interactive system developmnt
Guided Independent StudyProject work172:0034:00Project implementation & structured discussion/ reflection on learning objectives of coursework
Scheduled Learning And Teaching ActivitiesDrop-in/surgery61:006:00Asynchronous online Supported work on applied project &writeup- video chat in lecturer contact hours
Guided Independent StudyIndependent study62:0012:00Background reading
Total100:00
Teaching Rationale And Relationship

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.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Report2M100Structured discussion / reflection on key learning objectives of coursework project + applied AI implementation and evaluation repor
Formative Assessments
Description Semester When Set Comment
Written exercise2MOutline of project report. Feedback will be given prior to summative assessment
Assessment Rationale And Relationship

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.

Reading Lists

Timetable