CSC8611 : Human-Artificial Intelligence (AI) Interaction & Futures
CSC8611 : Human-Artificial Intelligence (AI) Interaction & Futures
- Offered for Year: 2025/26
- Module Leader(s): Dr Lei Shi
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
N/A
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
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).
Learning Outcomes
Intended Knowledge Outcomes
• 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.
Intended Skill Outcomes
To be able to:
• Design and develop applied artificial intelligence/machine learning applications for given requirements.
• Critically assess potential benefits and possible negative effects of AI systems in situated use.
Teaching Methods
Teaching Activities
| 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 | 4 | 2:00 | 8:00 | 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 | 4 | 2:00 | 8:00 | In person – timetabled workshops. Practical aspects AI/ML interactive system development. |
| 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 | 20 | 1:00 | 20:00 | Background reading. |
| Total | 100:00 |
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.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| 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 report. |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Written exercise | 2 | M | Outline 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.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC8611's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- CSC8611's past Exam Papers
General Notes
N/A
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