Undergraduate Programme and Module Handbook 2026-2027
Module COMP3771: Applications of AI
Department: Computer Science
COMP3771: Applications of AI
| Type | Open | Level | 3 | Credits | 20 | Availability | Available in 2026/2027 | Module Cap | Location | Durham |
|---|
Prerequisites
- COMP2261 Artificial Intelligence
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To explore and apply frameworks, methods, and ethical principles for designing and evaluating Human-AI interactive systems.
- To explore and apply recommender systems (RS) techniques and methods.
Content
- Foundations of Human-AI Interaction (HAII)
- Design and Evaluation Methods for HAII
- Affective Computing in Human-AI Interaction
- Trustworthy Autonomous Systems and Trust in AI
- Responsible AI: Frameworks and Practices
- Key RS techniques and traditional methods: content-based and collaborative filtering, hybrid and context-aware recommenders
- Advances and state-of-the-art in RS approaches
- Evaluation methods for RS
- Ethical issues in RS
- Applications of RS and HAII (e.g., entertainment, healthcare, education, finance, etc.)
Learning Outcomes
Subject-specific Knowledge:
- Key concepts and principles underpinning Human-AI Interaction (HAII) and their implications for user experience (UX) and usability, including affective and cognitive factors.
- An understanding of the different types of recommender system techniques, their purpose, domains of application, usage and evaluation.
Subject-specific Skills:
- An ability to design and evaluate interactive AI systems.
- An ability to critically assess trust and trustworthiness in autonomous and intelligent systems.
- An ability to integrate ethical and responsible AI frameworks into the design and evaluation of Human-AI systems.
- An ability to design, implement and evaluate a personalised recommender system for a specific domain.
Key Skills:
- An ability to propose AI solutions to real-world problems.
- An ability to critically analyse and evaluate ethical issues, current practices and recent advances in AI.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures enable students to learn new materials relevant to Applications of AI.
- The Assignment is a Data Design Template which will involve designing a structured record template and collecting a sample dataset using it. The Report is a Data Analysis Report which will involve conducting an in-depth analysis of a larger dataset to present key findings, interpretations, and evidence-based conclusions for a specific HAII topic.
- The Presentation assesses knowledge in the threshold RS concepts and skills in applying RS techniques and methods by designing, developing and evaluating AI systems that deliver personalised recommendations.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 40 | 2 per week | 1 hour | 40 | |
| Preparation and Reading | 160 | ||||
| Total | 200 |
Summative Assessment
| Component: Coursework | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Assignment | 10% | ||
| Report | 40% | ||
| Presentation | 50% | ||
Formative Assessment:
Example formative exercises are given during the course.
■ Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.