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Department of Computer Science and Technology

Principal lecturer: 
Other lecturers: 
MPhil ACS, Part III
Lent term
Course code: 
Students will benefit from pre-requisite content in machine learning that is delivered in Michaelmas.
Class limit: 

This module can accommodate up to 20 students consisting of both Part II students and MPhil / Part III Students


This is an advanced course in human-computer interaction, with a specialist focus on intelligent user interfaces and interaction with machine-learning and artificial intelligence technologies. The format will be largely Practical, with students working in pairs to carry out a mini-project involving empirical research investigation. These studies will investigate human interaction with some kind of model-based system for planning, decision-making, automation etc. Possible study formats might include: System evaluation, Field observation, Hypothesis testing experiment, Design intervention or Corpus analysis, following set examples from recent research publications. Project work will be formally evaluated through a report and presentation.


(note that Lectures 2-7 also include one hour class discussion of practical work)
        • Current research themes in intelligent user interfaces
        • Program synthesis
        • Mixed initiative interaction
        • Interpretability / explainable AI
        • Labelling as a fundamental problem
        • Machine learning risks and bias
        • Visualisation and visual analytics
        • Student research presentations


By the end of the course students should:
        • be familiar with current state of the art in intelligent interactive systems
        • understand the human factors that are most critical in the design of such systems
        • be able to evaluate evidence for and against the utility of novel systems
        • have experience of conducting user studies meeting the quality criteria of this field
        • be able to write up and present user research in a professional manner

Recommended reading

Brad A. Myers and Richard McDaniel (2000). Demonstrational Interfaces: Sometimes You Need a Little Intelligence, Sometimes You Need a Lot.


There will be a minor assessment component (20%) in which students compile a reflective diary throughout the term, reporting on the weekly sessions. Diary entries should include citations to any key references, notes of possible further reading, summary of key points, questions relevant to the personal project, and points of interest noted in relation to the work of other students.

The major assessment component (80%) will involve a report on research findings, in the style of a submission to the ACM CHI or IUI conferences. This work will be submitted incrementally through the term, in order that feedback can be provided before final assessment of the full report. Phased submissions will cover the following aspects of the empirical study:

  1. Research question
  2. Method
  3. Literature Review
  4. Introduction
  5. Results
  6. Discussion / Conclusion

Feedback on each phased submission will include an indicative mark for guidance, but with the understanding that the final grade will be based on the final delivered report, and that this may go up (or possibly down), depending on how well the student responds to earlier feedback.

Reflective diary entries will also be assessed, but graded at a relatively coarse granularity corresponding to the ACS grading bands, and with minimal written feedback. Informal and generic feedback will be offered verbally in class, and also potentially supplemented with peer assessment.

Further Information

Due to COVID-19, the method of teaching for this module will be adjusted to cater for physical distancing and students who are working remotely. We will confirm precisely how the module will be taught closer to the start of term.

This module also has a large practical element. If the module is run remotely due to COVID-19 restrictions, changes to the practical work will be required

This module is shared with the Part II Computer Science Tripos course Interaction with Machine Learning. Assessment will be adjusted for the two groups of students to be at an appropriate level for whichever course the student is enrolled on. Further information about assessment and practicals will follow at the first lecture.