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Department of Computer Science and Technology



Course pages 2023–24

Practical Research in Human-centred AI

Principal lecturer: Prof Alan Blackwell
Additional lecturer: Dr Advait Sarkar
Taken by: MPhil ACS, Part III
Code: P342
Term: Michaelmas
Hours: 16 (8 x 2hr sessions)
Format: In-person lectures
Prerequisites: Students will benefit from pre-requisite content in machine learning that is delivered in Michaelmas.
Moodle, timetable


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 carrying 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

Class Size

This module can accommodate upto 20 Part II, Part III and MPhil students.

Recommended reading

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

Alan Blackwell (2024). Moral Codes: Designing alternatives to AI

Assessment - Part II Students

The format will be largely practical, with students carrying out an individual mini-project involving empirical research investigation.

Assignment 1: six incremental submissions which together contribute 20% to the final module mark.

Assignment 2: Final report - 80% of the final module mark

Assessment - MPhil / Part III Students

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

Current Cambridge undergraduate students who are continuing onto Part III or the MPhil in Advanced Computer Science may only take this module if they did NOT take 'Interaction with Machine Learning' as a Unit of Assessment in Part II.

This module is shared with Part II of the Computer Science Tripos. 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.