Module code: EEEM081

Module Overview

This module aims to introduce students primarily with an engineering or science background with a variety of research methods, necessary to conduct research in the field of people-centred AI. The module also serves as a vehicle into equipping students with the necessary research skills to carry out a project in this cross-disciplinary area as well as important generic skills that are considered valuable to MSc graduates. The module will cover key aspects pertaining to planning and conducting research from literature reviewing to research methodology, to research ethics through to examples of innovative outputs in people-centred AI. Students will complete the module by thinking about research designs that are systematic and will inspire new research ideas.
This module will be taken in the first of study, thus ensuring students gain the appropriate research skills prior to the commencement of their project in the second year. Depending on their interest, students will be able to exercise the competencies developed in this module in the context of either a technical problem concerned with developed of core AI technology or in a particular application domain concerned with the societal implications of the use of AI

Module provider

SOL - Computer Science and Elec Eng

Module Leader


Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 113

Tutorial Hours: 4

Guided Learning: 22

Captured Content: 11

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

Indicative content includes: 

  • Introduction to people-centred research methods.

  • Literature review, including critical reading and referencing.

  • Qualitative and quantitative research methods.

  • Computer science/AI specific research methods.

  • Data management and ethical considerations.

  • Research design.

  • Project planning and research coordination.

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Literature Review 30
Coursework Research Analysis and Planning Assignment 70

Alternative Assessment


Assessment Strategy

The assessment strategy for this module is designed to provide students with the opportunity to demonstrate:

  • Knowledge and understanding of topics covered in Q&A tutorials, discussion posts, captured content, guided learning and independent learning materials.

  • Reasoned analysis

  • Analytical ability to draw appropriate conclusions, based upon analysis of the issues raised by the questions.

  • Critical engagement in scholarly debate

  • Effective critical self-reflection 

  • Ability to link theory to practice

The coursework assessment method addresses all learning outcomes listed above. Thus, the summative assessment for this module consists of:

  • Coursework: Literature Review (30%).

  • Coursework: Research Analysis and Planning Assignment (70%).

Formative assessment and feedback:

  • Q&A tutorials.

  • Discussion forums.

  • Self-guided activities.

The assessments are designed to give students the opportunity to demonstrate critical thinking and a deep level of knowledge and understanding. Students reflect on their own work and the skills and qualities acquired, such as, the methodology used when reviewing literature, selecting the relevant research method approach, collecting and managing data whilst ensuring research ethics.

Module aims

  • To equip students with knowledge of a range of research methods and their use in the field of people-centred AI.
  • To enable students to identify appropriate research methods to address a particular research problem and understand their limitations.
  • To develop students' competence at drawing methodological arguments and arriving at an independent, reasoned position in relation to them.
  • To enable students to develop a systematic and well justified research design.
  • To equip students with the key research skills necessary in preparation for their project in people-centred AI.

Learning outcomes

Attributes Developed
001 Conduct literature searching and reference management. PT
002 Design people-centred AI research with the knowledge of fundamental issues underpinning different research methodologies. KC
003 Assess the technical and social implications of developments in people-centred AI. KC
004 Be able to select appropriate tools from a variety of methodological techniques to tackle a particular research problem in the field of people-centred AI. KCPT
005 Consider the merits of different formats of research dissemination and data visualisation. PT
006 Identify, plan and develop a project proposal. PT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

  • Introduce students to explore the most common forms of research methods and analysis techniques used in the people-centred AI field.

  • Equip students with the research and writing skills they will need to produce critically informed, original academic writing.

  • Develop students understanding of how to conduct research in an effective and ethical manner.

  • Develop students’ understanding of key methodological issues in both qualitative and quantitative field.

  • Develop student’s ability to recognise and evaluate the appropriateness and the advantages and disadvantages of a range of research methods .

  • Develop students’ knowledge, experience and confidence in using a range of research methods.

  • Provide students with expertise in data collection and data management, and the ability to apply them when planning, designing and examining research questions.

  • Encourage students to make their own choices in the employment of appropriate research methods.

  • Encourage students to think creatively and critically on a research problem.

The learning and teaching methods include a combination of Q&A tutorials, discussion posts, captured content, guided learning and independent learning. Students are expected to engage with research extensively and consider the appropriate research methods in relevant areas. The module aims to equip students with knowledge and understanding of qualitative and quantitative research methods, data collection, data management, research design and research ethics. It will also provide them with an opportunity to explore and apply this knowledge when drawing on relevant literature and planning research in the context of either a technical or an application domain-specific problem relating to people-centred AI.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: EEEM081

Other information

We are committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
Sustainability: One or more of the 17 of the United Nation’s goals of sustainability could be met as part of the development of the research proposal which will consider the implications for people. The extend to which the goals are supported will depend on the topic of the research proposal considered. Students will be encouraged to identify where these benefits lie in their coursework.
Global and cultural intelligence: Students will take on a body of work to develop their research proposal and to meet the objectives successfully, they will develop independently some contribution to knowledge. High quality proposal may have the potential to inform further research which could in turn result in dissemination activities and benefiting the broader research community.
Digital capabilities: Research design and planning will require a clear understanding of data collection, management, and processing and analysis. Furthermore, the report will be written electronically and will require competent use of a word processor or LaTeX compiler to neatly and clearly present a body of work as an academic document. Other skills in use of publication databases and a reference manager will be important to carry out a literature search and write a professionally presented survey. 
Employability: Knowledge of research methods and the ability to apply them to various problems is a critical skill to conduct responsible research in people-centred AI due to the cross disciplinary nature of the field and the importance to be able to understand its implications on people.
Resourcefulness and resilience: The need to draw information and knowledge from many sources often across disciplines, and to develop competency in a range of research methods spanning both qualitative and quantitative approaches will form resourcefulness. Being able to keep up to date with state of the art and carry out original research in the fast-moving field of AI will also require resilience.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
People-Centred Artificial Intelligence (Online) MSc 2 Compulsory A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.