Module Specification

Leadership for Innovation

Module Specification

Leadership for Innovation




Mode(s) of Study Code CATS Credits ECTS Credits Framework HECoS code
Full-time blended
Part-time blended
LI61 15 7 FHEQ - L6

Prerequisites and Co-requisites

None

Learning Outcomes

There are no module learning outcomes to display.

Student Workload

The methods of teaching and learning for this module are based on the School's Professional 15 teaching system, consisting of the following activities.

Activity Total hours
Introductory lecture 1.50
Concept learning (knowledge graph) 18.00
AI formative assessment 9.00
Case Study Review 9.00
AI Roleplay 13.50
Workshop/Lab Sessions 13.50
Independent reading, exploration and practice 61.50
Summative assessment 24.00
Total: 150.00

Teaching and Learning Methods

Activity Description
Introductory lecture

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.

Concept learning (knowledge graph)

Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS.

This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed.

AI formative assessment

Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios.

The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability.

Case Study Review

In this learning activity, students explore recent real-world case studies relevant to their course topic. The case studies will have been selected and curated by the module leader to represent up-to-date examples. They guide students through key details, contextual factors, and outcomes. This approach enhances students' understanding of current industry trends, challenges, and solutions, preparing them for real-world scenarios they may encounter in their future careers.

The learning experienced will be augmented by AI (virtual private tutor) allowing the students to critically engage with the content and discuss the case studies.

AI Roleplay

AI Roleplay is an innovative educational approach that leverages artificial intelligence to create immersive, interactive learning experiences for university students. In this activity, students are presented with a professional challenge or scenario relevant to their course. They then engage in a simulated interaction with one or more AI-powered characters, each programmed to embody specific roles, personalities, and expertise.

These AI characters respond dynamically to the student's inputs, creating a realistic and adaptive roleplay environment. Students can practice their communication skills, decision-making, problem-solving, and other professional competencies in a safe, low-stakes setting. After the session, the AI system provides detailed feedback on the student's performance, highlighting strengths and areas for improvement. This personalised guidance helps students refine their skills and gain confidence in handling real-world professional situations.

Workshop/Lab Sessions

Those studying in the blended learning mode will attend these 9 weekly classes (in person or remotely) during weeks 2 to 10. These sessions will complement the theory already studied during the preceding week (in our flipped-learning model), with discussions, analysis, practice or experience . They will be interactive and participatory, rather than one-way lectures. There will also be an opportunity for Q&A in every session. Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning. They may include hands-on project work, group discussions or debates, roleplay, simulation, case studies, presentations, and other learning activities and opportunities. These workshops present an opportunity to apply critical thinking and problem-solving skills. They also encourage collaboration and foster a sense of community among students.

Independent reading, exploration and practice

This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge.

Summative assessment

Summative assessments are used to evaluate student learning at the end of a module. These assessments can take many forms, including exams, papers, or presentations. Instructors can use summative assessments to measure whether students have achieved the learning outcomes for the module and provide them with a sense of their overall progress. Summative assessments can also be used to evaluate the effectiveness of the teaching methods used in the module.


Assessment Patterns

Weighting Format Outcomes assessed
50% Simulation and Role Playing Assessment
This assessment requires students to engage in AI-assisted simulations or role-playing scenarios that mirror real-world professional situations. It evaluates their practical knowledge, decision-making, and adaptability.

Students are given a detailed brief outlining a dynamic, evolving problem involving various issues like business, legal, professional, and ethical considerations. They must interpret the situation, consult relevant sources, and present a solution based on their knowledge from the module.

At the start of the module, students attend a workshop on effective participation in simulations aligned with the learning outcomes. Throughout the term, they practice through formative simulations, receiving feedback from AI, peers, staff, and their module leader.
50% Invigilated Exam
This is a time-limited and closed-book exam with a mix of multiple-choice and analytical written questions that students undertake during the summative assessment period as scheduled under the School’s remote invigilation conditions to ensure quality and academic integrity.

The exam enables the students to demonstrate their successful attainment of the module learning outcomes, primarily related to knowledge and understanding, and secondarily related to Professional/Transferable Skills.

The analytical written questions will consist of problem questions representing issues and dilemmas students are likely to encounter in professional life and students have to synthesise and apply what they have learnt on the module in order to produce sound and reasoned judgements with respect to the problem.

To enable the students to practice and prepare, various formative assessment activies, including quizzes and a AI-augmented assignments and mock exams are built into the module. Additionally, throughout the course, students will regularly receive feedback on their knowledge and assignments from AI as well as peers and staff to indicate how to improve future work and how to give constructive feedback to others.

References/Indicative Reading List

There are no module reference books to display.

Programmes Linked to This Module


Module Approval

Stage Version Date of approval Authority Chair Revalidation
Compliance 1.0 Academic Board Dr Paresh Kathrani
Pre-Teaching 1.0 Director of Education Dr Paresh Kathrani
Note: The information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: Leadership for Innovation (LI61)