Module Specification

Advanced Leadership for Innovation

London school of INNOVATION

Module Specification

Advanced Leadership for Innovation: Lead with Foresight, Drive Transformation


In today's tech-driven world, innovation leadership requires more than just vision—it demands strategic action. This module delves into the leadership essentials required to navigate and thrive in high-tech environments. You will be equipped to tackle disruption head-on, balancing immediate execution with strategic foresight.

This module will explore a range of leadership styles tailored for innovation, from transformational to situational. You will learn to build and manage cross-functional teams, harness emerging technologies, and foster a culture supporting risk-taking and creativity. Topics include decision-making in fast-moving contexts and scaling innovation from ideation to execution.

By completing this module, you will gain advanced skills in risk management, change management, and leveraging modern collaboration tools. You will be well-prepared to lead through crises, drive digital transformations, and align your team's efforts with overarching organisational objectives.

Embark on a journey to become a catalyst for innovation, equipped with the knowledge and tools to make a significant impact.


Code Number of Credits ECTS Credits Framework HECoS code
DA71 15 7 FHEQ - L7

Learning outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Critically analyse different leadership styles and their application in fostering a culture of creativity and experimentation.
LO2 Knowledge and Understanding Demonstrate a systematic understanding of core principles of innovation in high-tech, data-driven environments.
LO3 Intellectual Skills Develop strategic responses to align innovation initiatives with organisational goals in complex digital landscapes.
LO4 Intellectual Skills Evaluate and propose new methodologies for managing innovation teams during technological disruptions or crises.
LO5 Professional/Transferable Skills Critically evaluate and apply ethical and professional standards in managing change and innovation within organisations.
LO6 Professional/Transferable Skills Leverage modern collaboration tools to enhance performance in remote and distributed innovation teams.

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.		 K LO1
K LO2
I LO3
I LO4
P LO5
P LO6
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.		 K LO1
K LO2
I LO3
I LO4
P LO5
P LO6

Student workload

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
150.00

Content Structure

Week Chapter Name Chapter Description
Week 1 Principles of Innovation Understand the core principles of innovation and why they matter. Dive into various innovation types and theories to grasp how they can drive progress. Delve into strategic frameworks that guide innovative efforts, ensuring your approach aligns with organisational goals.
Week 2 Leadership Styles Learn about different leadership styles—adaptive, transformational, transactional, servant, situational, and strategic. Understand when to apply each style and how flexible leadership tailored to context can drive long-term success in high-tech, data-driven environments.
Week 3 Fostering Creativity Explore ways to foster a culture of creativity and experimentation within your team. Understand the importance of a supportive environment and practical methods to encourage risk-taking and innovative thinking.
Week 4 Managing Innovation Teams Learn techniques for effectively managing innovation teams, including cross-functional team-building and leveraging emerging technologies. Develop skills to align team efforts with organisational objectives and drive continuous improvement.
Week 5 Navigating Change Master change management techniques essential for leading through disruptions or crises. Study models like Kotter’s 8-Step Change Model, the ADKAR Model, and Lewin’s Change Management Model to effectively guide transformations and overcome resistance.
Week 6 Risk Management Dive deep into risk management principles, including risk identification, assessment, and mitigation. Use frameworks like RBS, FMEA, and Monte Carlo simulations to navigate uncertainties and foster a culture that supports calculated risks.
Week 7 Scaling Innovation Explore the innovation life cycle from ideation to execution. Learn strategies to scale your innovation efforts effectively while balancing short-term execution with long-term strategic goals.
Week 8 Collaboration Models Understand different approaches to innovation—Open, Closed, Collaborative, and Crowdsourced. Learn how to harness both external and internal ideas and technologies, leveraging partnerships to maximise innovation outcomes.
Week 9 Digital Transformation Examine the challenges specific to technical leadership in digital technology projects. Gain skills in leading teams through complex system implementations, managing remote and distributed teams, and using modern collaboration tools to enhance productivity.

Module References

There are no module reference contents to display.

Methods of teaching/learning


Introductory lecture (1.50 hours)

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) (18.00 hours)

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 (9.00 hours)

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 (9.00 hours)

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 (13.50 hours)

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 (13.50 hours)

The 9 weekly sessions following the introduction (weeks 2 to 10) will be dedicated to teaching the contents of the module during interactive workshops. These sessions will complement the theory with practice, experience or analysis. Their purpose is to advance the student's cognition from 'knowledge' to 'understand' and 'apply'.

Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning, and may include hands-on project work, group discussions or debates, roleplay, simulation, case study or other presentation, 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. There will be an opportunity also for Q&A in every session.


Independent reading, exploration and practice (61.50 hours)

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.

Programmes this module appears on

Programme Term Type
1 Software Technical Leadership (MSc) 1 Core
2 Digital Innovation and Entrepreneurship (MSc) 2 Core
3 Digital Project Management (MSc) 2 Core
4 AI for Business Transformation (MSc) 2 Core
5 AI and Machine Learning (MSc) 1 Optional
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: Advanced Leadership for Innovation: Lead with Foresight, Drive Transformation (DA71)