Module manager: Charles Umney
Email: c.r.umney@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
LUBS5326M Digitalization, Automation and the Future of Work
This module is not approved as an Elective
The module examines how Artificial Intelligence (AI) and related digital technologies are shaping the future of work and management. It examines this topic from different perspectives, including examining macro-level labour questions such as the possibility of unemployment; micro-level questions such as the role of AI in performance management or its implications for equality; and bigger societal questions such as AI and climate change. The module places particular emphasis on the human experience of AI at work. There are two key themes running through it. First, the module encourages students to adopt a critical perspective toward the more exaggerated claims surrounding AI. AI is not, on its own, transforming the world of work; what matters most is how people choose to implement and use these technologies in organisational settings. This requires understanding who makes these decisions and where tensions or conflicts may emerge. Second, the module foregrounds workers’ voices, emphasising that the outcomes of digital technological change are more positive when workers have the ability to negotiate and influence how these changes unfold.
The objectives of the module are to develop students’ critical understanding of how AI and related digital technologies are reshaping labour markets, encouraging them to question overly simplistic narratives, such as claims that AI will inevitably create a “jobless future.” The module also equips students to analyse how AI is transforming employment practices within organisations, enabling them to evaluate its implications for areas such as performance management, recruitment, equality and diversity initiatives, and collective bargaining, while building their capacity to make independent, evidence based judgements about the opportunities and risks associated with using AI at work. Finally, the module supports students in evaluating a range of policy recommendations on how AI should be deployed and regulated, and in developing their own informed understanding grounded in critical analysis and evidence.
On successful completion of the module students will be able to:
ALO1. Demonstrate a critical understanding of key theories about how AI is likely to shape the future of work and formulate their own independent view of which theories best fit the evidence.
ALO2. Evaluate evidence about how AI is being used in workplaces and formulate independent and critical arguments about best practice in relation to AI.
ALO3. Evaluate competing policy approaches to AI and the future of work- for example, considering how governments should regulate it, how HR practitioners should approach it, or how trade unions should seek to negotiate it.
ALO4. Formulate their own independent arguments about how stakeholders, such as, governments, employers, unions, and workers themselves, should be acting to shape AI and the future of work.
On successful completion of the module students will be able to:
SLO1 - Academic Skills
Compare and synthesise different arguments and perspectives, using supporting evidence to form opinions, arguments, theories and ideas.
Employ good study practices and shared values, giving credit to others where their work contributes to yours.
SLO2 - Work Ready Skills
Communicate effectively both orally and in writing to convey complex information.
Learn through practice, learning proactively and adopting effective learning strategies.
SLO3 - Technical Skills
Identify and respond to ethical issues which arise in relation to everyday use of technology, such as exclusion or inequalities.
The course will cover a range of topics focused on and around AI and the future of work. Key indicative topics to be investigated include, but are not limited to:
- AI and job quality: Will AI make people’s jobs better?
- AI and the “platform economy”: a new model of management?
- AI and worker voice: how can AI work for workers?
- AI, equality, and diversity
- AI and HRM processes: recruitment, skills, performance management
- AI and the climate emergency: can it support a Just Transition?
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 10 | 1 | 10 |
| Seminar | 10 | 1 | 10 |
| Private study hours | 130 | ||
| Total Contact hours | 20 | ||
| Total hours (100hr per 10 credits) | 150 | ||
This could include a variety of activities, such as reading, watching videos, question practice and exam preparation.
Students will be given an unassessed group task, where they will be asked to give a presentation in a seminar. The topic of this presentation will be closely related to the exam questions (exam questions will be concealed from students before the exam, but general topics will be known, enabling them to prepare in a precise way). Over the course of the term, all potential exam topics will be addressed in presentations, so all students will either give or watch a presentation on each topic. The tutor leading the seminar will give formative feedback on the presentation, including comments on the argument being made, the quality of evidence referenced, and the theoretical ideas used. This will help students in preparing how they will approach exam questions.
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) (S1) | 2.0 Hrs Mins | 100 |
| Total percentage (Assessment Exams) | 100 | |
The resit for this module will by 100% by 2 hour exam.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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