Anuschka Schmitt

This course is available on the CEMS Exchange, Global MSc in Management, Global MSc in Management (CEMS MIM), Global MSc in Management (MBA Exchange), MBA Exchange, MSc in Human Resources and Organisations (Human Resource Management/CIPD), MSc in Human Resources and Organisations (International Employment Relations/CIPD), MSc in Human Resources and Organisations (Organisational Behaviour), MSc in Management (1 Year Programme) and MSc in Management of Information Systems and Digital Innovation. This course is available with permission as an outside option to students on other programmes where regulations permit. This course uses controlled access as part of the course selection process. For information on controlled access courses, including eligibility, application processes, deadlines, and departmental contact details, please refer to the Controlled Access Courses webpage.

How to apply:

Places on oversubscribed courses will be allocated via a random ballot process with priority given to students with the course in their Programme Regulations, followed by other Department of Management students, then students from elsewhere in the school. Providing you apply before the Department deadline of 10am on Monday 28 September 2026, the time you applied will not be taken into account. Please be aware that places on high demand courses cannot be guaranteed. By submitting an application, students are confirming that they meet any pre-requisites specified. Providing an additional written statement will not aid a student’s chances of being accepted onto a course, and statements are not read.

For further information and updates on course availability during Week 1, please see the Department of Management Electives Moodle page.

Deadline for application:

Please apply by 10am on Monday 28 September 2026 with your first course choices. No allocations are made before this date and time.

For queries contact:

Please view the Department of Management Electives page for contact emails.

Priority will be given to students on the MSc in Management of Information Systems and Digital Innovation programme.

A basic understanding of experimental design and quantitative data analysis is expected to have or the be developed during the course. Most importantly, students should be curious about human-centred and organisational factors in the context of human-computer interaction.

Amid forecasts and prophecies of the impact of Artificial Intelligence (AI) on our daily life, our economy, society and workplace, people are debating whether we should be scared or excited about AI. For sure, advances in large language models made available to a wide audience represent a remarkable leap forward in digital innovation. As AI enables to automate and augment human work, corporate actors and processes are not immune to this movement. But AI is not an externally imposed fate.

What are the opportunities and the risks of these advancements if we want AI to be useful and usable for the most people possible? What objectives do we want AI-based systems to pursue, and how do we determine these objectives for specific contexts?

To answer these questions requires a critical but future-oriented understanding, as well as more human-centric and design-focused approaches. This course addresses skills and techniques relevant to the evaluation and design of innovative and human-facing systems, products, and processes. This course will make the case that AI-based systems that are designed for human and social factors can result in more beneficial, effective, and safer user interactions and interaction outcomes.

You will learn foundations of (conversational) AI and its potential for organisational contexts, a range of perspectives on the opportunities and risks of human-AI interaction, effective design-related practices for creating and evaluating useful and usable AI-based systems. You will also have several opportunities to formally communicate your ideas to a variety of audiences.

By the end of this course, you will have gained insights into AI and LLMs, as well as social, organizational, and human factors relevant to human-AI interaction and AI in the workplace. Armed with a robust toolkit of tools and techniques, you will be equipped to design and evaluate solutions and seize digital opportunities with AI where others see challenges.

15 hours of lectures and 16 hours of seminars in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

Students are expected to prepare both individually and collectively for the seminars. 

In its Ethics Code, LSE upholds a commitment to intellectual freedom. This means we will protect the freedom of expression of our students and staff and the right to engage in healthy debate in the classroom.

Formative for individual assessment: students submit a 500-word outline covering their chosen topic, selected readings, and the analytical lens they intend to apply. Feedback is provided in a one-to-one office hours session. Students are expected to acknowledge how feedback was incorporated in a brief note appended to their final submission making the formative-summative connection explicit and traceable.

Formative for group assessment: students deliver a 10-minute pitch presentation of their initial project idea in seminar. Peer and teacher feedback is structured around the marking criteria for the summative group assignment, familiarising students with the assessment standards. Teams submit a one-paragraph written response to the feedback received as part of their summative project, ensuring that feedback is actively processed.
 

• Chapter 2: Theory from Friedman, B., & Hendry, D. G. (2019). Value sensitive design: Shaping technology with moral imagination. MIT Press. 
• Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., ... & Rahwan, I. (2018). The moral machine experiment. Nature, 563(7729), 59-64. 
• Bailey, J. E., & Pearson, S. W. (1983). Development of a Tool for Measuring and Analyzing Computer User Satisfaction. Management Science, 29(5), 530-545. 
• Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258. 
• Kasy, M. (2025). The Means of Prediction: How AI Really Works (and Who Benefits). University of Chicago Press. 
• Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied psychology, 71(4), 1171-1204. 
• Recht, B. (2026). The Irrational Decision: How We Gave Computers the Power to Choose for Us. Princeton University Press.
  https://doi.org/10.2307/jj.33506889

Project (40%).

This component of assessment includes an element of group work.

Project (60%).

For detailed assessment information, including all deadlines and timings, please see the relevant course Moodle page. Assessment timings will be available at the start of each term.

Project (60%).
This component of assessment is an individual assignment.