The London Interdisciplinary School (LIS) Seeks Visiting Lecturer (Adjunct Faculty)

The London Interdisciplinary School (LIS) is seeking to appoint a Visiting Lecturer (Adjunct Faculty) with expertise in the ethical, legal, and organizational dimensions of artificial intelligence. This is an exciting opportunity to contribute to an innovative interdisciplinary curriculum that equips students to address complex real-world challenges.

We are looking for an academic who is enthusiastic about active, problem-based learning and able to integrate perspectives from across multiple disciplines. The role would suit an early-career academic who has recently completed, or is close to completing, a PhD, although applications from candidates with equivalent or greater expertise and experience are also welcome.

About The Role

The successful candidate will contribute teaching across undergraduate, postgraduate, and MBA programs.

They Will

• Have a primary responsibility for designing and delivering a second-year undergraduate Problem-Based Learning module on AI and Ethics which pays £5,000.
• Contribute to delivery of the ‘Problems: AI Futures’ module on the MASc programme which pays £1,500.
• Contribute c.2 weeks of teaching towards the ‘Intelligence Shift’ on MBA programme, covering the legal and regulatory background to AI which pays £1,500.
• Contribute c.2 weeks of teaching as part of the ‘Disciplinary Perspective’ on undergraduate ‘Problems 1B’ module - broadly focused on law and the environment with the candidate being able to connect their expertise to an aspect of the environment which pays £1,500.

The undergraduate Problem-Based Learning module on AI and Ethics is taught over ten weeks (January–March 2027) through twice-weekly, 90-minute sessions for cohorts of approximately 35–50 second-year students. In keeping with LIS's active learning approach, teaching combines carefully curated pre-session materials with interactive workshops, seminars, facilitated group work, and applied problem-solving rather than traditional lectures alone.

For the undergraduate AI problems module, which will be the primary area of responsibility, the successful candidate will be expected to:

• Design and deliver an engaging interdisciplinary module on AI and Ethics at undergraduate Level 5 appropriate for the Framework for Higher Education Qualifications and aligned with the overall programme learning outcomes. You can either adapt an existing module information form or create a new one, but the content should incorporate a range of perspectives on AI ethics - e.g., technical, socio-cultural, environmental, political, and legal perspectives - and make clear reference to at least 2-3 different academic disciplines.
• Design and mark a suitable collaborative assessment that can be completed by students working in groups of 4-5. This should include an output aimed at a'real-world' audience of policymakers and/or practitioners, and, optionally, a separate output for an academic audience. Ideally, the'real-world' audience should include a specific, named person or organisation that the students have engaged with during the module, although there can be some flexibility on this. You can either adapt an existing assessment brief or create a new one.
• Provide high-quality assessment, written feedback, and grading in accordance with institutional policies and academic standards.
• Support students throughout the module and contribute to an inclusive and intellectually challenging learning environment.
• You will be expected to deliver the content yourself, although there may be some flexibility, e.g., to invite representatives of relevant organisations to provide information on a real-world problem that they would like the students to work on (no more than two sessions).
• All students should be provided with text-based feedback as well as a numerical grade, using a clear rubric based on the assessment brief. We can provide samples of students' work and markers' feedback.
• Marking and feedback must be completed online in our Virtual Learning Environment, Canvas, within set timeframes.
• You will need to create mechanisms for ensuring that students cannot receive credit for the collaborative assessment if they have not participated actively in their group (e.g., authorship statements, allocation of sections to individuals, or requiring a separate individual reflection).
• The policy on use of AI tools must be clearly explained in the assessment brief and you should ensure that students use these tools only when needed, to enhance learning rather than substituting it.