LSE AI and Education Fellowships

•  A centrally funded one term teaching buy-out – exact details will be agreed by the Review Panel with the department and faculty member prior to the confirmation of a fellowship. This will give successful fellows time and space to reimagine and redesign the teaching, learning and assessment on their course or multiple courses in the department through exploring and taking advantage of the opportunities of AI tools. The buy-out will not include relief from administrative or mentoring responsibilities in the department.
• Support from a dedicated new 'AI in education development' expert. Recruited jointly across the Digital Skills Lab and the Eden Centre, they will work directly with the fellows on a one-to-one basis. They will provide advice, guidance and practical hands-on support for course redesign and the build and implementation of AI tools.
• Fellows will be provided with access to and training on applicable AI tools.
• Fellows will receive dedicated project management, administrative, and communications support to help with the promotion and dissemination of case studies and resulting course materials internally and externally (as appropriate).

• Deliver an AI integrated course or courses as per the aims, deliverables and timeline to be agreed with the Review Panel.
• Champion their work in LSE and beyond, including producing an open access case study across media formats, sharing course materials (as appropriate to the audience), presenting at events, and supporting wider work in the department and in LSE on development of AI in education. They will take an active role in the Fellowship Community of Practice for the full two years of the scheme.
• Work closely with colleagues in their own department including the Deputy Head (Education), relevant Programme Director(s), and feed into the Department Teaching Committee or equivalent body.

The Review Panel will evaluate applications using the following criteria: 

• Potential for impact: the proposal focuses on a course or courses that will significantly impact the learning of the student body, such as core courses, service teaching courses, or programme wide initiatives. The application should clearly outline how many students will benefit and the breadth of the impact.
• Innovation and scalability: the proposal presents either innovative applications of AI in education (teaching, learning, assessment) or approaches that can be easily replicated across LSE. The proposal should demonstrate how the AI integration will transform teaching and learning in ways that could serve as a model for other courses in their department or the broader School.
• Clarity of implementation plan: the applicant provides a clear outline of their aims, scope, and approach for integrating AI into their course(s). This includes specific resource requirements and a realistic timeline for implementation.
• Commitment to knowledge sharing: the applicant must be willing to actively participate in the LSE AI and Education Fellows Community of Practice for the full two years and to create open-access case studies in multiple formats (written, video, audio) documenting the transformation work.

• In addition to the evaluation criteria, the Review Panel will be looking to select a diverse set of aims and approaches when integrating AI, and courses that, in combination, will give us a broad range of disciplines. Possible themes could include:
  • Assessment innovation
  • Interactive and collaborative learning
  • Research led teaching enhancement
  • Inclusive education design
  • Personalised learning at scale