Creating an open, co-created and co-guided toolkit to support staff integration of AI literacy and skills into the curricula

Working with a cross-faculty team, and funded by the President and Principal's Fund for Educational Excellence 2024/25, the project aimed to develop an accessible, open-source toolkit co-created and co-guided by academic staff and students to effectively integrate AI literacy and skills across curricula at Queen Mary.

Project approach

1. Ethical approval - to ensure the project met institutional ethical standards
2. Data Collection - to gather rich, cross-disciplinary data through 35 in-depth case studies complemented by seven cross-faculty focus groups with students and two surveys, one for students and one for staff
3. Structured Mapping Exercise - to understand AI integration at the module and programme level
4. Sprint Day Workshop - with 45 attendees to showcase the framework’s adaptation at the module and programme levels. Eight colleagues from different faculties presented case studies on how they have integrated our framework and embedded it into their curriculum
5. Toolkit Development  - leveraging the case studies, the focus group and the feedback from the Sprint Day Workshop, the tea developed a practical, user-friendly toolkit comprising actionable guidance

Project outputs

1. AI in Teaching and Learning Framework, featured in the AI Literacy newsletter. To date, it has been requested by 31 academics globally, demonstrating its relevance and utility across diverse educational contexts. The framework also informed and contributed to the development of the AIM, ACT, and APT frameworks, practical tools designed to guide staff in designing assessments that develop both AI literacy and responsible AI use. 
2. Full-day Sprint Day Workshop, bringing together students, staff, and externals to co-design and validate elements of the framework. 
3. Participation in two workshops - one with the School of the Arts and another with the Queen Mary Centre for Excellence in Artificial Intelligence in Education.
4. Contributed to several blogs, including for the Queen Mary Centre, and we published our journal in JLDHE and AACSB. The project has been accepted into four conferences, including national events (Advance HE, LTSE and AOM), helping to disseminate insights and share effective practices.
5. The AI in Teaching and Learning Toolkit, developed as a key project output, is published and includes actionable guidance, case study examples, and AI tools to support institutions in embedding AI into teaching, learning, and assessment practices.

Project team

Lilian Schofield (School of Business and Management), Xue Zhou (School of Business and Management), Daniela Tavasci (School of Economics and Finance), Lesley Howell (School of Physical and Chemical Sciences), Aisha Abuelmaatti (School of Electronic Engineering and Computer Science) and Cassandra Lewis (Institute of Dentistry)