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Responsible curriculum canvas
The Responsible Curriculum Canvas is a practical tool to help you bring AI into your teaching thoughtfully, fairly, and in a way that's grounded in what your students actually need.
Working through it helps you make deliberate, defensible choices about AI rather than reacting task by task, and will help to reduce student anxiety from unclear policies, protecting students who don't have equal access to AI tools or fluent English, and reducing your reliance on unreliable detection tools. The result is a curriculum that uses AI responsibly and inclusively.
This isn't about policing students or banning AI. It's about designing your curriculum so AI supports learning rather than undermining it.
The canvas is made up of nine connected blocks, flowing from understanding your cohort, through assignment design and academic oversight, to clear communication with students, all grounded in student voice. Use it as a starting point for conversations with your team, and revisit it as your modules evolve.
This resource was developed with Teaching Excellence Project (TEP) funding and media production. TEPs support research into the development of innovative learning and teaching across the university. We're grateful to Digital Epix for their collaboration in shaping this work.
An introductory video to the Responsible Curriculum Canvas
Download the canvas and flashcards
The research behind the Responsible Curriculum Canvas
The Ethical and Inclusive AI Curriculum and Assessment Design Canvas was developed by Hadi Kazemi and Dr Hajar Fatorachian, in collaboration with Behshad Mousavi from Digitalepix, through a research project funded by Leeds Beckett University’s Centre for Learning and Teaching (CLT).
The project investigated how artificial intelligence could be integrated into teaching, learning and assessment in ways that promote academic integrity, fairness and inclusion. A central part of the research involved focus groups with undergraduate and postgraduate students from diverse backgrounds, exploring their experiences of using AI, its benefits for learning and the challenges they encountered.
Students highlighted how AI could support understanding complex concepts, generating ideas, structuring academic work, improving efficiency and overcoming language barriers. They also emphasised the need for clearer guidance, practical AI training, responsible use and safeguards against overdependence on AI tools.
These findings were combined with a review of academic literature, research papers, relevant policy documents and sector guidance to develop the underpinning research framework and practical guidelines that informed the Canvas. The framework brings together seven principles covering learning value, proportionate AI assistance, AI literacy, academic integrity, equitable participation, communication and human judgement, with privacy, security and tool suitability considered throughout.
The completed Canvas is now available to support academic colleagues in reviewing and designing curricula, learning activities and assessments. The website also provides downloadable resources and informational videos to help staff understand and apply the framework in their teaching practice.
Building on this work, Hadi and Hajar jointly secured a £10,000 BA/Leverhulme Small Research Grant in 2026 for Enhanced and Inclusive Curriculum Transformation through Ethical AI Integration. This next phase will extend the research through comparative analysis across national and international higher education institutions, involving Leeds Beckett University, Bangor University and IBA in Denmark.
By exploring staff and student perspectives across different institutional and educational settings, the research aims to identify similarities and differences in approaches to ethical and inclusive AI integration, including opportunities, challenges and effective practices. The findings will contribute to further refinement of the framework and guidance and inform the planned development of an interactive, app-based version of the Canvas, making the research more accessible and practical for educators across higher education.
This framework explains how the ethical and inclusive AI curriculum and assessment design canvas can support responsible AI integration in higher education. It connects student consultation findings with peer-reviewed research and sector guidance, translating evidence into practical design principles, assessment considerations and staff guidance for curriculum development.
The publications below underpin the Canvas research framework. Select a linked title to open the source.
Suggested starting points: UNESCO’s generative AI guidance, the AI Assessment Scale and QAA’s 2026 assessment report.
Research and educational design
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Miao, F. and Holmes, W. (2023) Guidance for generative AI in education and research. Paris: UNESCO. ISBN 978-92-3-100612-8.
Human-centred guidance for choosing educationally appropriate AI uses while protecting equity, privacy and learner agency.
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Miao, F. and Cukurova, M. (2024) AI competency framework for teachers. Paris: UNESCO. ISBN 978-92-3-100707-1.
A competency model for educators developing AI-related pedagogy, ethical awareness and professional learning.
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Ng, D.T.K., Leung, J.K.L., Chu, S.K.W. and Qiao, M.S. (2021) ‘Conceptualizing AI literacy: An exploratory review’, Computers and Education: Artificial Intelligence, 2, article 100041.
Explains AI literacy dimensions that can inform teaching activities and expectations for student understanding.
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Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J. and Kasneci, G. (2023) ‘ChatGPT for good? On opportunities and challenges of large language models for education’, Learning and Individual Differences, 103, article 102274.
Examines opportunities for learning support alongside bias, unreliable information and overdependence.
Assessment design and academic integrity
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Perkins, M., Furze, L., Roe, J. and MacVaugh, J. (2024) ‘The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment’, Journal of University Teaching and Learning Practice, 21(6).
A task-level framework for communicating how much AI assistance is permitted in an assessment.
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Cotton, D.R.E., Cotton, P.A. and Shipway, J.R. (2024) ‘Chatting and cheating: Ensuring academic integrity in the era of ChatGPT’, Innovations in Education and Teaching International, 61(2), pp. 228–239. Published online 13 March 2023.
Considers institutional responses to generative AI, including integrity, guidance and support for staff and students.
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Tertiary Education Quality and Standards Agency (TEQSA) (2023) Assessment reform for the age of artificial intelligence. Assessment Reform Guiding Principles, published 23 November. Expert-led report with lead authors Jason M. Lodge, Sarah Howard, Margaret Bearman and Phillip Dawson.
Practical principles for designing assessments that still provide credible evidence of student learning.
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Quality Assurance Agency for Higher Education (QAA) (2026) The perfect storm: AI, assessment and a sector under pressure. State of the Nation report, 9 July.
Recent UK sector analysis of assessment validity and consistency in institutional AI guidance.
Student perspectives and inclusive practice
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Quality Assurance Agency for Higher Education (QAA) (2025) Supporting the ethical and equitable use of Generative AI (GenAI) for Formative and Summative Assessment: a transnational perspective (PDF). Collaborative Enhancement Project final report, 30 June.
International perspectives on equitable AI use in formative and summative assessment.
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Attewell, S. (2025) Student perceptions of AI 2025. Jisc report, 22 May.
Student views on appropriate guidance, access to AI and opportunities for dialogue with academic staff.
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Stephenson, R. and Armstrong, C. (2026) Student Generative Artificial Intelligence Survey 2026. HEPI Report 199. Higher Education Policy Institute, 12 March.
UK survey evidence on student AI use, perceived learning benefits and emerging concerns.
Research methods, accessibility and data protection
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Braun, V. and Clarke, V. (2006) ‘Using thematic analysis in psychology’, Qualitative Research in Psychology, 3(2), pp. 77–101.
Methodological grounding for coding, interpreting and comparing themes in qualitative focus-group research.
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World Wide Web Consortium (W3C) (2024) Web Content Accessibility Guidelines (WCAG) 2.2. W3C Recommendation (originally published 5 October 2023; revised 12 December 2024). Edited by Alastair Campbell, Chuck Adams, Rachael Bradley Montgomery, Michael Cooper and Andrew Kirkpatrick.
Accessibility requirements relevant to the Canvas website, downloadable guidance and planned application.
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Information Commissioner’s Office (ICO) (n.d.) Guidance on AI and data protection: About this guidance. UK Information Commissioner’s Office. Accessed 15 October 2026; guidance subject to revision.
UK guidance for protecting personal information and considering transparency in AI-enabled processes.
Project team
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![Dr Hajar Fatorachian staff profile image]()
Dr Hajar Fatorachian
Reader / Leeds Business School -
![Hadi Kazemi]()
Hadi Kazemi
Course Director / School of Built Environment, Engineering and Computing -
![]()
Behshad Mousavi
Founder and Managing Director, Digital Epix / Leeds Beckett Alumnus
Project team
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![Dr Hajar Fatorachian staff profile image]()
Dr Hajar Fatorachian
Reader / Leeds Business School -
![Hadi Kazemi]()
Hadi Kazemi
Course Director / School of Built Environment, Engineering and Computing -
![]()
Behshad Mousavi
Founder and Managing Director, Digital Epix / Leeds Beckett Alumnus
