AI in Learning and Education

 

 

Education exemplifies AI's qualitative turn, where generative systems produce cultural outputs such as essays, explanations, and creative work that demand contextual interpretation rather than simple benchmarking. Yet current educational AI often operates through narrow metrics that homogenise learning, perpetuating limited conceptions of intelligence whilst marginalising alternative ways of knowing. Your research could challenge this, exploring how AI might engage meaningfully with educational complexity rather than flattening it. 

You might investigate human-AI ensembles in learning contexts, moving beyond assistant models toward genuine collaboration that amplifies collective capabilities. Potential directions include developing interpretive educational AI capable of representing multiple valid perspectives, employing participatory approaches where diverse communities shape systems reflecting their epistemologies, examining how algorithmic homogenization affects learners and educators, or reimagining assessment to value contextual judgement over standardised outputs. This addresses real-world challenges: fewer than 10% of schools have AI guidance, assessment systems fail when AI generates acceptable work, and marginalised communities risk exclusion from design processes. 

We welcome proposals employing participatory design that centres community voice, critical approaches examining power dynamics in educational technology, mixed methods integrating interpretive depth with empirical patterns, and speculative methods exploring alternatives to techno-determinism. 

Place-Based and Regional Context 

The North East offers opportunities for investigating how educational AI might resist homogenization and honour local contexts. You could collaborate with Newcastle City Council and North of Tyne Combined Authority, exploring community-centred approaches that develop AI with rather than for diverse populations. Regional partners like Sunderland Software City and Innovation SuperNetwork provide access to EdTech developers where your research could advocate for interpretive, culturally responsive systems over standardised solutions. 

The area's educational diversity enables investigation of how algorithmic systems perpetuate or challenge inequalities. Rather than treating this as a technical problem requiring scaled solutions, you could explore how different communities might shape AI, reflecting distinct epistemologies. NHS partnerships enable examination of human-AI ensembles in healthcare education. The region's industrial transformation offers sites for investigating workplace learning where AI might enhance rather than displace situated expertise. 

Relevant Partner Organisations 

Partners enable challenging, narrow EdTech paradigms. Newcastle City Council and North of Tyne Combined Authority support exploring alternatives to dominant models. Technology partners (Nokia Bell Labs, Google, Thoughtworks) offer opportunities influencing development toward interpretive approaches. Policy bodies (Ofcom, DSIT, National Cyber Security Centre, Cabinet Office) enable examining governance supporting AI plurality. Third sector organizations (VONNE, Trussell Trust, Digital Safety CIC) connect with communities often excluded from design. Cultural partners (Tyne & Wear Archives & Museums, International Centre for Life Trust) provide contexts where interpretive depth matters profoundly. Healthcare partners enable the investigation of professional learning ensembles. 

Related Articles and Reading 

Participatory Design and Co-Creation 

• Zheng, C. et al. (2025). How Can We Learn and Use AI at the Same Time?: Participatory Design of GenAI with High School Students. Proceedings of IDC '25. 
• Exploring AI Problem Formulation with Children via Teachable Machines (2024). Proceedings of CHI '24. 
• Expansive Dreaming with Historically Minoritised Youth About AI in Classroom Collaboration (2024). Proceedings of CHI '24. 
• Carvalho, L., Martinez-Maldonado, R., Tsai, Y-S., Markauskaite, L., & De Laat, M. (2022). How Can We Design for Learning in an AI World? Computers and Education: Open, 3. 
• ARTIS Project (2025). A Participatory Strategy for AI Ethics in Education and Rehabilitation Grounded in the Capability Approach. arXiv preprint. 

Learner Agency and Autonomy 

• Darvishi, A., Khosravi, H., Sadiq, S., & Gašević, D. (2024). Impact of AI Assistance on Student Agency. Computers & Education, 210. 
• Alm, A. (2024). Exploring Autonomy in the AI Wilderness: Learner Challenges and Choices. Education Sciences, 14(12). 

Equity and Digital Divides 

• Trucano, M. (2023). AI and the Next Digital Divide in Education. Brookings Institution. 
• OECD (2024). The Potential Impact of Artificial Intelligence on Equity and Inclusion in Education. 
• Vesna, L., Sawale, P.S., Kaul, P., Pal, S., & Murthy, B.S.R. (2025). Digital Divide in AI-Powered Education: Challenges and Solutions for Equitable Learning. Journal of Information Systems Engineering and Management, 10(21s). 
• Accessibility and Disability in PreK-12 CS (2024). Proceedings of RESPECT 2024. 

Assessment and Academic Integrity 

• Kofinas, A. et al. (2024). The Impact of Generative AI on Academic Integrity of Authentic Assessments Within a Higher Education Context. British Journal of Educational Technology. 
• Evangelista, E.D.L. (2025). Ensuring Academic Integrity in the Age of ChatGPT: Rethinking Exam Design, Assessment Strategies, and Ethical AI Policies in Higher Education. Contemporary Educational Technology, 17(1). 

Teacher Roles and Professional Development 

• Kim, J. (2024). Types of Teacher-AI Collaboration in K-12 Classroom Instruction: Chinese Teachers' Perspective. Education and Information Technologies, 29, 17433-17465. 

AI Literacy Frameworks 

• Long, D. & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of CHI '20. 
• Digital Promise (2024). Revealing an AI Literacy Framework for Learners and Educators. 
• Open University (2025). A Framework for the Learning and Teaching of Critical AI Literacy Skills. 
• OECD & European Commission (2025). AI Literacy Framework for Primary and Secondary Education. 
• AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review (2025). Proceedings of ICER 2025. 
• Framing AI Literacy for K-12 Education: Insights from Multi-Perspective and International Stakeholders (2025). Proceedings of ACE 2025. 

UK Government Policy 

• Department for Education (2023, updated 2025). Generative Artificial Intelligence (AI) in Education. 
• Ofsted (2025). AI in Schools and Further Education: Findings from Early Adopters. 

European Union Policy 

• European Parliament and Council (2024). Artificial Intelligence Act (Regulation (EU) 2024/1689). 
• European Commission (2022). Ethical Guidelines on the Use of Artificial Intelligence and Data in Teaching and Learning for Educators. 
• European Parliament (2021). Artificial Intelligence in Education, Culture and the Audiovisual Sector. Report A9-0127/2021. 

International Guidance 

• UNESCO (2023). Guidance for Generative AI in Education and Research. 

Interpretive and Alternative Approaches to AI 

• Hemment, D., Kommers, C., et al. (2025). Doing AI Differently: Rethinking the Foundations of AI via the Humanities. White Paper, The Alan Turing Institute.