The conference brings together scholars, practitioners, students, and innovators to explore how artificial intelligence is reshaping higher education. From ethical design to transformative pedagogy, the conference offers a platform for critical dialogue, cutting-edge research, and collaborative innovation.

The conference will include digital thinking hands-on workshops to boost digital skills and knowledge, explore practical automation tools and discover best practices.

Conference Opportunities

• Inspirational talks, panel discussions, and networking opportunities, and hands-on workshops to enhance your understanding of effective teaching practices.
• Showcase of cutting-edge research on AI in education.
• Practical tutorials and workshops on AI tools and pedagogies.
• Policy and strategy discussions on AI integration in higher education.
• Promotion of ethical, inclusive, and equitable use of AI technologies.

Submission Guidelines and Publications

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome.

• Abstracts: Research papers, posters, provocations or position papers: 300–400 words
• Full paper: 5,000 - 6,000 words
• Short paper: 4,000 words
• Workshop proposal: 500 - 700 words
• Tutorial proposal: 500 - 700 words

• All submissions must be original and not under review elsewhere.
• Single-blind peer review for papers.
• Accepted papers and abstracts will be published in the ScienceOpen conference collection proceedings "Future Facing Learning" with DOIs.
• Selected papers will be invited to submit to be published in a peer-reviewed edited volume with a prominent international publisher.
• The use of artificial intelligence (AI)-generated text in an article shall be disclosed in the acknowledgements section of any paper submitted to the conference. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text.

List of Topics

Theme 1: Reimagining curriculum for an AI-enabled economy

Curriculum is no longer a stable sequence of knowledge to be delivered, it is becoming a contested design space where knowledge, capability, and identity are continuously redefined. In an era where AI systems generate, apply, and restructure knowledge in real time, we ask what it means to design curriculum that remains meaningful, adaptive, and consequential. We invite work that challenges inherited assumptions about curriculum design and explores new models that are co-constructed with industry, responsive to uncertainty, and capable of evolving alongside technological and societal change.

Indicative topics:

• curriculum co-design with industry and enterprise partners
• modular, flexible, and interdisciplinary curriculum models
• work-integrated, project-based, and authentic learning
• curriculum design for adaptability and lifelong learning
• institutional and system-level curriculum transformation.

Theme 2: Knowledge, expertise, and human-AI collaboration

The foundations of expertise are shifting. As AI systems increasingly generate analysis, reasoning, and decision support, long-standing assumptions about knowledge ownership and professional competence are breaking down. Expertise is no longer defined by what one knows, but by how one acts when knowledge is distributed, uncertain, and machine mediated. This theme invites critical and empirical work on what expertise becomes when intelligence is no longer exclusively human.

Indicative topics:

• changing nature of knowledge in AI-mediated contexts
• redefining expertise under conditions of uncertainty
• human-AI collaboration and augmented decision-making
• AI, data, and systems literacies
• interdisciplinary and machine-assisted knowledge production.

Theme 3: Professional identity, ethics, and trust in AI-mediated practice

Professions are being reassembled in real time. Roles such as doctor, lawyer, architect, and designer are no longer defined by stable task boundaries, but by shifting relationships between humans, systems, and automation. This raises profound questions about responsibility, legitimacy, and trust. We invite work that interrogates not only how professions change, but what it means to be a professional at all.

Indicative topics:

• evolving professional identities in AI-enabled environments
• ethical judgment and accountability in AI-supported decision-making
• critical oversight of intelligent systems
• professional standards and regulatory frameworks
• trust in algorithmically mediated practice.

Theme 4: Industry-linked learning and authentic assessment

Assessment systems were designed for a world in which performance was human, observable, and separable from tools. That world no longer exists. As AI reshapes what it means to perform, learn, and produce value, assessment itself must be rethought. This theme explores how education systems can move beyond proxy measures of achievement toward authentic evaluation of capability in real-world contexts.

Indicative topics:

• scalable models of industry-linked and work-integrated learning
• sustainable education-industry partnerships
• authentic, performance-based, and competency-based assessment
• digital credentials and portfolio-based assessment
• measuring impact on learner capability and outcomes.

Committees

General Chair

• Professor Chrisina Jayne, Teesside University, UK

Programme Chairs

• Dr Ann Thanaraj, Teesside University, UK
• Paul Durston, Teesside University, UK

Organising Chairs

• Dr Andrew Bingham, Teesside University, UK
• Dr Ann French, Teesside University, UK
• Dr Matthew Watson, Teesside University, UK

Venue

The conference will be held in Middlesbrough, Digital Life Building, TS1 3BX.

Contact

All questions about submissions should be emailed to FFL.AI.Conference@tees.ac.uk