HP-UCL IOE Fellowship in AI in Education 2027: Programme Details, Application Requirements and Deadline

The HP-UCL IOE Fellowship in AI in Education is a global executive leadership programme designed to strengthen the knowledge, governance capabilities, and strategic leadership of senior education leaders working to guide the responsible use of artificial intelligence in education systems. Delivered by the UCL Institute of Education (IOE) in partnership with HP, the programme focuses on helping education leaders understand AI, develop responsible strategies, and address the opportunities and challenges associated with integrating artificial intelligence into education.

As artificial intelligence continues to influence teaching, learning, educational management, and policy development, leaders are increasingly expected to make informed decisions about how these technologies should be introduced and governed. The fellowship responds to this need by bringing together expertise in artificial intelligence, learning sciences, ethics, policy, and human-computer interaction.

Through expert-led learning, collaborative discussions, practical case studies, peer learning, and a mentored capstone project, participants explore how AI can be used responsibly and effectively within their professional and institutional contexts.

For education policymakers and organisational leaders seeking to strengthen their understanding of AI in education, this fellowship provides a structured opportunity to examine emerging developments, explore leadership challenges, and develop practical approaches to educational transformation.

Important deadline update: The official application deadline was 7 October 2026. As of 20 October 2026, that deadline has passed. Anyone interested in the programme should consult the official website or contact the programme team to confirm whether any further application arrangements are available.

Official source: UCL Institute of Education – HP-UCL IOE Fellowship in AI in Education.

About the HP-UCL IOE Fellowship in AI in Education

The fellowship is a leadership programme focused on the responsible transformation of education through artificial intelligence. It is delivered by the UCL Institute of Education and HP and is housed within the UCL Knowledge Lab.

The programme recognizes that AI has the potential to influence how educational institutions operate, how teachers support learners, how students interact with educational resources, and how policymakers make decisions. However, introducing AI into education also raises important questions about fairness, accountability, privacy, inclusion, educational quality, and institutional responsibility.

Education leaders may be expected to make decisions about AI tools and systems without always having sufficient evidence, technical knowledge, or governance frameworks to guide those decisions. The fellowship aims to help address this challenge by developing participants’ understanding of AI and their ability to lead its implementation responsibly.

Rather than focusing only on the technology itself, the programme examines the relationship between AI, learning, institutional leadership, education policy, and human needs. Participants are encouraged to consider how AI-related decisions can support meaningful learning outcomes while protecting learners’ interests and strengthening institutional capacity.

The programme combines conceptual learning with practical application. Fellows explore global developments in AI in education, examine real-world challenges, engage with experts and peers, and work towards strategies that are relevant to their own professional settings.

Objectives of the HP-UCL IOE Fellowship

The fellowship’s central purpose is to prepare senior education leaders to make informed, strategic, and responsible decisions about artificial intelligence in education.

Its focus includes several important objectives.

1. Strengthening leadership in AI-driven education transformation

Introducing AI into an educational institution or wider education system requires more than selecting a technology. It also involves understanding institutional priorities, preparing staff, evaluating risks, establishing governance arrangements, and deciding how technological changes should be implemented.

The fellowship explores how leaders can approach these responsibilities strategically. Participants consider the decisions required to introduce AI in ways that support educational goals and respond to the needs of learners, teachers, and institutions.

2. Developing knowledge of AI and learning

Understanding AI in education requires consideration of both technological developments and the principles of teaching and learning.

The programme explores the foundations of AI, learning sciences, and human-AI interaction. These areas help participants examine how AI systems may influence educational experiences and how technology can be designed and used with human learning needs in mind.

This perspective is important because technological innovation should be assessed according to its educational value rather than simply its novelty.

3. Promoting responsible and ethical AI use

AI applications can raise questions about fairness, accountability, transparency, privacy, and the treatment of different groups of learners.

The fellowship addresses responsible AI by examining ethical frameworks and governance approaches that can help leaders assess these concerns. Participants are encouraged to consider how educational institutions can make decisions that recognize the benefits of AI while addressing potential risks.

4. Exploring AI policy and governance

Education leaders may need to establish policies for the selection, procurement, deployment, and oversight of AI systems. These decisions can affect teaching practices, institutional resources, learner experiences, and public trust.

The programme examines governance, policy frameworks, and procurement processes to help participants understand the broader responsibilities associated with AI adoption.

5. Encouraging inclusive and learning-centred innovation

The use of AI in education should take account of different learner needs, educational circumstances, and institutional capabilities.

The fellowship explores learner agency, inclusion, wellbeing, equitable design, and the role of teachers. These themes encourage participants to consider how AI-related strategies can be developed around educational priorities and the needs of the people who use or are affected by these systems.

Who Is the Fellowship Designed For?

According to the official programme description, the HP-UCL IOE Fellowship in AI in Education is a global executive leadership programme for senior education policymakers and organizational leaders.

Its emphasis on leadership, governance, and system-level transformation means that applicants should carefully assess whether their current responsibilities and professional experience align with the programme’s intended audience.

The application process asks candidates to explain their current roles, professional responsibilities, leadership experience, areas of influence, and relevant experience in digital transformation, AI, education, innovation, or policy.

Applicants should therefore be prepared to demonstrate how their professional responsibilities connect with the challenges and opportunities of AI in education.

The official website describes the programme as a selective leadership opportunity. Applications are reviewed competitively, with selection criteria focused on professional relevance, leadership potential, commitment, and the strength of the proposed project.

The programme aims to build an international cohort representing different professional, institutional, and geographical contexts.

Applicants should consult the official programme page for the full current eligibility and selection information rather than assuming that all education professionals or students qualify.

Key Benefits and Learning Opportunities

The fellowship offers a combination of expert-led learning, practical activities, professional collaboration, and project development. These elements are intended to help participants connect emerging developments in AI with real challenges in their organizations and education systems.

1. Learning from specialists in AI and education

Participants engage with expertise spanning artificial intelligence, learning sciences, education policy, ethics, and human-computer interaction.

This interdisciplinary approach helps leaders examine AI from multiple perspectives. It encourages them to consider not only how a technology works, but also how it affects learning, professional practice, governance, and institutional decision-making.

2. Exploring practical education challenges

The programme includes applied case studies and opportunities to discuss real policy and implementation challenges.

These activities allow participants to reflect on how AI-related decisions may work in different settings. They can examine the considerations involved in developing strategies, assessing evidence, and addressing implementation challenges within their own professional contexts.

3. Building a stronger understanding of AI governance

The fellowship explores responsible AI, policy, governance, and procurement. These areas are relevant to leaders who must evaluate technology options, consider institutional risks, and develop appropriate approaches to implementation.

Participants can use these perspectives to inform their professional decision-making and contribute to discussions about the responsible use of AI in education.

4. Learning through international peer exchange

The programme brings together fellows from different professional and geographical contexts.

Peer learning can help participants compare challenges, exchange perspectives, and consider how different institutions approach AI in education. This international engagement is particularly relevant because educational systems differ in their resources, policies, priorities, and technological capabilities.

5. Developing a practical capstone project

The fellowship includes a mentored capstone project connected to participants’ work contexts.

This component provides an opportunity to translate learning into a practical leadership or implementation initiative. Participants are expected to identify a relevant challenge, explain why it matters, and consider how their proposed project could be developed or implemented.

6. Joining a professional network

The official programme page states that programme alumni will be able to join a global network of fellows.

Such a network can provide a basis for continued professional exchange and collaboration with people working on AI and education-related challenges in different contexts.

HP-UCL IOE Fellowship Programme Structure

The official programme describes the fellowship as a selective, 12-month hybrid leadership programme. It combines online learning, independent study, coaching, an in-person residential week in London, and a mentored capstone project.

The structure is intended to connect foundational knowledge with leadership practice and project development.

Phase One: Online Learning and Leadership Development

The programme begins with three months of live online workshops, supported by independent study and coaching.

The learning journey covers themes related to AI in education, including global trends, the foundations of AI and learning, ethics, governance, inclusion, teachers’ professional agency, evidence, implementation, and leadership.

The official programme outline identifies the following learning themes:

Week 1: Framing AI in Education Systems

Participants explore global AI trends, governance challenges, and the implications of AI for education systems. This provides a starting point for examining AI as a leadership and institutional issue rather than only a technical development.

Week 2: Foundations of AI, Learning, and Human-AI Interaction

This theme introduces foundational perspectives on AI, learning sciences, and human-centred AI design. It helps participants consider the relationship between technological capabilities and educational experiences.

Week 3: Ethics and Responsible AI

Participants examine ethical frameworks and questions involving fairness, accountability, and responsible AI use. These issues are important when evaluating how AI systems should be selected, introduced, and overseen in education.

Week 4: Governance, Policy, and Procurement

This theme addresses policy frameworks, governance arrangements, and procurement processes. It explores considerations that leaders may need to address when making decisions about AI tools and institutional implementation.

Week 5: Learners, Inclusion, and Equitable Design

Participants consider learner agency, inclusion, wellbeing, and equitable approaches to educational design. The focus encourages attention to the needs of different learners and the potential consequences of AI-related decisions.

Week 6: Teachers and Professional Agency

This theme examines how AI relates to teachers’ expertise and professional decision-making. It considers the role of educators in shaping how technology is used within teaching and learning.

Week 7: Evidence, Implementation, and Scaling

Participants explore how to assess evidence, plan implementation, and consider the challenges of scaling initiatives across different educational contexts.

Week 8: Leadership Clinics and Capstone Launch

The programme turns towards participants’ leadership challenges and the development of practical AI strategies. This stage supports the definition and initial planning of capstone projects relevant to their professional contexts.

Weeks 9–11: Project Development and Mentorship

Participants refine their proposed projects through mentorship, peer learning, and strategic feedback. This phase helps connect the fellowship’s learning themes with practical project development.

Week 12: Residential Week in London

The programme includes a one-week residential in London, designed to advance collaboration, innovation, and policy dialogue through immersive in-person engagement.

The official programme page lists the residential week as beginning in the week of 5 April 2027.

Phase Two: Mentored Capstone Project

Following the initial online learning period and residential component, the programme includes nine months of mentoring and capstone development.

The capstone is intended to focus on a challenge connected to the participant’s own work context. Fellows receive guidance from a facilitator and participate in follow-up support as outlined by the programme.

This project-based component is an important part of the fellowship because it encourages participants to apply their learning to a practical leadership challenge rather than treating the programme as a purely theoretical experience.

Applicants are therefore expected to identify a potential project and explain its importance as part of their application.

Application Requirements for the HP-UCL IOE Fellowship

The application form requests personal and professional information, a personal statement, confirmation of availability, and a Letter of Support from an employer or sponsoring organization.

Candidates preparing an application should review each requirement carefully.

1. Personal and Professional Information

Applicants are asked to provide the following information:

• Full name.
• Primary email address.
• Current role or job title.
• Current organization or institution.
• Country of residence.

The information should be accurate and consistent with the applicant’s current professional circumstances.

2. Personal Statement: Maximum Four A4 Pages

The personal statement is a central part of the application. It must not exceed four A4 pages and must address three sections.

Section One: Professional Background and Current Role — Maximum Two A4 Pages

Applicants should describe their current role and professional responsibilities, leadership experience, and areas of influence.

They should also explain the context in which they work, including relevant details about their organization, institution, or education system.

Experience related to digital transformation, artificial intelligence, education, innovation, or policy should be included where relevant.

A strong response should clearly explain the connection between the applicant’s professional background and the fellowship’s focus on leadership and AI in education.

Section Two: Motivation and Commitment — Maximum One A4 Page

This section asks applicants to explain why they wish to participate in the fellowship and how the programme aligns with their current work and future goals.

Applicants should also identify the perspectives, expertise, or experience they would contribute to the fellowship community.

In addition, they should demonstrate their capacity and commitment to engage actively throughout the programme.

The response should communicate a clear motivation for participating and explain how the applicant expects to use the experience in their professional context.

Section Three: Proposed Capstone or Leadership Project — Maximum One A4 Page

Applicants must outline a potential capstone or leadership project that they may develop through the fellowship.

The proposed project should address three key questions:

1. What is the project idea? Explain the central idea and the challenge or opportunity it addresses.
2. Why is it important? Describe its relevance to the applicant’s organisation, institution, education system, or professional context.
3. How could the fellowship help? Explain how the programme could support the project’s development or implementation.

The proposal should be connected to the fellowship’s focus on AI in education, leadership, innovation, or policy. Applicants should present a clear and realistic idea that demonstrates an understanding of the context in which the project could be implemented.

3. Personal Statement File Format

The application form allows the personal statement to be uploaded as a PDF or document file.

The maximum permitted file size is 10 MB. Applicants should check that their document is complete, readable, correctly formatted, and within the four-page limit before uploading it.

4. Confirmation of Availability and Commitment

Applicants must confirm that they have the capacity and commitment to attend all required programme sessions, including the residential week in London.

Because the fellowship combines online sessions, independent study, coaching, mentoring, and an in-person component, applicants should consider their professional responsibilities and other commitments before confirming their availability.

5. Letter of Support from an Employer or Sponsoring Organization

Applicants must upload a Letter of Support from their employer or sponsoring organization.

The letter should confirm the organization’s support for the applicant’s participation, attendance, and proposed project.

Candidates should arrange this document in advance and ensure that it addresses the support requested in the application form.

How Applicants Are Selected

The official programme page states that applications are reviewed competitively by the programme team.

The published selection criteria include:

• Relevance of the applicant’s role: The relationship between the applicant’s professional responsibilities and AI and education transformation.
• Leadership experience and potential: Evidence of leadership experience and the potential to contribute to meaningful impact.
• Capacity to participate fully: The ability and commitment to engage in all programme activities.
• Commitment to responsible AI: An interest in responsible, equitable, and human-centred approaches to artificial intelligence.
• Strength of the proposed project: The clarity and quality of the proposed capstone or leadership project.
• Contribution to peer learning: The potential to contribute to international collaboration and shared learning.

The programme aims to bring together a diverse international cohort representing different professional, institutional, and geographical backgrounds.

Applicants should use their personal statements to explain their experience and motivation clearly, demonstrate the relevance of their work, and present a well-defined project idea.

Fellowship Costs and Financial Support

According to the official UCL programme page, the programme covers all costs upon acceptance, excluding airfares and visa expenses.

This means that accepted participants should not assume that international flights or visa-related expenses are included in the programme’s cost coverage. Applicants should consult the official programme team for clarification about the precise costs covered and any practical arrangements that apply to their participation.

The programme also includes a residential week in London, so participants should consider the travel and visa arrangements relevant to their circumstances.

Candidates should verify the current terms directly with UCL before making financial or travel commitments.

Important Dates for the HP-UCL IOE Fellowship

The official programme page lists the following dates for the 2026 application cycle and 2027 programme:

Event	Date
Applications opened	29 June 2026
Application deadline	7 October 2026
Notification of application outcomes	29 October 2026
Programme start date	11 January 2027
UCL Easter closure	25–30 March 2027
Residential week in London	Week beginning 5 April 2027

Application status: The deadline of 7 October 2026 has passed. The official schedule lists 29 October 2026 as the date for notifying applicants of outcomes. Anyone who has not applied should contact the programme team to ask whether any further application options are available. The published dates should be checked against the official programme page for any updates.

How to Find the Official Application Information

The application process involves completing the online form and submitting the required documents. Since the published deadline has passed, prospective applicants should first confirm whether the application form remains available and whether the programme team is accepting any further submissions.

Use the official links below to review the programme information and application materials.

Official fellowship website:
https://www.ucl.ac.uk/ioe/hp-ucl-ioe-fellowship-ai-education

Online application form:
https://docs.google.com/forms/d/e/1FAIpQLSedVnYlAas2m5TVdu3SxIF3LMApFbibs5cizd8WX_aPquDqAA/viewform?usp=dialog

Programme contact email:
hp-ucl-aied@ucl.ac.uk

Applicants can use the contact email to ask the programme team about the current application status, eligibility requirements, participation arrangements, or other questions not fully answered on the website.

Frequently Asked Questions

What is the HP-UCL IOE Fellowship in AI in Education?

It is a global executive leadership programme delivered by the UCL Institute of Education and HP. It focuses on developing leadership, knowledge, and governance capabilities for responsible AI transformation in education systems.

Who is the fellowship intended for?

The official programme describes its intended audience as senior education policymakers and organisational leaders. Applicants should review the official selection information to determine whether their professional roles and experience align with the programme.

What documents are required?

The application form requests a personal statement of no more than four A4 pages and a Letter of Support from an employer or sponsoring organization. Applicants must also provide personal and professional information and confirm their ability to attend the required programme activities.

What should the personal statement include?

It must cover the applicant’s professional background and current role, motivation and capacity to participate, and a proposed capstone or leadership project. Each section has a specified page limit.

Is the programme delivered online or in person?

The programme uses a hybrid format. It includes live online workshops, independent study, coaching, a one-week residential in London, and a mentored capstone project.

Does the fellowship cover programme costs?

The official programme page states that programme costs are covered upon acceptance, excluding airfares and visa expenses. Applicants should consult UCL for clarification about the applicable arrangements.

What was the application deadline?

The published deadline was 7 October 2026. As of 20 October 2026, it has passed. Prospective applicants should contact the programme team to confirm whether any further submissions are possible.

When is the programme scheduled to begin?

The published programme start date is 11 January 2027.

Conclusion

The HP-UCL IOE Fellowship in AI in Education is designed for senior education leaders who want to strengthen their capacity to guide responsible artificial intelligence transformation. Through expert-led learning, policy and governance discussions, international peer exchange, and a mentored capstone project, the programme connects emerging AI developments with practical leadership challenges in education.

The application process requires candidates to explain their professional experience, demonstrate their motivation and commitment, propose a relevant leadership project, and provide a Letter of Support from an employer or sponsoring organization. Participants must also be able to commit to the programme’s full schedule, including the residential week in London.

The published application deadline was 7 October 2026, which has now passed. Interested readers should consult the official UCL website or contact the programme team for the latest status and any further opportunities to participate.

For more education, leadership, technology, fellowship, and professional development opportunities, continue exploring Opportunities for Youth (OFY).