Special Issue on Digital Education Innovation and Shaping the Future University in the Age of AI

Editorial Board

Special Issue Co - Editor:

• Professor Lucy LU, Provost, ELITE Innovation College Cambridge, Director of Centre for Women Leadership and Innovation, Global Innovation Research Institute, Xi'an Peihua University
• Professor CHEN Li, Beijing Normal University; Vice President, China Association for Educational Technology; Internet + Education, lifelong learning and evaluation

International Advisory Board

• Professor YANG Zongkai, Chairman of China Associate for Education Technology,
• Professor Craig Mahoney, Former Vice Chancellor, University of West Scotland
• Professor Maurits Van Rooijen, President, Europe University of Applied Science, German
• Professor Phil Laird, Vice President, University of Trinity West, Canada
• Professor Richard Li-Hua, President, Global Innovation Research Institute, Xi’An Peihua University.

Special Issue Associate Editors

• Dr Nian Zhiying, Director, PBL Teaching and Research Centre, College Education for the Future, Beijing Normal University (Zhuhai Campus)
• Dr Wang Jingxin，Assistant Professor, Institute for Advanced Studies in Education, School of International Education, Shandong University.
• Dr Vivian Li, Director of President Office, ELITE Innovation College Cambridge, UK

Special Issue Assistant Editors:

• Ms Wang Nan, Global Innovation Research Institute, Xi’An Peihua University,P.R.China
• Ms Amber Jasmine Mclean, Luiss University, Italy

1. Strategic rationale: why this issue, why now

Artificial intelligence (AI) has passed from a discrete educational technology into cognitive and organisational infrastructure. It now participates in writing, feedback, assessment, curriculum design, student support, research, administration and strategic decision-making. The urgent research problem is therefore no longer simply whether education adopts AI. It is how educational purposes, professional roles, institutional arrangements and public value are reshaped as AI becomes embedded across the system.

AI is shaped through pedagogical choices, data and platform architectures, professional cultures, regulation, funding pressures, labour markets, institutional histories and cultural expectations. When embedded in different organisational arrangements, comparable AI capabilities may support dialogic learning and epistemic inquiry, automate academic and administrative decisions, intensify data-driven monitoring, standardise knowledge production, or redistribute professional work.

China has moved from education digitalisation to system-wide “AI + Education”. The 2024-2035 education-strengthening blueprint and the April 2026 'AI + Education' Action Plan connect AI with student development, teacher capability, school governance, scientific research, lifelong learning and national innovation capacity. The May 2026 World Digital Education Conference in Hangzhou made “Transformation, Development and Governance” its organising frame; the July 2026 World Artificial Intelligence Conference in Shanghai included a major AI + Education Forum on reimagining the university. These developments make China not merely a market for educational technology, but a system-scale site in which policy, infrastructure, professional capability and institutional practice can be studied together.

The global agenda is also changing. UNESCO has placed human agency, rights, inclusion and critical AI literacy at the centre of education policy. In the EU, policy now links AI literacy with organisational responsibility and treats high-stakes educational uses as a governance concern. OECD's 2026 Digital Education Outlook distinguishes AI-supported task performance from durable learning and warns that unguided cognitive offloading can create an illusion of mastery. Recent research on the “agency gap”, metacognitive AI literacy, cognitive offloading and university AI governance further shifts attention from individual tool use to the conditions under which learners, educators and institutions retain judgement, participation and accountability.

These discussions were brought into focus by the 2026 Sino-UK Digital Education Innovation Forum, jointly organised by the China Association for Educational Technology (CAET) and ELITE Innovation College Cambridge (EICC), with Bournemouth University, the Council for Digitalization of International Education (CDIE-CAET), the China Association for Management of Technology, UK, and Xi'an Peihua University as co-organisers. In their welcome message, the International Organizing Committee co-chairs, Professor Yang Zongkai and Professor Richard Li-Hua, framed the partnership as bringing system-scale digital-education implementation into dialogue with humanistic and institutional innovation, while asking how human learning should evolve as AI becomes a cognitive partner.

The Cambridge-Eton-Bournemouth programme connected national strategy, higher education, secondary education, vocational pathways, research systems, institutional leadership and international cooperation, bringing together participants from more than 40 universities and organisations across 12 countries and regions. The Forum is not the subject of the Special Issue and the collection will not function as conference proceedings. Rather, it served as an academic platform that exposed researchable tensions: personalisation versus governance; scale versus deep and dialogic learning; efficiency versus human relationships and professional judgement; employability versus broader human formation; data-driven evaluation versus contested educational value; and accelerated knowledge production versus research integrity and epistemic diversity.

Against this background, the Special Issue does not ask simply how AI can be integrated into education. It asks instead how societies and institutions decide what AI is for, who has authority to make those decisions, which forms of learning and knowledge count as valuable, what evidence can establish educational benefit, whose agency is protected, and what kind of university is being built. This repositions AI in education as a problem of innovation direction, institutional choice and public value.

2. Purpose, research questions and objectives

The Special Issue will connect cognition and learning with professional work, institutional strategy, governance, political economy and public purpose. Its focus is not prediction for its own sake, but the choices being made now that will determine which educational futures become normal, scalable and difficult to reverse.

Future-defining research questions

1. Decision authority and educational purpose: Who should decide what AI is permitted to optimise in education - students, educators, institutional leaders, technology providers, regulators or wider publics - and how should competing claims about educational value be negotiated?
2. Scale without shallowness: Under what pedagogical, organisational and policy conditions can AI at scale support deep, dialogic and durable learning rather than improve only the speed or quality of task completion?
3. Leadership and governance for agentic institutions: As AI moves from assistance towards recommendation and action, which decisions may be delegated, which must remain human, who is answerable for consequences, and what new leadership capabilities and governance architectures are required?
4. Employability, academic work and human formation: How should curricula, credentials, academic roles and university-industry relationships be redesigned when AI changes both entry-level work and professional expertise - without reducing education to preparation for today's labour market?
5. Evidence and the definition of outcomes: What should count as credible evidence of AI-enabled educational value across cognition, agency, equity, teacher work, cost, institutional learning and public trust; who validates that evidence; and over what time horizon?
6. Alternative future-university trajectories: How do policy, culture, finance, infrastructure, platform power and knowledge traditions shape different AI-enabled institutional models across China, the UK and other regions; what can travel across systems, and what must remain locally shaped?

Objectives 

• Develop an innovation-studies account of AI-enabled educational change that explains how technological capabilities, organisational choices, professional practices and institutional conditions interact.
• Explain how decision authority, leadership, organisational politics and governance translate AI capabilities into particular educational priorities, practices and institutional forms.
• Identify the conditions under which scale and efficiency strengthen - or displace - deep learning, cognitive agency, professional judgement and human relationships.
• Reframe employability and capability formation for a labour market in which AI changes tasks, entry routes, disciplinary expertise and the meaning of professional responsibility.
• Advance rigorous, multi-stakeholder and longitudinal approaches to evaluating educational value, including intended benefits, distributional effects and unintended consequences.
• Compare innovation trajectories across education levels, institution types and national systems, using China-UK dialogue as a starting point for wider international analysis.
• Establish a forward research programme that connects theoretical development with empirical, comparative, design-based and practice-grounded evidence.

3. Fit with the International Journal of Innovation Studies

IJIS is an international, interdisciplinary journal that seeks to advance theoretical and practical knowledge on innovation. Its scope includes innovation processes, strategic management, national and sectoral innovation systems, organisational change, the creation of innovators, and the philosophy and psychology of innovation. The proposed issue therefore treats education not as an application setting peripheral to innovation studies, but as a major institutional domain in which the direction, organisation and public value of technological change can be examined.

The proposed issue fits the journal's mission in five direct ways:

• Innovation as a shaped process. It studies problem definition, selection, adaptation, co-design, implementation, appropriation, evaluation and possible abandonment rather than treating AI as a fixed intervention.
• Innovation as an organisational and system phenomenon. It connects learning practices with professional capability, leadership, institutions, platforms, policy, labour markets and cross-border ecosystems.
• Innovation as contested direction and value. It asks who determines the purposes of innovation, which outcomes count, whose interests are privileged, and how efficiency, equity, agency and public purpose are negotiated.
• Innovation as capability and knowledge formation. It examines how universities cultivate judgement, creativity, interdisciplinary knowledge and responsible innovators in human-AI environments.
• Innovation as future-building. It studies how present decisions create path dependencies, institutional alternatives and different forms of the future university.

4. Topics covered

Submissions may address, but are not limited to, the following interconnected themes. Strong papers should make the innovation mechanism, institutional context and educational stakes explicit.

Educational purpose, cognitive formation and AI literacy. Wisdom, judgement, cognitive agency, cognitive friction and expansion; student, teacher, researcher and leader AI literacy; foundational knowledge and independent thinking.

Pedagogy, learning and student experience. Human-AI collaboration; dialogic and deep learning; personalised support and collective learning; teacher augmentation; accessibility, belonging, wellbeing and digital character.

Assessment, credentials and academic integrity. Authentic and process-based assessment; oral/dialogic evidence; authorship, disclosure and verification; whole-person evaluation; micro-credentials and trust in qualifications.

Knowledge, disciplines and AI-mediated research. AI-enabled scientific inquiry and knowledge production; disciplinary and interdisciplinary change; research methods, infrastructures and integrity; epistemic diversity; open science and university-industry research ecosystems.

Leadership, institutional transformation and intelligent campus models. Strategy, decision authority, academic and professional roles, workforce redesign, student services, campus ecosystems, organisational learning, finance and institutional identity.

Governance, agentic AI and responsible innovation. Human oversight and delegation; data governance, privacy, bias, transparency, contestability, procurement, platform power, cybersecurity, quality assurance and accountability.

Internationalisation, inclusion and educational civilisation. Transnational education, multilingual AI, cross-border knowledge exchange, global digital divides, local adaptation, Global South perspectives and education as a common good.

Innovation systems, policy, employability and lifelong pathways. National and regional AI-education strategies; policy implementation; vocational and professional education; university-industry collaboration; skills ecosystems; changing entry-level work; lifelong learning and future capability.

5. Theoretical openness and analytical architecture

The public Call for Papers will remain theoretically open. Suitable shared entry points include innovation systems, institutional theory, organisational change and ambidexterity, capability development, responsible and open innovation, sociomateriality, learning sciences, extended cognition, critical data studies and the social shaping of technology.

The shared analytical approach is deliberately non-determinist. It begins with contemporary AI capabilities and then examines how their consequences are mediated by problem framing, strategic choice, organisational interests and power, professional work, implementation and user adaptation. This mode of inquiry learns from established organisational analyses of technological change without importing earlier technologies, or any single scholar's model, as the theoretical foundation of the Special Issue.

Accordingly, papers should make visible the mechanisms that convert AI capability into educational consequence:

• how an institution defines the problem;
• who participates in selection and design;
• which values are encoded in procurement, data and evaluation;
• how academic and professional work is reorganised;
• where authority and accountability sit; and
• how implementation produces adaptation, resistance, unintended effects or abandonment.

Educational value is an outcome to be explained, not an attribute to be assumed.

Level	Guiding question	Indicative evidence
Cognitive	What happens to understanding, memory, questioning, creativity, judgement and agency of AI?	Learning-process evidence, transfer, metacognition, assessment and longitudinal measures
Professional	How are expertise, discretion, identity, workload and responsibility redistributed in education institutions that apply AI?	Role redesign, professional practice, AI literacy, judgement and capability evidence
Institutional	Who sets teaching and learning direction and decisions, and how are teaching, research, governance and campus systems reorganized to support students learning?	Process studies, governance arrangements, operating models, investment and organisational outcomes
System and societal	How do policy, culture, markets, infrastructure and public purpose shape alternative trajectories and what is the impact of digital leadership in shaping the process?	Comparative policy, innovation systems, distributional effects and longitudinal change

6. Contribution formats and methodological openness

The issue will combine scholarly rigour with deliberate research-practice exchange. Final article categories and limits will be agreed with the Editor-in-Chief and aligned with the current IJIS Guide for Authors.

• Agenda-setting editorial. A Guest Editor synthesis that integrates the collection, explains how AI capabilities are institutionally shaped, identifies unresolved contradictions and sets a next-stage international research programme.
• Original research articles. Quantitative, qualitative or mixed-method studies with explicit theoretical and innovation contributions.
• Comparative case clusters. Two or more papers using a shared question or protocol across countries, institutions or education levels, followed by a short editorial synthesis.
• Conceptual and integrative reviews. Theory development, systematic or integrative reviews, and agenda-setting papers that generate testable propositions.
• Design, action and practice-based research. Design-based research, action research, institutional experiments and theoretically informed practice studies with transparent evidence and limitations.
• Discussion and commentary articles. Critical observations, policy/practice perspectives and paired debates on contested questions, using IJIS's Discussion category and subject to editorial approval.

Innovative issue design

• Debate pairs on personalisation and governance, scale and deep learning, efficiency and human relationship, and employability and educational purpose.
• A common evidence template for invited institutional cases: problem, context, innovation mechanism, stakeholders, outcomes, unintended effects, equity, cost and transferability.
• Cross-level synthesis connecting classroom evidence with organisational and policy consequences.
• A final research-agenda section identifying priority constructs, measures, comparative datasets and longitudinal questions for the field.

7. Original academic contribution

AI in education is now a crowded field. Much current work is organised around GenAI integration, instructional design, assessment integrity, learning outcomes, responsible use, AI literacy or employability. Human-centred interaction and cognitive offloading form a further strand, while institutional leadership and governance are beginning to develop as substantial agendas. These contributions are important, but they are often pursued in parallel and commonly retain the tool, classroom or individual user as the primary unit of analysis.

This Special Issue's originality lies in an integrated explanatory shift from what AI can do to how educational systems shape what AI becomes. It will make four connected contributions:

• Innovation trajectories rather than isolated applications. It examines how problems are framed, technologies selected, authority allocated, professional work reorganised, outcomes evaluated and systems revised or abandoned.
• A cross-level account of institutional change. It connects cognition and learning with professional roles, leadership, governance, finance, research, labour and institutional identity.
• Educational value as an empirical and contested outcome. It develops stronger evidence and governance questions around who defines benefit, which evidence is accepted, who bears costs and risks, and who can challenge decisions.
• Comparative explanation rather than national showcase. It uses China-UK dialogue as a starting point for wider international analysis of why similar AI capabilities generate different educational and institutional trajectories.

The resulting academic outcome will be China-anchored but not China-bounded, focused on the future university without being limited to classroom teaching, and centred on contemporary AI without reducing innovation to technology integration. By explaining why apparently similar AI capabilities produce different forms of learning, work, governance and institutional purpose, the collection will advance innovation theory and establish a coherent agenda for comparative, longitudinal and design-based research.

8. Important deadlines

Date	Milestone
Early September 2026	Editorial approval, finalisation of Guest Editors and launch of Call for Papers
30th November 2026	Full manuscript submission deadline
31st December 2026	Editorial screening, desk decisions and reviewer assignment
10th January 2027	First-round peer-review decisions, subject to reviewer availability
28 February 2027	Target date for revised manuscripts
April-June 2027	Final decisions and rolling online publication, subject to journal process

Note: The 30th November 2026 deadline is deliberately ambitious and depends on immediate launch and activation of the Forum's invitation-ready pipeline. Optional abstracts allow early quality and fit guidance without restricting the open call.

9. Submission instructions

Please read the [Guide for Authors] before submitting. All articles should be [submitted online] , please select [VSI: AI-Enabled Education and the Future University] on submission.

10. Relevant topics and keywords

Educational innovation; future university; artificial intelligence; institutional shaping; social shaping of technology; innovation systems; educational value; decision authority; human-AI collaboration; cognitive agency; deep learning; AI literacy; professional work; institutional transformation; AI governance; responsible innovation; assessment; research innovation; internationalisation; intelligent campus; employability; education policy.