1 Introduction
Generative artificial intelligence (GenAI) is rapidly reshaping what university teachers need to know, how they exercise professional judgment, and how institutions support teaching and learning. The challenge is no longer limited to learning to use a new tool. Faculty now confront questions about assessment, authorship, academic integrity, privacy, data governance, accessibility, disciplinary variation, and the shifting distribution of responsibility between human and machine actors. UNESCO’s guidance on GenAI and its
AI Competency Framework for Teachers both emphasize that technical capability must be developed alongside pedagogical judgment, ethical awareness, and ongoing professional learning (
Miao & Cukurova, 2024;
Miao & Holmes, 2023). The OECD (
2024) likewise frames teacher development as a system-level issue that depends on supportive professional environments rather than on isolated skill acquisition.
This shift has two implications. First, faculty development must become more adaptive. The rapid pace of GenAI change means that a workshop focused on a single platform or prompt technique can quickly become outdated. Second, faculty development increasingly spans organizational boundaries. Universities learn from one another, compare institutional policies, share practices, and participate in alliances and professional networks. Recent research also shows that faculty use of AI is heterogeneous and that professional-development needs vary significantly across educators and contexts (
Mah & Groß, 2024). At the same time, universities are adopting different approaches to AI governance, so cross-institutional collaboration must navigate rather than ignore regulatory and normative differences (
Wu et al., 2024).
Against this background, a familiar assumption warrants reconsideration: Greater connectivity does not automatically foster more collaboration. Digital platforms can make contact easier, but durable professional learning requires continuity, common reference points, institutional support, and opportunities for local adaptation. This commentary therefore asks a practical question: What organizational priorities can help transnational faculty development shift from episodic contact to sustained, shared coordination?
We use “shared governance” here as an aspirational design principle rather than as a claim that a particular alliance has already achieved a stable governance model. The term does not refer to the conventional faculty-senate model of internal university governance. Instead, it describes a practical balance that provides sufficient coordination to sustain common work while allowing institutions to retain meaningful influence over content, priorities, and local implementation.
2 Why Connectivity Is Not Enough
Digital connectivity has transformed the scale at which faculty encounter ideas, colleagues, and resources. International webinars, online communities, shared platforms, and hybrid conferences can connect educators at relatively low cost and with unprecedented speed. These opportunities matter. They broaden access to expertise and expose participants to practices that would otherwise be confined to specific institutions or to geographically bounded contexts.
The limitation is that the connection is episodic by default. A webinar can disseminate information without establishing a shared agenda. A platform can host resources without producing agreement on how to interpret or use them. An international event can generate visibility without establishing responsibility for what happens next. The problem is not a failure of technology, but a gap between access and institutionalization.
This distinction is especially important for future faculty development. Professional learning has value when it shapes what faculty understand, discuss, design, and revise over time. In the GenAI context, that process often requires repeated interpretation because institutional policies, available tools, ethical expectations, and disciplinary practices continue to evolve. A systematic review of empirical research on GenAI in education (
Zhang et al., 2024) highlights both the breadth of emerging applications and persistent concerns about competencies, ethics, and the alignment of AI-enabled practices with educational goals. The challenge is therefore not simply to circulate information more widely but to build arrangements that sustain professional learning over time and across institutional differences.
This is why future faculty development should be treated as both an organizational coordination challenge and an instructional one. Institutions need ways to translate broad ambitions into actionable agendas, connect separate activities into a coherent sequence, keep normative questions visible, and enable different partners to adapt shared ideas without becoming passive recipients.
3 Why GenAI Intensifies the Coordination Challenge
None of these coordination problems is unique to GenAI. International faculty development networks have always had to manage differences in priorities, resources, calendars, and institutional cultures. However, GenAI intensifies these pressures in at least four ways.
First, competency expectations are unusually unstable. What counts as useful AI literacy for university teachers is still evolving. Technical fluency matters, but so do the abilities to evaluate output quality, redesign assessments, preserve academic integrity, recognize bias, protect data, and decide when not to use AI. Therefore, faculty development agendas must evolve more quickly than traditional multi-year training cycles allow.
Second, normative and regulatory divergence is increasingly evident. Universities and national systems differ in their approaches to privacy, platform access, assessment, intellectual property, responsible use, and disclosure. Comparative studies of university AI governance (
Wu et al., 2024) reveal substantial variation in institutional guidance, and research on academic responsibility (
McGrath et al., 2023) highlights the difficulty of assigning responsibility when AI systems are involved in teaching and professional judgment. Transnational faculty development cannot assume that an institution’s acceptable practices are automatically portable to another institution.
Third, capacity is uneven. Institutions differ in their access to commercial platforms, technical infrastructure, legal support, instructional-design expertise, and AI-related professional learning. If cross-border collaboration is organized primarily around the practices of the best-resourced participants, network activity can unintentionally reproduce center–periphery dynamics.
Fourth, technological time moves faster than organizational time. Universities operate through committee cycles, approval processes, academic calendars, and budget routines. GenAI tools and policies can change within weeks or months. This mismatch creates a practical need for coordination mechanisms that are stable enough to sustain collaboration yet flexible enough to accommodate rapid change. Recent governance-oriented discussions in digital education similarly argue that AI reshapes questions of authority, responsibility, and accountability rather than functioning solely as a new instructional technology (
Pesek, 2026).
4 Five Priorities for Transnational Faculty Development
4.1 Translate Broad AI Ambitions into Actionable Faculty Development Agendas
International alliances often begin with broad terms such as digital intelligence, innovation, responsible AI, inclusion, or global cooperation. These terms help set a common direction, but they are too abstract to guide faculty learning on their own. A coordinating hub should therefore translate broad ambitions into specific questions, themes, and activities that faculty can engage with.
For example, “responsible AI” can be translated into faculty development sessions on assessment redesign, acceptable use, data protection, bias, disclosure, and disciplinary judgment. “AI literacy” can be broken into layers for novice users, experienced adopters, instructional leaders, and policy-facing staff. Translation does not mean fixing a single definition for all partners. It means creating an operational starting point that enables meaningful comparison across contexts.
4.2 Build Temporal Continuity and Organizational Memory
One-off events are valuable for visibility and entry, but continuity requires a rhythm. Recurring workshops, follow-up sessions, annual cycles, shared calendars, and clear connections among activities can help a network sustain momentum. The purpose is not to maximize the number of events; it is to ensure that activities accumulate.
Continuity also requires organizational memory. Agendas, guidance notes, case examples, recorded discussions, and reusable learning resources can serve as reference points for future work. Without this memory, each new cohort or event starts from zero. With it, the network can revisit questions as technology and policy evolve. Toh and Looi (
2024) emphasize that digital transformation involves managing persistent tensions rather than solving them once and for all. The same logic applies to faculty development: Recurring questions require recurring institutional spaces.
4.3 Keep Pedagogical and Ethical Judgment Alongside Technical Experimentation
Faculty development in the GenAI era can easily become tool-centered. New models and applications are visible, fast-moving, and attractive. But training that focuses solely on functional use risks divorcing technology from its educational purpose.
Every technical conversation should therefore be paired with questions about pedagogy and judgment. What educational problem is the tool intended to address? What new risks does it pose? Which forms of assessment remain valid? What should faculty disclose? What should remain under human control? Chan (
2023) argues for principled institutional policy rather than purely tool-centered adoption, and UNESCO’s competency framework likewise integrates technical, pedagogical, ethical, and professional dimensions (
Miao & Cukurova, 2024).
For transnational collaboration, normative curation is especially important because the objective should not be to eliminate differences among institutional policies. Rather, the objective is to make those differences discussable and usable as learning resources. Divergence can become an asset when partners compare how their institutions draw boundaries and what those differences reveal about local values and responsibilities.
4.4 Preserve Distributed Participation and Local Adaptation
Coordination is necessary, but over-centralization can weaken ownership. A network becomes fragile when partners participate only by receiving content produced elsewhere. Durable collaboration requires substantive participation: Institutions should be able to contribute cases, lead sessions, challenge assumptions, and adapt shared themes to local contexts.
This is especially important in GenAI because institutional context matters. A policy discussion at a university with broad access to commercial AI platforms may not translate directly to a partner operating under different regulatory or resource constraints. Therefore, the same faculty development theme may require different examples, emphasis, and implementation strategies.
Shared governance, as used here, depends on this division of labor. A coordinating hub can provide continuity and a common structure, while partner institutions retain meaningful authority over substantive content and local translation. Coordination and autonomy are not opposites; effective collaboration requires both.
4.5 Turn Events into Reusable Institutional Infrastructure
The final priority is to treat outputs as infrastructure rather than souvenirs. International activities often conclude with presentations, photographs, and short reports. These materials have communicative value, but future faculty development also requires resources that endure over time and across institutions.
Reusable outputs might include adaptable competency prompts, discussion protocols, comparative policy notes, case libraries, faculty development modules, checklists for responsible use, and templates for local workshops. Their value lies in their portability. They provide common reference points while remaining open to revision.
This approach also changes how success is understood. The most meaningful outcome of an international event may not be attendance alone, but whether participants leave with something that can be used, adapted, debated, and improved within their home institutions. In that sense, shared governance grows through the gradual development of common working infrastructure rather than through formal centralization.
5 DI-IDEA and IELP as an Illustrative Institutional Context
The Digital Intelligence International Development Education Alliance (DI-IDEA) provides a useful institutional context for considering these priorities, but it should not be treated as empirical evidence for the propositions advanced above. Launched at the Beijing Forum in November 2023, DI-IDEA brought together Peking University and nearly 30 participating universities, with an agenda focused on international cooperation in digital-intelligence education, program development, resource sharing, and digital governance (Digital Intelligence International Development Education Alliance, 2023a,
2023b).
Within this broader alliance ecology, the International Education Leadership Programme (IELP) exemplifies how a network-level mission can align with a more concrete, recurring activity stream. Publicly available DI-IDEA materials describe IELP as a member-led initiative that began in 2023 with King’s College London, the London School of Economics and Political Science, Monash University, and the University of Warwick, with Peking University joining in a subsequent phase. DI-IDEA later listed IELP among its programs and described related activities as operating under the alliance framework (Digital Intelligence International Development Education Alliance, 2025, n.d.).
The relevance of this example is organizational rather than evidentiary. It illustrates a common challenge for international alliances: A broad mission requires programmatic structures that enable regular meetings, comparative practice, and the translation of shared concerns into action. It also underscores the importance of the relationship between alliance-level coordination and partner-led participation. An alliance can create visibility and convening power, but its professional-development value depends on whether participating institutions can shape the substantive work and bring it back to local settings.
The DI-IDEA/IELP relationship should therefore be read here as an illustrative setting for reflection, not as validation of a universal governance model. The broader argument of this commentary rests on a practical synthesis of current policy, scholarship, and institutional experience: Transnational faculty development is more likely to endure when common coordination is paired with distributed ownership.
6 Agenda for University Leaders and Alliances
For leaders designing future faculty development collaborations, the central question is not how to build the largest network but how to make participation meaningful. Five practical questions can guide the design.
First, what will translate the alliance’s broad ambitions into specific faculty development agendas? Second, what will connect one event to the next, giving collaboration a temporal rhythm rather than a sequence of disconnected activities? Third, where will ethical, pedagogical, and regulatory questions be discussed alongside technical experimentation? Fourth, how will partner institutions retain substantive influence over content and local adaptation? Fifth, what reusable resources or routines will remain after the event ends?
These questions also suggest a different approach to evaluation. Attendance, institutional representation, and geographic reach remain useful indicators, but they are insufficient. Alliances should also ask whether activities generate repeat participation, whether outputs are reused locally, whether partners contribute substantive content, whether shared resources are revised over time, and whether emerging differences are surfaced rather than suppressed.
Importantly, shared governance should remain a direction of travel rather than a label applied too quickly. Recurring meetings or a coordinating institution alone do not, by themselves, demonstrate shared decision-making or shared responsibility. A more cautious and useful approach is to ask whether the conditions for shared governance are strengthening: continuity, reciprocal influence, distributed contributions, transparent coordination, and the capacity for local adaptation.
7 Conclusions
The GenAI era is reshaping faculty development and its organizational context. Universities need technical capabilities and mechanisms to coordinate professional learning amid rapidly changing tools, divergent institutional policies, uneven capacities, and diverse educational cultures.
For transnational collaboration, connectivity is a starting point rather than an outcome. Platforms, webinars, and alliances can create contact, but sustained professional learning depends on what happens after the connection: whether broad ambitions translate into actionable agendas, whether activities accumulate over time, whether ethical and pedagogical questions remain visible, whether partners retain substantive ownership, and whether events generate reusable institutional resources.
Shared governance offers a useful aspiration for this work when understood modestly: not as uniformity or as evidence that all partners share identical authority, but as a deliberate effort to balance coordination with local autonomy. Future faculty development will be more resilient when international alliances invest not only in connecting people but also in the institutional routines that enable them to learn, adapt, and act together over time.
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