As generative AI reshapes higher education, instructors across the globe have become increasingly interested in integrating AI into the classroom, but they require trusted, timely, and relevant resources for such integration. To enable the development of an open educational resource (OER) that could be remixed and adapted to different teaching contexts and support inclusive access, this study examined what instructors want and need from an open resource on AI in teaching and learning in higher education and documented a process for developing professional development resources in a time of rapid technological change. This study employed a multiphase mixed-methods design. First, a statewide needs assessment survey was conducted with 149 instructors to identify needs, challenges, and priorities across diverse institutional and disciplinary contexts. Participants prioritized three core content domains for the resource—AI literacy, pedagogical strategies, and ethics—and favored interactive, adaptable, and open resources grounded in practical classroom applications. These findings were used to develop an OER prototype, on which 75 instructors provided feedback, affirming the utility of the resource and suggesting areas for further refinement. This study contributes an educator-informed process for translating instructors’ needs into the design and formative evaluation of an adaptable, openly licensed faculty development resource. This evidence-based, educator-driven approach demonstrates how those tasked with faculty development on AI can develop and refine timely resources that meet the needs of educators from diverse contexts, offering a flexible, scalable, and community-oriented pathway for bridging institutional resource gaps and advancing inclusive faculty development in the age of AI.

Professional judgement, the situated, normative practice of weighing what is educationally desirable rather than merely technically effective, is central to educational professionalism. Yet international frameworks for faculty development converge on what individual faculty should know and do, without conceptualising the conditions under which professional judgement is exercised, or how AI restructures them. This paper introduces the judgement space: the pedagogical condition within which professional judgement is not only exercised but articulated, examined, and sustained through pedagogical interaction. In AI-supported higher education, where students engage with AI independently and beyond the pedagogical relationship, the judgement space becomes a three-party condition: AI-generated evaluative outputs enter the interaction alongside the judgement of teacher and student. We draw on Biesta’s account of educational purpose across qualification, socialisation, and subjectification. From this account, we advance three claims. First, professional judgement is foundational to faculty development, not reducible to one competency among many. Second, AI restructures the evidence base on which professional judgement operates. It captures qualification-domain evidence more readily than evidence of socialisation and subjectification, privileging responsive practice unless the pedagogical framework positions AI to enrich rather than impoverish the judgement space. Third, the dialogic inquiry triangle, developed within the dialogic inquiry tradition and examined in school-based practitioner research, provides such a framework. Extended to AI-supported higher education, it structures the three-party interaction through which the judgement space can be created and sustained. We derive six structural principles for the teacher–student–AI interaction and a professional judgement audit for faculty development.

Generative artificial intelligence (GenAI) has intensified the debate in higher education about academic integrity, student learning, and the future of academic work. Faculty development, however, often remains concentrated on tool demonstrations, compliance advice, and risk management rather than on the redesign of intellectually demanding learning. This conceptual review develops the PEDAL model—purpose, epistemic challenge, dialogue, accountability, and learning evidence—as a practical synthesis for faculty development in AI-rich higher education. The model was derived through an integrative, abductive synthesis of scholarship on higher-order thinking, GenAI, constructive alignment, cognitive apprenticeship, co-regulation, AI literacy, assessment, and professional learning, supplemented by recent empirical work from resource-diverse settings. PEDAL positions AI as a bounded pedagogical resource that can generate alternatives, simulations, misconceptions, and prompts for critique without replacing disciplinary judgment. The paper explains the derivation and relationships of the five principles, provides worked redesigns in the social sciences and engineering, and proposes evidence strategies intended to be feasible for large and diverse classes. It also distinguishes minimum program-level conditions from more mature institutional support. The contribution is not a new general theory of learning but a meso-level design heuristic that translates established insights into a coherent sequence of decisions for faculty development, assessment redesign, and empirical testing.

Against the backdrop of human–artificial intelligence (AI) synergy reshaping higher education, university teachers are required to transition from traditional knowledge-transmission professionalism to a new form of educational professionalism for the digital intelligence era. This transformation entails a fundamental shift beyond mere tool adaptation, redefining teachers as value leaders, learning designers, human–AI collaboration organizers, and teaching researchers. Drawing on literature analysis and empirical evidence, this study analyzes the reshaping of the higher education ecosystem through five key dimensions: talent cultivation, knowledge supply, instructional focus, knowledge production, and assessment. It further proposes systemic empowerment paths encompassing top-level design, digital intelligence infrastructure, tiered training systems, and reformed evaluation mechanisms. Ultimately, the study concludes that teacher transformation is not merely a matter of technical adaptation; rather, the core lies in cultivating educational professionalism for the digital intelligence era and converting technological advantages into genuine educational quality so that human–AI collaboration can drive deep learning, holistic development, and responsible innovation in higher education.

As Big Data and AI technologies continue to evolve rapidly, fundamental social structures, knowledge production models, and the logic of talent cultivation are undergoing profound transformation. The traditional roles of faculty members, once focused on knowledge transmission, are now evolving into learning designers, ethical mentors, innovation facilitators, and lifelong learners. This paper examines the restructuring of faculty members’ roles and proposes a framework of five core competencies for future educators in higher education. It also explores the Future Faculty Training Program implemented by Wuhan University. Characterized by digital intelligence, personalized learning, and continuous professional development, this program offers valuable insights into translating the theoretical framework of faculty development into practice.

Faculty development (FD) and the preparation of future faculty are central to university teaching quality, yet in Japan they are studied separately and remain underexamined in English. Pre-faculty development (pre-FD), a term coined within Japanese higher education, exclusively denotes the pre-service teaching preparation of doctoral students and other prospective academics, broadly corresponding to the American “Preparing Future Faculty” movement. Treating the two as tracks of a single academic-development continuum, this article asks how each has been institutionalized and localized in Japan, and what constrains the translation of policy into teaching improvement. The study is a narrative, document-based conceptual review of policy documents, statutory standards, national surveys, and Japanese- and English-language scholarship, using the Scholarship of Teaching and Learning (SoTL) as an analytical lens. Three findings emerge. First, both tracks advanced through top-down policy-driven and policy-induced reform: In-service FD became a legal mandate in 2008, whereas pre-FD remains a best-effort obligation. Second, in-service FD has developed a distinctive cluster of locally grounded practices: inter-university networks, mutual FD, the collegial model, and student-engaged FD. Third, national survey data show participation rising while collegial formats and dedicated provision declined over the same period, indicating that participation rates are a poor proxy for teaching-development quality. Reading the tracks together reveals what single-track accounts obscure: shared organizational carriers and a common ceiling set by the absence of a continuing professional role in academic development. The article argues, as a proposition derived from the analysis, that sustainable development depends on connecting the two tracks into a continuous career pathway framed by SoTL.

With the accelerating tempo of technological change, universities’ teaching reforms should be timely and principled rather than slow, ad hoc or reactive. This article anchors such reform in an invariant core: learning is underpinned by neuropsychological processes in which the cognitive and affective dimensions are inseparable. Using the Structure of Observed Learning Outcome taxonomy developed by Biggs as a conceptual lens, we argue that generative AI now produces artefacts exhibiting relational-level structure, and that the learner may bypass the deeper thinking that assignments are designed to elicit. We then discuss the evaluative judgements about cognitive offloading as both a risk and an opportunity in learning, guided by a principled test: Whether the process being bypassed is constitutive of the intended learning outcome. Three shifts in faculty development are proposed. First, development should deepen pedagogical knowledge and practice, placing greater emphasis on the mechanisms of human learning. Second, as AI-assisted coding lowers the barrier to creating educational tools, development should move from technology adoption towards pedagogical authoring of digital educational tools, anchored in the invariant core. Third, as our scientific understanding of learning develops, faculty should extend their scholarly identity as researchers to their teaching. They will also need to propose principled and measured adjustments to teaching approaches and curricula in step with the changing priorities of society. Faculty will have to be role models of ethical AI use and of ethical practices of their respective disciplines. The article closes with the reasons for learning and the institutional conditions universities must provide.

AI is reshaping higher education in ways that extend beyond the adoption of new tools, challenging established assumptions about assessment, curriculum, ethics, graduate preparation, and the work of university teachers. Drawing on insights from the Teacher Panel at the International Conference on GenAI and Pedagogical Innovations held in Hong Kong in May 2026, this commentary argues that faculty development in the AI era must move beyond generic technology training towards a more role-based, discipline-sensitive, future-oriented, and ecosystemic approach. The commentary identifies four key insights from the panel discussion: AI use is already widespread and assessment has become an immediate pressure point; AI literacy is necessary but insufficient without disciplinary, professional, ethical, and human-centred dimensions; universities must connect learning more explicitly with the authentic world beyond campus while renewing the role of the campus as a site of experimentation and futures literacy; and institutions need stronger capacity for evidence-informed, forward-looking AI governance. Building on these insights, the commentary proposes seven emerging roles for university teachers: human-centred educator; disciplinary and professional expert; learning architect and intellectual guide; assessment (re)designer; technically competent and critically informed professional; ethical steward; and educational experimenter and evidence-based practitioner. Corresponding faculty development needs are then discussed, including support for human-centred education, curriculum renewal, world-connected learning, assessment redesign, technical-pedagogical AI capability, ethical stewardship, AI policy literacy, and scholarship-informed educational improvement. The commentary concludes with a preliminary role-based framework for faculty development. It emphasises that these roles need not be performed by a single individual teacher. Rather, as educational design and delivery become more complex in the age of AI, university education should increasingly be understood as a collective endeavour involving academic staff, educational developers, technology specialists, professional staff, students, external partners, employers, and community organisations.

The emergence of generative artificial intelligence (GenAI) systems has fundamentally altered the epistemological conditions under which educational research is conducted, rendering existing faculty preparation frameworks necessary but insufficient. Building on a call for a computational mindset and subsequent work on educational data literacy, this article proposes a three-stage developmental progression culminating in algorithmic agency, which is the capacity to critically co-construct scientific knowledge with large language models and automated analytical pipelines while maintaining methodological rigor and preserving human epistemic authority. Unlike prior frameworks, which conceive of artificial intelligence as an analytical instrument under human direction, algorithmic agency addresses the qualitatively distinct challenge posed by GenAI systems that function as epistemic participants capable of synthesizing literature, constructing arguments, and producing knowledge claims that enter scholarly discourse alongside human-authored work. To operationalize this progression, we propose the Co-Intelligent Academic Practice (CAP) framework, a theoretically grounded developmental architecture comprising four cumulative competency domains organized across three developmental stages. A methodology integrating design and systems thinking provides the developmental process through which faculty build and sustain these competencies. We further argue that individual competency development is insufficient without commensurate institutional enablement, including governed infrastructure, value-aligned policy, scholarly recognition, community-based developmental support, and equitable global participation. The article concludes by identifying empirical validation of the CAP staging model as the field’s most urgent methodological priority and arguing that faculty development is the primary institutional lever through which epistemic norms for the co-intelligent era are established or abandoned.

Generative artificial intelligence (GenAI) is intensifying the need for universities to support faculty learning across institutional and national boundaries. Yet digital connectivity—platforms, webinars, online communities, and international events—does not, by itself, produce durable collaboration. This commentary argues that future faculty development should be treated not only as a professional-learning task but also as an organizational coordination challenge. Drawing on recent scholarship and policy discussions, and using the Digital Intelligence International Development Education Alliance and the International Education Leadership Programme as an illustrative institutional context rather than an empirical case, we outline five priorities for transnational faculty development: translating broad AI ambitions into actionable agendas; creating temporal continuity; embedding ethical and pedagogical judgment in technical development; preserving distributed participation and local adaptation; and converting events into reusable institutional infrastructure. We use shared governance as an aspirational design principle rather than an empirically established outcome. The central proposition is practical: International faculty development collaboration becomes more resilient when coordination and local autonomy are deliberately combined.

Generative artificial intelligence (GenAI) is reshaping higher education at a pace that outstrips universities’ policy, pedagogical, and procurement responses. While guidance documents proliferate, consistent governance does not. This paper adapts assurance and accountability scholarship to GenAI in higher education by proposing the Triple-A Framework (AI Assurance and Accountability in Higher Education), a multi-stakeholder, five-control-function framework. The resulting 5 × 5 matrix crosses five higher-education stakeholders—students, educators, institutions, technology providers, and a combined group of policymakers, regulators, and accreditation bodies—with five control functions: declarative, confirmative, preventative, detective, and corrective. We then apply a trust lens, showing how declarative, confirmative, and preventative functions support assurance and trust building, while detective and corrective functions support accountability and trust repair; findings from detection and correction can subsequently inform preventative redesign. The framework is a diagnostic and design tool, not a compliance checklist, standard, or maturity model: It shows where trust work must occur. We illustrate the framework with vignettes and set out a research and policy agenda for strengthening assurance and accountability capacity across higher education institutions with differing resources and governance capabilities.