1. Introduction

This paper introduces the C.L.A.I.R. for IHE framework – a scholarly and practical approach to Artificial Intelligence (AI) literacy designed for the unique and complex domain of International Higher Education (IHE). As AI continues its rapid permeation into all facets of global society, its impact on IHE—a sector characterized by cross-border collaboration, diverse student and staff populations, and multifaceted international operations—is particularly profound and warrants a specialized strategy for workforce development. The CLAIR for IHE framework, an acronym for Capability, Literacy, Adaptability, Interculturality, and Responsibility, offers a comprehensive and capability-centric model to cultivate AI fluency among IHE professionals.

The framework is underpinned by the CLAIRvoyance Cycle, a distinct, iterative methodology guiding IHE professionals to: Contextualize AI within their specific IHE role and global setting; Learn about fundamental AI capabilities and ethical principles; Apply AI tools and capability understanding to IHE tasks innovatively; Interculturally engage with and evaluate AI outputs and processes; and Responsibly adapt practices and strategies based on AI’s evolution and impact. This approach moves beyond transient tool-based training, focusing instead on a durable understanding of AI’s core abilities, primarily informed by the OECD AI Capability Indicators.¹

The IHE sector’s inherent complexity, encompassing diverse stakeholders, cross-border operations, and varied cultural contexts, necessitates an AI literacy framework that is not merely adapted from generic models but is fundamentally architected with these intricacies at its core.¹ Standard AI literacy is insufficient for IHE; the sector requires a nuanced AI intercultural fluency. This is because AI tools and their outputs can harbor inherent cultural biases or prove unsuitable in specific international settings, a critical consideration for a field that operates across myriad cultural, legal, and operational landscapes.⁵ The CLAIR framework, with “Interculturality” as a central pillar, directly confronts this gap, advancing beyond general ethical considerations to foster specific competencies in intercultural AI engagement.

Furthermore, the “competency lag”—the gap between rapid AI adoption and the slower development of critical understanding—is particularly acute in IHE.¹ This is not merely a skills deficit but a deeper “capability comprehension lag,” intensified by the swift, often uncritical, integration of AI into diverse and sensitive international functions such as student recruitment, admissions, and support.² Without a comprehensive grasp of AI’s underlying capabilities, IHE staff risk misapplying these technologies, potentially leading to ethical breaches, biased outcomes, or ineffective communication in high-stakes international contexts. CLAIR for IHE, through its emphasis on “Capability” and “Responsibility,” aims to bridge this critical comprehension deficit.

This paper details the CLAIR for IHE framework, its constituent pillars, the CLAIRvoyance Cycle methodology, a comprehensive competency matrix for various IHE roles, and innovative pedagogical and assessment strategies. It concludes with strategic recommendations for institutional adoption, underscoring the framework’s potential to empower IHE staff, enhance institutional effectiveness, and foster ethical and innovative AI engagement across the global higher education landscape.

2. The Evolving Imperative: AI Fluency in the Globalized Higher Education Ecosystem

2.1 AI across the International-Student Lifecycle

From first enquiry to post-arrival retention, AI systems now permeate each stage of the international student journey. Multilingual, generative-AI chatbots such as ApplyBoard’s Abbie and or Cardiff Metropolitan University’s Gecko implementation now handle time-zone-agnostic recruitment queries, while similarity-matching and speech-analysis engines in iSchoolConnect offer programme suggestions and visa-interview coaching.¹ Once prospective students apply, partnerships such as INTO University’s predictive-screening models and Enroly’s Apply platform automate document checking and Confirmation of Acceptance for Studies (CAS) workflows, reducing decision times without sacrificing regulatory compliance. Currently this is possible and imperative that institutions maintain robust human oversight in line with the EU AI Act.² After enrolment, retention chatbots (Pounce, Georgia State University) and adaptive tutoring agents (Syntea, IU International) deliver 24/7 guidance that has demonstrably lowered attrition and study time. However, over-automatisation risks isolating applicants, underscoring the need for culturally sensitive, blended support.³

2.2 AI in Teaching, Curriculum, and Transnational-Education Quality Assurance

Generative-AI tools are increasingly embedded in learning design, enabling academics to create formative content, simulate intercultural scenarios, and scaffold project work.⁴ Pedagogical experts caution, however, that genuine learning gains arise only when students critically interrogate AI outputs rather than consume them passively. Within transnational-education (TNE) partnerships, institutions are also piloting AI-enhanced quality-assurance dashboards that trace curriculum alignment across jurisdictions and flag divergence from local regulations in real time. Evidence presented to the European Quality Assurance Forum indicates that traceability and auditability are essential design criteria if such systems are to satisfy diverse national accrediting bodies.⁵

The main issue in teaching and learning is that there is a disconnect between students and educators, with the former adopting and using GenAI tools in all aspects of their learning, including to cheat. If educators don’t have enough competencies in AI and restriction to the most powerful models out there (ChatGPT , Claude) they risk failing to understand the full capabilities of these models, identify and recognise misuse of AI and properly guide students on using AI ethically and efficiently as part of the learning journey. [needs some referencing and last stats from Guardian, CanvaCon etc) 

2.3 Strategic Planning, Market Intelligence, and Institutional Governance

At the institutional level, AI-driven scenario modelling and event-workflow automations (e.g., the University of Leeds’ use of Gecko) streamline capacity planning, target-market segmentation, and risk analysis.⁶ Yet predictive outputs can magnify data-biases or false correlations, making expert validation indispensable. Governance frameworks must therefore integrate algorithmic-risk assessment, transparency on model provenance, and escalation pathways for high-stakes decisions—especially where student visas, immigration compliance, or resource allocation are involved.

2.4 Distinct AI-Literacy Requirements for the Sector

Generic AI-literacy models insufficiently account for the intercultural, ethical, and regulatory complexity of IHE. The sector’s “competency lag”—rapid technological uptake paired with slower staff development—is exacerbated by cross-border data-protection regimes (e.g., GDPR), culturally variant communication norms, and the high stakes of student wellbeing.⁷ Staff must therefore cultivate an AI literacy that combines technical understanding with intercultural skills: evaluating outputs for linguistic nuance, culturally contingent meaning, and potential bias before dissemination to diverse audiences.

2.5 Limitations of Existing Frameworks and the Need for CLAIR

Recent attempts to graft OECD AI-Capability Indicators onto generic literacy schemes mark incremental progress towards “AI capability fluency” but remain retrofits rather than sector-specific designs. They lack dedicated pillars for interculturality and adaptability—both critical in a global education market. The CLAIR for IHE framework (Section 3) responds to this deficit by embedding Capability, Literacy, Adaptability, Interculturality, and Responsibility within a cyclical methodology (the CLAIRvoyance cycle) that directly aligns AI-fluency development with IHE’s strategic, ethical, and operational realities.

3. Introducing CLAIR for IHE: A Capability-Driven AI Literacy Framework for International Higher Education

In response to the distinct AI literacy needs of the International Higher Education sector, this paper introduces the CLAIR for IHE framework, an acronym representing five interconnected and indispensable pillars: Capability, Literacy, Adaptability, Interculturality, and Responsibility. This framework is designed to be capability-driven, deeply contextualized for IHE, and oriented towards fostering genuine AI literacy among professionals across the diverse roles within the sector.

3.1 Unveiling CLAIR: Capability, Literacy, Adaptability, Interculturality, and Responsibility

The five pillars of CLAIR for IHE are defined as follows:

• Capability: This pillar forms the bedrock of the framework, emphasizing a deep understanding of Artificial Intelligence’s fundamental abilities, operational principles, and inherent limitations. Moving beyond proficiency with specific, often transient, AI tools, it focuses on grasping what AI systems can and cannot reliably do. This understanding is critically informed by established benchmarks such as the OECD AI Capability Indicators, which describe AI’s progression across levels from basic to human-equivalent performance in areas like “Language,” “Problem Solving,” and “Social Interaction”.¹ For IHE professionals, this means, for example, discerning whether an AI tool for market analysis is operating at a level suitable for strategic decision-making or if its outputs require substantial human augmentation.

• Literacy: Building upon the understanding of capabilities, this pillar encompasses foundational knowledge of core AI concepts (e.g., machine learning, natural language processing, generative AI), awareness of key ethical principles and guidelines (e.g., fairness, transparency, accountability), and the development of practical skills for effective AI application in IHE contexts. This aligns with broader definitions of AI literacy and fluence such as UNESCO and Anthropic ²³ but is specifically tailored to IHE scenarios, such as using AI for multilingual communication or analyzing international student data ethically.

• Adaptability: Given the extraordinarily rapid evolution of AI technologies and their impact, this pillar focuses on cultivating the capacity for continuous learning, critical foresight, and agile responsiveness among IHE professionals. It involves not only adapting to new AI tools and features but also anticipating how shifts in AI’s core capabilities (e.g., an AI achieving a higher OECD Language or Problem-Solving level) will necessitate changes in IHE roles, processes, and strategic priorities.¹ This pillar fosters a mindset of lifelong engagement with AI’s development. The concept of “Adaptability” within CLAIR for IHE extends beyond individual skill updates; it encompasses the capacity of IHE professionals to contribute to the adaptation of institutional strategies and roles in anticipation of AI reaching higher OECD capability levels in domains crucial to IHE, such as cross-border education counselling, complex cross-cultural communication or complex problem-solving in globalized scenarios.¹ For example, if AI demonstrably advances in its ability to understand and generate nuanced intercultural communication (beyond current OECD Language L3 or Social Interaction L2), the roles of international student advisors or global marketing specialists would need to fundamentally evolve, requiring strategic institutional rethinking rather than mere tool adoption.

• Interculturality: This pillar is a distinctive and critical component of CLAIR for IHE, addressing the unique demands of the international higher education landscape. It involves the ability to critically select, use, evaluate, and mediate AI tools and their outputs within diverse global and cultural contexts. This includes recognizing AI’s potential for cultural and linguistic biases, ensuring culturally sensitive and appropriate AI-human interactions, and adapting AI-generated content for specific international audiences.¹ For instance, an international recruitment officer would use this competency to assess whether an AI-generated promotional campaign is culturally resonant and respectful in multiple target countries. The “Interculturality” pillar emphasizes active mediation of AI in cross-cultural IHE settings. It is not merely about being sensitive to cultural differences, but about developing the skills to “translate,” “reframe,” or critically assess the appropriateness of AI outputs for diverse cultural audiences. This active mediation is crucial when AI is used in direct communication or decision-making processes affecting international stakeholders, as AI tools often lack deep cultural nuance. [Add her a reference to Geert Hofstede, Erin Meyer, and an algorythmic bias reference] 

• Responsibility: This pillar underscores the commitment to ethical AI deployment and stewardship within IHE. It encompasses upholding academic integrity in an AI-suffused environment, ensuring robust data privacy and security (especially with cross-border data flows), promoting equity and fairness in AI-assisted decision-making (e.g., in admissions or resource allocation), and actively contributing to the development and adherence of responsible AI governance frameworks within the institution and the broader IHE sector.⁹

[Here we could mention ISO, EU AI Act, Ethical Frameworks)

3.2 Core Philosophy of CLAIR for IHE

The CLAIR for IHE framework is guided by a set of core philosophical principles designed to ensure its relevance, effectiveness, and ethical grounding:

• Global Competence through AI Literacy: The primary aim is to equip IHE staff with the AI literacy necessary to operate effectively, ethically, and innovatively in an increasingly globalized and AI-suffused higher education ecosystem.
• Ethical Agility: The framework seeks to develop the capacity within IHE professionals to navigate the complex and often novel ethical dilemmas posed by AI applications in diverse international settings, promoting thoughtful and principled decision-making.
• Capability-Centric, Not Tool-Reliant: The framework intentionally moves the focus from proficiency with specific, rapidly changing AI software to a more enduring and profound understanding of AI’s fundamental, evolving capabilities and their implications. As AI systems advance, there will be decreasing need to know how to use digital tools, and more emphasis on how to specify requirements, communicate needed outcomes and orchestrate processes through natural language.
• Human-AI Collaboration for IHE Excellence: The framework promotes a vision of synergistic partnership, where IHE professionals leverage AI to augment their expertise, enhance efficiency, and drive innovation, rather than viewing AI as a replacement for human judgment and interaction.
• Inclusivity and Equity by Design: A core tenet is to ensure that AI is implemented in ways that enhance access, promote fairness, and mitigate bias within international higher education, actively working against the potential for AI to exacerbate existing inequalities.¹⁵

3.3 Grounding CLAIR: Key Influences and Differentiators

The CLAIR for IHE framework is built upon a synthesis of leading research and international standards, while also offering unique contributions tailored to the IHE sector. Its “Capability” pillar is explicitly grounded in the OECD AI Capability Indicators, providing a robust, internationally recognized system for understanding and tracking AI’s development relative to human abilities.¹ This capability-centric approach directly addresses common critiques of earlier AI literacy models that were often too tool-focused and quickly became outdated.¹

While drawing inspiration from broader educational competency frameworks, such as those developed by UNESCO for AI in education ³⁶, and acknowledging the regulatory landscape, including the EU AI Act’s mandate for AI literacy ²⁵, CLAIR for IHE distinguishes itself through its specific design for the international higher education context. Unlike revisions of general frameworks, CLAIR’s pillar structure—particularly the explicit inclusion of “Interculturality”—and its bespoke CLAIRvoyance Cycle methodology are novel contributions, conceived from the outset to address the unique operational realities, ethical complexities, and strategic imperatives of IHE. This ensures that the framework is not merely a generic model with IHE examples, but a deeply integrated and contextually relevant guide for cultivating advanced AI fluency within the global higher education workforce.

4. Deconstructing CLAIR for IHE: Pillars and the CLAIRvoyance Cycle Methodology

The CLAIR for IHE framework is operationalized through its five core pillars and a dynamic, iterative methodology,  CLAIRvoyance Cycle. Together, these elements provide a comprehensive structure for developing AI literacy among International Higher Education professionals.

4.1 The Five Pillars of CLAIR for IHE in Detail

Each pillar of the CLAIR framework represents a critical domain of knowledge, skill, and disposition necessary for navigating the AI-suffused IHE landscape:

• Capability:

• Definition and Scope: This pillar focuses on developing a nuanced understanding of AI’s fundamental operational capacities and inherent limitations, as benchmarked by frameworks like the OECD AI Capability Indicators.¹ It involves recognizing the current developmental stage of AI across various cognitive and functional domains (e.g., Language, Problem-Solving, Social Interaction, Creativity) and appreciating the implications of these capability levels for AI’s reliability and suitability for specific IHE tasks.
• Importance in IHE: In IHE, where decisions can have significant cross-border impact (e.g., international student recruitment strategies, TNE partnership viability), understanding an AI’s true capability—not just its marketed features—is crucial for risk assessment and effective deployment. For example, an International Marketing Manager must understand that an AI operating at OECD Language Level 3 can generate fluent text but may lack the deep cultural nuance or robust reasoning (characteristic of Level 4+) needed for a truly persuasive global campaign.¹
• IHE Example: An Education Abroad Coordinator evaluating an AI tool for matching students to international programs would use this pillar to assess if the tool is merely performing advanced pattern recognition (akin to OECD Vision Levels 2-3) or engaging in complex problem-solving that considers multifaceted student needs and program characteristics (approaching OECD Problem-Solving Levels 3-4).¹
• Literacy:

• Definition and Scope: This pillar encompasses foundational knowledge of AI terminology, core concepts (AI, Machine Learning, Deep Learning, NLP, GenAI), historical evolution, current applications, and key ethical principles (fairness, transparency, accountability, privacy). It also includes the practical skills needed to effectively use common AI tools relevant to IHE roles, such as prompt engineering and data interpretation.²³
• Importance in IHE: A shared AI literacy provides a common language and understanding across diverse IHE departments and roles, facilitating collaboration and informed discussion about AI adoption and governance. It ensures staff can engage with AI tools productively and critically.
• IHE Example: An Admissions Officer uses their AI literacy to understand how an AI-powered application screening tool works, recognizes the potential for bias in its algorithms if trained on historical data, and knows how to interpret its outputs critically, cross-referencing with other information sources.
• Adaptability:

• Definition and Scope: This pillar emphasizes the development of skills and mindsets necessary for continuous learning and agile responsiveness in the face of rapid AI advancements. It involves not only keeping abreast of new AI tools and techniques but, more fundamentally, anticipating how evolving AI capabilities (e.g., a shift from OECD Problem-Solving Level 2 to Level 3) will impact IHE roles, operational processes, and strategic institutional goals.¹ It fosters proactive engagement with change rather than reactive adjustment.
• Importance in IHE: The global IHE landscape is already dynamic; AI adds another layer of rapid transformation. Staff must be prepared to reskill, upskill, and potentially redefine their roles as AI takes on more sophisticated tasks. For instance, if AI reaches “Creativity Level 4,” an International Marketing Specialist’s role in campaign strategy would need to evolve significantly beyond simply using AI image generators.¹
• IHE Example: A TNE Operations Manager, understanding the trajectory of AI capabilities, proactively explores how enhanced AI-driven quality assurance tools might reshape compliance monitoring for international branch campuses, and begins to develop new skills in managing AI-augmented QA processes.

• Interculturality:

• Definition and Scope: This pillar focuses on the ability to effectively and ethically deploy, interpret, and mediate AI and its outputs within diverse global and cultural contexts. It involves a critical awareness of how cultural values, communication styles, and social norms can influence the design, application, and reception of AI technologies. It also means recognizing and mitigating AI’s inherent cultural biases and ensuring that AI-human interactions are culturally sensitive, appropriate, and respectful in all IHE engagements.¹
• Importance in IHE: IHE is inherently intercultural. AI tools, often developed in specific cultural contexts (predominantly Western), may not be universally applicable or appropriate. Staff need to evaluate AI-generated content (e.g., student communications, marketing materials) for cultural nuances, potential misinterpretations, or unintended offense in specific international settings. This active mediation is vital.
• IHE Example: An International Student Advisor using an AI chatbot for student support is skilled in recognizing when the chatbot’s generic advice (based on OECD Language L3, Social Interaction L2) might be culturally inappropriate for a student from a specific background. The advisor then intervenes to provide more nuanced, culturally attuned guidance, effectively mediating the AI’s output.
• Responsibility:

• Definition and Scope: This pillar underscores the commitment to ethical conduct, accountability, and stewardship in all AI-related activities within IHE. It includes adherence to data privacy regulations (e.g., GDPR in Europe, and other national/regional laws relevant to international student data), safeguarding academic integrity, promoting fairness and equity in AI-driven processes, mitigating algorithmic bias, and contributing to the development and implementation of robust institutional AI governance policies.⁹
• Importance in IHE: The use of AI in IHE involves sensitive student data, high-stakes decisions (e.g., admissions, grading), and interactions with vulnerable populations. A strong sense of responsibility is paramount to maintain trust, uphold ethical standards, and ensure AI is used for the benefit of all stakeholders.
• IHE Example: A Senior IHE Leader champions the development of an institutional AI ethics framework, ensuring that AI tools used in international admissions are regularly audited for bias, that student data is handled in compliance with all relevant international regulations, and that there are clear channels for accountability if AI systems produce unfair outcomes.

4.2 The CLAIRvoyance Cycle: A Methodology for Cultivating AI Fluency

The CLAIRvoyance Cycle is an iterative, reflective, and action-oriented methodology designed to help IHE professionals develop and apply the CLAIR competencies in their work. It consists of five stages:

1. Contextualize:

• Action: IHE professionals begin by identifying and analyzing the specific ways AI is currently impacting, or could potentially impact, their particular role, tasks, and responsibilities within the IHE context. This involves understanding the unique opportunities and challenges AI presents in their specific international or intercultural setting (e.g., a recruitment officer considers AI for reaching diverse global markets; a COIL facilitator explores AI for multilingual collaboration support).
• Focus: Understanding the ‘why’ and ‘where’ of AI in their IHE world.

2. Learn:

• Action: Professionals engage in targeted learning activities to build their knowledge and skills related to the CLAIR pillars. This includes studying core AI concepts, understanding the OECD AI Capability Indicators relevant to their work ¹, familiarizing themselves with ethical frameworks and institutional AI policies ²⁵, and exploring best practices for AI application in IHE.
• Focus: Acquiring the necessary knowledge and conceptual tools.

3. Apply:

• Action: Professionals skillfully and ethically experiment with and implement AI tools and techniques to enhance their IHE tasks, innovate processes, or solve specific problems. This stage involves practical application, such as effective prompt engineering, using AI for data analysis, or leveraging AI for content creation, always with a critical and discerning mindset.
• Focus: Putting knowledge into practice; experiential engagement with AI.

4. Interculturally Engage & Evaluate:

• Action: This critical stage involves analytically assessing AI systems, processes, and outputs for accuracy, relevance, potential bias (including cultural bias), and overall impact within specific IHE operational environments and diverse international contexts.¹ Professionals actively consider the intercultural implications of AI use, mediating AI interactions and outputs to ensure they are culturally appropriate and effective. This stage moves beyond generic critical evaluation to an active process of intercultural negotiation with AI. For example, an AI-generated student communication might be factually correct (OECD Language L3) but culturally insensitive (OECD Social Interaction L2 limitations); the IHE professional evaluates this and adapts the communication accordingly.
• Focus: Critical assessment through an intercultural lens; ensuring AI is fit for global purpose.

5. Responsibly Adapt:

• Action: Professionals reflect on the outcomes and implications of their AI use, considering its effectiveness, ethical dimensions, and impact on stakeholders. Based on this reflection and an ongoing awareness of AI’s evolution (e.g., shifts in OECD capability levels), they adapt their personal practices, workflows, and even contribute to broader institutional strategic adjustments. This stage fosters a commitment to continuous professional development and responsible innovation. This “Responsibly Adapt” stage uniquely connects individual professional growth to institutional strategic evolution in IHE. It encourages staff to provide insights from their AI engagement that can inform how the institution adapts its global strategies, creating a vital feedback loop for organizational agility.
• Focus: Learning from experience, continuous improvement, and strategic foresight.

The CLAIRvoyance Cycle is not a one-off, linear process but a continuous loop. Each cycle of engagement with AI should lead to deeper understanding and more refined practices, fostering an upward spiral of AI fluency. This methodology ensures that the CLAIR competencies are not just learned abstractly but are actively developed and applied in the real-world complexities of International Higher Education.

5. CLAIR for IHE in Action: Cultivating AI Literacy Across IHE Professional Roles

To translate the CLAIR for IHE framework and the CLAIRvoyance Cycle into practical application, this section introduces a competency matrix tailored for diverse professional roles within International Higher Education. This matrix serves as a blueprint for targeted training, individualized learning pathways, and competency benchmarking, ensuring that AI fluency is cultivated in a manner relevant to the specific responsibilities and contexts of IHE staff.

5.1 The CLAIR Competency Matrix for IHE Professionals

The CLAIR Competency Matrix for IHE Professionals (Table 1) outlines specific competencies and proficiency indicators for key roles across the IHE landscape, mapped against the five CLAIR pillars. It aims to provide concrete examples of the knowledge, skills, attitudes, and awareness that constitute AI literacy  in each role, explicitly integrating an understanding of OECD AI Capability levels and the application of the CLAIRvoyance Cycle.

The matrix must explicitly address how IHE professionals engage with AI not just as end-users, but also, where relevant, as individuals involved in the procurement, design, or development oversight of AI solutions for IHE. This is particularly pertinent for roles in HEDTech support, curriculum development, or senior leadership who may be evaluating AI vendors or commissioning bespoke AI tools.⁹ Competencies under “Capability” and “Responsibility” should therefore include aspects of ethical AI sourcing, understanding AI development lifecycles, assessing vendor claims against actual AI capabilities, and ensuring that procured or developed AI systems align with institutional values and IHE-specific needs. This represents a higher-order AI literacy crucial for sound institutional governance and responsible innovation.

Furthermore, a key differentiator of the CLAIR Competency Matrix is its inherent dynamism, achieved by framing competencies within the context of the CLAIRvoyance Cycle. Rather than presenting a static list of skills, the matrix implies an ongoing process of contextualization, learning, application, intercultural engagement/evaluation, and responsible adaptation. This approach emphasizes continuous development and responsiveness to the rapidly evolving AI landscape, which is vital for achieving and maintaining true AI fluency.¹

Table 1: The CLAIR Competency Matrix for IHE Professionals

IHE Role/Cluster	CLAIR Pillar 1: Capability (OECD Awareness)	CLAIR Pillar 2: Literacy (Concepts & Ethics)	CLAIR Pillar 3: Adaptability (Lifelong Learning & Strategic Foresight)	CLAIR Pillar 4: Interculturality (AI in Global Contexts)	CLAIR Pillar 5: Responsibility (Governance & Equity)	Example Application of CLAIRvoyance Cycle
International Recruitment Officer	Articulates typical OECD Language (e.g., L3) and Problem-Solving (e.g., L2) levels of AI tools used for market analysis and content generation; understands limitations in nuanced reasoning or cultural understanding.1	Understands core AI concepts in recruitment tech (CRMs, ad platforms); aware of ethical implications of AI in targeting/selection (bias, privacy); familiar with data sources and GenAI for content.11	Proactively explores new AI-powered recruitment tools and adapts strategies as AI capabilities for personalization and market insight evolve (e.g., to Language L4).	Critically evaluates AI-generated marketing content for cultural appropriateness across diverse target markets (e.g., Asia vs. Europe); adapts messaging to avoid stereotypes.5	Ensures compliance with data privacy regulations (e.g., GDPR) for applicant data; transparent about AI use in outreach; advocates for equitable AI in recruitment.22	Contextualize: Identifies need for personalized outreach in new emerging markets. Learn: Studies OECD Language L3 capabilities of current GenAI. Apply: Uses GenAI to draft initial email templates. Interculturally Engage & Evaluate: Assesses templates for cultural relevance in target markets, noting AI’s limitations in deep nuance. Responsibly Adapt: Refines templates with human oversight, documents AI use, and shares feedback on AI tool’s intercultural effectiveness.
Admissions Assessor/Officer	Understands OECD Knowledge (L3) and Metacognition (L2) levels of AI tools for application summarization or initial screening; aware of AI’s limitations in handling ambiguity or novel information.1	Knowledge of AI in application screening; understands potential biases in algorithms trained on historical data; principles of fairness in AI-assisted admissions.7	Adapts review processes as AI tools for document verification or anomaly detection improve in capability (e.g., advanced OECD Vision for document authenticity).	Assesses AI-generated applicant summaries for potential cultural biases in interpreting non-traditional qualifications or experiences from diverse educational systems.	Upholds institutional policies on AI in admissions; accountable for AI-assisted decisions; ensures holistic review beyond AI scores; verifies AI-flagged inconsistencies with cultural sensitivity.29	Contextualize: Faces high volume of international applications with diverse credentials. Learn: Understands AI’s (OECD Metacognition L2) ability to flag inconsistencies but also its potential for bias. Apply: Uses AI to summarize applications but verifies flagged issues manually. Interculturally Engage & Evaluate: Critically assesses AI flags for bias against non-standard educational backgrounds. Responsibly Adapt: Recommends policy adjustments for AI use in admissions to ensure fairness.
International Student Advisor	Aware of OECD Language (L3) and Social Interaction (L2) capabilities of student support chatbots; understands AI’s limits in empathy and complex emotional understanding.1	Understands AI in student support platforms; ethical considerations for AI in providing advice; limitations of AI in nuanced student needs assessment.8	Continuously learns about new AI tools for student well-being and mental health support, adapting advisory practices as AI demonstrates more sophisticated (but still limited) interaction capabilities.	Evaluates AI chatbot responses for cultural appropriateness and sensitivity when interacting with students from diverse backgrounds; mediates AI communication to prevent misunderstandings.5	Ensures student data privacy and confidentiality in AI-mediated support; maintains ethical boundaries; transparent with students about AI use; advocates for accessible AI support tools.8	Contextualize: Needs to provide scalable support for common international student queries. Learn: Researches AI chatbot capabilities (OECD Language L3, Social Interaction L2). Apply: Deploys chatbot for FAQs. Interculturally Engage & Evaluate: Monitors chatbot interactions, noting instances of culturally insensitive or overly generic advice. Responsibly Adapt: Trains chatbot with more nuanced responses, provides clear escalation paths to human advisors, and educates students on chatbot limitations.
Education Abroad Coordinator	Understands OECD Problem-Solving (L2) and Knowledge (L3) levels of AI tools for program matching or risk assessment; aware of limitations in complex, multi-factor decision-making.1	Knowledge of AI in mobility platforms; ethical implications of AI in opportunity allocation; data privacy in cross-border data exchange.1	Adapts program promotion and risk management strategies as AI tools for virtual site visits (OECD Vision L3) or real-time crisis monitoring evolve.	Critiques AI-generated pre-departure materials for cultural sensitivity relevant to diverse host countries and student origins.	Adheres to data security for student mobility data; ethical considerations in promoting programs using AI-generated content; ensures equitable access to AI-supported mobility opportunities.	Contextualize: Aims to improve student matching to suitable education abroad programs. Learn: Explores AI tools for program recommendation (OECD Problem Solving L2). Apply: Uses AI to generate initial program suggestions based on student profiles. Interculturally Engage & Evaluate: Assesses AI recommendations for fairness and suitability, considering non-quantifiable student goals and diverse program environments. Responsibly Adapt: Refines AI matching criteria and ensures human advisors make final recommendations.
COIL Facilitator / Virtual Exchange Coordinator	Aware of OECD Language (L3) for AI translation tools and Social Interaction (L2) for AI in supporting group dynamics; understands limits in fostering deep intercultural dialogue.1	Knowledge of AI tools for collaborative learning and intercultural exchange; ethical considerations of AI in virtual classrooms (equity of access, data privacy).1	Explores and integrates new AI tools for enhancing virtual collaboration (e.g., AI for real-time sentiment analysis in group discussions, if ethically deployed) as these mature.	Evaluates AI translation tools not just for accuracy but for conveying cultural nuance in COIL projects; guides students on interpreting AI-mediated communication interculturally.20	Ensures ethical use of student contributions in AI-assisted COIL projects; promotes digital citizenship and responsible AI use among participants; addresses accessibility of AI tools for all students.29	Contextualize: Needs to support multilingual COIL projects effectively. Learn: Assesses AI translation tools (OECD Language L3). Apply: Implements AI translation in COIL platforms. Interculturally Engage & Evaluate: Observes how AI translation impacts student interaction, noting limitations in conveying tone or idiomatic expressions. Responsibly Adapt: Provides guidance to students on using AI translation critically and supplements with human facilitation for nuanced discussions.
TNE Operations Manager	Articulates OECD Problem-Solving (L2-3) and Knowledge (L3) levels of AI tools for market intelligence, competitor analysis, or QA processes in TNE.1	Understands AI in TNE quality assurance; ethical considerations of AI in cross-border education (data sovereignty, academic standards); AI for policy compliance analysis.1	Adapts TNE operational strategies and QA protocols as AI capabilities for predictive analytics (e.g., student success in TNE programs) or automated compliance checking advance.	Critically assesses AI-generated market analysis reports for TNE for cultural biases or oversimplification of complex local educational landscapes; ensures QA processes are culturally appropriate for host contexts.19	Ensures ethical AI use in all TNE operations; robust data governance across borders; transparency with partners about AI use; adapts TNE strategies to AI trends and evolving capabilities relevant to QA and delivery.43	Contextualize: Tasked with assessing new market viability for TNE programs. Learn: Investigates AI tools for market analysis (OECD Problem Solving L2). Apply: Uses AI to gather and analyze market data. Interculturally Engage & Evaluate: Cross-references AI findings with local expert knowledge, noting AI’s potential lack of understanding of nuanced local regulations or cultural preferences in education. Responsibly Adapt: Integrates AI insights with human expertise for final market entry decisions, documents AI limitations.
Senior IHE Leader / Policy Maker	Understands the strategic trajectory of OECD AI Capabilities (e.g., potential for AI to reach L4/L5 in key domains) and its implications for long-term IHE planning, competitiveness, and workforce development.1	Deep understanding of AI governance models (e.g., EU AI Act 25), institutional risk management for AI; ethical leadership principles for AI adoption; AI’s impact on the future of work and skills.41	Leads institutional adaptation to fundamental shifts in AI capabilities, fostering a culture of responsible AI innovation and strategic foresight across the institution.	Champions the development of interculturally competent AI strategies for the institution’s global engagement, ensuring AI supports equitable and respectful international partnerships.	Develops and enforces institution-wide AI policies and ethical guidelines based on CLAIR principles; ensures responsible AI stewardship; advocates for responsible AI at national/international levels; promotes lifelong AI adaptation for the entire IHE workforce.21	Contextualize: Aims to develop an institutional AI strategy for internationalization. Learn: Studies OECD capability trajectories, EU AI Act, and best practices in HEI AI governance. Apply: Leads task force to draft AI strategy incorporating CLAIR principles. Interculturally Engage & Evaluate: Ensures strategy considers diverse global stakeholder perspectives and promotes equitable AI use across international activities. Responsibly Adapt: Establishes mechanisms for ongoing review and adaptation of the AI strategy as AI and global contexts evolve.

Note: Proficiency indicators (e.g., Foundational, Intermediate, Advanced) would be further defined with specific benchmarks for each competency within a full implementation guide.

5.2 Illustrative Case Vignettes

To further illuminate the practical application of the CLAIR for IHE framework and the CLAIRvoyance Cycle, consider the following vignettes:

• Vignette 1: The International Marketing Specialist and a Global Campaign

• Mei, an International Marketing Specialist, is tasked with developing a recruitment campaign for a new master’s program targeting students in Southeast Asia and Latin America.
• Contextualize: Mei recognizes the need for culturally tailored messaging for two distinct regions and considers using GenAI for initial content creation due to tight deadlines.
• Learn: She reviews the OECD Language (L3) and Creativity (L3) capabilities of her chosen GenAI tool, noting its strengths in generating diverse content but potential weaknesses in deep cultural understanding and originality.¹ She also revisits ethical guidelines on avoiding stereotypes in marketing.
• Apply: Mei uses the GenAI tool to draft initial versions of website copy, social media posts, and email templates, prompting for themes relevant to both regions.
• Interculturally Engage & Evaluate: She critically evaluates the AI-generated content. For Southeast Asia, she notes the AI’s tendency towards overly formal language and generic imagery. For Latin America, some phrases appear to be direct translations that lose their intended warmth. She identifies these as limitations of the AI’s current Social Interaction (L2) and advanced Language (L3, not L4) capabilities.
• Responsibly Adapt: Mei significantly reworks the AI content, collaborating with local student interns for cultural validation. She documents the AI’s role and her adaptations, providing feedback to her team on the GenAI tool’s limitations for nuanced intercultural marketing and suggesting future training on prompting for cultural specificity.
• Vignette 2: The Education Abroad Advisor and AI-Powered Program Matching

• David, an Education Abroad Advisor, is exploring an AI tool designed to match students with suitable international exchange programs based on their academic profiles, interests, and preferences.
• Contextualize: David sees the potential for AI to help students navigate a large portfolio of programs but is concerned about fairness and the tool’s ability to capture non-quantifiable student goals.
• Learn: He investigates the AI tool’s claimed capabilities, trying to ascertain its underlying OECD Problem-Solving level (likely L2, focused on structured data) and Knowledge, Learning & Memory level (L3, accessing program databases).¹ He also reviews institutional policies on student data privacy.
• Apply: David runs pilot tests with anonymized student profiles, comparing AI recommendations to those he would make based on his experience.
• Interculturally Engage & Evaluate: He observes that the AI tends to prioritize programs in well-known Western destinations and sometimes overlooks excellent opportunities in less-represented regions or programs with unique experiential components not easily captured by data fields. He assesses this as a potential bias in the AI’s training data or algorithmic weighting, and a limitation of its current capability to understand nuanced qualitative aspects of program fit.
• Responsibly Adapt: David decides to use the AI tool as a preliminary exploration aid for students but mandates that all AI-generated recommendations are discussed with a human advisor. He provides feedback to the AI vendor about the observed biases and advocates for features allowing more nuanced input on student preferences and program characteristics, aiming for more equitable and globally diverse recommendations.

These vignettes illustrate how the CLAIR framework and CLAIRvoyance Cycle can guide IHE professionals in various roles to engage with AI thoughtfully, critically, and effectively, always keeping the specific demands and diverse contexts of international higher education at the forefront.

6. Pedagogical Pathways and Assessment Innovations for CLAIR for IHE

Cultivating the competencies outlined in the CLAIR for IHE framework requires carefully designed pedagogical approaches and innovative assessment strategies that align with the CLAIRvoyance Cycle. The goal is to move beyond traditional knowledge transmission to foster deep understanding, critical thinking, ethical reasoning, intercultural sensitivity, and adaptive expertise in relation to AI.

6.1 Designing Learning Experiences for CLAIR Fluency

Professional development programs for IHE staff based on CLAIR for IHE should embody active, experiential, and collaborative learning principles. Pedagogical approaches should directly support progression through the CLAIRvoyance Cycle:

• Problem-Based Learning (PBL) in International Contexts: Staff engage with real-world IHE challenges where AI could offer solutions (e.g., improving support for at-risk international students, developing a TNE quality assurance framework, designing an inclusive COIL module). They would progress through the CLAIRvoyance Cycle to analyze the problem, learn about relevant AI capabilities and ethics, apply AI tools, interculturally evaluate solutions, and reflect on their adaptation strategies.
• Collaborative Global Projects using AI: Teams of IHE professionals, potentially from different institutions or cultural backgrounds, could collaborate on a project that requires the use of AI tools (e.g., co-developing an international student orientation module using AI for content generation and translation). This would provide direct experience in intercultural AI engagement.
• Reflective Practice on Intercultural AI Interactions: Structured activities where staff analyze and discuss their experiences (or case studies of others’ experiences) using AI in cross-cultural IHE scenarios. This could involve critiquing AI-generated communications for cultural appropriateness or discussing ethical dilemmas arising from AI use in diverse settings.⁵
• Case Study Analysis from Diverse IHE Settings: Using curated case studies (drawing from institutional practices globally, e.g., University of Michigan’s AI initiatives ⁵⁰, Georgia Tech’s AI in admissions ⁴², or examples of AI in student support ⁸) to illustrate the application of CLAIR pillars and the CLAIRvoyance Cycle.
• Experiential Workshops and AI “Sandboxes”: Providing hands-on opportunities for staff to experiment with various AI tools in a safe, supported “sandbox” environment, focusing on understanding their capabilities (OECD levels) and limitations rather than just feature sets. QS offers an AI Sandbox for its consortium members.⁵³
• Peer Learning and Communities of Practice (CoPs): Facilitating CoPs where IHE staff can share their learning, challenges, and successes in applying CLAIR for IHE, fostering a collective intelligence and continuous improvement culture.¹

Learning modules could be structured around the CLAIR pillars or specific IHE functions, always embedding the CLAIRvoyance Cycle. For example:

• Module 1: Understanding AI Capabilities & the IHE Landscape: Focus on CLAIR’s “Capability” and “Literacy” pillars, introducing OECD indicators ¹ and core AI concepts within IHE contexts.
• Module 2: Ethical, Intercultural, and Responsible AI Application in IHE: Deep dive into “Interculturality” and “Responsibility,” exploring ethical frameworks ²⁵, data privacy, bias mitigation ³², and culturally sensitive AI use.
• Module 3: Adaptive AI Strategy and Innovation for IHE Futures: Focus on “Adaptability,” encouraging strategic thinking about AI’s long-term impact on IHE roles and institutional strategies.

6.2 Advanced Assessment Methods for CLAIR Competencies

Assessment of CLAIR competencies must be authentic, performance-based, and capable of evaluating higher-order thinking skills, ethical judgment, and adaptive capacity. It is also crucial that assessment methods themselves are designed with intercultural sensitivity, especially given the diverse backgrounds of IHE staff. Tasks should avoid cultural biases and allow for varied expressions of competence, ensuring fairness in evaluation.

• AI Capability Diagnosis Tasks (IHE-Contextualized): Adapting the concept from ¹[p.34], these tasks present staff with AI-generated outputs relevant to IHE (e.g., an AI-drafted TNE partnership proposal, an AI-generated analysis of international student feedback, an AI-created marketing video for a global audience). Staff would be required to:

1. Diagnose the primary OECD capability (e.g., Language, Problem-Solving, Creativity, Social Interaction) and the likely performance level (1-5) demonstrated by the AI in producing that output.
2. Provide a detailed justification for their diagnosis, referencing OECD level descriptors.³
3. Critique the output’s suitability, accuracy, and potential biases within a specific IHE scenario, particularly considering intercultural implications. These tasks can serve as powerful formative assessments within the “Learn” and “Interculturally Engage & Evaluate” stages of the CLAIRvoyance Cycle, allowing staff to practice identifying AI capabilities in low-risk scenarios before applying this understanding to complex, real-world IHE tasks.

• Intercultural AI Ethical Dilemma Scenarios: Staff are presented with complex ethical dilemmas where AI use in an IHE context (e.g., an AI system for admissions showing demographic bias against applicants from certain regions ¹⁵, an AI translation tool misrepresenting sensitive information in a student disciplinary case) has significant intercultural dimensions. Assessment would focus on the staff member’s reasoning, their application of CLAIR principles (especially “Interculturality” and “Responsibility”), their proposed solutions, and their consideration of diverse stakeholder perspectives.
• CLAIRvoyance Cycle Reflective Portfolio: Staff select a significant IHE project or initiative where they have intentionally applied AI. They document their journey through each stage of the CLAIRvoyance Cycle:

• Contextualize: Their analysis of the IHE problem/opportunity and AI’s potential role.
• Learn: Key AI capabilities, ethical principles, or intercultural considerations they researched.
• Apply: How they used AI, including prompts, tools, and processes.
• Interculturally Engage & Evaluate: Their critical assessment of AI outputs, identification of biases, and any intercultural adaptations made.
• Responsibly Adapt: Reflections on what they learned, how their practice changed, and recommendations for future AI use or institutional adaptation. This portfolio assesses not just the outcome but the process of developing AI fluency.
• Rubrics for AI-Assisted IHE Work: Developing specific rubrics for evaluating work produced with AI assistance. These rubrics should assess:

• The quality and appropriateness of the final output for the IHE context.
• The student’s/staff member’s critical engagement with the AI (e.g., evidence of prompt iteration, evaluation of AI suggestions).
• The ethical use of AI, including proper acknowledgment and data handling.⁵⁴
• The intercultural sensitivity demonstrated in adapting or mediating AI outputs for diverse audiences.
• The justification for using AI and an understanding of its capabilities and limitations in the given task. Guidance from sources like Conestoga’s AI-savvy rubrics ⁵⁴ or AI for Education’s rubric prompts ⁵⁷ can be adapted, emphasizing critical AI literacy.²³

Table 2: Mapping CLAIR Components to IHE-Specific Learning Modules and Assessment Strategies

CLAIR Pillar / CLAIRvoyance Cycle Stage	Illustrative IHE Learning Module	Key Learning Objectives	Suggested Pedagogical Approaches	Innovative Assessment Method	Relevant OECD Capabilities Addressed
Capability / Learn Stage	Module: “AI Capabilities in Global HE: An OECD-Informed Perspective”	– Describe the 9 OECD AI Capability Indicators and their 5-level scales.1 <br> – Analyze current AI performance levels (e.g., L2-L3) and their implications for IHE tasks.	Interactive lectures, analysis of AI tool outputs against OECD descriptors, expert guest speakers (AI researchers).	AI Capability Diagnosis Task: Analyze an AI-generated international market research report, diagnose OECD Problem-Solving & Language levels, justify, and critique its strategic reliability for a new TNE venture.1	Language, Problem-Solving, KML, Metacognition.
Literacy / Learn Stage	Module: “Foundations of AI: Concepts, Ethics, and IHE Applications”	– Define core AI terms (AI, ML, GenAI, NLP) and their relevance to IHE. <br> – Identify key ethical principles for AI in IHE (fairness, transparency, privacy).29 <br> – Practice effective prompt engineering for IHE tasks.	Case studies of AI use in IHE (recruitment, student support), hands-on prompting workshops, ethical debates on AI scenarios in IHE.	Scenario-Based Ethical Analysis: Evaluate an IHE scenario involving AI use (e.g., AI in admissions screening) against institutional ethical guidelines and CLAIR’s “Responsibility” pillar.	N/A (focus on concepts & ethics rather than specific capability levels).
Adaptability / Responsibly Adapt Stage	Module: “Strategic AI Futures for IHE: Leading and Adapting to Change”	– Analyze the potential long-term impact of evolving AI capabilities on specific IHE roles and institutional strategies.1 <br> – Develop a personal action plan for continuous AI learning and professional adaptation.	Futures thinking workshops, scenario planning exercises (e.g., “What if AI reaches Language L4?”), peer coaching on adaptive strategies.	CLAIRvoyance Cycle Reflective Portfolio (Adaptation Focus): Document how anticipation of a specific AI capability shift (e.g., advanced AI in intercultural communication) leads to a proposed adaptation in an IHE process or personal role.	All 9 OECD indicators, as adaptability requires anticipating shifts across the spectrum.
Interculturality / Interculturally Engage & Evaluate Stage	Module: “AI in Global Communication & Collaboration: An Intercultural Approach”	– Critically evaluate AI-generated communications for cultural appropriateness in diverse international markets/contexts.5 <br> – Mediate AI outputs to ensure culturally sensitive interactions with international stakeholders.	Analysis of AI-generated cross-cultural communication (successes and failures), role-playing AI-mediated intercultural dialogues, collaborative projects with international partners involving AI tools.	Intercultural AI Ethical Dilemma Scenario: Resolve a dilemma where an AI tool used in COIL facilitation provides culturally insensitive feedback to students from different backgrounds. Justify actions using CLAIR’s “Interculturality” and “Responsibility” pillars.	Language, Social Interaction, Metacognition.
Responsibility / All Stages	Module: “AI Governance, Equity, and Responsible Stewardship in IHE”	– Apply principles of the EU AI Act to IHE contexts.25 <br> – Identify and propose strategies to mitigate AI bias in IHE systems (e.g., admissions, student support).31 <br> – Draft AI usage guidelines for a specific IHE department.	Analysis of institutional AI policies (e.g., U of Sydney 55, U of Toronto 61), development of mock AI governance proposals, debates on AI and academic integrity.	AI Policy Critique & Redesign Task: Critique an existing (hypothetical or real) university AI policy using CLAIR principles and propose revisions to enhance its ethical robustness and intercultural sensitivity for IHE.	N/A (focus on governance & ethics).
CLAIRvoyance Cycle (Overall Application)	Capstone Project: “Innovating an IHE Process with AI”	– Apply all stages of the CLAIRvoyance Cycle to a chosen IHE challenge or opportunity.	Mentored, self-directed project work, peer feedback sessions, final presentation of project and reflections.	Full CLAIRvoyance Cycle Reflective Portfolio: Comprehensive documentation and reflection on the entire cycle for the capstone project, assessed via a holistic rubric aligned with CLAIR competencies.	Varies depending on project focus, likely multiple OECD indicators.

This integrated approach to pedagogy and assessment, grounded in the CLAIR for IHE framework and its CLAIRvoyance Cycle, aims to cultivate a deeply knowledgeable, critically aware, ethically astute, interculturally competent, and proactively adaptive IHE workforce, ready to harness AI’s potential responsibly for the advancement of global higher education.

7. Strategic Institutionalization: Embedding CLAIR for IHE for a Future-Ready IHE Sector

The successful cultivation of an AI-literate workforce across International Higher Education requires more than the development of a robust framework; it demands strategic institutional commitment, proactive leadership, thoughtful resource allocation, and a culture that champions continuous learning and responsible innovation. Embedding the CLAIR for IHE framework effectively necessitates a multi-pronged approach.

7.1 Leadership and Governance for AI Fluency

Strong and visible commitment from senior IHE leadership is paramount for the successful institutionalization of CLAIR for IHE.¹ Leaders must champion AI fluency not merely as a training initiative but as a core institutional capacity, essential for navigating the future of global higher education. This involves:

• Strategic Alignment: Explicitly linking the CLAIR for IHE framework and its goals with the institution’s overarching mission, values, internationalization objectives, and digital transformation strategies.⁴¹ When AI literacy is framed as a critical enabler of strategic priorities—such as enhancing global student recruitment, improving TNE quality, or fostering impactful international research—it gains greater traction and resourcing.
• Developing Institutional AI Policies and Ethical Guidelines: IHE institutions must establish clear, comprehensive, and ethically grounded AI policies and usage guidelines. These should be informed by CLAIR principles (especially “Responsibility” and “Interculturality”) and aligned with international standards and regulations, such as the EU AI Act ²⁵ and best practices from other universities.²¹ These policies should address data privacy, academic integrity, bias mitigation, transparency, and accountability in AI use across all IHE functions.
• Establishing AI Governance Structures: The creation of dedicated AI governance bodies, such as an “IHE AI Ethics and Capability Steering Group,” is recommended. This council, comprising diverse stakeholders (leadership, faculty, staff, technical experts, legal counsel, and potentially student representatives), would oversee the implementation of the CLAIR framework, monitor the evolution of AI and its implications for IHE, ensure adherence to ethical guidelines, and guide the institution’s responsible AI adoption. Insights from university AI task force reports can inform the structure and remit of such bodies.⁴⁸

7.2 Resource Allocation and Capacity Building

Transforming an institution’s workforce towards AI fluency requires tangible investment:

• Professional Development Programs: Allocating resources for the design and delivery of comprehensive professional development programs based on the CLAIR for IHE framework and the CLAIRvoyance Cycle for all relevant staff. This includes training for trainers and ongoing support.
• Access to AI Tools and Platforms: Providing staff with access to appropriate, vetted, and university-endorsed AI tools and platforms in a secure and ethical manner.⁵⁵ This may involve institutional licenses for certain AI software or guidance on using publicly available tools responsibly.
• Fostering Communities of Practice (CoPs): Actively encouraging, resourcing, and supporting the formation of internal CoPs focused on AI in IHE.¹ These CoPs are invaluable for staff to share experiences, discuss challenges, showcase successful applications of CLAIR principles in their specific IHE contexts, and collaboratively explore new AI tools and capabilities. Effective institutionalization of CLAIR for IHE benefits significantly from such bottom-up engagement. CoPs can become crucial for sharing tacit knowledge, particularly regarding the nuanced application of CLAIR’s “Interculturality” pillar in diverse, real-world IHE scenarios—knowledge that formal training alone may not fully capture. These communities can act as living repositories and incubators for best practices in intercultural AI engagement, making the CLAIR framework more robust and adaptive over time.
• Appointing CLAIR Champions/AI Capability Stewards: To facilitate widespread adoption and provide localized support, institutions should consider appointing “CLAIR Champions” or “AI Capability Stewards” within different departments or international units. These individuals, deeply trained in the CLAIR framework, would mentor colleagues, facilitate the CLAIRvoyance Cycle within their teams, tailor CLAIR principles to specific departmental needs (e.g., how “Interculturality” applies differently in international marketing versus TNE academic quality assurance), and act as liaisons with the central AI governance body. This distributed leadership model can accelerate and deepen the framework’s impact.

7.3 Addressing Equity, Diversity, and Inclusion (EDI) in AI for IHE

AI technologies carry the risk of perpetuating or even amplifying existing biases if not developed and deployed thoughtfully. The CLAIR for IHE framework, particularly through its “Responsibility” and “Interculturality” pillars, provides a lens for proactively addressing these concerns:

• Bias Mitigation in IHE Systems: Institutions must use CLAIR principles to critically examine and mitigate potential AI biases in IHE processes such as international student admissions, scholarship allocation, student support services, and even in the AI tools used for teaching and learning. This is crucial to avoid disadvantaging international students or those from underrepresented backgrounds.¹⁵
• Equitable Access: Ensuring that all staff have equitable access to CLAIR-based training and the AI tools necessary for their roles is vital. Similarly, when AI is used in student-facing applications, considerations of equitable student access to technology and digital literacy support are paramount.

7.4 Continuous Adaptation and Future-Proofing

The field of AI is characterized by exceptionally rapid development. Therefore, the CLAIR for IHE framework and associated training programs must be conceived as living entities, designed for continuous adaptation and improvement:

• Monitoring AI Advancements: Institutions need a sustainable mechanism to monitor AI advancements, not just in terms of new software but, crucially, through the lens of evolving capabilities (e.g., as tracked by OECD updates ³) and emerging ethical challenges. This intelligence must inform curriculum revisions and training updates.
• Regular Framework and Curriculum Review: The CLAIR framework, its competency matrix (Table 1), learning modules, and assessment strategies (Table 2) must be regularly reviewed and updated. For example, if future OECD assessments indicate a significant advancement in AI’s “Social Interaction” capability, training for International Student Advisors using AI chatbots would need to be adjusted to reflect this new baseline and its implications.
• Cultivating a Culture of Lifelong Learning: Beyond formal training, institutions must foster a culture that encourages and supports continuous, self-directed learning about AI among staff, reinforcing the “Adaptability” pillar of CLAIR and the “Lifelong Adaptation” aspect of responsible AI stewardship.¹ This includes providing access to curated resources, webinars, and relevant professional networks.

By strategically embedding the CLAIR for IHE framework through committed leadership, robust governance, adequate resourcing, a focus on EDI, and a commitment to continuous adaptation, IHE institutions can build a truly AI-fluent workforce capable of navigating the complexities and harnessing the opportunities of the AI era for the betterment of global higher education.

8. Conclusion: Advancing Global Higher Education through CLAIR-Powered AI Fluency

The pervasive and accelerating influence of Artificial Intelligence presents both unprecedented opportunities and significant challenges for the International Higher Education sector. Effectively, ethically, and innovatively navigating this complex and rapidly evolving landscape demands a concerted, strategic, and institution-wide effort to cultivate widespread AI literacy and fluency among all IHE staff. This report has proposed the CLAIR for IHE framework—grounded in Capability, Literacy, Adaptability, Interculturality, and Responsibility—and its accompanying CLAIRvoyance Cycle methodology as a novel and tailored approach to meet this urgent need.

The CLAIR for IHE framework moves beyond generic AI literacy models by being fundamentally designed for the unique intricacies of the international higher education context. Its emphasis on understanding AI’s core Capabilities (informed by OECD indicators ¹) rather than transient tools, fostering foundational Literacy in AI concepts and ethics, cultivating Adaptability to AI’s rapid evolution and its impact on IHE roles, embedding Interculturality as a critical lens for AI engagement in diverse global settings, and championing Responsibility in AI governance and application, collectively offers a robust pathway for developing genuine AI fluency. The CLAIRvoyance Cycle provides a practical, iterative process for IHE professionals to internalize and apply these competencies, ensuring that learning is continuous, reflective, and deeply contextualized.

The detailed CLAIR Competency Matrix for IHE Professionals (Table 1) and the mapping of CLAIR components to learning modules and assessment strategies (Table 2) translate the framework into actionable guidance. They provide a blueprint for designing role-specific training, fostering individualized professional development, and measuring the growth of AI fluency in ways that are meaningful for the IHE sector.

The successful institutionalization of CLAIR for IHE hinges on strategic leadership, the development of ethical AI governance structures, adequate resource allocation for training and tools, a proactive approach to equity and inclusion in AI deployment, and an unwavering commitment to continuous adaptation. Addressing the “capability comprehension lag” ¹—the gap between the rapid adoption of AI and the development of informed, critical, and interculturally aware usage—is a key ongoing challenge that CLAIR for IHE is specifically designed to tackle.

Ultimately, achieving AI fluency is not a one-time accomplishment but an ongoing journey. The true value of the CLAIR for IHE framework lies in its potential to shift the International Higher Education sector from being a reactive consumer of AI technologies to a proactive shaper of ethical, equitable, and interculturally intelligent AI applications within a global context. By fostering deep capability understanding and critical, ethical, and intercultural engagement, CLAIR empowers IHE professionals not just to use AI, but to demand, co-create, and steward AI solutions that are genuinely fit for the complex and diverse purposes of international higher education. This represents a move beyond mere literacy towards active agency in the evolving AI ecosystem, allowing the IHE sector to influence AI development towards more responsible and globally beneficial ends.

In an increasingly competitive global higher education market, institutions whose staff are genuinely AI-fluent—understanding not just current tools but the trajectory of AI’s fundamental capabilities and how to navigate them interculturally and responsibly—will be distinctly advantaged. They will be better equipped to innovate, operate with greater efficiency and efficacy, personalize services to meet diverse stakeholder needs, and ultimately provide a superior educational experience and stronger global impact. The cultivation of AI fluency among staff, through a dedicated framework like CLAIR for IHE, is therefore not merely an internal development goal. It is a profound strategic investment in a future-ready, resilient, and relevant International Higher Education sector, capable of leading through profound technological change and shaping a more equitable, intelligent, and interconnected global future.

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