A Comparative Study of AI Readiness in Language Teacher Education in the Global South

A Comparative Study of AI Readiness in Language Teacher Education in the Global South | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Comparative Study of AI Readiness in Language Teacher Education in the Global South Syed Naeem Ahmed, Heena Amjad, Saira Abbas, Zarrina Salieva This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7614466/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract A growing body of global research supports the United Nations' Sustainable Development Goal 4, specifically Target 4.c, which emphasizes the importance of improving teacher quality through international cooperation and professional development. While Artificial Intelligence (AI) integration has garnered attention, language teacher education remains insufficiently addressed, underscoring the need for a dedicated framework to assess and enhance AI readiness in this field. Addressing this gap, the study utilizes Holmström's AI Readiness Framework to examine perceived and demonstrated readiness among language educators in Pakistan, Uzbekistan, and Saudi Arabia, exploring infrastructure, expertise, and institutional support. It identifies barriers and enablers to AI integration and proposes a replicable framework for contextually diverse teacher education systems. A two-phase mixed methods design was employed. In Phase 1, 400 language education professionals completed surveys, while focus group discussions were conducted with a purposive sample of 40 willing participants to explore contextual challenges in AI integration. Survey findings revealed the highest AI readiness in Saudi Arabia (M = 4.10), followed by Uzbekistan (M = 3.10) and Pakistan (M = 2.92). Readiness was correlated with infrastructure (r = 0.673), while faculty training explained 39.7% of the variance. Focus group discussions reinforced these findings, revealing ethical concerns, policy gaps, and limited training as persistent challenges. In Phase 2, 100 participants were stratified and completed a task-based AI readiness assessment. Strengths were noted in tool use and privacy awareness, while ethics and peer training were identified as areas for improvement. Recommendations include modular training, institutional readiness benchmarks, and localized policy reform. Future research should investigate the relationship between AI readiness and learner outcomes. AI in language teacher education generative AI tools for teaching educational technology adoption Holmström AI Readiness Framework technological readiness in education Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Artificial Intelligence (AI) is increasingly recognized as a transformative force in education, potentially addressing persistent challenges and innovating teaching and learning practices [ 29 , 26 ]. When properly integrated, AI can personalize instruction, provide instantaneous feedback, and act as a “co-intelligence” for teachers by enhancing their productivity and effectiveness [ 14 ]. These benefits are highly relevant in language education. AI-powered tools (e.g., intelligent tutoring systems, automated translators, and graders) can augment teachers’ capacity to support diverse learners. However, the rapid rise of AI also brings new risks and demands that have often outpaced educational policies [ 26 ]. International agencies stress that AI’s promise must be harnessed inclusively to avoid exacerbating existing inequities [ 29 , 26 ]. Access to AI-enhanced learning remains uneven, with well-resourced schools experimenting with chatbots and intelligent tutors. In contrast, many others, especially in developing countries, lack basic electricity or internet connectivity [ 26 ]. This stark digital divide raises questions about whether integrating AI into education is a privilege for a few. In low- and middle-income contexts, many teachers have limited digital and AI literacy [ 26 ], and their exposure to AI in teaching may be confined to news or social media rather than hands-on practice. Ultimately, the success of AI in education will depend on how well-prepared educators are to utilize these technologies effectively. Teacher education programs, therefore, play a pivotal role: they must equip current and future teachers with the competencies and conditions to harness AI, ensuring that "AI for all" includes all classrooms and learners. The present study investigates AI readiness in language teacher education within three multilingual, resource-constrained higher education settings: Pakistan, Uzbekistan, and Saudi Arabia. These countries represent contexts where English and other languages are taught concurrently, and educational institutions vary in their access to and use of technology. Multilingual environments add complexity to AI integration. For instance, AI tools often have limited capabilities in local languages, impeding their usefulness for non-English speakers. Meanwhile, resource constraints such as limited bandwidth, outdated hardware, and scarce funding can hinder even the most motivated educators. There is a dearth of empirical data on how such contexts prepare for AI in teacher education, as most research on AI in education has been concentrated in a few high-income countries, notably the United States and China [ 10 ] In contrast, low-income regions have produced relatively few studies on this topic, and little is known about the readiness of teacher education institutions in the Global South to embrace AI [ 10 , 25 ]. This study fills that gap through a cross-national analysis spanning South Asia, Central Asia, and the Middle East. Uniquely, the research was conceived and conducted through a grassroots collaboration among an international team of scholars from the three countries, ensuring on-the-ground insights that outside researchers might miss. The result is one of the first comparative datasets on teacher education and AI from these contexts, offering replicable evidence to inform both local practice and the broader discourse on AI in education. The following research questions guide the study: What is the current state of AI readiness among language teacher education professionals in Pakistan, Uzbekistan, and Saudi Arabia regarding digital infrastructure, faculty expertise, and institutional support? What is the current state of faculty readiness for AI in these language teacher education contexts, specifically regarding educators’ training, skills, and knowledge for integrating AI into teaching and teacher preparation? What forms of institutional support (policies, leadership initiatives, funding, technical support) exist for AI integration in language teacher education, and how do these compare across the three country contexts? Significance of the study This study makes novel contributions on three levels. Substantively, it provides some of the first systematic data on AI readiness in teacher education from Pakistani, Uzbekistani, and Saudi Arabian contexts, which are often underrepresented in the literature [ 10 ]. The findings identify key infrastructure, training, and policy gaps, offering guidance for aligning national strategies and external interventions with capacity-building needs. Methodologically, the cross-national, grassroots approach provides a replicable model for culturally responsive research. The instruments developed can be adapted for use in other multilingual, low-resource settings. Theoretically, the study refines Holmström’s AI readiness framework within the teacher education domain, revealing overlooked dynamics such as language diversity. It helps bridge the gap between global aspirations for AI and local educational realities. LITERATURE REVIEW Artificial Intelligence (AI) has emerged as a powerful tool, particularly in language teacher education. AI-powered technologies such as automated assessment systems, adaptive learning platforms, and intelligent tutoring systems have shown promise in enhancing teaching efficiency, supporting personalized learning, and improving student engagement [ 11 ]. However, while some studies highlight AI’s benefits in instructional efficiency [ 18 ], others caution that over-reliance on AI tools may undermine teacher autonomy and reduce pedagogical flexibility [ 23 ]. Global Alignment and Analytical Framework The global imperative to enhance teacher capacity is firmly grounded in Sustainable Development Goal 4 (SDG 4), which emphasizes inclusive and equitable education, as well as lifelong learning. Specifically, Target 4.c [ 24 ] calls for substantially increasing the number of qualified teachers through international cooperation and professional development initiatives, particularly in developing regions [ 29 ]. This priority is highly relevant for AI integration in teacher education, where institutional readiness must be matched by trained human capital. In countries such as Pakistan, Uzbekistan, and Saudi Arabia, SDG 4 serves as a normative benchmark for evaluating national education strategies in the context of global digital transformation goals [ 29 ]. While SDG 4 outlines the policy vision, the present study operationalizes this agenda through Holmström’s AI Readiness Framework. Initially developed for organizational digital transformation, this model provides a structured approach for evaluating institutional, technological, and human resource readiness for AI adoption. Its dimensions of strategic alignment, infrastructure, organizational capacity, and data governance have been adapted here to evaluate AI readiness within the context of language teacher education. This dual alignment with global development goals and analytical precision supports a robust, context-sensitive evaluation of AI preparedness across diverse educational systems. Theoretical Framework: AI Readiness Framework by Holmström The AI Readiness Framework by Holmström provides a structured approach to evaluating an institution’s capacity to integrate AI [ 12 ]. Unlike the Technology Acceptance Model (TAM) [ 8 ], which focuses on individual attitudes toward technology, or the SAMR model [ 20 ], which examines the stages of technology integration in classrooms, Holmström’s framework, as illustrated in Fig. 1 , offers a system-wide lens. It assesses institutional preparedness across four core dimensions: infrastructure, leadership, data strategy, and workforce. Initially applied in corporate automation contexts, the framework has gained relevance across various sectors seeking to scale AI innovation. Its emphasis on organizational culture and capability is a valuable foundation for education-focused adaptation. Figure 1 illustrates the four key aspects of the AI Readiness Framework: Infrastructure Readiness, Leadership and Vision, Data and AI Strategy, and Workforce Capabilities. The funnel shape illustrates how these key variables are interconnected, highlighting that these dimensions are crucial, with resources serving as the foundation and workforce training as the pinnacle for AI integration. Holmström originally applied the framework in business settings to guide the adoption of enterprise-level AI [ 12 ]. However, AI readiness in education requires additional considerations, including faculty development, ethical standards, and the pedagogical applicability of AI. This study extends Holmström’s model by adapting it for language teacher education in Pakistan, Uzbekistan, and Saudi Arabia. The revised model collapses the original four dimensions into three integrated domains more applicable to multilingual teacher education: technological infrastructure, faculty expertise and training, and institutional support. As illustrated in Fig. 2 , this adapted framework maintains the holistic perspective of Holmström’s model [ 12 ] but foregrounds education-specific enablers and constraints. Figure 2 is an adapted framework that guides the development of the study’s conceptual model. This model conceptualizes AI readiness in language teacher education as the dynamic interplay among infrastructure, human capital, and institutional environment. These three domains are explored in depth below. Technological Infrastructure Infrastructure, internet connectivity, hardware, digital content platforms, and access to AI-enabled tools are foundational to any successful AI integration. In teacher education, where the practical application of technology in pedagogy is emphasized, inadequate infrastructure can derail even the most well-intentioned digital innovation. The U.S. Department of Education identified infrastructure gaps as a significant bottleneck in AI readiness across several countries, particularly in rural and underserved regions [ 27 ]. In Pakistan and Uzbekistan, for instance, outdated computing facilities and inconsistent internet access have hindered educators' ability to adopt AI tools effectively [ 1 ]. In contrast, investment in educational infrastructure as part of Saudi Arabia's Vision 2030 has increased AI readiness, enabling greater utilization of generative AI in higher education [ 3 ]. However, investment in infrastructure does not guarantee success on its own. In particular, highly multilingual environments require linguistic support from teachers, translation technology, localized interfaces, and adaptive hardware and software that function effectively under restricted bandwidth. Strategic investments such as solar-powered labs, mobile AI learning kits, and hybrid offline platforms mitigate infrastructural barriers in low-resource settings. Faculty Expertise and Training Educator preparedness is the second pillar of AI readiness. Educators’ digital literacy, which encompasses their ability to utilize AI tools, apply them pedagogically, and manage AI-enhanced learning environments, is crucial for the meaningful integration of AI into instruction [ 32 ]. Sustained professional development significantly enhances teachers’ willingness to engage with AI, whereas one-off workshops often yield limited, short-term gains. Nevertheless, access to such training remains uneven across countries and institutions [ 19 ]. Reports that many rural educators have little to no exposure to AI tools, and where training exists, it is often generic rather than tailored to subject-specific or contextual needs [ 5 ]. While new graduates may be familiar with tools like ChatGPT, their ability to apply them critically and ethically in language instruction remains underdeveloped [ 2 ]. Practical faculty training must also consider the specific challenges of multilingual classrooms. AI translation tools, for example, often fall short in handling regional dialects or non-Western languages, requiring teachers to exercise discretion and adaptation [ 4 ]. Developing structured, context-specific faculty training is crucial for the meaningful and ethical integration of AI in language teacher education [ 30 , 31 ]. Teachers must operate AI tools and understand when and how to use them pedagogically, addressing biases, ethical concerns, and technical limitations [ 11 ]. AI risks becoming a superficial add-on rather than a transformative force in language teacher education without targeted, continuous training. Institutional Support The third core factor in AI readiness is institutional support. Even the most technologically equipped and well-trained educators need enabling environments to integrate AI sustainably. Institutional support encompasses leadership vision, policy coherence, resource allocation, and mechanisms for monitoring and sustaining innovation. When aligned with bottom-up pedagogical innovation, [ 7 ] highlights that top-down strategic leadership creates the most fertile ground for AI integration. In Saudi Arabia, institutional support has played a crucial role in this endeavor. Governmental and university-level strategies have provided funding, established AI research centers, and included AI-related competencies in faculty performance metrics [ 3 ]. Conversely, in Pakistan and Uzbekistan, institutional support remains inconsistent. [ 1 ] While some urban institutions pilot AI initiatives, many lack formal policies, guidelines, or support structures, resulting in fragmented and isolated efforts. Rakha notes the absence of strategic direction for AI in teacher education across many Uzbekistani institutions until recently [ 22 ]. Furthermore, institutional support must also involve ethical frameworks, transparency protocols, and professional incentives. Without guidelines on data privacy, algorithmic bias, or vetting AI tools, educators are left vulnerable to misapplication or liability [ 17 , 6 ]. [ 11 ] Stress that institutions must provide access and training and uphold ethical standards that align with inclusive, learner-centered education. Regional Literature and Collaboration Rationale While there has been significant global interest recently, studies measuring AI readiness in language teacher education are lacking in Pakistan, Uzbekistan, and Saudi Arabia [ 3 , 21 ]. Within Saudi Arabia, emerging research is examining the role of AI in education and Vision 2030, with a focus on higher education and its digital transformation [ 10 ]. There have been limited studies from Pakistan, aside from some theoretical work and general digital learning, which do not pertain significantly to language teacher training or multilingual contexts [ 22 ]. The same applies to Uzbekistan, where there appears to be less research on AI in education, highlighting the early stage of policy development and academic interest in AI [ 9 ]. The gaps in these regions motivated the authors, who reside in one of the three countries, to encourage cross-national collaboration through an international WhatsApp research group. To provide contextual knowledge and utilize their regional perspectives, the authors formulated a study that addresses the realities of AI usage in teaching. Before drafting the ideas, the group analyzed the country’s educational policymaking documents, strategic plans for higher education, and other relevant materials related to digital learning to ensure that they accurately reflected the current realities. This collaboration has produced a unique dataset that aims to fill the existing research gap and is designed to be replicable through longitudinal and cross-regional methodologies. Synthesis Technological infrastructure, faculty expertise and training, and institutional support form the foundation of AI readiness in language teacher education. These factors are mutually reinforcing: without reliable infrastructure, even the most well-trained educators are constrained; without training, technology remains underutilized; and without institutional backing, innovation lacks sustainability. These dynamics are particularly acute in multilingual and under-resourced contexts, where educational equity, linguistic diversity, and economic disparity intersect with digital transformation. Although some recent studies have begun exploring AI in teacher education, most are limited in scope, geographically concentrated, or focused on short-term impacts [ 28 , 29 ]. This study addresses these limitations by offering a comparative, multi-country perspective rooted in the lived experiences of educators and institutions. As such, it contributes to theoretical understanding and practical strategies for building AI readiness in the most needed contexts. METHODOLOGY A two-phase convergent mixed-methods approach was adopted to address the research questions comprehensively. Phase 1 included quantitative surveys to provide breadth and comparability across Pakistan, Uzbekistan, and Saudi Arabia, complemented by qualitative focus groups that yielded depth on contextual challenges and supports. The design was explicitly linked to the study’s guiding factors and research questions. Each research question targets one of the three AI readiness factors across the three national contexts, ensuring that the methodology directly examines technological infrastructure, faculty expertise and training, and institutional support in language teacher education. A methodological matrix (Table 2 ) was developed to map each research question to the data sources, sample, and analysis techniques, providing a clear rationale for how the mixed methods approach answers the study's aims. Combining methods ensures that statistical trends can be explained with contextual evidence, strengthening the validity of findings through triangulation. Phase 2 was introduced to mitigate the limitations of self-reported survey data in Phase 1. It employed a Task-Based Instrument (AIR-TBI) to assess observable competencies in AI integration, ethical reasoning, and institutional awareness. A stratified subsample of 100 participants was drawn from the original pool of 400 survey respondents. Stratification was based on three criteria: country (Pakistan, Uzbekistan, Saudi Arabia), type of institution (public university, private university, teacher training institution), and professional role (such as lecturer, professor, or educational technologist). This ensured balanced and proportionate demographic representation across key variables. This approach increased the reliability of the task-based results while maintaining comparability with Phase 1 findings. Participants and Sampling The target population for this study was professionals involved in English language teacher education and related educational technology roles in Pakistan, Saudi Arabia, and Uzbekistan. Participants were selected through purposive sampling to include those knowledgeable about AI integration in education. Inclusion criteria required individuals to have at least two years of experience teaching English with AI-enhanced tools or in educational policymaking for teacher training; respondents with no AI-related experience were excluded. Initially, approximately 532 invitations were distributed. Due to non-response and incomplete data rate, the final sample comprised 400 participants (after omitting ~ 132 incomplete responses). These 400 respondents included 111 from Pakistan, 102 from Uzbekistan, and 187 from Saudi Arabia, reflecting a broad cross-section of the three contexts. Data were collected in English through an online English survey (using Google Forms) to ensure all participants understood the questions clearly. Tables 1 and 2 below summarize the participants' demographics for Phase 1, while Table 3 summarizes the participants’ demographics for Phase 2. This diverse representation, encompassing teacher educators (lecturers and professors), AI researchers, data scientists, and ed-tech specialists, enabled a comprehensive assessment of AI readiness from both pedagogical and technological perspectives within language teacher education. Table 1 Participant Demographics (N = 400) Variable Category Count Percentage (%) Country Saudi Arabia 187 46.8 Pakistan 111 27.8 Uzbekistan 102 25.5 Gender Male 210 52.5 Female 175 43.8 I prefer not to say 15 3.8 Age Group 20–30 66 16.5 31–40 132 33.0 41–50 121 30.3 51 and above 81 20.3 Experience 5–10 years 154 38.5 11–15 years 124 31.0 More than 15 years 122 30.5 Education Level Master’s 152 38.0 PhD [1] 248 62.0 Institution Type Public University 192 48.0 Private University 133 33.3 Teacher Training Institution 75 18.8 Table 2 Participant Roles by Country Country Total (N) AI Researcher Data Scientist Doctor of Education Educational Technologist Lecturer Professor Pakistan 111 16 18 23 20 16 18 Saudi Arabia 187 30 20 38 39 33 27 Uzbekistan 102 21 15 15 16 20 15 Most participants were faculty in higher education or teacher training institutes, ensuring relevance to language teacher education contexts. In addition to the survey respondents, several participants were invited for follow-up qualitative sessions. To gain deeper insights, focus group discussions were conducted in each country, with one per country and 12–14 participants each, totaling 40 participants. These focus group participants were selected from the survey pool based on their willingness to assume various roles and experiences, enriching the qualitative data with diverse perspectives. In Phase 2, a stratified subsample of 100 participants was selected from the original 400 respondents to complete the AI Readiness Task-Based Instrument (AIR-TBI). Stratification was based on country (30 from Pakistan, 30 from Uzbekistan, and 40 from Saudi Arabia), institutional affiliation (public university, private university, or teacher training institution), and academic role (e.g., lecturer, professor, educational technologist). This ensured proportional and diverse representation across key demographic variables. Participants completed structured tasks to assess their practical use of AI, ethical reasoning, and engagement with institutional policies. The resulting performance-based data validated and enriched the perceptual trends identified in Phase 1. Table 3 Participant Demographics (Phase 2, N = 100) Variable Category Count Percentage (%) Country Saudi Arabia 40 40.0 Pakistan 30 30.0 Uzbekistan 30 30.0 Gender Male 50 50.0 Female 48 48.0 I prefer not to say 2 2.0 Age Group 20–30 17 17.0 31–40 35 35.0 41–50 31 31.0 51 and above 17 17.0 Experience Less than 5 years 15 15.0 5–10 years 35 35.0 11–15 years 28 28.0 More than 15 years 22 22.0 Education Level Bachelor’s 10 10.0 Master’s 42 42.0 PhD 46 46.0 Postgraduate Diploma 2 2.0 Institution Type Public University 50 50.0 Private University 30 30.0 Teacher Training Institution 15 15.0 Community College 5 5.0 Role AI Researcher 11 11.0 Doctor of Education 23 23.0 Educational Technologist 14 14.0 Lecturer 34 34.0 Professor 18 18.0 The study used a structured 31-item survey in Phase 1 to assess AI readiness across three primary dimensions: digital infrastructure, faculty expertise, and institutional support. The availability of AI-related tools, internet access, and institutional and technological support measured digital infrastructure. Faculty expertise was assessed through indicators such as professional development, self-reported proficiency, and perceived relevance of AI in language education. Institutional support encompassed items such as AI policies, leadership involvement, and funding availability. The survey employed a five-point Likert scale, ranging from "strongly disagree" to "agree strongly." It was piloted with 10 educators from each country to ensure clarity and contextual appropriateness, with a Cronbach’s alpha of 0.89 indicating high internal consistency. In Phase 2, participants completed the AI Readiness Task-Based Instrument (AIR-TBI), designed to generate evidence-based performance data across three readiness domains: (1) technical and pedagogical competence, (2) ethical and reflective judgment, and (3) institutional and collaborative readiness. The instrument included eight structured tasks: designing an AI-integrated lesson plan, responding to ethical dilemmas, evaluating AI tools, and describing institutional policies. Each response was scored on a rubric from 0 (no evidence) to 2 (competent demonstration). This approach mitigated social desirability and self-report bias by anchoring analysis in observable educator behaviors. Both survey and AIR-TBI responses were collected through online forms, with Phase 2 participants given two weeks to complete the task-based instrument. Clear guidelines and examples were provided to ensure consistent interpretation of task requirements. Participants in both phases were informed of their rights and provided consent for their participation to be anonymous. To complement the quantitative findings, focus group discussions were conducted with a subset of 12 to 14 participants from each country, totaling 40 educators. These discussions aimed to explore perceptions, challenges, and institutional perspectives on integrating AI. Focus group discussion guides were developed, covering themes such as perceived barriers to AI adoption, institutional readiness, leadership support, and opportunities for collaboration with industry and government. The discussions were recorded, transcribed, and coded thematically using NVivo software to ensure systematic analysis. Data Analysis The study employed a multi-phase data analysis strategy to reflect the dual design. In Phase 1, descriptive statistics were used to calculate country-wise means and standard deviations of AI readiness, assessing central tendency and variation. Pearson correlation coefficients were calculated to evaluate relationships between AI readiness and key variables such as digital infrastructure and faculty training access. For example, the analysis examined whether institutions with stronger infrastructure reported higher AI readiness. One-way ANOVA was performed to identify statistically significant differences in AI readiness across the three countries. These analyses were conducted using SPSS 27, ensuring statistical rigor and reliability. In Phase 2, quantitative scores from the AIR-TBI were analyzed descriptively to assess participant performance on each of the eight tasks. Mean scores were calculated by task and domain, allowing for cross-task comparison and identification of regional patterns. A composite AI readiness score (out of 16) was generated for each participant. Group comparisons by country and demographic factors were conducted using ANOVA and cross-tabulation, while correlation analysis explored relationships between AIR-TBI performance and self-reported Phase 1 responses. This result enabled validation of Phase 1 trends through performance-based evidence. For the qualitative component, thematic analysis was employed to analyze focus group transcripts and open-ended needs assessments, utilizing NVivo 12 software for data management and coding. An inductive coding approach allowed themes to emerge organically, later structured according to the study's conceptual framework. Four researchers conducted and cross-verified coding, achieving an intercoder agreement of 85%. Key themes included Barriers to AI Implementation, Institutional Policy and Support, and Perceived Benefits of AI. Cross-national insights highlighted infrastructural gaps in Pakistan and Uzbekistan, as well as strategic implementation challenges in Saudi Arabia. Integrating survey metrics, task-based scores, and qualitative themes offered a multidimensional view of AI readiness, reinforcing the validity of findings through methodological triangulation. Replicability The methodology provides high replicability because all instruments, including the survey and focus group guides, are uniform and standardized. Other researchers may employ different contextual strategies by adjusting specific variables to measure AI responsiveness in various educational environments. Moreover, this study unapologetically presents evidence of AI responsiveness in language teacher education by applying a detailed mixed-methods approach, thus supporting educators, policymakers, and institutional decision-makers with meaningful data. RESULTS This section presents the study's findings and is organized into two phases, corresponding to the dual-method research design. Phase 1 reports the results from the quantitative Likert-scale survey and qualitative focus group discussions conducted with 400 participants across Pakistan, Uzbekistan, and Saudi Arabia. This phase focuses on self-reported perceptions of technological infrastructure, faculty expertise, and institutional support. Phase 2 introduces performance-based data from a stratified subsample of 100 participants who completed the AI Readiness Task-Based Instrument (AIR-TBI). This instrument assessed participants' competencies in integrating AI tools, navigating ethical dilemmas, and engaging with institutional support mechanisms. The two phases provide perceptual and behavioral insights into AI readiness in language teacher education across the three national contexts. Phase 1 - Quantitative Findings The composite AI Readiness Index was computed by integrating infrastructure (35%), faculty training (40%), institutional support (15%), and perceived ease of integration (10%). Results revealed cross-country disparities, with Saudi Arabia achieving the highest readiness score (M = 4.10), followed by Uzbekistan (M = 3.10) and Pakistan (M = 2.92). Figure 3 presents a boxplot illustrating the distribution of AI readiness scores within each country. Saudi Arabia shows the least variance, indicating consistency in institutional AI adoption efforts, while Pakistan shows the widest variance. Figure 3. Distribution of AI Readiness Scores by Country This is a boxplot showing country-level variation in AI readiness scores among participants. Saudi Arabia shows the least variance, while Pakistan shows the highest. Further analysis revealed a strong positive correlation between infrastructure availability and AI readiness (r = 0.673), affirming that infrastructural sufficiency underpins institutional readiness. Faculty training alone accounted for 39.7% of the variance in readiness scores (R² = 0.397), underscoring its central role. Conversely, infrastructure and faculty training exhibited a weak correlation (r ≈ 0.06), suggesting that the availability of resources does not automatically translate into faculty preparedness. Survey responses also revealed country-specific contrasts in access to AI-related training: over 70% of Saudi participants reported receiving formal training, compared to just under 40% in Uzbekistan and less than 30% in Pakistan. This discrepancy aligns with observed differences in institutional investment and strategic policy orientation. Infrastructure-related findings are summarized in Table 4 , which presents the average scores across six readiness indicators. As shown in Table 4 , Saudi Arabia reported the strongest internet access, while Uzbekistan led in computing and software resources. Pakistan consistently scored the lowest across most infrastructure indicators, particularly internet reliability. Table 4 Infrastructure Readiness by Country Country Reliable Internet Modern Computing AI Software Tech Support AI Labs Infra Sufficiency Pakistan 2.50 2.96 2.76 2.90 2.89 2.95 Saudi Arabia 4.20 2.97 3.10 2.95 3.10 2.96 Uzbekistan 2.70 3.21 3.19 2.92 2.96 3.05 Saudi Arabia stands out in reliable Internet (M = 4.20), while Uzbekistan performs better in AI software availability and modern computing. Pakistan lags in nearly all indicators, particularly in terms of reliable internet access. Table 5 Faculty Training by Country Country Training Confidence Workshops Use AI Belief Access Collaboration Pakistan 2.83 3.04 2.99 3.09 3.06 2.68 2.76 Saudi Arabia 2.88 2.87 2.93 2.98 2.78 3.04 2.94 Uzbekistan 3.10 3.06 3.16 3.07 3.03 3.43 3.20 Faculty training scores are detailed in Table 5 . Uzbekistan scored highest on most indicators, particularly access to AI courses and peer collaboration, while Pakistan reported the lowest averages in training access and peer collaboration. Institutional support, summarized in Table 6 , reveals Saudi Arabia as the strongest in policy development, while Uzbekistan performs well in access to AI resources. Saudi Arabia is perceived as having the most established AI policies (M = 3.46), while Uzbekistan stands out in AI resource access. Pakistan lags in policy and resource indicators, reflecting challenges in formal institutional backing for AI efforts. Pakistan scored lowest in nearly all institutional support indicators, including policy presence and funding. These patterns are highlighted in Table 6 . Table 6 Institutional Support Country Policies Leadership Funding Industry Research Incentives Resources Pakistan 2.65 2.77 2.97 3.06 3.06 2.96 2.70 Saudi Arabia 3.46 2.60 3.04 2.91 3.08 3.00 3.06 Uzbekistan 3.15 2.71 3.02 2.99 2.93 2.93 3.30 Figure 4 reports perceived barriers to AI adoption. Pakistan ranks highest in resistance and financial concerns, while Uzbekistan reports heightened ethical and policy-related barriers. Policy clarity and ethical concerns emerge as critical challenges in Uzbekistan (M = 3.18 and 3.33, respectively), while Pakistani educators report heightened resistance to AI and significant financial barriers. Overall, perceived barriers remain above average in all three countries, with Pakistan experiencing the most pronounced challenges, warranting targeted interventions. To complement these quantitative patterns, the following section presents qualitative findings that delve deeper into participants' lived experiences and the contextual challenges they face. Phase 1 - Qualitative Findings The focus group analysis involved 40 participants from Pakistan, Saudi Arabia, and Uzbekistan. Thematically coded data revealed four dominant themes: barriers to AI adoption, institutional and policy support, ethical concerns, and cross-border opportunities. Their frequency is summarized in Table 7 . Table 7 Frequency of Themes in Focus Group Data Theme Frequency Barriers to AI Adoption 12 Institutional and Policy Support 11 Ethical Concerns 9 Cross-Border Opportunities 8 Barriers to AI Adoption Participants across Pakistan and Uzbekistan expressed acute infrastructural challenges, including inconsistent electricity and outdated computing hardware. Faculty often lacked access to AI tools and reported little institutional support for professional development. Educators in rural areas highlighted that limited bandwidth and unreliable Internet disrupted classroom experimentation with AI platforms. Faculty hesitation was a cross-cutting barrier, particularly from inadequate training and fears of role replacement. One Uzbek lecturer (FG005) remarked, " Having faced a Lack of Training directly, I believe institutional inertia in Uzbekistan is a core part of the issue .” A Pakistani participant (FG017) shared, “ In my experience as a Professor, Outdated Devices limit the potential impact of AI in our educational context .” A Saudi respondent (FG002) added, “ From an administrative and instructional standpoint, Unstable Internet continues hindering progress in Saudi Arabia. Although we have labs, many teachers resist using AI because they feel unprepared .” Institutional and Policy Support Institutional readiness emerged as a powerful enabler. Multiple participants cited Saudi Arabia’s Vision 2030 as the backbone of the country’s proactive approach to AI in education. Educators noted the presence of well-funded AI labs, leadership incentives, and structured training programs. One Saudi lecturer (FG008) observed, “ It is evident that Inconsistent Leadership is not just a technical hurdle, but also a cultural one here in Saudi Arabia. ” One Pakistani respondent (FG015) shared, “ It is evident that Supportive Administration is not just a technical hurdle, but also a cultural one here in Pakistan. ” An Uzbek participant (FG027) commented, “ Our department keeps hitting walls due to Funding Gaps; this is something I have brought up repeatedly. There is enthusiasm at the ministry level, but institutions do not feel that support trickling down. ” Ethical Concerns Across all three countries, faculty raised ethical questions about fairness, transparency, and student data privacy. A Saudi educational technologist (FG031) stated, "Our department keeps hitting walls due to Privacy Concerns; this is something I have brought up repeatedly." A respondent from Uzbekistan (FG010) highlighted, "My experience in Uzbekistan suggests that Bias in AI Tools remains a major stumbling block to advancing AI." In Pakistan, one lecturer (FG013) observed, "There is an ongoing conversation about Privacy Concerns in our institution, and as a Lecturer, I find it unresolved. ” We fear standard AI models will wipe out the cultural nuances we embed in our lessons.” Cross-Border Opportunities Despite barriers, focus group discussions reflected a shared desire for regional collaboration. Participants advocated for AI learning communities, faculty exchange programs, and shared resource platforms tailored to the linguistic and educational contexts of Pakistan and Uzbekistan. A Saudi professor (FG004) suggested, " Saudi Arabia has great research opportunities, often reflecting deep institutional gaps. Also, Saudi Arabia is one of the best in infrastructure. ” A Pakistani participant (FG038) added, “ There is an ongoing conversation about Collaborative Research Opportunities in our institution, and as an AI Researcher, I find it unresolved .” An Uzbek educator (FG022) remarked, “ Overcoming Collaborative Research Opportunities is essential to make meaningful AI integration possible in Uzbekistan. ” Many participants envisioned a regional AI knowledge hub that would bridge infrastructure, capacity, and strategy gaps. A regional AI knowledge hub that could bridge gaps in infrastructure, capacity, and strategy. Phase 2: Performance-Based Assessment (AIR-TBI) Phase 2 evaluated educators’ competencies using the AI Readiness Task-Based Instrument (AIR-TBI), completed by a stratified subsample of 100 participants. The instrument comprised eight structured tasks designed to assess three domains of AI readiness: technical and pedagogical competence, ethical judgment, and institutional engagement. Each task was scored on a rubric from 0 (no evidence) to 2 (competent demonstration), yielding a maximum possible total score of 16. The average total AIR-TBI score across participants was 10.51 (SD = 2.77), indicating a moderate to high level of demonstrated AI readiness. Task-level analysis revealed differentiated performance across the eight tasks: The highest average scores were observed in Task 2: Tool Selection (M = 1.49, SD = 0.59) and Task 7: Privacy Awareness (M = 1.43, SD = 0.64), indicating that participants were generally competent in selecting appropriate AI tools and acknowledging ethical considerations related to data protection. Strong performances were also found in Task 4: Multilingual Adaptation (M = 1.39) and Task 8: Policy Awareness (M = 1.38), reflecting awareness of localized teaching contexts and institutional dynamics. Task 1: AI-Integrated Lesson Planning (M = 1.33) showed moderate competency, while relatively lower scores were recorded in Task 3: Ethics (M = 1.20), Task 5: Comparative Evaluation (M = 1.15), and Task 6: Peer Training (M = 1.14), indicating areas where deeper reflection or pedagogical articulation may be needed. Task Performance by Gender and Role Figure 5 presents a comparison of task-based scores by gender. Female participants demonstrated higher mean scores across most tasks, particularly in ethical awareness (Task 3) and multilingual adaptation (Task 4), suggesting stronger reflection and contextual sensitivity. Male participants scored marginally higher on technical tasks, such as AI lesson planning (Task 1) and tool selection (Task 2). In contrast, responses from those who preferred not to disclose their gender showed the most significant variability, likely due to the low sample size. Figure 6 compares task-based scores across professional roles. Lecturers and professors outperformed other roles on tasks related to lesson planning, AI tool comparison, and institutional policy awareness, reflecting their direct involvement in curriculum design and policy interpretation. AI researchers demonstrated strong performance in tool selection (Task 2) but achieved relatively lower scores in peer training (Task 6), possibly indicating a more individualized approach to engaging with AI tools. Educational technologists performed consistently across all tasks but did not excel in any specific domain, whereas Doctors of Education demonstrated balanced competencies, particularly in ethical and institutional dimensions. These disaggregated findings provide essential insights for tailoring professional development. Programs can be designed to address role-specific gaps, such as improving collaborative teaching among researchers and advancing policy literacy among technologists, while also encouraging gender-equitable access to technical and reflective training. DISCUSSION Interpreting AI Readiness in Light of Global and Analytical Frameworks This study extends existing knowledge by interpreting AI readiness in teacher education through Holmström’s AI Readiness Framework [ 12 ] and the development-oriented lens of SDG 4, notably Target 4.c on teacher training [ 29 ]. While the results have established national variations in infrastructure, training, and institutional support, these variations are most significant in what they reveal about system-level preparedness. Holmström’s framework facilitated a structured readiness analysis across infrastructure, human capital, and strategic alignment. The framework was valuable in identifying national strengths, such as Saudi Arabia’s policy coherence and institutional investment, as well as cross-cutting vulnerabilities, particularly limited faculty preparedness and gaps in ethical engagement. At the same time, the study revealed that applying this model to multilingual, under-resourced educational contexts necessitates adaptation. Digital language literacy, access to localized AI tools, and clarity on institutional AI policy emerged as critical dimensions beyond those emphasized in the original model. Framing these insights within the Sustainable Development Goals highlights their global relevance. Target 4.c calls for enhanced teacher capacity through international cooperation [ 29 ]. This study contributes to that call by offering a scalable model for evaluating AI readiness in diverse contexts grounded in perception-based and performance-based evidence. It also demonstrates that AI integration cannot depend solely on infrastructure; it requires sustained faculty development, ethical competence, and institutional alignment [ 32 ]. By merging a global framework with empirical data from the Global South, the study helps bridge aspirational policies with educational realities. It affirms that readiness must be understood as access or awareness, as well as the institutional and pedagogical capacity to use AI meaningfully and ethically in teacher education. Interpreting Disparities in AI Readiness The most evident disparity emerged in overall readiness scores, with Saudi Arabia scoring highest (M = 4.10), followed by Uzbekistan (M = 3.10) and Pakistan (M = 2.92). These differences reflect uneven implementation of digital transformation policies and varying degrees of faculty preparation. Saudi Arabia’s score aligns with Vision 2030, emphasizing the structured integration of AI in higher education. Respondents there reported more consistent access to infrastructure and institutional support. In contrast, Pakistani and Uzbekistani educators cited outdated computing facilities, poor internet connectivity, and limited institutional encouragement. Although Uzbekistan outperformed Pakistan on most infrastructure indicators (Table 5 ), the overall readiness remained constrained by training deficiencies and fragmented policies. These findings are consistent with [ 13 , 15 ], who emphasize that systemic barriers and a lack of localized policy adaptations often limit the adoption of AI, despite technological availability. Faculty Training: A Decisive Factor Our regression analysis confirmed that faculty training explained 39.7% of the variance in AI readiness scores. Access was not evenly distributed despite similar training needs: over 70% of Saudi respondents had received formal training, while only ~ 40% in Uzbekistan and ~ 30% in Pakistan reported the same. The qualitative data further reinforced this finding. One Pakistani professor stated, “ Outdated devices limit the potential impact of AI in our educational context ” (FG017), while an Uzbek lecturer noted, “ Lack of training directly contributes to institutional inertia ” (FG005). These statements reflect how resource gaps and inadequate capacity-building measures suppress meaningful AI adoption. This aligns with [ 16 ], who found that AI literacy programs significantly improved student and teacher confidence, ethical awareness, and usage efficacy. The Infrastructure Readiness Disconnect Although infrastructure and AI readiness were strongly correlated (r = 0.673), we found a notably weak correlation (r ≈ 0.06) between infrastructure and faculty training. This suggests that institutions might invest in infrastructure without adequate parallel investments in human development. With its integrated policy design, Saudi Arabia achieved alignment across both dimensions; however, Pakistan and Uzbekistan exhibit a disconnect between access and utility. As one Saudi respondent emphasized, “ Although we have labs, many teachers resist using AI because they feel unprepared ” (FG002). This underscores the importance of coupling infrastructure with pedagogical support and professional development. [ 14 ] Stress that readiness is not merely a technical issue but depends on synchronized institutional strategies, training, and leadership. Institutional Support and Policy Coherence The disparity in AI policy support was stark. Only 22% of Pakistani respondents affirmed the existence of AI-related policies, compared to 61% in Saudi Arabia and 47% in Uzbekistan. Focus group participants frequently cited policy ambiguity and leadership inconsistency. An Uzbek participant noted, “ Funding gaps and unclear leadership stall our AI integration efforts ” (FG027). Conversely, structured programs in Saudi Arabia have encouraged adoption through funding, training incentives, and performance benchmarks. These findings align with the arguments made [ 13 ], which advocate for a clear national strategy that empowers institutions and reduces ambiguity surrounding the implementation of AI. Ethical Tensions and Regional Realities A recurring theme was Ethical concerns regarding privacy, bias, and overdependence on foreign tools. Educators from Pakistan and Uzbekistan felt that AI models lacked linguistic sensitivity and transparency. One Uzbek respondent warned, “ Bias in AI tools remains a major stumbling block to advancing AI ” (FG010), while a Pakistani lecturer expressed concern about “ Privacy concerns that remain unresolved ” (FG013). These issues highlight the need for ethical governance and culturally responsive AI design. According to [ 14 ], AI literacy should encompass both moral and social dimensions, enabling faculty and students to identify and address risks proactively. Opportunities for Regional Collaboration Despite these challenges, educators from all three countries called for increased cross-border cooperation. Many advocated for AI training hubs, multilingual toolkits, and collaborative research platforms. As one Saudi professor suggested, “ Saudi Arabia has great research opportunities and is a regional leader in infrastructure ” (FG004). Educators envisioned a regional knowledge hub to pool resources, share strategies, and collectively build AI capacity. Such initiatives align with the call [ 13 ] for multi-institutional, multilingual, and cross-regional partnerships to promote the equitable and sustainable adoption of AI in diverse education systems. Integrating Task-Based Assessment: Addressing Gaps in Self-Reported Readiness The inclusion of Phase 2 in this study was designed to overcome the limitations of self-reported data in Phase 1 by providing direct evidence of AI integration competencies. While the Likert-scale surveys offered broad insights into perceptions, they could not capture actual skill application or ethical reasoning. The AIR-TBI was administered to a stratified subsample of 100 participants to address this gap, assessing performance across eight tasks mapped to technical, reflective, and institutional AI readiness. Each task was scored on a 3-point scale: 0 = No evidence of competence, 1 = Partial demonstration of competence, and 2 = Full demonstration of competence. While the variables measured in Phase 2 differ in structure from those in Phase 1, this divergence is intentional. The task-based format complemented survey findings by triangulating observed behaviors against self-reported perceptions. This phase significantly enhanced the study’s validity. The performance-based results generally reinforced Phase 1 trends- for example, educators in Saudi Arabia, who reported the highest perceived readiness, also performed well in institutional awareness and tool selection. However, the task-based results also exposed notable discrepancies, especially in ethical judgment (Task 3), peer training (Task 6), and comparative evaluation (Task 5), where many participants scored only moderately. These results suggest that belief in AI's potential does not always translate into demonstrated readiness in applied teaching contexts. The gender-disaggregated analysis further contextualized the findings. Female educators slightly outperformed male participants in reflective and collaborative tasks such as Ethics (1.25 vs. 1.16) and Comparative Evaluation (1.19 vs. 1.12), aligning with Phase 1 focus group themes that emphasized a more cautious and inclusive approach among female educators. However, male educators scored higher in Privacy Awareness (1.50 vs. 1.40) and Peer Training (1.16 vs. 1.08), indicating greater confidence in handling data and providing collegial support. These nuances underscore that gendered patterns in AI readiness are not unidirectional and vary by task type. Role-based comparisons offered further insight. Lecturers, who formed the largest role group in the sample, exhibited the highest average performance across most tasks, particularly in Lesson Planning and Institutional Policy awareness. Professors also scored strongly, especially in Multilingual Adaptation and Tool Evaluation, likely due to their broader curriculum responsibilities. Doctor of Education performed consistently across most tasks, demonstrating balanced readiness in both pedagogical and ethical domains. Educational Technologists performed strongly in technical tasks such as Tool Selection and Privacy Awareness, but were less confident in ethics and peer-related tasks. While excelling in Tool Selection, AI Researchers scored relatively low in tasks involving collaboration or contextual adaptation, such as Peer Training and Comparative Evaluation. These patterns suggest that AI readiness is influenced by training, access, and role-specific orientations and responsibilities. The table below presents mean task scores by gender (out of a maximum score of 2 per task): (see Table 8 ). Table 8 Mean AIR-TBI Task Scores by Gender Task Female Male I prefer not to say Task 1: Lesson Plan 1.35 1.32 1.00 Task 2: Tool Selection 1.52 1.44 2.00 Task 3: Ethics 1.25 1.16 1.00 Task 4: Multilingual 1.40 1.40 1.00 Task 5: Comparison 1.19 1.12 1.00 Task 6: Peer Training 1.08 1.16 2.00 Task 7: Privacy 1.40 1.50 0.50 Task 8: Policy Support 1.40 1.38 1.00 These disaggregated findings affirm that Phase 2 was instrumental in surfacing practical differences in readiness that were not captured through self-report. To conclude the discussion, the following section outlines how these findings can inform future policies, institutional strategies, and professional development practices tailored to diverse educational contexts. Country-level comparisons further illustrate this divergence. While Phase 1 revealed Saudi Arabia as the most AI-ready country in terms of self-reported confidence, Phase 2 confirmed this with a strong performance in policy support (Task 8) and tool selection (Task 2). However, Pakistani and Uzbekistani educators, who in Phase 1 reported lower perceived readiness, demonstrated comparable or stronger performance in reflective and ethical tasks in Phase 2, particularly in Tasks 3 (Ethics) and 4 (Multilingual Adaptation). These results suggest that educators in lower-resourced environments may cultivate deeper reflective practices under certain contextual constraints, possibly as a compensatory strategy for limited infrastructure. Task 1 (AI-Integrated Lesson Plan) received among the lowest average scores in Pakistan (1.03), while Uzbekistan (1.37) and Saudi Arabia (1.52) performed moderately. Although Saudi participants scored highest on this task, the overall average remained below the 'complete competence' threshold of 2.00, suggesting that lesson plans lacked pedagogical rigor or depth even in the strongest-performing country. Many educators struggled to create lessons that reflected best practices in language teaching or to integrate meaningful AI effectively. This reinforces the need for targeted capacity building around lesson planning that incorporates AI meaningfully, especially in language teacher education contexts. This nuance would not have emerged from self-report data alone. The study presents a more nuanced and realistic portrayal of regional AI readiness in language teacher education by combining country-specific task performance data with survey trends (see Table 9 ). Table 9 Mean AIR-TBI Task Scores by Country (scores out of 2.00) Task Pakistan Saudi Arabia Uzbekistan Task 1: Lesson Plan 1.03 1.52 1.37 Task 2: Tool Selection 1.10 1.80 1.47 Task 3: Ethics 0.87 1.62 0.97 Task 4: Multilingual 1.13 1.62 1.33 Task 5: Comparison 0.93 1.50 0.90 Task 6: Peer Training 1.13 1.27 0.97 Task 7: Privacy 1.03 1.75 1.40 Task 8: Policy Support 0.87 1.80 1.33 While Phase 1 provided a helpful overview of perceived readiness, the AIR-TBI added needed granularity to interpret actual readiness for AI integration. Phase 2 made several critical contributions: It validated and refined Phase 1 findings by confirming specific patterns, such as Saudi Arabia's perceived institutional strength, while challenging others, especially in ethics and collaborative practice. It revealed a consistent gap between confidence and competence, showing that educators often overestimated their readiness in surveys. It highlighted role- and gender-based performance trends, offering actionable insights for differentiated training. It identified AI-integrated lesson planning as a persistent weakness, even in high-performing contexts. It revealed that educators in under-resourced contexts, such as Pakistan and Uzbekistan, demonstrated reflective strengths that surveys failed to capture. These insights would not have emerged through perception data alone. Phase 2 provides an empirical foundation for designing more contextually grounded, role-sensitive, and evidence-informed AI training interventions in language teacher education. As institutions seek to create equitable and targeted professional development programs, these findings provide a robust evidence base for aligning training with demonstrated needs across gender, role, and country contexts. In light of these findings, the following country-specific steps are recommended: Saudi Arabia should capitalize on its institutional momentum by embedding AI-integrated lesson planning modules into national teacher training frameworks. This means focusing on pedagogical depth rather than technical exposure alone. Pakistan should prioritize capacity building in ethical reasoning and curriculum design by offering scenario-based training and mentorship for lesson planning using AI tools in under-resourced regions. Uzbekistan can strengthen its AI readiness by investing in infrastructure upgrades and incentivizing faculty collaboration across teacher training institutions to scale reflective and multilingual AI practices. These tailored interventions reflect each country's performance strengths and gaps, as well as the importance of contextualized support for sustainable AI integration in language teacher education. Recommendations for Policy and Practice The findings support the following updated recommendations: Integrate Faculty Training with Infrastructure Investment : Institutions should jointly invest in technological infrastructure (e.g., reliable Internet, AI tools) and continuous professional development. Without appropriate training, infrastructure remains underutilized, and training remains theoretical and ineffective. Develop and Localize AI Policies : Policymakers must collaborate with educators to co-develop national AI policies that are context-sensitive and culturally relevant. These should include frameworks for language inclusion, ethics, and classroom implementation to ensure practical usability and alignment with local teaching realities. Embed Ethical AI Education into Curriculum : Ethics training must go beyond theory. Modules should focus on algorithmic bias, data privacy, and misuse scenarios through real-world case studies and applied dilemmas [ 14 ]. This should be mandatory for all pre-service and in-service faculty development programs. Establish Regional AI Resource Hubs : Cross-border AI resource centers can support faculty by providing access to open-source tools, multilingual digital content, and facilitating interdisciplinary knowledge exchange. These hubs can foster regional innovation and narrow gaps in preparedness across institutions [ 13 ]. Strengthen Institutional and Leadership Incentives : Institutions must formally recognize AI leadership within their faculty by providing grants, research opportunities, and leadership roles. Strong institutional support is crucial for scaling up AI practices and integrating them into the institutional culture [ 14 ]. These recommendations align with the differentiated readiness levels revealed in Phases 1 and 2, providing a roadmap for the scalable and equitable integration of AI in language teacher education. CONCLUSION This study provided a comprehensive, mixed-method investigation of AI readiness in language teacher education across Saudi Arabia, Uzbekistan, and Pakistan. By combining perceptual (Phase 1) and performance-based (Phase 2) approaches, the research uncovered both reported and demonstrated competencies across key domains: technological infrastructure, faculty training, institutional support, ethical awareness, and pedagogical integration. Key Research Findings highlight that Saudi Arabia showed the highest overall readiness due to stronger infrastructure and institutional backing. At the same time, Pakistan and Uzbekistan revealed critical gaps in policy, training access, and depth of implementation. Gender and role-based patterns showed that AI readiness is not uniform; for example, female educators showed stronger reflective awareness, while lecturers and professors outperformed in planning and institutional alignment. Task 1 (Lesson Planning) exposed a universal challenge across countries, underscoring the difficulty in translating AI awareness into effective pedagogical practice. Broader Implications suggest that AI integration in teacher education must move beyond digital access to focus on holistic, contextual implementation. Institutional leadership, regional partnerships, and curriculum reform must work to ensure that AI contributes meaningfully to inclusive, culturally relevant, and pedagogically sound education systems. The study's main contributions include the development and validation of a new task-based assessment instrument (AIR-TBI), the identification of previously unmeasured performance gaps through triangulation, and the provision of country-specific recommendations grounded in data. The study offers a replicable model for evaluating AI readiness across other national and institutional contexts. Future Directions include conducting longitudinal studies to measure readiness progression post-policy reform or training intervention, extending the survey to additional regions for comparative insights, and incorporating observational or classroom-based data to validate task performance. Policymakers, academic leaders, and international stakeholders are urged to use these findings to develop responsive, role-specific AI training programs and institutional strategies. AI readiness must be framed not as a technology issue but as a question of teaching quality, equity, and educational sustainability. This study contributes to the growing field of AI and education by offering practical recommendations and empirical tools that support transformational change in language teacher education across various contexts. Declarations ETHICAL APPROVAL This study was conducted by international researchers based in Saudi Arabia, Uzbekistan, and Pakistan. As the research was not affiliated with a single host institution and did not involve clinical or high-risk procedures, formal institutional ethical approval was not applicable. A formal ethical waiver vide # IRP/25/0051 has been issued by the Yanbu English Language Institute, a division of the Royal Commission for Education, Saudi Arabia. The study adhered to the moral principles outlined in the Declaration of Helsinki (2013) and UNESCO’s Recommendation on Science and Scientific Researchers (2017). CONSENT TO PARTICIPATE Before their involvement, all participants were informed of the study’s objectives, procedures, and the voluntary nature of their participation. Written informed consent was obtained from all survey participants, as well as from those participating in focus group discussions and task-based assessments (AIR-TBI). Participants were assured of confidentiality, anonymity, and the right to withdraw from any phase of the study at any point without consequence. Data were collected and used solely for academic and research purposes. CONSENT TO PUBLISH Not applicable, as the study does not include any identifiable images or personal data. All authors have reviewed and approved the final manuscript and consent to its submission and publication in Discover Education . CONFLICT OF INTEREST The authors declare that there is no conflict of interest. DATA AVAILABILITY STATEMENT The datasets generated and analyzed during the current study are not publicly available due to institutional confidentiality agreements and ethical restrictions involving human participants. However, upon reasonable request, anonymized data may be made available from the corresponding author, Dr. Syed Naeem Ahmed. FUNDING STATEMENT The authors received no financial support or funding for this study's research, authorship, or publication. CLINICAL TRIAL NUMBER Clinical trial number: not applicable. AUTHOR CONTRIBUTIONS Dr. Syed Naeem Ahmed (Corresponding Author) – Supervision, Methodology, Original Draft Preparation, and Data Analysis (Quantitative & Qualitative) Dr. Heena Amjad – Conceptualization, Literature Review, and Data Collection Ms. Saira Abbas – Instrument Design, Qualitative Data Coding, Writing, Results & Discussion Sections, and Writing Editing & Reviewing Dr. Zarrina Salieva – Participant Recruitment & Data Collection, Contextual Interpretation, Critical Review & Revisions, and Writing, Editing & Reviewing References Ahmad SF, Alam MM, Rahmat MK, Shahid MK, Aslam M, Salim NA, Al-Abyadh MHA. Leading edge or bleeding edge: Designing a framework for adopting AI technology in an educational organization. Sustainability. 2023;15(8):6540. https://doi.org/10.3390/su15086540 . Alexandrowicz V. Artificial intelligence integration in teacher education: Navigating benefits, challenges, and transformative pedagogy. 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Available from: https://sdgs.un.org/goals/goal4 Ng W, Wang H, Li X, Chen H, Wong CH. Understanding K–12 teachers' technological, pedagogical, and content knowledge (TPACK) readiness and attitudes toward AI teaching. Educ Inf Technol. 2024. https://doi.org/10.1007/s10639-024-12621-2 . Viberg O, Hatlevik OE, Macgilchrist F, Piro J, Sharma R, Vermunt JD. What explains teachers' trust in AI in education across six countries? Int J Artif Intell Educ. 2024. https://doi.org/10.1007/s40593-024-00433-x . Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges. JUTLP [Internet]. 2024 Apr 19 [cited 2025 Apr 2];21(06). Available from: https://open-publishing.org/journals/index.php/jutlp/article/view/818 Footnotes Note. A Doctor of Education (Ed.D./Ph.D.) refers to educators who hold a doctorate in education or serve as senior teacher trainers. Each participant is counted once under their primary role. Totals per country are in the second column. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Feb, 2026 Reviews received at journal 30 Nov, 2025 Reviews received at journal 29 Nov, 2025 Reviewers agreed at journal 29 Nov, 2025 Reviewers agreed at journal 29 Nov, 2025 Reviewers invited by journal 14 Nov, 2025 Editor assigned by journal 30 Oct, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 29 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7614466","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550095635,"identity":"c7236b66-c261-481a-9ba8-1db2432485fe","order_by":0,"name":"Syed Naeem 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Arabia","correspondingAuthor":false,"prefix":"","firstName":"Heena","middleName":"","lastName":"Amjad","suffix":""},{"id":550095637,"identity":"51901e8e-596f-4c82-9e3d-001241530a43","order_by":2,"name":"Saira Abbas","email":"","orcid":"","institution":"Royal Commission for Jubail and Yanbu","correspondingAuthor":false,"prefix":"","firstName":"Saira","middleName":"","lastName":"Abbas","suffix":""},{"id":550095640,"identity":"934e4926-93d7-4257-bfb4-7374493994da","order_by":3,"name":"Zarrina Salieva","email":"","orcid":"","institution":"Samarkand State Institute of Foreign Languages","correspondingAuthor":false,"prefix":"","firstName":"Zarrina","middleName":"","lastName":"Salieva","suffix":""}],"badges":[],"createdAt":"2025-09-14 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1","display":"","copyAsset":false,"role":"figure","size":117423,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical Framework Adapted from Holmström\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7614466/v1/bbb4146c6eb03d02b9f869ff.png"},{"id":96804051,"identity":"f39507ed-0a6e-4d6d-bf89-abea2052383e","added_by":"auto","created_at":"2025-11-26 09:05:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119090,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical Framework Adapted from Holmström\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7614466/v1/70e752cfb1cec0c60bc42739.png"},{"id":96804048,"identity":"8eff52f1-5b02-4e39-b694-a7a9bed1457c","added_by":"auto","created_at":"2025-11-26 09:05:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61579,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of AI Readiness Scores by Country\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7614466/v1/4c7e1b5b5313892983db7c00.png"},{"id":96916286,"identity":"327fdb10-95c0-4938-aae1-2f8156ba93f4","added_by":"auto","created_at":"2025-11-27 14:08:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":90964,"visible":true,"origin":"","legend":"\u003cp\u003ePerceived Barriers by Country\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7614466/v1/f0af52f916344ee9a7be0a32.png"},{"id":96804054,"identity":"980e292a-0b13-4685-b838-74dbc2c4d931","added_by":"auto","created_at":"2025-11-26 09:05:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":119753,"visible":true,"origin":"","legend":"\u003cp\u003eTask-Based Scores by Gender\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7614466/v1/7a7beaa64ab2e316c032980e.png"},{"id":96804061,"identity":"4fbed178-337c-45ec-948f-cb4c2f62e761","added_by":"auto","created_at":"2025-11-26 09:05:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":105310,"visible":true,"origin":"","legend":"\u003cp\u003eTask-Based Scores by Role\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7614466/v1/1b7132c7cf251e6bebbd66e7.png"},{"id":97135705,"identity":"6f1c8564-99dc-4860-87a3-9efed8749210","added_by":"auto","created_at":"2025-12-01 09:52:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2099607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7614466/v1/498cb963-53d3-4dd8-8acb-67c753efa7e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Comparative Study of AI Readiness in Language Teacher Education in the Global South","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eArtificial Intelligence (AI) is increasingly recognized as a transformative force in education, potentially addressing persistent challenges and innovating teaching and learning practices [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. When properly integrated, AI can personalize instruction, provide instantaneous feedback, and act as a “co-intelligence” for teachers by enhancing their productivity and effectiveness [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These benefits are highly relevant in language education. AI-powered tools (e.g., intelligent tutoring systems, automated translators, and graders) can augment teachers’ capacity to support diverse learners. However, the rapid rise of AI also brings new risks and demands that have often outpaced educational policies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. International agencies stress that AI’s promise must be harnessed inclusively to avoid exacerbating existing inequities [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Access to AI-enhanced learning remains uneven, with well-resourced schools experimenting with chatbots and intelligent tutors.\u003c/p\u003e\u003cp\u003eIn contrast, many others, especially in developing countries, lack basic electricity or internet connectivity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This stark digital divide raises questions about whether integrating AI into education is a privilege for a few. In low- and middle-income contexts, many teachers have limited digital and AI literacy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and their exposure to AI in teaching may be confined to news or social media rather than hands-on practice. Ultimately, the success of AI in education will depend on how well-prepared educators are to utilize these technologies effectively. Teacher education programs, therefore, play a pivotal role: they must equip current and future teachers with the competencies and conditions to harness AI, ensuring that \"AI for all\" includes all classrooms and learners. The present study investigates AI readiness in language teacher education within three multilingual, resource-constrained higher education settings: Pakistan, Uzbekistan, and Saudi Arabia. These countries represent contexts where English and other languages are taught concurrently, and educational institutions vary in their access to and use of technology. Multilingual environments add complexity to AI integration. For instance, AI tools often have limited capabilities in local languages, impeding their usefulness for non-English speakers.\u003c/p\u003e\u003cp\u003eMeanwhile, resource constraints such as limited bandwidth, outdated hardware, and scarce funding can hinder even the most motivated educators. There is a dearth of empirical data on how such contexts prepare for AI in teacher education, as most research on AI in education has been concentrated in a few high-income countries, notably the United States and China [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn contrast, low-income regions have produced relatively few studies on this topic, and little is known about the readiness of teacher education institutions in the Global South to embrace AI [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This study fills that gap through a cross-national analysis spanning South Asia, Central Asia, and the Middle East. Uniquely, the research was conceived and conducted through a grassroots collaboration among an international team of scholars from the three countries, ensuring on-the-ground insights that outside researchers might miss. The result is one of the first comparative datasets on teacher education and AI from these contexts, offering replicable evidence to inform both local practice and the broader discourse on AI in education. The following research questions guide the study:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eWhat is the current state of AI readiness among language teacher education professionals in Pakistan, Uzbekistan, and Saudi Arabia regarding digital infrastructure, faculty expertise, and institutional support?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWhat is the current state of faculty readiness for AI in these language teacher education contexts, specifically regarding educators’ training, skills, and knowledge for integrating AI into teaching and teacher preparation?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWhat forms of institutional support (policies, leadership initiatives, funding, technical support) exist for AI integration in language teacher education, and how do these compare across the three country contexts?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSignificance of the study\u003c/h3\u003e\n\u003cp\u003eThis study makes novel contributions on three levels. Substantively, it provides some of the first systematic data on AI readiness in teacher education from Pakistani, Uzbekistani, and Saudi Arabian contexts, which are often underrepresented in the literature [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The findings identify key infrastructure, training, and policy gaps, offering guidance for aligning national strategies and external interventions with capacity-building needs. Methodologically, the cross-national, grassroots approach provides a replicable model for culturally responsive research. The instruments developed can be adapted for use in other multilingual, low-resource settings. Theoretically, the study refines Holmström’s AI readiness framework within the teacher education domain, revealing overlooked dynamics such as language diversity. It helps bridge the gap between global aspirations for AI and local educational realities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"LITERATURE REVIEW","content":"\u003cp\u003eArtificial Intelligence (AI) has emerged as a powerful tool, particularly in language teacher education. AI-powered technologies such as automated assessment systems, adaptive learning platforms, and intelligent tutoring systems have shown promise in enhancing teaching efficiency, supporting personalized learning, and improving student engagement [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, while some studies highlight AI’s benefits in instructional efficiency [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], others caution that over-reliance on AI tools may undermine teacher autonomy and reduce pedagogical flexibility [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003ch3\u003eGlobal Alignment and Analytical Framework\u003c/h3\u003e\u003cp\u003eThe global imperative to enhance teacher capacity is firmly grounded in Sustainable Development Goal 4 (SDG 4), which emphasizes inclusive and equitable education, as well as lifelong learning. Specifically, Target 4.c [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] calls for substantially increasing the number of qualified teachers through international cooperation and professional development initiatives, particularly in developing regions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This priority is highly relevant for AI integration in teacher education, where institutional readiness must be matched by trained human capital. In countries such as Pakistan, Uzbekistan, and Saudi Arabia, SDG 4 serves as a normative benchmark for evaluating national education strategies in the context of global digital transformation goals [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile SDG 4 outlines the policy vision, the present study operationalizes this agenda through Holmström’s AI Readiness Framework. Initially developed for organizational digital transformation, this model provides a structured approach for evaluating institutional, technological, and human resource readiness for AI adoption. Its dimensions of strategic alignment, infrastructure, organizational capacity, and data governance have been adapted here to evaluate AI readiness within the context of language teacher education. This dual alignment with global development goals and analytical precision supports a robust, context-sensitive evaluation of AI preparedness across diverse educational systems.\u003c/p\u003e\u003ch3\u003eTheoretical Framework: AI Readiness Framework by Holmström\u003c/h3\u003e\u003cp\u003eThe AI Readiness Framework by Holmström provides a structured approach to evaluating an institution’s capacity to integrate AI [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Unlike the Technology Acceptance Model (TAM) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which focuses on individual attitudes toward technology, or the SAMR model [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which examines the stages of technology integration in classrooms, Holmström’s framework, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, offers a system-wide lens. It assesses institutional preparedness across four core dimensions: infrastructure, leadership, data strategy, and workforce. Initially applied in corporate automation contexts, the framework has gained relevance across various sectors seeking to scale AI innovation. Its emphasis on organizational culture and capability is a valuable foundation for education-focused adaptation.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the four key aspects of the AI Readiness Framework: Infrastructure Readiness, Leadership and Vision, Data and AI Strategy, and Workforce Capabilities. The funnel shape illustrates how these key variables are interconnected, highlighting that these dimensions are crucial, with resources serving as the foundation and workforce training as the pinnacle for AI integration.\u003c/p\u003e\u003cp\u003eHolmström originally applied the framework in business settings to guide the adoption of enterprise-level AI [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, AI readiness in education requires additional considerations, including faculty development, ethical standards, and the pedagogical applicability of AI. This study extends Holmström’s model by adapting it for language teacher education in Pakistan, Uzbekistan, and Saudi Arabia. The revised model collapses the original four dimensions into three integrated domains more applicable to multilingual teacher education: technological infrastructure, faculty expertise and training, and institutional support. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, this adapted framework maintains the holistic perspective of Holmström’s model [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] but foregrounds education-specific enablers and constraints.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is an adapted framework that guides the development of the study’s conceptual model. This model conceptualizes AI readiness in language teacher education as the dynamic interplay among infrastructure, human capital, and institutional environment. These three domains are explored in depth below.\u003c/p\u003e\u003ch3\u003eTechnological Infrastructure\u003c/h3\u003e\u003cp\u003eInfrastructure, internet connectivity, hardware, digital content platforms, and access to AI-enabled tools are foundational to any successful AI integration. In teacher education, where the practical application of technology in pedagogy is emphasized, inadequate infrastructure can derail even the most well-intentioned digital innovation. The U.S. Department of Education identified infrastructure gaps as a significant bottleneck in AI readiness across several countries, particularly in rural and underserved regions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In Pakistan and Uzbekistan, for instance, outdated computing facilities and inconsistent internet access have hindered educators' ability to adopt AI tools effectively [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In contrast, investment in educational infrastructure as part of Saudi Arabia's Vision 2030 has increased AI readiness, enabling greater utilization of generative AI in higher education [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, investment in infrastructure does not guarantee success on its own. In particular, highly multilingual environments require linguistic support from teachers, translation technology, localized interfaces, and adaptive hardware and software that function effectively under restricted bandwidth. Strategic investments such as solar-powered labs, mobile AI learning kits, and hybrid offline platforms mitigate infrastructural barriers in low-resource settings.\u003c/p\u003e\u003ch3\u003eFaculty Expertise and Training\u003c/h3\u003e\u003cp\u003eEducator preparedness is the second pillar of AI readiness. Educators’ digital literacy, which encompasses their ability to utilize AI tools, apply them pedagogically, and manage AI-enhanced learning environments, is crucial for the meaningful integration of AI into instruction [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Sustained professional development significantly enhances teachers’ willingness to engage with AI, whereas one-off workshops often yield limited, short-term gains. Nevertheless, access to such training remains uneven across countries and institutions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Reports that many rural educators have little to no exposure to AI tools, and where training exists, it is often generic rather than tailored to subject-specific or contextual needs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While new graduates may be familiar with tools like ChatGPT, their ability to apply them critically and ethically in language instruction remains underdeveloped [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePractical faculty training must also consider the specific challenges of multilingual classrooms. AI translation tools, for example, often fall short in handling regional dialects or non-Western languages, requiring teachers to exercise discretion and adaptation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Developing structured, context-specific faculty training is crucial for the meaningful and ethical integration of AI in language teacher education [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Teachers must operate AI tools and understand when and how to use them pedagogically, addressing biases, ethical concerns, and technical limitations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. AI risks becoming a superficial add-on rather than a transformative force in language teacher education without targeted, continuous training.\u003c/p\u003e\u003ch2\u003eInstitutional Support\u003c/h2\u003e\u003cp\u003eThe third core factor in AI readiness is institutional support. Even the most technologically equipped and well-trained educators need enabling environments to integrate AI sustainably. Institutional support encompasses leadership vision, policy coherence, resource allocation, and mechanisms for monitoring and sustaining innovation. When aligned with bottom-up pedagogical innovation, [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] highlights that top-down strategic leadership creates the most fertile ground for AI integration.\u003c/p\u003e\u003cp\u003eIn Saudi Arabia, institutional support has played a crucial role in this endeavor. Governmental and university-level strategies have provided funding, established AI research centers, and included AI-related competencies in faculty performance metrics [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Conversely, in Pakistan and Uzbekistan, institutional support remains inconsistent. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] While some urban institutions pilot AI initiatives, many lack formal policies, guidelines, or support structures, resulting in fragmented and isolated efforts. Rakha notes the absence of strategic direction for AI in teacher education across many Uzbekistani institutions until recently [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, institutional support must also involve ethical frameworks, transparency protocols, and professional incentives. Without guidelines on data privacy, algorithmic bias, or vetting AI tools, educators are left vulnerable to misapplication or liability [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Stress that institutions must provide access and training and uphold ethical standards that align with inclusive, learner-centered education.\u003c/p\u003e\u003ch3\u003eRegional Literature and Collaboration Rationale\u003c/h3\u003e\u003cp\u003eWhile there has been significant global interest recently, studies measuring AI readiness in language teacher education are lacking in Pakistan, Uzbekistan, and Saudi Arabia [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Within Saudi Arabia, emerging research is examining the role of AI in education and Vision 2030, with a focus on higher education and its digital transformation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. There have been limited studies from Pakistan, aside from some theoretical work and general digital learning, which do not pertain significantly to language teacher training or multilingual contexts [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The same applies to Uzbekistan, where there appears to be less research on AI in education, highlighting the early stage of policy development and academic interest in AI [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe gaps in these regions motivated the authors, who reside in one of the three countries, to encourage cross-national collaboration through an international WhatsApp research group. To provide contextual knowledge and utilize their regional perspectives, the authors formulated a study that addresses the realities of AI usage in teaching. Before drafting the ideas, the group analyzed the country’s educational policymaking documents, strategic plans for higher education, and other relevant materials related to digital learning to ensure that they accurately reflected the current realities. This collaboration has produced a unique dataset that aims to fill the existing research gap and is designed to be replicable through longitudinal and cross-regional methodologies.\u003c/p\u003e\u003ch3\u003eSynthesis\u003c/h3\u003e\u003cp\u003eTechnological infrastructure, faculty expertise and training, and institutional support form the foundation of AI readiness in language teacher education. These factors are mutually reinforcing: without reliable infrastructure, even the most well-trained educators are constrained; without training, technology remains underutilized; and without institutional backing, innovation lacks sustainability. These dynamics are particularly acute in multilingual and under-resourced contexts, where educational equity, linguistic diversity, and economic disparity intersect with digital transformation.\u003c/p\u003e\u003cp\u003eAlthough some recent studies have begun exploring AI in teacher education, most are limited in scope, geographically concentrated, or focused on short-term impacts [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This study addresses these limitations by offering a comparative, multi-country perspective rooted in the lived experiences of educators and institutions. As such, it contributes to theoretical understanding and practical strategies for building AI readiness in the most needed contexts.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003eA two-phase convergent mixed-methods approach was adopted to address the research questions comprehensively. Phase 1 included quantitative surveys to provide breadth and comparability across Pakistan, Uzbekistan, and Saudi Arabia, complemented by qualitative focus groups that yielded depth on contextual challenges and supports. The design was explicitly linked to the study’s guiding factors and research questions. Each research question targets one of the three AI readiness factors across the three national contexts, ensuring that the methodology directly examines technological infrastructure, faculty expertise and training, and institutional support in language teacher education. A methodological matrix (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was developed to map each research question to the data sources, sample, and analysis techniques, providing a clear rationale for how the mixed methods approach answers the study's aims. Combining methods ensures that statistical trends can be explained with contextual evidence, strengthening the validity of findings through triangulation.\u003c/p\u003e\u003cp\u003ePhase 2 was introduced to mitigate the limitations of self-reported survey data in Phase 1. It employed a Task-Based Instrument (AIR-TBI) to assess observable competencies in AI integration, ethical reasoning, and institutional awareness. A stratified subsample of 100 participants was drawn from the original pool of 400 survey respondents. Stratification was based on three criteria: country (Pakistan, Uzbekistan, Saudi Arabia), type of institution (public university, private university, teacher training institution), and professional role (such as lecturer, professor, or educational technologist). This ensured balanced and proportionate demographic representation across key variables. This approach increased the reliability of the task-based results while maintaining comparability with Phase 1 findings.\u003c/p\u003e\u003ch2\u003eParticipants and Sampling\u003c/h2\u003e\u003cp\u003eThe target population for this study was professionals involved in English language teacher education and related educational technology roles in Pakistan, Saudi Arabia, and Uzbekistan. Participants were selected through purposive sampling to include those knowledgeable about AI integration in education. Inclusion criteria required individuals to have at least two years of experience teaching English with AI-enhanced tools or in educational policymaking for teacher training; respondents with no AI-related experience were excluded. Initially, approximately 532 invitations were distributed. Due to non-response and incomplete data rate, the final sample comprised 400 participants (after omitting ~ 132 incomplete responses). These 400 respondents included 111 from Pakistan, 102 from Uzbekistan, and 187 from Saudi Arabia, reflecting a broad cross-section of the three contexts. Data were collected in English through an online English survey (using Google Forms) to ensure all participants understood the questions clearly.\u003c/p\u003e\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below summarize the participants' demographics for Phase 1, while Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the participants’ demographics for Phase 2. This diverse representation, encompassing teacher educators (lecturers and professors), AI researchers, data scientists, and ed-tech specialists, enabled a comprehensive assessment of AI readiness from both pedagogical and technological perspectives within language teacher education.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipant Demographics (N = 400)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUzbekistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI prefer not to say\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20–30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31–40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41–50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5–10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11–15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMore than 15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaster’s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhD\u003csup\u003e[1]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstitution Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePublic University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTeacher Training Institution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipant Roles by Country\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAI Researcher\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData Scientist\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDoctor of Education\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEducational Technologist\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLecturer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eProfessor\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUzbekistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e Most participants were faculty in higher education or teacher training institutes, ensuring relevance to language teacher education contexts. In addition to the survey respondents, several participants were invited for follow-up qualitative sessions. To gain deeper insights, focus group discussions were conducted in each country, with one per country and 12–14 participants each, totaling 40 participants. These focus group participants were selected from the survey pool based on their willingness to assume various roles and experiences, enriching the qualitative data with diverse perspectives.\u003c/p\u003e\u003cp\u003eIn Phase 2, a stratified subsample of 100 participants was selected from the original 400 respondents to complete the AI Readiness Task-Based Instrument (AIR-TBI). Stratification was based on country (30 from Pakistan, 30 from Uzbekistan, and 40 from Saudi Arabia), institutional affiliation (public university, private university, or teacher training institution), and academic role (e.g., lecturer, professor, educational technologist). This ensured proportional and diverse representation across key demographic variables. Participants completed structured tasks to assess their practical use of AI, ethical reasoning, and engagement with institutional policies. The resulting performance-based data validated and enriched the perceptual trends identified in Phase 1.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipant Demographics (Phase 2, N = 100)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUzbekistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI prefer not to say\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20–30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31–40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41–50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than 5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5–10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11–15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMore than 15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBachelor’s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaster’s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePostgraduate Diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstitution Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePublic University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTeacher Training Institution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCommunity College\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRole\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI Researcher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoctor of Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducational Technologist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLecturer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfessor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe study used a structured 31-item survey in Phase 1 to assess AI readiness across three primary dimensions: digital infrastructure, faculty expertise, and institutional support. The availability of AI-related tools, internet access, and institutional and technological support measured digital infrastructure. Faculty expertise was assessed through indicators such as professional development, self-reported proficiency, and perceived relevance of AI in language education. Institutional support encompassed items such as AI policies, leadership involvement, and funding availability. The survey employed a five-point Likert scale, ranging from \"strongly disagree\" to \"agree strongly.\" It was piloted with 10 educators from each country to ensure clarity and contextual appropriateness, with a Cronbach’s alpha of 0.89 indicating high internal consistency.\u003c/p\u003e\u003cp\u003eIn Phase 2, participants completed the AI Readiness Task-Based Instrument (AIR-TBI), designed to generate evidence-based performance data across three readiness domains: (1) technical and pedagogical competence, (2) ethical and reflective judgment, and (3) institutional and collaborative readiness. The instrument included eight structured tasks: designing an AI-integrated lesson plan, responding to ethical dilemmas, evaluating AI tools, and describing institutional policies. Each response was scored on a rubric from 0 (no evidence) to 2 (competent demonstration). This approach mitigated social desirability and self-report bias by anchoring analysis in observable educator behaviors.\u003c/p\u003e\u003cp\u003eBoth survey and AIR-TBI responses were collected through online forms, with Phase 2 participants given two weeks to complete the task-based instrument. Clear guidelines and examples were provided to ensure consistent interpretation of task requirements. Participants in both phases were informed of their rights and provided consent for their participation to be anonymous.\u003c/p\u003e\u003cp\u003eTo complement the quantitative findings, focus group discussions were conducted with a subset of 12 to 14 participants from each country, totaling 40 educators. These discussions aimed to explore perceptions, challenges, and institutional perspectives on integrating AI. Focus group discussion guides were developed, covering themes such as perceived barriers to AI adoption, institutional readiness, leadership support, and opportunities for collaboration with industry and government. The discussions were recorded, transcribed, and coded thematically using NVivo software to ensure systematic analysis.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eThe study employed a multi-phase data analysis strategy to reflect the dual design. In Phase 1, descriptive statistics were used to calculate country-wise means and standard deviations of AI readiness, assessing central tendency and variation. Pearson correlation coefficients were calculated to evaluate relationships between AI readiness and key variables such as digital infrastructure and faculty training access. For example, the analysis examined whether institutions with stronger infrastructure reported higher AI readiness. One-way ANOVA was performed to identify statistically significant differences in AI readiness across the three countries. These analyses were conducted using SPSS 27, ensuring statistical rigor and reliability.\u003c/p\u003e\u003cp\u003eIn Phase 2, quantitative scores from the AIR-TBI were analyzed descriptively to assess participant performance on each of the eight tasks. Mean scores were calculated by task and domain, allowing for cross-task comparison and identification of regional patterns. A composite AI readiness score (out of 16) was generated for each participant. Group comparisons by country and demographic factors were conducted using ANOVA and cross-tabulation, while correlation analysis explored relationships between AIR-TBI performance and self-reported Phase 1 responses. This result enabled validation of Phase 1 trends through performance-based evidence.\u003c/p\u003e\u003cp\u003eFor the qualitative component, thematic analysis was employed to analyze focus group transcripts and open-ended needs assessments, utilizing NVivo 12 software for data management and coding. An inductive coding approach allowed themes to emerge organically, later structured according to the study's conceptual framework. Four researchers conducted and cross-verified coding, achieving an intercoder agreement of 85%. Key themes included Barriers to AI Implementation, Institutional Policy and Support, and Perceived Benefits of AI. Cross-national insights highlighted infrastructural gaps in Pakistan and Uzbekistan, as well as strategic implementation challenges in Saudi Arabia.\u003c/p\u003e\u003cp\u003eIntegrating survey metrics, task-based scores, and qualitative themes offered a multidimensional view of AI readiness, reinforcing the validity of findings through methodological triangulation.\u003c/p\u003e\u003ch2\u003eReplicability\u003c/h2\u003e\u003cp\u003eThe methodology provides high replicability because all instruments, including the survey and focus group guides, are uniform and standardized. Other researchers may employ different contextual strategies by adjusting specific variables to measure AI responsiveness in various educational environments. Moreover, this study unapologetically presents evidence of AI responsiveness in language teacher education by applying a detailed mixed-methods approach, thus supporting educators, policymakers, and institutional decision-makers with meaningful data.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThis section presents the study's findings and is organized into two phases, corresponding to the dual-method research design. Phase 1 reports the results from the quantitative Likert-scale survey and qualitative focus group discussions conducted with 400 participants across Pakistan, Uzbekistan, and Saudi Arabia. This phase focuses on self-reported perceptions of technological infrastructure, faculty expertise, and institutional support. Phase 2 introduces performance-based data from a stratified subsample of 100 participants who completed the AI Readiness Task-Based Instrument (AIR-TBI). This instrument assessed participants' competencies in integrating AI tools, navigating ethical dilemmas, and engaging with institutional support mechanisms. The two phases provide perceptual and behavioral insights into AI readiness in language teacher education across the three national contexts.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003ePhase 1 - Quantitative Findings\u003c/h2\u003e\u003cp\u003eThe composite AI Readiness Index was computed by integrating infrastructure (35%), faculty training (40%), institutional support (15%), and perceived ease of integration (10%). Results revealed cross-country disparities, with Saudi Arabia achieving the highest readiness score (M\u0026thinsp;=\u0026thinsp;4.10), followed by Uzbekistan (M\u0026thinsp;=\u0026thinsp;3.10) and Pakistan (M\u0026thinsp;=\u0026thinsp;2.92). Figure\u0026nbsp;3 presents a boxplot illustrating the distribution of AI readiness scores within each country. Saudi Arabia shows the least variance, indicating consistency in institutional AI adoption efforts, while Pakistan shows the widest variance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 3.\u003c/b\u003e Distribution of AI Readiness Scores by Country\u003c/p\u003e\u003cp\u003eThis is a boxplot showing country-level variation in AI readiness scores among participants. Saudi Arabia shows the least variance, while Pakistan shows the highest.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurther analysis revealed a strong positive correlation between infrastructure availability and AI readiness (r\u0026thinsp;=\u0026thinsp;0.673), affirming that infrastructural sufficiency underpins institutional readiness. Faculty training alone accounted for 39.7% of the variance in readiness scores (R\u0026sup2; = 0.397), underscoring its central role. Conversely, infrastructure and faculty training exhibited a weak correlation (r\u0026thinsp;\u0026asymp;\u0026thinsp;0.06), suggesting that the availability of resources does not automatically translate into faculty preparedness.\u003c/p\u003e\u003cp\u003eSurvey responses also revealed country-specific contrasts in access to AI-related training: over 70% of Saudi participants reported receiving formal training, compared to just under 40% in Uzbekistan and less than 30% in Pakistan. This discrepancy aligns with observed differences in institutional investment and strategic policy orientation.\u003c/p\u003e\u003cp\u003eInfrastructure-related findings are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which presents the average scores across six readiness indicators. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Saudi Arabia reported the strongest internet access, while Uzbekistan led in computing and software resources. Pakistan consistently scored the lowest across most infrastructure indicators, particularly internet reliability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInfrastructure Readiness by Country\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReliable Internet\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModern Computing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAI Software\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTech Support\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAI Labs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eInfra Sufficiency\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUzbekistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSaudi Arabia stands out in reliable Internet (M\u0026thinsp;=\u0026thinsp;4.20), while Uzbekistan performs better in AI software availability and modern computing. Pakistan lags in nearly all indicators, particularly in terms of reliable internet access.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFaculty Training by Country\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraining\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConfidence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWorkshops\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUse AI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBelief\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAccess\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCollaboration\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUzbekistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFaculty training scores are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Uzbekistan scored highest on most indicators, particularly access to AI courses and peer collaboration, while Pakistan reported the lowest averages in training access and peer collaboration.\u003c/p\u003e\u003cp\u003eInstitutional support, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, reveals Saudi Arabia as the strongest in policy development, while Uzbekistan performs well in access to AI resources. Saudi Arabia is perceived as having the most established AI policies (M\u0026thinsp;=\u0026thinsp;3.46), while Uzbekistan stands out in AI resource access. Pakistan lags in policy and resource indicators, reflecting challenges in formal institutional backing for AI efforts. Pakistan scored lowest in nearly all institutional support indicators, including policy presence and funding. These patterns are highlighted in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInstitutional Support\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolicies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLeadership\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFunding\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndustry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eResearch\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIncentives\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eResources\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUzbekistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports perceived barriers to AI adoption. Pakistan ranks highest in resistance and financial concerns, while Uzbekistan reports heightened ethical and policy-related barriers. Policy clarity and ethical concerns emerge as critical challenges in Uzbekistan (M\u0026thinsp;=\u0026thinsp;3.18 and 3.33, respectively), while Pakistani educators report heightened resistance to AI and significant financial barriers. Overall, perceived barriers remain above average in all three countries, with Pakistan experiencing the most pronounced challenges, warranting targeted interventions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo complement these quantitative patterns, the following section presents qualitative findings that delve deeper into participants' lived experiences and the contextual challenges they face.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePhase 1 - Qualitative Findings\u003c/h2\u003e\u003cp\u003eThe focus group analysis involved 40 participants from Pakistan, Saudi Arabia, and Uzbekistan. Thematically coded data revealed four dominant themes: barriers to AI adoption, institutional and policy support, ethical concerns, and cross-border opportunities. Their frequency is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequency of Themes in Focus Group Data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTheme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBarriers to AI Adoption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstitutional and Policy Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthical Concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-Border Opportunities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eBarriers to AI Adoption\u003c/h2\u003e\u003cp\u003eParticipants across Pakistan and Uzbekistan expressed acute infrastructural challenges, including inconsistent electricity and outdated computing hardware. Faculty often lacked access to AI tools and reported little institutional support for professional development. Educators in rural areas highlighted that limited bandwidth and unreliable Internet disrupted classroom experimentation with AI platforms. Faculty hesitation was a cross-cutting barrier, particularly from inadequate training and fears of role replacement. One Uzbek lecturer (FG005) remarked, \"\u003cem\u003eHaving faced a Lack of Training directly, I believe institutional inertia in Uzbekistan is a core part of the issue\u003c/em\u003e.\u0026rdquo; A Pakistani participant (FG017) shared, \u0026ldquo;\u003cem\u003eIn my experience as a Professor, Outdated Devices limit the potential impact of AI in our educational context\u003c/em\u003e.\u0026rdquo; A Saudi respondent (FG002) added, \u0026ldquo;\u003cem\u003eFrom an administrative and instructional standpoint, Unstable Internet continues hindering progress in Saudi Arabia. Although we have labs, many teachers resist using AI because they feel unprepared\u003c/em\u003e.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eInstitutional and Policy Support\u003c/h2\u003e\u003cp\u003eInstitutional readiness emerged as a powerful enabler. Multiple participants cited Saudi Arabia\u0026rsquo;s Vision 2030 as the backbone of the country\u0026rsquo;s proactive approach to AI in education. Educators noted the presence of well-funded AI labs, leadership incentives, and structured training programs. One Saudi lecturer (FG008) observed, \u0026ldquo;\u003cem\u003eIt is evident that Inconsistent Leadership is not just a technical hurdle, but also a cultural one here in Saudi Arabia.\u003c/em\u003e\u0026rdquo; One Pakistani respondent (FG015) shared, \u0026ldquo;\u003cem\u003eIt is evident that Supportive Administration is not just a technical hurdle, but also a cultural one here in Pakistan.\u003c/em\u003e\u0026rdquo; An Uzbek participant (FG027) commented, \u0026ldquo;\u003cem\u003eOur department keeps hitting walls due to Funding Gaps; this is something I have brought up repeatedly. There is enthusiasm at the ministry level, but institutions do not feel that support trickling down.\u003c/em\u003e\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eEthical Concerns\u003c/h2\u003e\u003cp\u003e Across all three countries, faculty raised ethical questions about fairness, transparency, and student data privacy. A Saudi educational technologist (FG031) stated, \"Our department keeps hitting walls due to Privacy Concerns; this is something I have brought up repeatedly.\" A respondent from Uzbekistan (FG010) highlighted, \"My experience in Uzbekistan suggests that Bias in AI Tools remains a major stumbling block to advancing AI.\" In Pakistan, one lecturer (FG013) observed, \"There is an ongoing conversation about Privacy Concerns in our institution, and as a Lecturer, I find it unresolved. \u0026rdquo; We fear standard AI models will wipe out the cultural nuances we embed in our lessons.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eCross-Border Opportunities\u003c/h2\u003e\u003cp\u003eDespite barriers, focus group discussions reflected a shared desire for regional collaboration. Participants advocated for AI learning communities, faculty exchange programs, and shared resource platforms tailored to the linguistic and educational contexts of Pakistan and Uzbekistan. A Saudi professor (FG004) suggested, \"\u003cem\u003eSaudi Arabia has great research opportunities, often reflecting deep institutional gaps. Also, Saudi Arabia is one of the best in infrastructure.\u003c/em\u003e\u0026rdquo; A Pakistani participant (FG038) added, \u0026ldquo;\u003cem\u003eThere is an ongoing conversation about Collaborative Research Opportunities in our institution, and as an AI Researcher, I find it unresolved\u003c/em\u003e.\u0026rdquo; An Uzbek educator (FG022) remarked, \u0026ldquo;\u003cem\u003eOvercoming Collaborative Research Opportunities is essential to make meaningful AI integration possible in Uzbekistan.\u003c/em\u003e\u0026rdquo; Many participants envisioned a regional AI knowledge hub that would bridge infrastructure, capacity, and strategy gaps. A regional AI knowledge hub that could bridge gaps in infrastructure, capacity, and strategy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003ePhase 2: Performance-Based Assessment (AIR-TBI)\u003c/h2\u003e\u003cp\u003ePhase 2 evaluated educators\u0026rsquo; competencies using the AI Readiness Task-Based Instrument (AIR-TBI), completed by a stratified subsample of 100 participants. The instrument comprised eight structured tasks designed to assess three domains of AI readiness: technical and pedagogical competence, ethical judgment, and institutional engagement. Each task was scored on a rubric from 0 (no evidence) to 2 (competent demonstration), yielding a maximum possible total score of 16.\u003c/p\u003e\u003cp\u003eThe average total AIR-TBI score across participants was 10.51 (SD\u0026thinsp;=\u0026thinsp;2.77), indicating a moderate to high level of demonstrated AI readiness. Task-level analysis revealed differentiated performance across the eight tasks:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe highest average scores were observed in Task 2: Tool Selection (M\u0026thinsp;=\u0026thinsp;1.49, SD\u0026thinsp;=\u0026thinsp;0.59) and Task 7: Privacy Awareness (M\u0026thinsp;=\u0026thinsp;1.43, SD\u0026thinsp;=\u0026thinsp;0.64), indicating that participants were generally competent in selecting appropriate AI tools and acknowledging ethical considerations related to data protection.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrong performances were also found in Task 4: Multilingual Adaptation (M\u0026thinsp;=\u0026thinsp;1.39) and Task 8: Policy Awareness (M\u0026thinsp;=\u0026thinsp;1.38), reflecting awareness of localized teaching contexts and institutional dynamics.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTask 1: AI-Integrated Lesson Planning (M\u0026thinsp;=\u0026thinsp;1.33) showed moderate competency, while relatively lower scores were recorded in Task 3: Ethics (M\u0026thinsp;=\u0026thinsp;1.20), Task 5: Comparative Evaluation (M\u0026thinsp;=\u0026thinsp;1.15), and Task 6: Peer Training (M\u0026thinsp;=\u0026thinsp;1.14), indicating areas where deeper reflection or pedagogical articulation may be needed.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eTask Performance by Gender and Role\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents a comparison of task-based scores by gender. Female participants demonstrated higher mean scores across most tasks, particularly in ethical awareness (Task 3) and multilingual adaptation (Task 4), suggesting stronger reflection and contextual sensitivity. Male participants scored marginally higher on technical tasks, such as AI lesson planning (Task 1) and tool selection (Task 2). In contrast, responses from those who preferred not to disclose their gender showed the most significant variability, likely due to the low sample size.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e compares task-based scores across professional roles. Lecturers and professors outperformed other roles on tasks related to lesson planning, AI tool comparison, and institutional policy awareness, reflecting their direct involvement in curriculum design and policy interpretation. AI researchers demonstrated strong performance in tool selection (Task 2) but achieved relatively lower scores in peer training (Task 6), possibly indicating a more individualized approach to engaging with AI tools. Educational technologists performed consistently across all tasks but did not excel in any specific domain, whereas Doctors of Education demonstrated balanced competencies, particularly in ethical and institutional dimensions.\u003c/p\u003e\u003cp\u003eThese disaggregated findings provide essential insights for tailoring professional development. Programs can be designed to address role-specific gaps, such as improving collaborative teaching among researchers and advancing policy literacy among technologists, while also encouraging gender-equitable access to technical and reflective training.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003eInterpreting AI Readiness in Light of Global and Analytical Frameworks\u003c/h2\u003e\u003cp\u003eThis study extends existing knowledge by interpreting AI readiness in teacher education through Holmstr\u0026ouml;m\u0026rsquo;s AI Readiness Framework [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and the development-oriented lens of SDG 4, notably Target 4.c on teacher training [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. While the results have established national variations in infrastructure, training, and institutional support, these variations are most significant in what they reveal about system-level preparedness. Holmstr\u0026ouml;m\u0026rsquo;s framework facilitated a structured readiness analysis across infrastructure, human capital, and strategic alignment. The framework was valuable in identifying national strengths, such as Saudi Arabia\u0026rsquo;s policy coherence and institutional investment, as well as cross-cutting vulnerabilities, particularly limited faculty preparedness and gaps in ethical engagement. At the same time, the study revealed that applying this model to multilingual, under-resourced educational contexts necessitates adaptation. Digital language literacy, access to localized AI tools, and clarity on institutional AI policy emerged as critical dimensions beyond those emphasized in the original model. Framing these insights within the Sustainable Development Goals highlights their global relevance. Target 4.c calls for enhanced teacher capacity through international cooperation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This study contributes to that call by offering a scalable model for evaluating AI readiness in diverse contexts grounded in perception-based and performance-based evidence. It also demonstrates that AI integration cannot depend solely on infrastructure; it requires sustained faculty development, ethical competence, and institutional alignment [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. By merging a global framework with empirical data from the Global South, the study helps bridge aspirational policies with educational realities. It affirms that readiness must be understood as access or awareness, as well as the institutional and pedagogical capacity to use AI meaningfully and ethically in teacher education.\u003c/p\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eInterpreting Disparities in AI Readiness\u003c/h2\u003e\u003cp\u003eThe most evident disparity emerged in overall readiness scores, with Saudi Arabia scoring highest (M\u0026thinsp;=\u0026thinsp;4.10), followed by Uzbekistan (M\u0026thinsp;=\u0026thinsp;3.10) and Pakistan (M\u0026thinsp;=\u0026thinsp;2.92). These differences reflect uneven implementation of digital transformation policies and varying degrees of faculty preparation. Saudi Arabia\u0026rsquo;s score aligns with Vision 2030, emphasizing the structured integration of AI in higher education. Respondents there reported more consistent access to infrastructure and institutional support.\u003c/p\u003e\u003cp\u003eIn contrast, Pakistani and Uzbekistani educators cited outdated computing facilities, poor internet connectivity, and limited institutional encouragement. Although Uzbekistan outperformed Pakistan on most infrastructure indicators (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the overall readiness remained constrained by training deficiencies and fragmented policies. These findings are consistent with [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], who emphasize that systemic barriers and a lack of localized policy adaptations often limit the adoption of AI, despite technological availability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eFaculty Training: A Decisive Factor\u003c/h2\u003e\u003cp\u003eOur regression analysis confirmed that faculty training explained 39.7% of the variance in AI readiness scores. Access was not evenly distributed despite similar training needs: over 70% of Saudi respondents had received formal training, while only\u0026thinsp;~\u0026thinsp;40% in Uzbekistan and ~\u0026thinsp;30% in Pakistan reported the same.\u003c/p\u003e\u003cp\u003eThe qualitative data further reinforced this finding. One Pakistani professor stated, \u0026ldquo;\u003cem\u003eOutdated devices limit the potential impact of AI in our educational context\u003c/em\u003e\u0026rdquo; (FG017), while an Uzbek lecturer noted, \u0026ldquo;\u003cem\u003eLack of training directly contributes to institutional inertia\u003c/em\u003e\u0026rdquo; (FG005). These statements reflect how resource gaps and inadequate capacity-building measures suppress meaningful AI adoption. This aligns with [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], who found that AI literacy programs significantly improved student and teacher confidence, ethical awareness, and usage efficacy.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eThe Infrastructure Readiness Disconnect\u003c/h2\u003e\u003cp\u003eAlthough infrastructure and AI readiness were strongly correlated (r\u0026thinsp;=\u0026thinsp;0.673), we found a notably weak correlation (r\u0026thinsp;\u0026asymp;\u0026thinsp;0.06) between infrastructure and faculty training. This suggests that institutions might invest in infrastructure without adequate parallel investments in human development. With its integrated policy design, Saudi Arabia achieved alignment across both dimensions; however, Pakistan and Uzbekistan exhibit a disconnect between access and utility. As one Saudi respondent emphasized, \u0026ldquo;\u003cem\u003eAlthough we have labs, many teachers resist using AI because they feel unprepared\u003c/em\u003e\u0026rdquo; (FG002). This underscores the importance of coupling infrastructure with pedagogical support and professional development. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Stress that readiness is not merely a technical issue but depends on synchronized institutional strategies, training, and leadership.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eInstitutional Support and Policy Coherence\u003c/h2\u003e\u003cp\u003eThe disparity in AI policy support was stark. Only 22% of Pakistani respondents affirmed the existence of AI-related policies, compared to 61% in Saudi Arabia and 47% in Uzbekistan. Focus group participants frequently cited policy ambiguity and leadership inconsistency. An Uzbek participant noted, \u0026ldquo;\u003cem\u003eFunding gaps and unclear leadership stall our AI integration efforts\u003c/em\u003e\u0026rdquo; (FG027).\u003c/p\u003e\u003cp\u003eConversely, structured programs in Saudi Arabia have encouraged adoption through funding, training incentives, and performance benchmarks. These findings align with the arguments made [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which advocate for a clear national strategy that empowers institutions and reduces ambiguity surrounding the implementation of AI.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical Tensions and Regional Realities\u003c/h3\u003e\n\u003cp\u003eA recurring theme was Ethical concerns regarding privacy, bias, and overdependence on foreign tools. Educators from Pakistan and Uzbekistan felt that AI models lacked linguistic sensitivity and transparency. One Uzbek respondent warned, \u0026ldquo;\u003cem\u003eBias in AI tools remains a major stumbling block to advancing AI\u003c/em\u003e\u0026rdquo; (FG010), while a Pakistani lecturer expressed concern about \u0026ldquo;\u003cem\u003ePrivacy concerns that remain unresolved\u003c/em\u003e\u0026rdquo; (FG013).\u003c/p\u003e\u003cp\u003eThese issues highlight the need for ethical governance and culturally responsive AI design. According to [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], AI literacy should encompass both moral and social dimensions, enabling faculty and students to identify and address risks proactively.\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eOpportunities for Regional Collaboration\u003c/h2\u003e\u003cp\u003eDespite these challenges, educators from all three countries called for increased cross-border cooperation. Many advocated for AI training hubs, multilingual toolkits, and collaborative research platforms. As one Saudi professor suggested, \u0026ldquo;\u003cem\u003eSaudi Arabia has great research opportunities and is a regional leader in infrastructure\u003c/em\u003e\u0026rdquo; (FG004). Educators envisioned a regional knowledge hub to pool resources, share strategies, and collectively build AI capacity.\u003c/p\u003e\u003cp\u003eSuch initiatives align with the call [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] for multi-institutional, multilingual, and cross-regional partnerships to promote the equitable and sustainable adoption of AI in diverse education systems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eIntegrating Task-Based Assessment: Addressing Gaps in Self-Reported Readiness\u003c/h2\u003e\u003cp\u003eThe inclusion of Phase 2 in this study was designed to overcome the limitations of self-reported data in Phase 1 by providing direct evidence of AI integration competencies. While the Likert-scale surveys offered broad insights into perceptions, they could not capture actual skill application or ethical reasoning. The AIR-TBI was administered to a stratified subsample of 100 participants to address this gap, assessing performance across eight tasks mapped to technical, reflective, and institutional AI readiness. Each task was scored on a 3-point scale: 0\u0026thinsp;=\u0026thinsp;No evidence of competence, 1\u0026thinsp;=\u0026thinsp;Partial demonstration of competence, and 2\u0026thinsp;=\u0026thinsp;Full demonstration of competence. While the variables measured in Phase 2 differ in structure from those in Phase 1, this divergence is intentional. The task-based format complemented survey findings by triangulating observed behaviors against self-reported perceptions. This phase significantly enhanced the study\u0026rsquo;s validity. The performance-based results generally reinforced Phase 1 trends- for example, educators in Saudi Arabia, who reported the highest perceived readiness, also performed well in institutional awareness and tool selection. However, the task-based results also exposed notable discrepancies, especially in ethical judgment (Task 3), peer training (Task 6), and comparative evaluation (Task 5), where many participants scored only moderately. These results suggest that belief in AI's potential does not always translate into demonstrated readiness in applied teaching contexts. The gender-disaggregated analysis further contextualized the findings. Female educators slightly outperformed male participants in reflective and collaborative tasks such as Ethics (1.25 vs. 1.16) and Comparative Evaluation (1.19 vs. 1.12), aligning with Phase 1 focus group themes that emphasized a more cautious and inclusive approach among female educators. However, male educators scored higher in Privacy Awareness (1.50 vs. 1.40) and Peer Training (1.16 vs. 1.08), indicating greater confidence in handling data and providing collegial support. These nuances underscore that gendered patterns in AI readiness are not unidirectional and vary by task type. Role-based comparisons offered further insight.\u003c/p\u003e\u003cp\u003eLecturers, who formed the largest role group in the sample, exhibited the highest average performance across most tasks, particularly in Lesson Planning and Institutional Policy awareness. Professors also scored strongly, especially in Multilingual Adaptation and Tool Evaluation, likely due to their broader curriculum responsibilities. Doctor of Education performed consistently across most tasks, demonstrating balanced readiness in both pedagogical and ethical domains. Educational Technologists performed strongly in technical tasks such as Tool Selection and Privacy Awareness, but were less confident in ethics and peer-related tasks. While excelling in Tool Selection, AI Researchers scored relatively low in tasks involving collaboration or contextual adaptation, such as Peer Training and Comparative Evaluation. These patterns suggest that AI readiness is influenced by training, access, and role-specific orientations and responsibilities. The table below presents mean task scores by gender (out of a maximum score of 2 per task): (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean AIR-TBI Task Scores by Gender\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI prefer not to say\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 1: Lesson Plan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 2: Tool Selection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 3: Ethics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 4: Multilingual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 5: Comparison\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 6: Peer Training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 7: Privacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 8: Policy Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThese disaggregated findings affirm that Phase 2 was instrumental in surfacing practical differences in readiness that were not captured through self-report. To conclude the discussion, the following section outlines how these findings can inform future policies, institutional strategies, and professional development practices tailored to diverse educational contexts. Country-level comparisons further illustrate this divergence. While Phase 1 revealed Saudi Arabia as the most AI-ready country in terms of self-reported confidence, Phase 2 confirmed this with a strong performance in policy support (Task 8) and tool selection (Task 2). However, Pakistani and Uzbekistani educators, who in Phase 1 reported lower perceived readiness, demonstrated comparable or stronger performance in reflective and ethical tasks in Phase 2, particularly in Tasks 3 (Ethics) and 4 (Multilingual Adaptation). These results suggest that educators in lower-resourced environments may cultivate deeper reflective practices under certain contextual constraints, possibly as a compensatory strategy for limited infrastructure. Task 1 (AI-Integrated Lesson Plan) received among the lowest average scores in Pakistan (1.03), while Uzbekistan (1.37) and Saudi Arabia (1.52) performed moderately. Although Saudi participants scored highest on this task, the overall average remained below the 'complete competence' threshold of 2.00, suggesting that lesson plans lacked pedagogical rigor or depth even in the strongest-performing country. Many educators struggled to create lessons that reflected best practices in language teaching or to integrate meaningful AI effectively. This reinforces the need for targeted capacity building around lesson planning that incorporates AI meaningfully, especially in language teacher education contexts. This nuance would not have emerged from self-report data alone. The study presents a more nuanced and realistic portrayal of regional AI readiness in language teacher education by combining country-specific task performance data with survey trends (see Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean AIR-TBI Task Scores by Country (scores out of 2.00)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUzbekistan\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 1: Lesson Plan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 2: Tool Selection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 3: Ethics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 4: Multilingual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 5: Comparison\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 6: Peer Training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 7: Privacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask 8: Policy Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eWhile Phase 1 provided a helpful overview of perceived readiness, the AIR-TBI added needed granularity to interpret actual readiness for AI integration. Phase 2 made several critical contributions:\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e It validated and refined Phase 1 findings by confirming specific patterns, such as Saudi Arabia's perceived institutional strength, while challenging others, especially in ethics and collaborative practice.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIt revealed a consistent gap between confidence and competence, showing that educators often overestimated their readiness in surveys.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIt highlighted role- and gender-based performance trends, offering actionable insights for differentiated training.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIt identified AI-integrated lesson planning as a persistent weakness, even in high-performing contexts.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIt revealed that educators in under-resourced contexts, such as Pakistan and Uzbekistan, demonstrated reflective strengths that surveys failed to capture.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThese insights would not have emerged through perception data alone. Phase 2 provides an empirical foundation for designing more contextually grounded, role-sensitive, and evidence-informed AI training interventions in language teacher education. As institutions seek to create equitable and targeted professional development programs, these findings provide a robust evidence base for aligning training with demonstrated needs across gender, role, and country contexts.\u003c/p\u003e\u003cp\u003eIn light of these findings, the following country-specific steps are recommended:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSaudi Arabia should capitalize on its institutional momentum by embedding AI-integrated lesson planning modules into national teacher training frameworks. This means focusing on pedagogical depth rather than technical exposure alone.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePakistan should prioritize capacity building in ethical reasoning and curriculum design by offering scenario-based training and mentorship for lesson planning using AI tools in under-resourced regions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUzbekistan can strengthen its AI readiness by investing in infrastructure upgrades and incentivizing faculty collaboration across teacher training institutions to scale reflective and multilingual AI practices.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese tailored interventions reflect each country's performance strengths and gaps, as well as the importance of contextualized support for sustainable AI integration in language teacher education.\u003c/p\u003e\u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\u003ch2\u003eRecommendations for Policy and Practice\u003c/h2\u003e\u003cp\u003eThe findings support the following updated recommendations:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIntegrate Faculty Training with Infrastructure Investment\u003c/b\u003e: Institutions should jointly invest in technological infrastructure (e.g., reliable Internet, AI tools) and continuous professional development. Without appropriate training, infrastructure remains underutilized, and training remains theoretical and ineffective.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDevelop and Localize AI Policies\u003c/b\u003e: Policymakers must collaborate with educators to co-develop national AI policies that are context-sensitive and culturally relevant. These should include frameworks for language inclusion, ethics, and classroom implementation to ensure practical usability and alignment with local teaching realities.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEmbed Ethical AI Education into Curriculum\u003c/b\u003e: Ethics training must go beyond theory. Modules should focus on algorithmic bias, data privacy, and misuse scenarios through real-world case studies and applied dilemmas [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This should be mandatory for all pre-service and in-service faculty development programs.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEstablish Regional AI Resource Hubs\u003c/b\u003e: Cross-border AI resource centers can support faculty by providing access to open-source tools, multilingual digital content, and facilitating interdisciplinary knowledge exchange. These hubs can foster regional innovation and narrow gaps in preparedness across institutions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStrengthen Institutional and Leadership Incentives\u003c/b\u003e: Institutions must formally recognize AI leadership within their faculty by providing grants, research opportunities, and leadership roles. Strong institutional support is crucial for scaling up AI practices and integrating them into the institutional culture [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThese recommendations align with the differentiated readiness levels revealed in Phases 1 and 2, providing a roadmap for the scalable and equitable integration of AI in language teacher education.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study provided a comprehensive, mixed-method investigation of AI readiness in language teacher education across Saudi Arabia, Uzbekistan, and Pakistan. By combining perceptual (Phase 1) and performance-based (Phase 2) approaches, the research uncovered both reported and demonstrated competencies across key domains: technological infrastructure, faculty training, institutional support, ethical awareness, and pedagogical integration. Key Research Findings highlight that Saudi Arabia showed the highest overall readiness due to stronger infrastructure and institutional backing. At the same time, Pakistan and Uzbekistan revealed critical gaps in policy, training access, and depth of implementation. Gender and role-based patterns showed that AI readiness is not uniform; for example, female educators showed stronger reflective awareness, while lecturers and professors outperformed in planning and institutional alignment. Task 1 (Lesson Planning) exposed a universal challenge across countries, underscoring the difficulty in translating AI awareness into effective pedagogical practice. Broader Implications suggest that AI integration in teacher education must move beyond digital access to focus on holistic, contextual implementation. Institutional leadership, regional partnerships, and curriculum reform must work to ensure that AI contributes meaningfully to inclusive, culturally relevant, and pedagogically sound education systems. The study's main contributions include the development and validation of a new task-based assessment instrument (AIR-TBI), the identification of previously unmeasured performance gaps through triangulation, and the provision of country-specific recommendations grounded in data. The study offers a replicable model for evaluating AI readiness across other national and institutional contexts. Future Directions include conducting longitudinal studies to measure readiness progression post-policy reform or training intervention, extending the survey to additional regions for comparative insights, and incorporating observational or classroom-based data to validate task performance. Policymakers, academic leaders, and international stakeholders are urged to use these findings to develop responsive, role-specific AI training programs and institutional strategies. AI readiness must be framed not as a technology issue but as a question of teaching quality, equity, and educational sustainability. This study contributes to the growing field of AI and education by offering practical recommendations and empirical tools that support transformational change in language teacher education across various contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICAL APPROVAL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted by international researchers based in Saudi Arabia, Uzbekistan, and Pakistan. As the research was not affiliated with a single host institution and did not involve clinical or high-risk procedures, formal institutional ethical approval was not applicable. A formal ethical waiver vide # IRP/25/0051 has been issued by the Yanbu English Language Institute, a division of the Royal Commission for Education, Saudi Arabia. The study adhered to the moral principles outlined in the Declaration of Helsinki (2013) and UNESCO\u0026rsquo;s Recommendation on Science and Scientific Researchers (2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore their involvement, all participants were informed of the study\u0026rsquo;s objectives, procedures, and the voluntary nature of their participation. Written informed consent was obtained from all survey participants, as well as from those participating in focus group discussions and task-based assessments (AIR-TBI). Participants were assured of confidentiality, anonymity, and the right to withdraw from any phase of the study at any point without consequence. Data were collected and used solely for academic and research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT TO PUBLISH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as the study does not include any identifiable images or personal data.\u003cbr\u003eAll authors have reviewed and approved the final manuscript and consent to its submission and publication in \u003cem\u003eDiscover Education\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to institutional confidentiality agreements and ethical restrictions involving human participants. However, upon reasonable request, anonymized data may be made available from the corresponding author, Dr. Syed Naeem Ahmed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support or funding for this study\u0026apos;s research, authorship, or publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCLINICAL TRIAL NUMBER\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Syed Naeem Ahmed (Corresponding Author) \u0026ndash; Supervision, Methodology, Original Draft Preparation, and Data Analysis (Quantitative \u0026amp; Qualitative)\u003c/p\u003e\n\u003cp\u003eDr. Heena Amjad \u0026ndash; Conceptualization, Literature Review, and Data Collection\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMs. Saira Abbas \u0026ndash; Instrument Design, Qualitative Data Coding, Writing, Results \u0026amp; Discussion Sections, and Writing Editing \u0026amp; Reviewing\u003c/p\u003e\n\u003cp\u003eDr. Zarrina Salieva \u0026ndash; Participant Recruitment \u0026amp; Data Collection, Contextual Interpretation, Critical Review \u0026amp; Revisions, and Writing, Editing \u0026amp; Reviewing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad SF, Alam MM, Rahmat MK, Shahid MK, Aslam M, Salim NA, Al-Abyadh MHA. 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Int J Artif Intell Educ. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40593-024-00433-x\u003c/span\u003e\u003cspan address=\"10.1007/s40593-024-00433-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEnhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges. JUTLP [Internet]. 2024 Apr 19 [cited 2025 Apr 2];21(06). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://open-publishing.org/journals/index.php/jutlp/article/view/818\u003c/span\u003e\u003cspan address=\"https://open-publishing.org/journals/index.php/jutlp/article/view/818\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cem\u003eNote. A Doctor of Education (Ed.D./Ph.D.) refers to educators who hold a doctorate in education or serve as senior teacher trainers. Each participant is counted once under their primary role. Totals per country are in the second column.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AI in language teacher education, generative AI tools for teaching, educational technology adoption, Holmström AI Readiness Framework, technological readiness in education","lastPublishedDoi":"10.21203/rs.3.rs-7614466/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7614466/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA growing body of global research supports the United Nations' Sustainable Development Goal 4, specifically Target 4.c, which emphasizes the importance of improving teacher quality through international cooperation and professional development. While Artificial Intelligence (AI) integration has garnered attention, language teacher education remains insufficiently addressed, underscoring the need for a dedicated framework to assess and enhance AI readiness in this field. Addressing this gap, the study utilizes Holmstr\u0026ouml;m's AI Readiness Framework to examine perceived and demonstrated readiness among language educators in Pakistan, Uzbekistan, and Saudi Arabia, exploring infrastructure, expertise, and institutional support. It identifies barriers and enablers to AI integration and proposes a replicable framework for contextually diverse teacher education systems. A two-phase mixed methods design was employed. In Phase 1, 400 language education professionals completed surveys, while focus group discussions were conducted with a purposive sample of 40 willing participants to explore contextual challenges in AI integration. Survey findings revealed the highest AI readiness in Saudi Arabia (M\u0026thinsp;=\u0026thinsp;4.10), followed by Uzbekistan (M\u0026thinsp;=\u0026thinsp;3.10) and Pakistan (M\u0026thinsp;=\u0026thinsp;2.92). Readiness was correlated with infrastructure (r\u0026thinsp;=\u0026thinsp;0.673), while faculty training explained 39.7% of the variance. Focus group discussions reinforced these findings, revealing ethical concerns, policy gaps, and limited training as persistent challenges. In Phase 2, 100 participants were stratified and completed a task-based AI readiness assessment. Strengths were noted in tool use and privacy awareness, while ethics and peer training were identified as areas for improvement. Recommendations include modular training, institutional readiness benchmarks, and localized policy reform. Future research should investigate the relationship between AI readiness and learner outcomes.\u003c/p\u003e","manuscriptTitle":"A Comparative Study of AI Readiness in Language Teacher Education in the Global South","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 09:04:58","doi":"10.21203/rs.3.rs-7614466/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-09T11:56:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-30T15:14:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T08:18:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39537716059731326530858639816748017490","date":"2025-11-29T08:03:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243165276451687289347607946895811185090","date":"2025-11-29T06:50:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-14T16:58:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-30T13:14:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-29T19:26:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2025-09-29T19:22:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e63e3ef5-7a3d-4850-b555-28ffb84d8f72","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-20T04:38:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 09:04:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7614466","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7614466","identity":"rs-7614466","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}