Beyond “Camel, Desert, Ramadan Defaults”: Faculty Use of GenAI for Culturally Responsive Teaching in UAE

Beyond “Camel, Desert, Ramadan Defaults”: Faculty Use of GenAI for Culturally Responsive Teaching in UAE | 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 Beyond “Camel, Desert, Ramadan Defaults”: Faculty Use of GenAI for Culturally Responsive Teaching in UAE Ajda Osifo, Mustafa Aydogan, Esra Izmir This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8722631/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Generative artificial intelligence (GenAI) is rapidly reshaping higher education teaching practices, yet we still know little about how faculty in culturally specific, non-Western contexts evaluate and adapt GenAI outputs for culturally responsive teaching (CRT). This qualitative interpretivist study examines how faculty members in UAE higher education use GenAI to support cultural fit in multicultural classrooms while navigating local norms, institutional expectations, and ethical constraints. Drawing on Gay’s CRT framework, we conducted semi-structured interviews with 20 faculty members across disciplines and national backgrounds who actively use GenAI in their teaching. Using reflexive thematic analysis, we identified four themes: (1) Culturally responsive pedagogical strategies using GenAI; (2) Faculty sensemaking of GenAI for cultural responsiveness; (3) Cultural and relational benefits of GenAI-supported teaching; and (4) Institutional, ethical, and cultural risks/barriers. Findings suggest that GenAI primarily functions as an accelerator of surface localization (e.g., examples, tone, translation) rather than a driver of deeper CRT transformation unless faculty apply explicit cultural decision rules and critical consciousness to interrogate bias, representation, and epistemic authority. Implications highlight the need for UAE-specific guidance, culturally grounded professional development focused on evaluative judgment, and shared vetted case resources to support responsible GenAI use at scale. GenAI Culturally Responsive Teaching Higher Education UAE Faculty Perspectives Introduction The launch of ChatGPT in late 2022 triggered widespread and rapid shifts in higher education practices. GenAI, powered by Large Language Models (LLMs), is a subset of artificial intelligence that has the ability to generate various content types including texts, images, audios, and videos enhancing personalized learning experiences and skill development (Cano et al., 2023 ; Zawacki-Richter et al., 2024 ). GenAI has immense potential to transform pedagogical approaches and to enhance teaching, learning, and assessment for both educators and students as it can assume multiple roles including that of a co-creator (Jiménez Romanillos & Andersson, 2024), collaborator and perceptive partner (Adam et al., 2024 ), virtual tutor (Tu, 2024 ), and cognitive support tool (Essien et al., 2024 ) for faculty members. This multifunctionality has facilitated its widespread adoption across a broad range of academic disciplines. Despite their powerful capabilities, GenAI tools also present shortcomings that can impede faculty members’ intellectual work and professional judgment in higher education. When instructors rely on GenAI uncritically, it can encourage superficial engagement with disciplinary content and reduce opportunities for deeper reasoning, thereby weakening critical thinking and evaluative judgment (Monib et al., 2025 ; Zawacki-Richter et al., 2024 ). Such reliance may also diminish sustained cognitive engagement in academic work, including lesson design, assessment construction, and feedback practices. For faculty, this risk is not simply doing tasks faster , but potentially outsourcing core scholarly and pedagogical thinking, moving too quickly from prompt to polished output without sufficient conceptual grounding or reflection. As a result, instructors may become more vulnerable to accepting plausible but inaccurate information and to overlooking cultural or epistemic biases embedded in GenAI outputs, making verification and critical review essential components of responsible use (Barana et al., 2023 ; Tao et al., 2024 ). Relatedly, a central challenge concerns faculty members’ evaluative judgement when working with GenAI-generated outputs. Bearman et al. ( 2024 ) conceptualize evaluative judgement as a core dimension of critical thinking that is essential for working safely and productively with GenAI. As findings from Liu et al. ( 2024 ) show, within human-GenAI collaboration, while GenAI may assist with idea generation and support the exploration and synthesis of diverse perspectives, it does not assume responsibility for the final content. Consequently, responsibility remains with human users who must be actively involved throughout the process, monitor and verify, and critically examine the outputs (Henadirage et al., 2026 ; Liu et al., 2024 ), and, above all, make informed judgements about their quality and validity, known as human-in-the-loop process (Qiu et al., 2025). Exercising such evaluative judgement is far from straightforward. Many higher education stakeholders, including faculty members may encounter difficulties in this regard, particularly when GenAI systems reproduce Western-centric assumptions (Grab, 2025 ; Watson et al., 2024 ), cultural and contextual biases, or normative frameworks that are not readily visible without strong cultural awareness (Agarwal et al., 2025 ). Because evaluating GenAI is not only a technical or cognitive task, but also a cultural and epistemic one, such assumptions and biases may remain unnoticed by those who lack sufficient cultural and critical awareness (Watson et al., 2024 ; Yusuf et al., 2024 ). As a result, instructors may uncritically accept outputs that reflect misaligned values, implicit stereotypes, or culturally inappropriate framings (Tao et al., 2024 ). These challenges are particularly troubling given the pace at which GenAI has been integrated into teaching and learning across diverse geographical contexts, often without sufficient pedagogical alignment, cultural sensitivity, or critical evaluation (Wu, 2024 ). Accordingly, these concerns emphasize the need for culturally responsive and locally grounded approaches to GenAI implementation in educational settings, particularly in non-Western and culturally diverse contexts. These issues become especially salient in higher education contexts where cultural norms strongly shape classroom interaction, institutional expectations, and what counts as appropriate communication. Higher education in the United Arab Emirates (UAE) has become a markedly internationalized sector, bringing together students and faculty from diverse linguistic, national, and cultural backgrounds within institutions shaped by local cultural traditions, religious values, and social expectations (Aydogan et al., 2025 ; Eppard et al., 2021 ; Shahin et al., 2025a ). Expatriates make up 88.5% of the UAE’s approximately 11 million population (Global Media Insight, 2025 ), and within universities this demographic reality contributes to highly multicultural learning environments where Emirati students study alongside international peers and are frequently taught by internationally recruited faculty whose academic training is often grounded in Western higher education systems (Aydogan et al., 2025 ; Shahin et al., 2025b ). Importantly, the nature of diversity is not uniform across contexts. While diversity in some countries is primarily expressed through race, ethnicity, and language, differences related to gender, social class, and ability exist within national contexts, yet manifest in culturally specific ways (Gay, 2015 ). In the UAE, cultural sensitivity in higher education therefore requires attention not only to visible indicators of cultural identity (e.g., nationality or language), but also to often-relational and religious local norms that regulate gender-based interaction, authority, communication style, and social boundaries (Aydogan et al., 2025 ; Singh et al., 2021 ; Williams et al., 2025 ). Teaching in the UAE is continually negotiated at the intersection of global academic practices and local cultural expectations, making culturally responsive pedagogy essential for fostering inclusion, engagement, and mutual respect (Moore-Jones, 2015 ). To conceptualize this cultural work of teaching, we draw on culturally responsive teaching (CRT) as a guiding framework. CRT is an approach that aims to increase teaching effectiveness by using learners’ cultural characteristics, experiences, and perspectives as pedagogical resources (Gay, 2002 ). In higher education settings such as the UAE, CRT is especially relevant because faculty must translate disciplinary expectations and institutional standards into classroom practices that land appropriately for students navigating English-medium instruction, culturally shaped interaction norms, and local expectations around respectful communication (Aydogan et al., 2025 ; Gay, 2015 ). As GenAI becomes embedded in planning, material development, and faculty–student communication, it can shape how (and how quickly) these translations are produced without guaranteeing cultural depth. From this lens, cultural responsiveness is not an abstract attitude; it is enacted through concrete teaching decisions, how examples are selected, how cases are framed, what tone is used in feedback, how participation is structured, and how knowledge is positioned as legitimate or relevant. Gay’s CRT model emphasizes that CRT requires more than general goodwill or broad awareness of diversity; it involves developing a knowledge base of cultural diversity and using it to design culturally relevant curricula, enact cultural caring and build a learning community, communicate across cultures, and achieve cultural congruity in instructional strategies (Gay, 2002 ; Gay, 2010a , 2010b ). Instructors’ cultural responsiveness is therefore expressed through routine pedagogical labor: adapting language demands, calibrating directness or “face” considerations, selecting culturally situated illustrations, and structuring learning activities in ways that align with students’ interactional expectations while sustaining academic challenge (Gay, 2002 , 2015 ; Moore-Jones, 2015 ). Within this framework, Gay ( 2010a ) further clarifies CRT through three interrelated emphases that are particularly useful for faculty work with GenAI: cultural competence (understanding and valuing learners’ cultural contexts), critical consciousness (questioning biases and assumptions that reproduce inequity), and academic success (supporting students’ academic flourishing through rigorous, culturally congruent teaching). Two additional issues in the CRT literature are particularly relevant for positioning the current study. First, CRT is often at risk of being operationalized as “adaptation” rather than deeper pedagogical transformation, e.g., making instruction feel more familiar through surface localization (names, examples, simplified language) while leaving underlying epistemic assumptions and authority structures intact (Gay, 2010a , 2015 ; Shahin et al., 2025b ; Smith, 1999 ). This matters in internationalized systems like the UAE where faculty may already experience their teaching materials as imported, Western-centric, or culturally distant, and where localization can become a routine necessity rather than a critical intervention (Shahin et al., 2025b ). In practice, CRT in such contexts requires sustained judgement about what constitutes fit beyond recognizability, how culture shapes meaning, participation norms, and the interpretability of scenarios, as well as whose knowledge is represented in the curriculum (Gay, 2010a ). Second, CRT foregrounds the educator as the primary agent of cultural judgement, which places attention on faculty competence development rather than on tools alone. Cultural competence and critical consciousness are not static end-states; they involve ongoing learning, reflective practice, and iterative adjustment in response to students and context (Gay, 2002 , 2010a ). This is especially salient when GenAI enters the workflow: GenAI can support drafting, localization, tone-polishing, and translation, but it cannot own the cultural reasoning that determines what is appropriate, what is risky, what is misrepresentative, and what reproduces bias (Grab, 2025 ; Liu et al., 2024 ). GenAI may accelerate teachers’ access to culturally situated materials, but the CRT literature suggests that cultural responsiveness still depends on human interpretive work by making explicit choices about language, representation, interaction, and equity rather than treating localization as a technical edit (Gay, 2010a , 2015 ; Shahin et al., 2025b ). In this sense, GenAI becomes a mediator of pedagogy. It can enable culturally responsive practices (e.g., localizing examples, clarifying language, varying activity design), yet it may also introduce vulnerabilities such as culturally inappropriate scenarios, tone that threatens face , or stereotypical representations (Agarwal et al., 2025 ; Grab, 2025 ). There are growing concerns that AI tools may not reliably support CRT across diverse educational settings, particularly where learners’ cultural backgrounds shape how they interpret instruction and experience learning (Grab, 2025 ). At the same time, educators can use AI systems to scaffold lesson design and evaluation around learners’ cultural contexts and lived realities, potentially making instruction more accessible and supportive (Henadirage et al., 2026 ; Mackey & Evans, 2020 ). However, we still know relatively little about how faculty make these cultural-fit judgements in practice, how they evaluate, localize, filter, and ethically constrain GenAI outputs when teaching in culturally specific contexts such as the UAE. Despite the growing body of research, there remains limited insight into how faculty in the UAE are pedagogically integrating GenAI into teaching and assessment, or how culturally embedded biases and normative assumptions in GenAI outputs are negotiated in practice. This lack of contextually grounded evidence represents a significant gap in the current literature. In response, this study examines how faculty in UAE higher education integrate GenAI into teaching and how they adapt, evaluate, and localize GenAI outputs to align with local cultural norms, student needs, and pedagogical goals across teaching preparation, learning materials, student engagement, and assessment. Guided by the CRT framework (Gay, 2002 , 2010a ), this research addresses the following question: How do faculty members utilize GenAI to support culturally responsive teaching in UAE higher education? Method A qualitative interpretivist approach (IPA) was adopted for this study because our primary aim was to understand how faculty members make sense of, negotiate, and enact the use of GenAI for culturally responsive teaching within the culturally diverse higher education context of the UAE. IPA is well suited to inquiries that foreground meaning making and the situated nature of practice, recognizing that participants’ accounts are shaped by the social, cultural, and institutional contexts in which they work (Denzin & Lincoln, 2011 ; Schwandt, 1994 ). Accordingly, we treated faculty members’ narratives as contextually grounded interpretations of their pedagogical decisions, how they assess cultural appropriateness, adapt GenAI outputs, and manage sensitivities in multicultural classrooms, rather than as neutral reports of “objective” behaviors (Denzin & Lincoln, 2011 ; Merriam & Tisdell, 2016 ). This stance aligns with a constructivist view of knowledge, in which understanding is co-produced through participants’ perspectives and the researchers’ analytic engagement with the data (Lincoln & Guba, 1985 ). Participants The study employed purposive and snowballing sampling strategies to recruit participants at main universities in the UAE. To be eligible, faculty members were required to (a) hold a higher education degree (master’s and/or doctoral), (b) actively utilize GenAI tools in their teaching practices, and (c) have had at least one year of teaching experience at a UAE institution. Invitation emails were sent to faculty members at the participating universities, inviting them to take part in an interview during the Fall 2025 semester. After each interview, participants were asked to suggest other potential participants. Data were subsequently collected through interviews with 20 volunteer academics. As shown in Table 1 , participants represented 10 nationalities, had high educational mobility, and were from a wide range of disciplines reflecting a wide range of cultural backgrounds and experiences. Table 1 Participant Demographics Pseudonym Gender Country of Origin Education Field Years in the UAE Degree Rana F Lebanon Germany Environmental Health and Safety 5 Ph.D. Yannis M Greece Australia Educational Technology 25 Ph.D. Dana F Egypt US Psychology 4 Ph.D. Arif M Pakistan Pakistan Linguistics 10 Masters Rami M Philippines Philippines Mathematics 10 Masters Omar M Cyprus Cyprus Business Administration 25 Masters Faisal M Pakistan Pakistan Mathematics and Statistics 9 Masters Michael M US US English/Writing 2 EdD Yulia F New Zealand New Zealand TESOL 17 Masters Lina F Lebanon UAE Special Education 17 Ph.D. Sean M US US Linguistics 8 Ph.D. Katerina F UK UK Education 2 Masters Sana F US US Psychology 6 Ph.D. Theresa F Egypt UAE Linguistics 2 Masters Moayad F Egypt Egypt Mass Communication 10 Ph.D. Fadi F US US Mathematics 13 Ph.D. Khalid M Jordan Jordan Educational Psychology 22 Ph.D. Saad M NA US Business 20 Ph.D. Sami M Egypt UK Statistics 8 Ph.D. Andreas M US UK Business 2 Ph.D. Note : NA: Data Not Available. In case of multiple degrees from different countries, the last degree obtained was recorded. Data Collection and Analysis The interview questions were developed in alignment with the study objectives and relevant literature on culturally responsive teaching and GenAI in higher education. Using a semi-structured format, the protocol included open-ended questions around (a) cultural considerations in teaching Emirati students, (b) how participants use GenAI to design culturally relevant materials and learning activities, and (c) how they evaluate the cultural appropriateness of AI-generated outputs. Participants were encouraged to reflect on their teaching practices in UAE classrooms, with particular attention to how cultural norms and student characteristics shape instructional decisions and how GenAI is incorporated to support culturally responsive learning environments. Following ethical approval (Approval number ZU25_036_F), data were collected through 20 interviews conducted online via Zoom. All interviews were audio-recorded with consent and transcribed verbatim. We employed reflexive thematic analysis to identify patterns of meaning across the dataset (Braun & Clarke, 2006, 2021). In line with IPA, the analytic aim was to extract meaning by segmenting transcripts, assigning codes, examining overlap across codes, and consolidating these into higher-order categories and themes (Creswell, 2012 ). Analytic memos also captured salient interactional cues (e.g., pauses, emphasis, and noticeable shifts in tone) when these provided context for interpreting participants’ intended meanings (Norris, 2013 ). Analysis proceeded inductively through iterative cycles of coding and theme development rather than applying predetermined categories. Transcripts were read by the second and third authors repeatedly to support familiarization, and initial codes were generated across the full dataset. Codes were then reviewed, compared, and clustered into higher-order categories through successive rounds of analysis, using constant comparison to refine boundaries and relationships among codes (Saldaña, 2016 ; Merriam & Tisdell, 2016 ).The research team met regularly to discuss coding decisions and candidate themes, resolving differences through discussion and returning to the transcripts to ensure interpretations were grounded in participants’ accounts. The first author served as an analytic auditor, challenging interpretations, requesting transcript evidence, and checking theme coherence. As shown in Table 2 , the final analytic structure consisted of four themes, with associated categories and codes. Authors’ Reflexivity Statements As a research team, we recognize that our positionalities and professional experiences shaped how we interpreted participants’ accounts and how we constructed patterns of meaning in the dataset. We approached analysis reflexively attending not only to what participants explicitly stated but also to the underlying assumptions, cultural logics, and tensions that organized faculty talk about GenAI and CRT. The team’s disciplinary backgrounds in multiculturalism, international higher education, and language teaching in diverse classrooms sensitized us to issues of cultural responsiveness and power in classroom communication, while also posing a risk of over-privileging pedagogical interpretations over technical ones. Because the first two authors work within majority-Emirati public universities, we were mindful that participants might frame their responses in institutionally acceptable terms; we therefore treated hesitations, indirect speech, and ambiguity as meaningful data rather than as gaps. As regular users of GenAI for teaching and academic work, we were attentive to the ways our own comfort with these tools could shape what we interpreted as effective or culturally responsive use. Additionally, interviews were conducted in English, and we remained attentive to how English-as-a-lingua-franca may have shaped participants’ phrasing, especially when discussing culturally sensitive issues. The first author is a Western-educated faculty member with approximately 10 years of experience in UAE higher education, which supported contextual sensitivity to local institutional norms and classroom dynamics but also risked taking familiar practices for granted. The second author is also Western-educated and has been based in the UAE for approximately 3 years; this position offered both a comparative lens and an awareness of the interpretive work required when navigating local cultural expectations as a relatively newer member of the system. The third author is internationally trained and resides outside the UAE, contributing analytic distance that helped surface implicit assumptions in our readings and prompted us to justify interpretations with evidence across transcripts. All authors use English as a second language, and we remained reflexive about how this shared linguistic positioning could influence both interview interaction (e.g., word choice, politeness strategies, and indirectness) and analytic interpretation (e.g., how we understood nuance, emphasis, or culturally loaded terms). Throughout the project, we used memoing, peer debriefing, and repeated return to the transcripts to interrogate our assumptions and preserve complexity, including ambivalence and critique, as analytically meaningful. Results Through IPA, we organized participants’ collective accounts into four overarching themes: (a) Culturally responsive pedagogical strategies using GenAI , (b) Faculty sensemaking of GenAI for cultural responsiveness , (c) Cultural and relational benefits of GenAI-supported teaching , and (d) Institutional, ethical, and cultural risks/barriers . These themes helped us explore and understand how faculty members in the UAE make sense of GenAI and how they use it to support culturally responsive pedagogy in this diverse learning environment. In the next sections, we present each theme and its subcategories, drawing on participants’ own words using pseudonyms assigned by the authors. Direct quotations are provided to preserve participants’ intended meanings and to represent the range of perspectives reflected in the dataset, consistent with a qualitative interpretivist approach. Theme 1: Culturally responsive pedagogical strategies using GenAI Most participants agreed that using GenAI strengthened their pedagogical approach by supporting content localization, language and communication scaffolding, and the design of culturally relevant engagement-focused learning experiences. GenAI tools provided speed and easy access to teaching materials for faculty who are often unfamiliar with the local students’ cultural characteristics. Khalid said “ it [GenAI] is saving time, giving good and quick ideas, generate a lot of fantastic outcomes. ” A salient theme was that participants described their teaching experiences and materials as deeply embedded in Western knowledge and examples, which they viewed as a major challenge for pedagogical adaptation; GenAI tools came into play as a pragmatic resource for reworking, localizing, and reframing these materials to better fit students’ cultural contexts. Localizing content happened in multiple ways. Creating case scenarios with UAE’s cultural dynamics was often emphasized by the faculty members. These participants valued GenAI’s support in finding information about local companies or government offices providing students a connection between global and local perspectives. Katerina recalled “ in one of my courses about leadership, [using GenAI] I might go with an example of a local company, let's say, an Emirati company, which is very well known for the students in the country, rather than using global examples. ” Importantly, participants offered mixed assessments regarding the extent to which localization amounted to superficial substitutions (e.g., changing names or minor contextual details) versus more substantive integration of culturally relevant elements into the narratives and scenarios embedded in teaching materials. As Dana put it: “ changing the company name from Amazon to Noon or Kareem is very superficial. ” A dominant narrative centered on the language and communication scaffolding role of GenAI. Participants valued the ability to manipulate language and its content based on learners’ needs. They welcomed GenAI for translating teaching materials into what Dana described as a “ relatable and acceptable ” medium for local students. Adaptation through translation ranged from cross-language translation to adjusting culturally specific nuances. Andreas aptly described this: “ I'm in a business school, and so very often I get a case meant for the US, or the UK, or Australia. It's meant for native English speakers in a particular environment. I'll very often take that, use Copilot or ChatGPT to clean it from idioms and phrases non-English speakers cannot understand… It is like English-to-English translation. ” Across interviews, a clear pattern emerged that faculty used GenAI to polish the tone of their communication with students. These included assignment descriptions, announcements, emails, or even in-class talks. Sean navigated cultural differences in communication styles using GenAI as a mediator: “ Sometimes I will paste an email and ask it to make it more polite, because you know, culturally here, you need to be careful how you phrase things. ” Participants also positioned GenAI-enabled multimedia and gamification as pedagogical tools for culturally aligned engagement. Moayad described how interactive games helped him maintain students’ attention and classroom momentum: “ Because they get bored very quickly, you know?… So in order to keep the class interactive and ongoing, I usually put some gaming… using HTML5 ,” Yet participants also cautioned that cultural responsiveness is not automatic; visuals and examples must be reviewed for audience fit, including tone, appropriateness, and relevance. Sean captured this ongoing need for judgment: “ If you ask it to create visuals or examples, it can do it quickly, but you still need to check if it fits the audience. ” Theme 2: Faculty sensemaking of GenAI for cultural responsiveness Across interviews, participants made sense of GenAI less as an answer machine and more as a pedagogical tool that must be guided, edited, and ethically constrained by the educator. Their accounts clustered around (a) clarifying the role of AI versus the role of the educator, (b) developing practical decision rules to make outputs fit culturally, and (c) articulating adoption stances that ranged from professional growth orientations to resistance and heightened awareness of bias and stereotyping. A dominant narrative positioned GenAI as extending capacity rather than replacing relational pedagogy and human judgment. As Rana emphasized, “... it's a tool. It doesn't replace a human being… We created that. The most important thing, it's a tool that helps us improve [higher education] further. ” Similarly, Lina framed GenAI as a way of scaling responsiveness while retaining the human role: “ It's not about replacing the human role. We need to always have that human role, but it's all about extending our capacity to be responsive to student needs. ” Faculty repeatedly located cultural responsiveness in the educator’s relational work, facilitation, mentoring, and emotional attunement, rather than in the technology itself. Theresa described this stance as facilitative teaching: “ I'm there as a facilitator, I'm there to guide them, but I'm not there to force knowledge into them, okay? ” Rana made the relationship explicit: “ I'm not a teacher. I'm not a teacher to you. I'm a mentor to you, you know? ” Andreas further anchored cultural responsiveness in affective human qualities GenAI cannot replicate: “ generative AI is not human. It's just gonna take whatever the average is, right? I feel like this empathy, these feelings that you do not see in AI, you know, this is what I care about. ” Participants described “ fit ” as something they produce through inputs and human-in-the-loop revision. Yulia was direct that cultural responsiveness depends on intentional prompting: “ the weakness is generally just a lack of specificity in its… generalities, unless you prompt it in a different way. …, AI will never give you a culture-responsive content ” This logic was echoed as a rule of thumb about inputs: Lina noted, “ I'll put the case study and then I'll ask, okay, can we just change this into something that is less Westernized ,” while Sean summarized it aptly: “ the output is dependent on the input. Garbage in, garbage out kind of things. ” Faculty also described routine editing as part of cultural care, especially around sensitive phrasing. Lina explained, “ I check them. I read it, I check it, some of the scenarios were not really possible or cannot be used in the classroom… and I just erased the scenarios ” Rami similarly described his role as explicitly adaptive: “ So, most of the roles that I do here would just be filtering and editing and adapting the outputs that AI will give me to resonate within the Emirati culture. ” Alongside verification and triangulation, participants warned against taking outputs for granted. Sean used a vivid metaphor for over-trusting outputs: “ you have to know that you are driving into a river, no matter what the device says… it's still at a stage where it occasionally directs you to drive your car straight into the river, and you've got to know enough. ” Participants’ orientations ranged from openness and professional growth to strong resistance grounded in perceived risk. For some, GenAI was tied to staying current: Theresa reflected, “ Well, it's… it's the buzzword. Everyone's talking about AI nowadays… if I failed to update my knowledge, or failed to evolve with what's going on in the world, then it's not going to be helping me… ” and Rana similarly noted, “ I like always to be up-to-date, because I believe this is part of professionally developing myself as well. ” Theme 3: Cultural and relational benefits of GenAI-supported teaching Across interviews, participants described benefits of GenAI that extended beyond efficiency, including inclusive access, stronger engagement and motivation, and greater cross-cultural connection and belonging. These outcomes were linked to concrete practices, subtitles, translation, interactive activities, and rapid contextualization, rather than assumed as automatic. Participants noted that GenAI-supported materials could reduce participation barriers for students with different communication needs. Michael highlighted real-time accessibility supports: “with the new technologies now, we use online screens, BenQ for students who cannot catch up with me, or who have problems.” Omar also referenced hearing-related support: “in hearing problems, this [GenAI-based subtitles] would be a one way of inclusion”. Translation and multilingual delivery were similarly framed as inclusion strategies. Yannis noted that students “can, ask the translation and can read it… in many different languages,” while Arif emphasized real-time language support in English-medium settings. Participants framed cultural responsiveness as relational design, structuring classroom interaction so students can participate safely and feel a sense of belonging across differences. Yulia described culture work as managing who works with who in mixed-gender settings: “ it’s thinking about who’s working with who, especially now that we’ve got mixed-gender classes. So who’s working with who ,” alongside “ being aware of… culturally sensitive topics. ” Similarly, Lina emphasized adjusting examples and group processes to reduce discomfort and support inclusion: “ now we changed into the mixed gender classes. So here is where… we had to change a bit ,” and “ for me… it’s about gender and culture and how you can tailor the class in a way that respects… the fact that they’re not really comfortable with each other ,” adding that in grouping and workplace-prep discussions “ you always make sure that they feel comfortable .” Lina also linked this relational work to resisting stereotyped “culture” shortcuts in GenAI outputs, when prompts over-localize, “ you immediately get the camel, the desert and Ramadan defaults, and that this is not the reality. ” Theme 4: Institutional, ethical, and cultural risks/barriers. Across interviews, participants emphasized that GenAI use in culturally responsive teaching is constrained by institutional governance and resources, academic integrity pressures, and cultural/ethical risks that require careful judgment rather than automated adoption. Participants described uneven institutional guidance, ranging from permissive policies to discouraging stances, shaping how confidently faculty could integrate GenAI. Andreas noted, “our policy is that the faculty members just have to be very clear about what their AI policies are, but it's a pretty inclusive policy,” while Fadi contrasted this with a more restrictive stance: “So our policy so far in computing, we discourage students, from using, actually, generative AI.” Participants also pointed to the need for capacity-building. Yannis described institutional efforts: “We have professional development program… part of it about AI, a certificate about how to incorporate AI. “Yet Theresa observed that cultural dimensions were often missing: “in the training. there wasn't much talk about culture, honestly.” Resource constraints further limited uptake; Yannis raised cost and access concerns: “my university does not, pay for our subscription… Who's gonna pay for it?” A dominant concern was how GenAI complicates authorship, effort, and detection. Rana described the dilemma of “ too perfect ” submissions: “You know that you have a level D or F students, and then his assignment is perfect. We are all suffering from this.” Others highlighted the limits of detection tools and the ambiguity of what “counts” as AI-assisted work; Saad captured this tension: “we are promoting it; students are using it… and sometimes AI is also not detected.” Participants emphasized that culturally responsive practice requires boundaries around sensitive content, active editing, and skepticism toward outputs. Yannis stated plainly, “We avoid to discuss politics, we avoid to discuss religion, we avoid to discuss sensitive issues,” while Rana described human-in-the-loop screening as a safeguard: “if there are certain phrases that I feel like it's culturally sensitive… I change it.” Several participants warned that GenAI can amplify stereotypes; Khalid noted “a kind of bias… describing women… that all of them wear abaya… and Emirati, all of them rich,” and Dana cautioned that it can be “fostering more stereotypes.” Privacy and data sensitivity were also salient; Rami drew a clear boundary: “I cannot give Real-life data with them.” Finally, trust and quality problems were framed as an ever-present risk; Rana warned, “Whatever you say, he will agree with you,” and Andreas advised, “double-check everything… never do something with generative AI that then you submit without really… re-reading.” Table 2 Thematic Framework and Code Tree GenAI and CRP Theme Category Codes Theme 1: Culturally responsive pedagogical strategies using GenAI 1A. Localizing content to UAE contexts T1.1 UAE case/scenario prompting T1.2 UAE policy/law anchoring T1.3 Culturally familiar names/examples 1B. Language & communication scaffolding T1.5 Translation / bilingual support T1.6 Tone-polishing for culturally acceptable communication 1C. Designing culturally engaging learning experiences T1.7 Gamification & interactive activities T1.8 AI-generated multimedia/visual explanation Theme 2: Faculty sensemaking of GenAI for cultural responsiveness 2A. Role of AI vs role of educator T2.1 “AI is a tool, not a replacement” T2.2 Educator as facilitator/mentor (human relationship as core) 2B. Cultural-fit decision rules (how they make AI “fit”) T2.3 “What you feed it” (prompting for cultural fit) T2.4 Human-in-the-loop editing for sensitive phrasing T2.5 Verification/triangulation (don’t take outputs for granted) 2C. Adoption stance & cultural reflexivity T2.6 Openness/professional growth orientation T2.7 Resistance/denial among some faculty T2.8 Noticing stereotype patterns in AI outputs Theme 3: Cultural and relational benefits of GenAI-supported teaching 3A. Inclusive access & support T3.1 Accessibility supports (subtitles/hearing/reading support) 3B. Engagement & motivation T3.2 Attention/retention in “fast-bored” student culture T3.3 Positive climate (fun, rewards, energy shifts) 3C. Cross-cultural connection & belonging T3.4 Breaking stereotypes through structured interaction T3.5 Faster localization help (especially for non-local faculty) Theme 4: Institutional, ethical, and cultural risks/barriers 4A. Governance & resources T4.1 Institutional policy/guidelines shaping use T4.2 Need for training/workshops to use GenAI well T4.3 Access/cost constraints (tools not free / uneven availability) 4B. Academic integrity T4.4 Plagiarism/“too perfect” work / ethical use concerns 4C. Cultural & ethical risks T4.5 Sensitive topics/taboos (what cannot be said directly) T4.6 Stereotype amplification risk T4.7 Privacy/data sensitivity concerns T4.8 Trust/quality problems (over-agreeing, need to check sources) Discussion The aim of this study was to examine how faculty in UAE higher education are making sense of and practically using GenAI to support culturally responsive teaching in culturally and linguistically diverse classrooms. Our findings contribute to emerging scholarship on GenAI adoption by showing that instructors largely position these tools as capacity-extenders for localization and communication, while also highlighting the conditions and tensions that shape whether GenAI-supported cultural fit becomes meaningful pedagogical responsiveness or remains largely surface-level adaptation. A core contribution of the current study was showing that faculty framed GenAI as a practical accelerator for CRT in the UAE, especially when their materials were “ embedded in Western knowledge and examples ” that felt distant from local cultural references and institutional realities. Rather than “innovating,” participants described using GenAI to quickly generate locally situated examples, cases, and materials that would otherwise take substantial time. A latent risk, however, is that some faculty may treat GenAI as a substitute for, rather than support to, the ongoing development of their own intercultural competence, producing surface-level relevance while deferring deeper cultural learning. Echoing this concern, Naidu and Sevnarayan ( 2025 ) warn that unmediated GenAI can reinforce stereotypes and marginalize cultural perspectives, and Nyaaba ( 2025 ) notes that even context-aware systems may hallucinate, especially in underrepresented languages. Thus, technical localization is helpful but insufficient: culturally responsive GenAI use ultimately depends on faculty cultural expertise and critical judgment, aligning with Gay’s ( 2002 ) argument that CRT rests on educators’ deep, group-specific knowledge, not just localized examples or language. Despite enthusiasm for GenAI, across Theme 2, faculty repeatedly positioned responsiveness in mentoring, facilitation, emotional attunement, and judgment as the human characteristics they believe GenAI cannot replicate. GenAI was described as capacity-extenders but not replacing the relational work through which students feel seen, supported, and guided: augmentation within relational pedagogy. This finding is coherent with the UAE’s relational culture (Eppard et al., 2021 ; Moore-Jones, 2015 ; Shahin et al., 2025a ) and CRT’s emphasis on cultural caring and community-building, where responsiveness is enacted through how instructors structure interaction, communicate respect, and sustain a learning climate that supports engagement without violating local norms around face, authority, and participation (Gay, 2002 , 2015 ). These findings are also in line with the broader GenAI-in-higher-education literature cautioning that responsibility for meaning-making and pedagogical judgement remains with the human instructor, even when GenAI assists with drafting and ideation (Henadirage et al., 2026 ; Liu et al., 2024 ; Bearman et al., 2024 ). Participants described benefits that went beyond saving time, linking GenAI to inclusive access, engagement, and classroom belonging when paired with purposeful instructional design. These outcomes were tied to specific practices, subtitles, multilingual supports, interactive activities, and structured collaboration, rather than assumed as automatic effects of the technology. This supports the argument that AI systems can scaffold instruction around learners’ linguistic and cultural realities when educators intentionally design for accessibility and participation (Mackey & Evans, 2020 ; Zawacki-Richter et al., 2024 ). Such inclusivity-oriented instructional practices may be understood as indirectly supporting academic success within Gay’s ( 2010a ) CRT framework. At the same time, it reinforces that inclusive outcomes are contingent on human judgement and design choices, rather than inherent properties of the tool, particularly in culturally specific contexts where communication style and appropriateness shape whether students experience instruction as respectful or misaligned (Gay, 2002 , 2015 ; Williams et al., 2025 ). Faculty emphasized that GenAI-supported culturally responsive teaching is bounded by institutional conditions, including uneven policies, limited training, and tool access disparities. Academic integrity pressures, privacy concerns, and sensitive-topic boundaries further shaped what instructors felt they could do in practice (Zawacki-Richter et al., 2024 ). This finding is consistent with scholarship noting that GenAI adoption is occurring rapidly, often ahead of coherent governance, pedagogical guidance, or culturally grounded professional development (Damiano et al., 2024 ; O’Dea, 2024 ; Wu, 2024 ; Yusuf et al., 2024 ). In the UAE context, these constraints may be amplified because local norms strongly regulate what counts as appropriate classroom discourse and because faculty already navigate high-stakes expectations around respectful communication, sensitive topics, and institutional risk (Aydogan et al., 2025 ; Singh et al., 2021 ; Williams et al., 2025 ). Participants spoke at length about localizing examples, polishing tone, translating language, and achieving fit , which can be interpreted primarily as cultural competence at the level of materials and communication (Gay, 2010a ). Yet their accounts were comparatively thin on questions of power and knowledge politics, who is represented, whose knowledge counts, and how curricula may reproduce hierarchy through what is taught and what is left unspoken (Shahjahan et al., 2022 ). In that sense, culturally responsive practice risks being framed primarily as adaptation (making Western materials more palatable or locally recognizable) rather than transformation (re-centering local epistemologies and interrogating dominant frames). As Gay ( 2010b ) emphasizes, it is the educator’s culturally informed interpretation of students’ actions, needs, and strengths, not merely the presence of culturally familiar content that ultimately determines whether teaching is genuinely responsive. Participants’ emphasis on localization without sustained attention to power, representation, or epistemic authority reflects Gay’s ( 2002 ) distinction between adding multicultural content and redesigning curriculum through a culturally responsive lens. This aligns with Smith’s ( 1999 ) critique and, in the UAE context, with Shahin et al.’s ( 2025b ) findings on localization as “cultural substitution without epistemic transformation (p. 1),” where representational tweaks (e.g., local names, places, and culturally “safe” examples) may increase surface relevance while leaving Western authority structures intact, and can even edge toward symbolic epistemicide when non-Western ways of knowing remain structurally absent (Dutta, 2018 ; Shahjahan et al., 2022 ). Recent literature suggests GenAI may inadvertently accelerate this biased pattern (Tao et al., 2024 ): it makes it easier to swap in UAE examples, remove idioms, and generate “acceptable” language, but it does not automatically bring power, positionality, or epistemic justice into the pedagogical work unless faculty explicitly pursue that agenda. Many participants said they “check,” “filter,” and “adapt” GenAI outputs, especially around sensitive phrasing, yet the criteria for doing so often remain vague (e.g., what counts as culturally sensitive, what is removed, and which ethical/CRT commitments guide revisions). This points to a gap in critical consciousness, a key element of Gay’s ( 2010a ) framework. It matters because evaluative judgment in human–GenAI collaboration is not only technical or cognitive, but also cultural and epistemic (Bearman et al., 2024 ; Liu et al., 2024 ; Watson et al., 2024 ). When educators’ cultural judgment is thin or deficit-framed, “human-in-the-loop” can simply reproduce superficial localization, polished tone, localized names, and “safe” topics, without deeper engagement with inequity or local knowledge systems (Barana et al., 2023 ). This concern aligns with work on the UAE’s Western-oriented higher education workforce, where expatriate-led localization may not become culturally embedded without meaningful local involvement (Austin et al., 2014 ), and with Shahin et al.’s ( 2025b ) argument that localization without shared authorship tends to default to substitution rather than epistemic co-production. A productive next step, therefore, is to frame “human-in-the-loop” not as a safeguard by itself, but as a practice requiring explicit decision rules grounded in CRT/decolonial commitments, transparent editing rationales, and capacity-building that centers culture, not just the tool (Dutta, 2018 ; Mignolo & Walsh, 2018 ). Limitations and Future Research While this study makes a timely contribution to understanding GenAI use for cultural relevance in a rapidly evolving landscape, the findings should be interpreted considering several limitations. First, the sample is likely affected by selection bias. Although qualitative work does not aim for statistical generalizability, volunteer participation can skew the dataset toward faculty who already use GenAI or who are more invested in culturally responsive teaching. Faculty with less confidence or competence in either GenAI or culturally responsive pedagogy may hold different views that are not adequately represented here. Second, the study relies on faculty self-reports, which are inherently vulnerable to social desirability effects, particularly within UAE higher education, where faculty mobility is high and job security can feel uncertain. Additionally, because the first two authors are faculty members at a public university in the UAE, our institutional proximity may have shaped the interview context and participants’ responses. Participants may have framed their practices in ways they perceived as professionally acceptable within local policy, cultural norms, or institutional expectations. Another limitation of the study was that participants’ accounts offered limited evidence of measurable learning outcomes (e.g., gains in critical thinking), changes in belonging, or student voice that would confirm whether these practices are experienced by learners as genuinely culturally responsive. Future research should, therefore, move beyond faculty perceptions alone by incorporating student perspectives and, where possible, learning evidence. Mixed or multi-perspective designs could examine not only how faculty use GenAI, but also whether students experience these GenAI-supported pedagogical choices as culturally respectful, engaging, or alternatively, patronizing or surveillant. More broadly, future studies could attend to both ends of the process: the instructor’s GenAI-supported design decisions and students’ lived experience of those decisions in the classroom. Conclusion and Implications Taken together, the findings point to a central paradox: GenAI can reduce cultural mismatch while simultaneously reproducing cultural harm through stereotyping, overconfidence, or culturally inappropriate content. This duality positions culturally responsive GenAI use as a high-judgment practice where ethical boundaries and critical review are not optional but foundational. As one participant noted, “ GenAI should be treated like a fallible GPS: Even when it sounds confident, it can still direct you to drive your car straight into the river,” so instructors must remain in control and verify outputs with informed judgment. In practical terms, GenAI currently functions more as a pedagogical capacity-extender in UAE higher education, supporting faster localization, tone-polishing, and language scaffolding, than as a transformative driver of CRT. To translate this potential into more consistently responsible practice, UAE institutions and faculty can act on several immediate, actionable implications: Move from AI allowed to AI guidance faculty can actually use : Universities should publish a short, UAE-specific GenAI teaching guide that gives faculty clear “do/don’t” boundaries, what to avoid (sensitive topics, stereotypical depictions, culturally risky scenarios), what to verify (facts, cultural assumptions, tone), and what to protect (privacy, student data, identifiable cases). Provide a standard prompting/revision template: Rather than leaving faculty to improvise, institutions can offer a small set of approved prompt frames (e.g., “rewrite for UAE higher education context,” “remove idioms,” “adjust for mixed-gender classroom dynamics,” “ensure respectful communication”) plus a required final step: “state what you changed and why.” This builds consistent practice across colleges. Create a shared, vetted UAE case bank to reduce ad-hoc localization: A practical step is to develop a repository of vetted UAE-relevant cases, examples, and bilingual resources (with periodic review). This reduces dependence on one-off GenAI outputs and helps prevent “name swaps” from becoming the default definition of localization. Reorient professional development toward evaluation, not only tool use : Universtities should provide workshops prioritizing verification routines (fact-checking, bias spotting, cultural appropriateness review), not only prompting skills. A learning outcome of these workshops would be that faculty can demonstrate how they detect hallucinations, remove stereotypes, and justify edits using UAE classroom norms. Reduce inequity in access and capacity across colleges: Institutions should ensure consistent access (subscriptions, approved tools) and baseline training across departments. Otherwise, culturally responsive GenAI use will remain uneven, limited to faculty who are already confident, well-resourced, or tech-forward. Treat GenAI as support for communication, but keep relationship work human-led: Faculty can use GenAI for drafting announcements, feedback tone, and simplifying language, but should avoid outsourcing culturally sensitive relational decisions (conflict, advising, performance concerns). A good practice is: GenAI drafts; the instructor revises with local norms in mind. In conclusion, GenAI can support culturally responsive teaching in UAE higher education when it is treated as a tool that faculty actively govern, through explicit decision rules, careful verification, and culturally grounded revision. The most useful institutional move is to shift from broad encouragement or restriction toward shared standards, practical training, and vetted resources that help faculty produce culturally appropriate teaching at scale without reducing cultural responsiveness to surface-level localization. Declarations Author Contribution A.O. and M.A. conceptualized and designed the study, developed the interview protocol, and conducted the interviews. M.A. and E.I. led the qualitative analysis (coding, theme development, and refinement) and drafted the Method and Results sections. A.O. served as an analytic auditor, reviewed and challenged coding decisions, and strengthened theme coherence. All authors contributed to interpretation of findings, revised the manuscript critically for intellectual content, and approved the final version for submission. References Adam, I. O., Alhassan, M. D., & Diack, A. (2024). Exploring students’ foundational skills in integrating generative AI in Ghanaian higher education: A constructive learning perspective . In AMCIS 2024 Proceedings (Paper 23). Association for Information Systems. https://aisel.aisnet.org/amcis2024/ai_aa/ai_aa/23/ Agarwal, D., Naaman, M., & Vashistha, A. (2025). AI suggestions homogenize writing toward Western styles and diminish cultural nuances. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (Article 1117). Association for Computing Machinery. https://doi.org/10.1145/3706598.3713564 Austin, A. E., Chapman, D. W., Farah, S., Wilson, E., & Ridge, N. (2014). Expatriate academic staff in the United Arab Emirates: The nature of their work experiences in higher education institutions. Higher Education, 68 (4), 541–557. Aydogan, M., İzmir, E., & Shahin, H. (2025). “Like a 6-year-old dropped on Mars without parents”: Faculty members’ intercultural competence and cultural adaptation in the UAE. Studies in Higher Education . Advance online publication. https://doi.org/10.1080/03075079.2025.2495713 Barana, A., Marchisio, M., & Roman, F. (2023). Fostering problem solving and critical thinking in mathematics through generative artificial intelligence. In Proceedings of the 20th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2023) (pp. 377–385). Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. Assessment & Evaluation in Higher Education, 49 (6), 893–905. Cano, Y. M., Venuti, F., & Martinez, R. H. (2023). ChatGPT and AI text generators: Should academia adapt or resist? Harvard Business Publishing Education. https://hbsp.harvard.edu/inspiring-minds/chatgpt-and-ai-text-generators-should-academia-adapt-or-resist Creswell, J. W. (2012). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). SAGE Publications. Damiano, A. D., Lauría, E. J. M., Sarmiento, C., & Zhao, N. (2024). Early perceptions of teaching and learning using generative AI in higher education. Journal of Educational Technology Systems, 52 (3), 346–375. https://doi.org/10.1177/00472395241233290 Denzin, N. K., & Lincoln, Y. S. (Eds.). (2011). The SAGE handbook of qualitative research (4th ed.). SAGE. Dutta, U. (2018). Decolonizing “community” in community psychology. American Journal of Community Psychology, 62 (3–4), 272–282. https://doi.org/10.1002/ajcp.12281 Eppard, J., Bailey, F., McKeown, K., & Singh, H. (2021). Expatriate faculty and student perspectives on teaching and learning in a United Arab Emirates university. Issues in Educational Research, 31 (2), 458–475. Essien, A., Bukoye, O. T., O’Dea, X., & Kremantzis, M. (2024). The influence of AI text generators on critical thinking skills in UK business schools. Studies in Higher Education, 49 (5), 865–882. https://doi.org/10.1080/03075079.2024.2316881 Gay, G. (2000). Culturally responsive teaching: Theory, practice, and research . Teachers College Press. Gay, G. (2002). Preparing for culturally responsive teaching. Journal of Teacher Education, 53 (2), 106–116. https://doi.org/10.1177/0022487102053002003 Gay, G. (2010a). Acting on beliefs in teacher education for cultural diversity. Journal of Teacher Education, 61 (1–2), 143–152. https://doi.org/10.1177/0022487109347320 Gay, G. (2010b). Culturally responsive teaching: Theory, research, and practice (2nd ed.). Teachers College Press. Gay, G. (2015). The what, why, and how of culturally responsive teaching: International mandates, challenges, and opportunities. Multicultural Education Review, 7 (3), 123–139. https://doi.org/10.1080/2005615X.2015.1072079 Global Media Insight. (2025). United Arab Emirates (UAE) population statistics 2025 . https://www.globalmediainsight.com/blog/uae-population-statistics/ Grab, M. O. (2025). Teaching for equity: An exploration of AI’s role in culturally responsive teaching in higher education settings. Innovative Higher Education , 1–22. https://doi.org/10.1007/s10755-025-09801-4 Henadirage, A., Chathuranga, B.T.K. & Gunarathne, N. (2026). Changing assessment landscape in management education with AI-driven technologies: Impacts and drivers. International Journal of Educational Technology in Higher Education, 23 , Article 4. https://doi.org/10.1186/s41239-026-00578-w Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry . SAGE. Liu, J., Li, S., & Dong, Q. (2024). Collaboration with generative artificial intelligence: An exploratory study based on learning analytics. Journal of Educational Computing Research, 62 (5), 1014–1046. Mackey, J., & Evans, C. (2020). The role of AI in promoting inclusive education practices. International Journal of Inclusive Education, 24 (10), 1061–1077. https://doi.org/10.1080/13603116.2019.1622172 Merriam, S. B., & Tisdell, E. J. (2016). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass. Mignolo, W. D., & Walsh, C. E. (2018). On decoloniality: Concepts, analytics, praxis . Duke University Press. Monib, W. K., Qazi, A., & Mahmud, M. M. (2025). Exploring learners’ experiences and perceptions of ChatGPT as a learning tool in higher education. Education and Information Technologies, 30 (1), 917–939. https://doi.org/10.1007/s10639-024-13065-4 Moore-Jones, P. J. 2015. The benefits and pitfalls of a multicultural teaching faculty and a monocultural student population: An interpretive analysis of tertiary teachers’ and students’ perceptions in the United Arab Emirates. Journal of Language and Cultural Education 3 (3), 69–84. https://doi.org/10.1515/jolace-2015-0021. Naidu, K., & Sevnarayan, K. (2025). Culturally relevant assessment and generative AI: A co-creation framework for pre-service teachers. Journal of Applied Learning & Teaching, 8 (2), 132–142. Norris, S. (2013). Analyzing multimodal interaction: A methodological framework . Routledge. Nyaaba, M. (2025). Glocalizing generative AI in education for the Global South: The design case of 21st century teacher educator AI for Ghana [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2504.07149 O’Dea, X. (2024). Generative AI: Is it a paradigm shift for higher education? Studies in Higher Education, 49 (5), 811–816. https://doi.org/10.1080/03075079.2024.2332944 Qiu, W., Thway, M., Lai, J. W., & Lim, F. S. (2025, March). GenAI for teaching and learning: A human-in-the-loop approach. In Proceedings of the Companion Proceedings of the 15th International Conference on Learning Analytics & Knowledge (LAK25) (pp. 3–7). Dublin, Ireland. Saldaña, J. (2016). The coding manual for qualitative researchers (3rd ed.). SAGE. Schwandt, T. A. (1994). Constructivist, interpretivist approaches to human inquiry. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (pp. 118–137). SAGE. Shahin, H., Patka, M., & Smail, L. (2025b). Balancing the local-international dialectic in community psychology pedagogy: Lessons from adapting American curricula in the United Arab Emirates. American Journal of Community Psychology , 1–11. https://doi.org/10.1002/ajcp.70014 Shahin, H., Patka, M., Aydogan, M., Al Ali, A., Bin Thalith, M., & Alhemeiri, S. (2025a). Relationality and learning: Insights from undergraduate student research assistant experiences. LEARNing Landscapes, 29 , 245–262. Shahjahan, R. A., Estera, A. L., Surla, K. L., & Edwards, K. T. (2022). “Decolonizing” curriculum and pedagogy: A comparative review across disciplines and global higher education contexts. Review of Educational Research, 92 (1), 73–113. https://doi.org/10.3102/00346543211042423 Singh, H., Bailey, F., J. Eppard, & McKeown, K. (2021). Partners in learning: An exploration of multi-cultural faculty and Emirati students’ perspectives of university learning experiences. Learning, Culture and Social Interaction, 31 , Article 100564. https://doi.org/10.1016/j.lcsi.2021.100564 Smith, L. T. (1999). Decolonizing methodologies: Research and Indigenous peoples . Zed Books. Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024). Cultural bias and cultural alignment of large language models. PNAS Nexus, 3 (9), Article 346. https://doi.org/10.1093/pnasnexus/pgae346 Tu, Y.-F. (2024). Roles and functionalities of ChatGPT for students with different growth mindsets: Findings of drawing analysis. Educational Technology & Society, 27 (1), 198–214. https://doi.org/10.30191/ETS.202401_27(1).TP01 Watson, E., Viana, T., Zhang, S., Sturgeon, B., & Petersson, L. (2024). Towards an end-to-end personal fine-tuning framework for AI value alignment. Electronics, 13 (20), Article 4044. Williams, C. D., Hojeij, Z., Johnson, J. D., Eppard, J., & McKeown, K. (2025). Navigating educational mistakes in learning: Cultural perspectives of Emirati students and expatriate instructors in higher education. Journal of International Students, 15 (12), 1–20. https://doi.org/10.32674/5z34ya54 Wu, Y. (2024). Revolutionizing learning and teaching: Crafting personalized, culturally responsive curriculum in the AI era. Creative Education, 15 , 1642–1651. https://doi.org/10.4236/ce.2024.158098 Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21 , Article 21. https://doi.org/10.1186/s41239-024-00453-6 Zawacki-Richter, O., Bai, J. Y., Lee, K., van Slagter Tryon, P. J., & Prinsloo, P. (2024). New advances in artificial intelligence applications in higher education? International Journal of Educational Technology in Higher Education, 21 (1), Article 32. https://doi.org/10.1186/s41239-024-00464-3 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8722631","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582099083,"identity":"16a4ce9c-8444-4db3-9f0f-047c113f8087","order_by":0,"name":"Ajda Osifo","email":"","orcid":"","institution":"Zayed University","correspondingAuthor":false,"prefix":"","firstName":"Ajda","middleName":"","lastName":"Osifo","suffix":""},{"id":582099097,"identity":"9e2819d6-1de0-4241-92bf-dbea7488a069","order_by":1,"name":"Mustafa Aydogan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYNACGzBpACLkiFHP2MCQhtBiTLqWxAZC6nVnpD9/8CPBjoF/RvLGh19q7qTPn5HA+OEHgx1OF5rdyDFs7ElIZpC4kVZsLHPsWW7jjARmyR6GZJwuBGphbOD9wczAcCPHTFqC7XBus0QCgzQDAzNOF5rdSH/Y+CehnkEerOXf4XQ2iQTm3wwM9fW4tSQYNvMkHGYwAGqR/Nh2OIFHIoENaMvhBJwOO/PGcLZMwnEewzPPio0Z+w4bzuB52GbZY3DcEKctx9MffHyTUC0ndxwYYj++HZaXb08+fONHRbU8LltggIdBIIGBmQfMBkYUJI4IAf4DDIw/iFE4CkbBKBgFIw4AAFsxV90GB4iGAAAAAElFTkSuQmCC","orcid":"","institution":"Zayed University","correspondingAuthor":true,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"Aydogan","suffix":""},{"id":582099106,"identity":"9394eaa1-50f3-4f22-af7c-aa84e3802680","order_by":2,"name":"Esra Izmir","email":"","orcid":"","institution":"Sinop University","correspondingAuthor":false,"prefix":"","firstName":"Esra","middleName":"","lastName":"Izmir","suffix":""}],"badges":[],"createdAt":"2026-01-28 15:23:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8722631/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8722631/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103664486,"identity":"5ad100e2-e68e-449f-8c0d-569f57fa7f5c","added_by":"auto","created_at":"2026-02-28 19:09:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":812906,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8722631/v1/7973dab9-514e-43d3-990a-e47831315f36.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond “Camel, Desert, Ramadan Defaults”: Faculty Use of GenAI for Culturally Responsive Teaching in UAE","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe launch of ChatGPT in late 2022 triggered widespread and rapid shifts in higher education practices. GenAI, powered by Large Language Models (LLMs), is a subset of artificial intelligence that has the ability to generate various content types including texts, images, audios, and videos enhancing personalized learning experiences and skill development (Cano et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zawacki-Richter et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). GenAI has immense potential to transform pedagogical approaches and to enhance teaching, learning, and assessment for both educators and students as it can assume multiple roles including that of a co-creator (Jim\u0026eacute;nez Romanillos \u0026amp; Andersson, 2024), collaborator and perceptive partner (Adam et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), virtual tutor (Tu, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and cognitive support tool (Essien et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for faculty members. This multifunctionality has facilitated its widespread adoption across a broad range of academic disciplines.\u003c/p\u003e \u003cp\u003eDespite their powerful capabilities, GenAI tools also present shortcomings that can impede faculty members\u0026rsquo; intellectual work and professional judgment in higher education. When instructors rely on GenAI uncritically, it can encourage superficial engagement with disciplinary content and reduce opportunities for deeper reasoning, thereby weakening critical thinking and evaluative judgment (Monib et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zawacki-Richter et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such reliance may also diminish sustained cognitive engagement in academic work, including lesson design, assessment construction, and feedback practices. For faculty, this risk is not simply \u003cem\u003edoing tasks faster\u003c/em\u003e, but potentially outsourcing core scholarly and pedagogical thinking, moving too quickly from prompt to polished output without sufficient conceptual grounding or reflection. As a result, instructors may become more vulnerable to accepting plausible but inaccurate information and to overlooking cultural or epistemic biases embedded in GenAI outputs, making verification and critical review essential components of responsible use (Barana et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tao et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRelatedly, a central challenge concerns faculty members\u0026rsquo; \u003cem\u003eevaluative judgement\u003c/em\u003e when working with GenAI-generated outputs. Bearman et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) conceptualize evaluative judgement as a core dimension of critical thinking that is essential for working safely and productively with GenAI. As findings from Liu et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) show, within human-GenAI collaboration, while GenAI may assist with idea generation and support the exploration and synthesis of diverse perspectives, it does not assume responsibility for the final content. Consequently, responsibility remains with human users who must be actively involved throughout the process, monitor and verify, and critically examine the outputs (Henadirage et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and, above all, make informed judgements about their quality and validity, known as human-in-the-loop process (Qiu et al., 2025).\u003c/p\u003e \u003cp\u003eExercising such evaluative judgement is far from straightforward. Many higher education stakeholders, including faculty members may encounter difficulties in this regard, particularly when GenAI systems reproduce Western-centric assumptions (Grab, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Watson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), cultural and contextual biases, or normative frameworks that are not readily visible without strong cultural awareness (Agarwal et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Because evaluating GenAI is not only a technical or cognitive task, but also a cultural and epistemic one, such assumptions and biases may remain unnoticed by those who lack sufficient cultural and critical awareness (Watson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yusuf et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As a result, instructors may uncritically accept outputs that reflect misaligned values, implicit stereotypes, or culturally inappropriate framings (Tao et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These challenges are particularly troubling given the pace at which GenAI has been integrated into teaching and learning across diverse geographical contexts, often without sufficient pedagogical alignment, cultural sensitivity, or critical evaluation (Wu, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Accordingly, these concerns emphasize the need for culturally responsive and locally grounded approaches to GenAI implementation in educational settings, particularly in non-Western and culturally diverse contexts.\u003c/p\u003e \u003cp\u003eThese issues become especially salient in higher education contexts where cultural norms strongly shape classroom interaction, institutional expectations, and what counts as appropriate communication. Higher education in the United Arab Emirates (UAE) has become a markedly internationalized sector, bringing together students and faculty from diverse linguistic, national, and cultural backgrounds within institutions shaped by local cultural traditions, religious values, and social expectations (Aydogan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Eppard et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shahin et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Expatriates make up 88.5% of the UAE\u0026rsquo;s approximately 11\u0026nbsp;million population (Global Media Insight, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and within universities this demographic reality contributes to highly multicultural learning environments where Emirati students study alongside international peers and are frequently taught by internationally recruited faculty whose academic training is often grounded in Western higher education systems (Aydogan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shahin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, the nature of diversity is not uniform across contexts. While diversity in some countries is primarily expressed through race, ethnicity, and language, differences related to gender, social class, and ability exist within national contexts, yet manifest in culturally specific ways (Gay, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the UAE, cultural sensitivity in higher education therefore requires attention not only to visible indicators of cultural identity (e.g., nationality or language), but also to often-relational and religious local norms that regulate gender-based interaction, authority, communication style, and social boundaries (Aydogan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Williams et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Teaching in the UAE is continually negotiated at the intersection of global academic practices and local cultural expectations, making culturally responsive pedagogy essential for fostering inclusion, engagement, and mutual respect (Moore-Jones, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo conceptualize this cultural work of teaching, we draw on culturally responsive teaching (CRT) as a guiding framework. CRT is an approach that aims to increase teaching effectiveness by using learners\u0026rsquo; cultural characteristics, experiences, and perspectives as pedagogical resources (Gay, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In higher education settings such as the UAE, CRT is especially relevant because faculty must translate disciplinary expectations and institutional standards into classroom practices that land appropriately for students navigating English-medium instruction, culturally shaped interaction norms, and local expectations around respectful communication (Aydogan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gay, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As GenAI becomes embedded in planning, material development, and faculty\u0026ndash;student communication, it can shape how (and how quickly) these translations are produced without guaranteeing cultural depth. From this lens, cultural responsiveness is not an abstract attitude; it is enacted through concrete teaching decisions, how examples are selected, how cases are framed, what tone is used in feedback, how participation is structured, and how knowledge is positioned as legitimate or relevant.\u003c/p\u003e \u003cp\u003eGay\u0026rsquo;s CRT model emphasizes that CRT requires more than general goodwill or broad awareness of diversity; it involves developing a knowledge base of cultural diversity and using it to design culturally relevant curricula, enact cultural caring and build a learning community, communicate across cultures, and achieve cultural congruity in instructional strategies (Gay, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Gay, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e). Instructors\u0026rsquo; cultural responsiveness is therefore expressed through routine pedagogical labor: adapting language demands, calibrating directness or \u0026ldquo;face\u0026rdquo; considerations, selecting culturally situated illustrations, and structuring learning activities in ways that align with students\u0026rsquo; interactional expectations while sustaining academic challenge (Gay, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Moore-Jones, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Within this framework, Gay (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e) further clarifies CRT through three interrelated emphases that are particularly useful for faculty work with GenAI: \u003cem\u003ecultural competence\u003c/em\u003e (understanding and valuing learners\u0026rsquo; cultural contexts), \u003cem\u003ecritical consciousness\u003c/em\u003e (questioning biases and assumptions that reproduce inequity), and \u003cem\u003eacademic success\u003c/em\u003e (supporting students\u0026rsquo; academic flourishing through rigorous, culturally congruent teaching).\u003c/p\u003e \u003cp\u003eTwo additional issues in the CRT literature are particularly relevant for positioning the current study. First, CRT is often at risk of being operationalized as \u0026ldquo;adaptation\u0026rdquo; rather than deeper pedagogical transformation, e.g., making instruction feel more familiar through surface localization (names, examples, simplified language) while leaving underlying epistemic assumptions and authority structures intact (Gay, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shahin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Smith, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This matters in internationalized systems like the UAE where faculty may already experience their teaching materials as imported, Western-centric, or culturally distant, and where localization can become a routine necessity rather than a critical intervention (Shahin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). In practice, CRT in such contexts requires sustained judgement about what constitutes \u003cem\u003efit\u003c/em\u003e beyond recognizability, how culture shapes meaning, participation norms, and the interpretability of scenarios, as well as whose knowledge is represented in the curriculum (Gay, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, CRT foregrounds the educator as the primary agent of cultural judgement, which places attention on faculty competence development rather than on tools alone. Cultural competence and critical consciousness are not static end-states; they involve ongoing learning, reflective practice, and iterative adjustment in response to students and context (Gay, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e). This is especially salient when GenAI enters the workflow: GenAI can support drafting, localization, tone-polishing, and translation, but it cannot \u003cem\u003eown\u003c/em\u003e the cultural reasoning that determines what is appropriate, what is risky, what is misrepresentative, and what reproduces bias (Grab, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). GenAI may accelerate teachers\u0026rsquo; access to culturally situated materials, but the CRT literature suggests that cultural responsiveness still depends on human interpretive work by making explicit choices about language, representation, interaction, and equity rather than treating \u003cem\u003elocalization\u003c/em\u003e as a technical edit (Gay, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shahin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this sense, GenAI becomes a mediator of pedagogy. It can enable culturally responsive practices (e.g., localizing examples, clarifying language, varying activity design), yet it may also introduce vulnerabilities such as culturally inappropriate scenarios, tone that threatens \u003cem\u003eface\u003c/em\u003e, or stereotypical representations (Agarwal et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Grab, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). There are growing concerns that AI tools may not reliably support CRT across diverse educational settings, particularly where learners\u0026rsquo; cultural backgrounds shape how they interpret instruction and experience learning (Grab, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). At the same time, educators can use AI systems to scaffold lesson design and evaluation around learners\u0026rsquo; cultural contexts and lived realities, potentially making instruction more accessible and supportive (Henadirage et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Mackey \u0026amp; Evans, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, we still know relatively little about how faculty make these cultural-fit judgements in practice, how they evaluate, localize, filter, and ethically constrain GenAI outputs when teaching in culturally specific contexts such as the UAE.\u003c/p\u003e \u003cp\u003eDespite the growing body of research, there remains limited insight into how faculty in the UAE are pedagogically integrating GenAI into teaching and assessment, or how culturally embedded biases and normative assumptions in GenAI outputs are negotiated in practice. This lack of contextually grounded evidence represents a significant gap in the current literature. In response, this study examines how faculty in UAE higher education integrate GenAI into teaching and how they adapt, evaluate, and localize GenAI outputs to align with local cultural norms, student needs, and pedagogical goals across teaching preparation, learning materials, student engagement, and assessment. Guided by the CRT framework (Gay, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e), this research addresses the following question: \u003cem\u003eHow do faculty members utilize GenAI to support culturally responsive teaching in UAE higher education?\u003c/em\u003e\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eA qualitative interpretivist approach (IPA) was adopted for this study because our primary aim was to understand how faculty members make sense of, negotiate, and enact the use of GenAI for culturally responsive teaching within the culturally diverse higher education context of the UAE. IPA is well suited to inquiries that foreground meaning making and the situated nature of practice, recognizing that participants\u0026rsquo; accounts are shaped by the social, cultural, and institutional contexts in which they work (Denzin \u0026amp; Lincoln, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Schwandt, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Accordingly, we treated faculty members\u0026rsquo; narratives as contextually grounded interpretations of their pedagogical decisions, how they assess cultural appropriateness, adapt GenAI outputs, and manage sensitivities in multicultural classrooms, rather than as neutral reports of \u0026ldquo;objective\u0026rdquo; behaviors (Denzin \u0026amp; Lincoln, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Merriam \u0026amp; Tisdell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This stance aligns with a constructivist view of knowledge, in which understanding is co-produced through participants\u0026rsquo; perspectives and the researchers\u0026rsquo; analytic engagement with the data (Lincoln \u0026amp; Guba, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe study employed purposive and snowballing sampling strategies to recruit participants at main universities in the UAE. To be eligible, faculty members were required to (a) hold a higher education degree (master\u0026rsquo;s and/or doctoral), (b) actively utilize GenAI tools in their teaching practices, and (c) have had at least one year of teaching experience at a UAE institution. Invitation emails were sent to faculty members at the participating universities, inviting them to take part in an interview during the Fall 2025 semester. After each interview, participants were asked to suggest other potential participants. Data were subsequently collected through interviews with 20 volunteer academics. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, participants represented 10 nationalities, had high educational mobility, and were from a wide range of disciplines reflecting a wide range of cultural backgrounds and experiences.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudonym\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry of Origin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eField\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYears in the UAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLebanon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnvironmental Health and Safety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYannis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEducational Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePsychology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArif\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLinguistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRami\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhilippines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhilippines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMathematics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCyprus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCyprus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBusiness Administration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaisal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMathematics and Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMichael\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnglish/Writing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEdD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYulia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTESOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLebanon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecial Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLinguistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaterina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePsychology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheresa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLinguistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoayad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMass Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFadi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMathematics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKhalid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJordan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJordan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEducational Psychology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBusiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSami\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBusiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: NA: Data Not Available. In case of multiple degrees from different countries, the last degree obtained was recorded.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection and Analysis\u003c/h3\u003e\n\u003cp\u003eThe interview questions were developed in alignment with the study objectives and relevant literature on culturally responsive teaching and GenAI in higher education. Using a semi-structured format, the protocol included open-ended questions around (a) cultural considerations in teaching Emirati students, (b) how participants use GenAI to design culturally relevant materials and learning activities, and (c) how they evaluate the cultural appropriateness of AI-generated outputs. Participants were encouraged to reflect on their teaching practices in UAE classrooms, with particular attention to how cultural norms and student characteristics shape instructional decisions and how GenAI is incorporated to support culturally responsive learning environments.\u003c/p\u003e \u003cp\u003eFollowing ethical approval (Approval number ZU25_036_F), data were collected through 20 interviews conducted online via Zoom. All interviews were audio-recorded with consent and transcribed verbatim. We employed reflexive thematic analysis to identify patterns of meaning across the dataset (Braun \u0026amp; Clarke, 2006, 2021). In line with IPA, the analytic aim was to extract meaning by segmenting transcripts, assigning codes, examining overlap across codes, and consolidating these into higher-order categories and themes (Creswell, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Analytic memos also captured salient interactional cues (e.g., pauses, emphasis, and noticeable shifts in tone) when these provided context for interpreting participants\u0026rsquo; intended meanings (Norris, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalysis proceeded inductively through iterative cycles of coding and theme development rather than applying predetermined categories. Transcripts were read by the second and third authors repeatedly to support familiarization, and initial codes were generated across the full dataset. Codes were then reviewed, compared, and clustered into higher-order categories through successive rounds of analysis, using constant comparison to refine boundaries and relationships among codes (Salda\u0026ntilde;a, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Merriam \u0026amp; Tisdell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).The research team met regularly to discuss coding decisions and candidate themes, resolving differences through discussion and returning to the transcripts to ensure interpretations were grounded in participants\u0026rsquo; accounts. The first author served as an analytic auditor, challenging interpretations, requesting transcript evidence, and checking theme coherence. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the final analytic structure consisted of four themes, with associated categories and codes.\u003c/p\u003e\n\u003ch3\u003eAuthors’ Reflexivity Statements\u003c/h3\u003e\n\u003cp\u003eAs a research team, we recognize that our positionalities and professional experiences shaped how we interpreted participants\u0026rsquo; accounts and how we constructed patterns of meaning in the dataset. We approached analysis reflexively attending not only to what participants explicitly stated but also to the underlying assumptions, cultural logics, and tensions that organized faculty talk about GenAI and CRT. The team\u0026rsquo;s disciplinary backgrounds in multiculturalism, international higher education, and language teaching in diverse classrooms sensitized us to issues of cultural responsiveness and power in classroom communication, while also posing a risk of over-privileging pedagogical interpretations over technical ones. Because the first two authors work within majority-Emirati public universities, we were mindful that participants might frame their responses in institutionally acceptable terms; we therefore treated hesitations, indirect speech, and ambiguity as meaningful data rather than as gaps. As regular users of GenAI for teaching and academic work, we were attentive to the ways our own comfort with these tools could shape what we interpreted as \u003cem\u003eeffective\u003c/em\u003e or \u003cem\u003eculturally responsive\u003c/em\u003e use. Additionally, interviews were conducted in English, and we remained attentive to how English-as-a-lingua-franca may have shaped participants\u0026rsquo; phrasing, especially when discussing culturally sensitive issues.\u003c/p\u003e \u003cp\u003e The first author is a Western-educated faculty member with approximately 10 years of experience in UAE higher education, which supported contextual sensitivity to local institutional norms and classroom dynamics but also risked taking familiar practices for granted. The second author is also Western-educated and has been based in the UAE for approximately 3 years; this position offered both a comparative lens and an awareness of the interpretive work required when navigating local cultural expectations as a relatively newer member of the system. The third author is internationally trained and resides outside the UAE, contributing analytic distance that helped surface implicit assumptions in our readings and prompted us to justify interpretations with evidence across transcripts. All authors use English as a second language, and we remained reflexive about how this shared linguistic positioning could influence both interview interaction (e.g., word choice, politeness strategies, and indirectness) and analytic interpretation (e.g., how we understood nuance, emphasis, or culturally loaded terms). Throughout the project, we used memoing, peer debriefing, and repeated return to the transcripts to interrogate our assumptions and preserve complexity, including ambivalence and critique, as analytically meaningful.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThrough IPA, we organized participants\u0026rsquo; collective accounts into four overarching themes: (a) \u003cem\u003eCulturally responsive pedagogical strategies using GenAI\u003c/em\u003e, (b) \u003cem\u003eFaculty sensemaking of GenAI for cultural responsiveness\u003c/em\u003e, (c) \u003cem\u003eCultural and relational benefits of GenAI-supported teaching\u003c/em\u003e, and (d) \u003cem\u003eInstitutional, ethical, and cultural risks/barriers\u003c/em\u003e. These themes helped us explore and understand how faculty members in the UAE make sense of GenAI and how they use it to support culturally responsive pedagogy in this diverse learning environment. In the next sections, we present each theme and its subcategories, drawing on participants\u0026rsquo; own words using pseudonyms assigned by the authors. Direct quotations are provided to preserve participants\u0026rsquo; intended meanings and to represent the range of perspectives reflected in the dataset, consistent with a qualitative interpretivist approach.\u003c/p\u003e\n\u003ch3\u003eTheme 1: Culturally responsive pedagogical strategies using GenAI\u003c/h3\u003e\n\u003cp\u003eMost participants agreed that using GenAI strengthened their pedagogical approach by supporting content localization, language and communication scaffolding, and the design of culturally relevant engagement-focused learning experiences. GenAI tools provided speed and easy access to teaching materials for faculty who are often unfamiliar with the local students\u0026rsquo; cultural characteristics. Khalid said \u0026ldquo;\u003cem\u003eit [GenAI] is saving time, giving good and quick ideas, generate a lot of fantastic outcomes.\u003c/em\u003e\u0026rdquo; A salient theme was that participants described their teaching experiences and materials as deeply embedded in Western knowledge and examples, which they viewed as a major challenge for pedagogical adaptation; GenAI tools came into play as a pragmatic resource for reworking, localizing, and reframing these materials to better fit students\u0026rsquo; cultural contexts.\u003c/p\u003e \u003cp\u003eLocalizing content happened in multiple ways. Creating case scenarios with UAE\u0026rsquo;s cultural dynamics was often emphasized by the faculty members. These participants valued GenAI\u0026rsquo;s support in finding information about local companies or government offices providing students a connection between global and local perspectives. Katerina recalled \u0026ldquo;\u003cem\u003ein one of my courses about leadership, [using GenAI] I might go with an example of a local company, let's say, an Emirati company, which is very well known for the students in the country, rather than using global examples.\u003c/em\u003e\u0026rdquo; Importantly, participants offered mixed assessments regarding the extent to which localization amounted to superficial substitutions (e.g., changing names or minor contextual details) versus more substantive integration of culturally relevant elements into the narratives and scenarios embedded in teaching materials. As Dana put it: \u0026ldquo;\u003cem\u003echanging the company name from Amazon to Noon or Kareem is very superficial.\u003c/em\u003e\u0026rdquo;\u003c/p\u003e \u003cp\u003eA dominant narrative centered on the language and communication scaffolding role of GenAI. Participants valued the ability to manipulate language and its content based on learners\u0026rsquo; needs. They welcomed GenAI for translating teaching materials into what Dana described as a \u0026ldquo;\u003cem\u003erelatable and acceptable\u003c/em\u003e\u0026rdquo; medium for local students. Adaptation through translation ranged from cross-language translation to adjusting culturally specific nuances. Andreas aptly described this: \u0026ldquo;\u003cem\u003eI'm in a business school, and so very often I get a case meant for the US, or the UK, or Australia. It's meant for native English speakers in a particular environment. I'll very often take that, use Copilot or ChatGPT to clean it from idioms and phrases non-English speakers cannot understand\u0026hellip; It is like English-to-English translation.\u003c/em\u003e\u0026rdquo;\u003c/p\u003e \u003cp\u003eAcross interviews, a clear pattern emerged that faculty used GenAI to polish the tone of their communication with students. These included assignment descriptions, announcements, emails, or even in-class talks. Sean navigated cultural differences in communication styles using GenAI as a mediator: \u0026ldquo;\u003cem\u003eSometimes I will paste an email and ask it to make it more polite, because you know, culturally here, you need to be careful how you phrase things.\u003c/em\u003e\u0026rdquo;\u003c/p\u003e \u003cp\u003eParticipants also positioned GenAI-enabled multimedia and gamification as pedagogical tools for culturally aligned engagement. Moayad described how interactive games helped him maintain students\u0026rsquo; attention and classroom momentum: \u0026ldquo;\u003cem\u003eBecause they get bored very quickly, you know?\u0026hellip; So in order to keep the class interactive and ongoing, I usually put some gaming\u0026hellip; using HTML5\u003c/em\u003e,\u0026rdquo; Yet participants also cautioned that cultural responsiveness is not automatic; visuals and examples must be reviewed for audience fit, including tone, appropriateness, and relevance. Sean captured this ongoing need for judgment: \u0026ldquo;\u003cem\u003eIf you ask it to create visuals or examples, it can do it quickly, but you still need to check if it fits the audience.\u003c/em\u003e\u0026rdquo;\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTheme 2: Faculty sensemaking of GenAI for cultural responsiveness\u003c/h2\u003e \u003cp\u003eAcross interviews, participants made sense of GenAI less as an \u003cem\u003eanswer machine\u003c/em\u003e and more as a pedagogical tool that must be guided, edited, and ethically constrained by the educator. Their accounts clustered around (a) clarifying the role of AI versus the role of the educator, (b) developing practical decision rules to make outputs \u003cem\u003efit\u003c/em\u003e culturally, and (c) articulating adoption stances that ranged from professional growth orientations to resistance and heightened awareness of bias and stereotyping.\u003c/p\u003e \u003cp\u003eA dominant narrative positioned GenAI as extending capacity rather than replacing relational pedagogy and human judgment. As Rana emphasized, \u0026ldquo;... \u003cem\u003eit's a tool. It doesn't replace a human being\u0026hellip; We created that. The most important thing, it's a tool that helps us improve [higher education] further.\u003c/em\u003e\u0026rdquo; Similarly, Lina framed GenAI as a way of scaling responsiveness while retaining the human role: \u0026ldquo;\u003cem\u003eIt's not about replacing the human role. We need to always have that human role, but it's all about extending our capacity to be responsive to student needs.\u003c/em\u003e\u0026rdquo; Faculty repeatedly located cultural responsiveness in the educator\u0026rsquo;s relational work, facilitation, mentoring, and emotional attunement, rather than in the technology itself. Theresa described this stance as facilitative teaching: \u0026ldquo;\u003cem\u003eI'm there as a facilitator, I'm there to guide them, but I'm not there to force knowledge into them, okay?\u003c/em\u003e\u0026rdquo; Rana made the relationship explicit: \u0026ldquo;\u003cem\u003eI'm not a teacher. I'm not a teacher to you. I'm a mentor to you, you know?\u003c/em\u003e\u0026rdquo; Andreas further anchored cultural responsiveness in affective human qualities GenAI cannot replicate: \u0026ldquo;\u003cem\u003egenerative AI is not human. It's just gonna take whatever the average is, right? I feel like this empathy, these feelings that you do not see in AI, you know, this is what I care about.\u003c/em\u003e\u0026rdquo;\u003c/p\u003e \u003cp\u003eParticipants described \u0026ldquo;\u003cem\u003efit\u003c/em\u003e\u0026rdquo; as something they produce through inputs and human-in-the-loop revision. Yulia was direct that cultural responsiveness depends on intentional prompting: \u0026ldquo;\u003cem\u003ethe weakness is generally just a lack of specificity in its\u0026hellip; generalities, unless you prompt it in a different way. \u0026hellip;, AI will never give you a culture-responsive content\u003c/em\u003e\u0026rdquo; This logic was echoed as a rule of thumb about inputs: Lina noted, \u0026ldquo;\u003cem\u003eI'll put the case study and then I'll ask, okay, can we just change this into something that is less Westernized\u003c/em\u003e,\u0026rdquo; while Sean summarized it aptly: \u0026ldquo;\u003cem\u003ethe output is dependent on the input. Garbage in, garbage out kind of things.\u003c/em\u003e\u0026rdquo; Faculty also described routine editing as part of cultural care, especially around sensitive phrasing. Lina explained, \u0026ldquo;\u003cem\u003eI check them. I read it, I check it, some of the scenarios were not really possible or cannot be used in the classroom\u0026hellip; and I just erased the scenarios\u003c/em\u003e\u0026rdquo; Rami similarly described his role as explicitly adaptive: \u0026ldquo;\u003cem\u003eSo, most of the roles that I do here would just be filtering and editing and adapting the outputs that AI will give me to resonate within the Emirati culture.\u003c/em\u003e\u0026rdquo; Alongside verification and triangulation, participants warned against taking outputs for granted. Sean used a vivid metaphor for over-trusting outputs: \u0026ldquo;\u003cem\u003eyou have to know that you are driving into a river, no matter what the device says\u0026hellip; it's still at a stage where it occasionally directs you to drive your car straight into the river, and you've got to know enough.\u003c/em\u003e\u0026rdquo;\u003c/p\u003e \u003cp\u003eParticipants\u0026rsquo; orientations ranged from openness and professional growth to strong resistance grounded in perceived risk. For some, GenAI was tied to staying current: Theresa reflected, \u0026ldquo;\u003cem\u003eWell, it's\u0026hellip; it's the buzzword. Everyone's talking about AI nowadays\u0026hellip; if I failed to update my knowledge, or failed to evolve with what's going on in the world, then it's not going to be helping me\u0026hellip;\u003c/em\u003e\u0026rdquo; and Rana similarly noted, \u0026ldquo;\u003cem\u003eI like always to be up-to-date, because I believe this is part of professionally developing myself as well.\u003c/em\u003e\u0026rdquo;\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTheme 3: Cultural and relational benefits of GenAI-supported teaching\u003c/h3\u003e\n\u003cp\u003eAcross interviews, participants described benefits of GenAI that extended beyond efficiency, including inclusive access, stronger engagement and motivation, and greater cross-cultural connection and belonging. These outcomes were linked to concrete practices, subtitles, translation, interactive activities, and rapid contextualization, rather than assumed as automatic.\u003c/p\u003e \u003cp\u003eParticipants noted that GenAI-supported materials could reduce participation barriers for students with different communication needs. Michael highlighted real-time accessibility supports: \u003cem\u003e\u0026ldquo;with the new technologies now, we use online screens, BenQ for students who cannot catch up with me, or who have problems.\u0026rdquo;\u003c/em\u003e Omar also referenced hearing-related support: \u003cem\u003e\u0026ldquo;in hearing problems, this [GenAI-based subtitles] would be a one way of inclusion\u0026rdquo;.\u003c/em\u003e Translation and multilingual delivery were similarly framed as inclusion strategies. Yannis noted that students \u003cem\u003e\u0026ldquo;can, ask the translation and can read it\u0026hellip; in many different languages,\u0026rdquo;\u003c/em\u003e while Arif emphasized real-time language support in English-medium settings.\u003c/p\u003e \u003cp\u003eParticipants framed cultural responsiveness as relational design, structuring classroom interaction so students can participate safely and feel a sense of belonging across differences. Yulia described culture work as managing who works with who in mixed-gender settings: \u0026ldquo;\u003cem\u003eit\u0026rsquo;s thinking about who\u0026rsquo;s working with who, especially now that we\u0026rsquo;ve got mixed-gender classes. So who\u0026rsquo;s working with who\u003c/em\u003e,\u0026rdquo; alongside \u0026ldquo;\u003cem\u003ebeing aware of\u0026hellip; culturally sensitive topics.\u003c/em\u003e\u0026rdquo; Similarly, Lina emphasized adjusting examples and group processes to reduce discomfort and support inclusion: \u0026ldquo;\u003cem\u003enow we changed into the mixed gender classes. So here is where\u0026hellip; we had to change a bit\u003c/em\u003e,\u0026rdquo; and \u0026ldquo;\u003cem\u003efor me\u0026hellip; it\u0026rsquo;s about gender and culture and how you can tailor the class in a way that respects\u0026hellip; the fact that they\u0026rsquo;re not really comfortable with each other\u003c/em\u003e,\u0026rdquo; adding that in grouping and workplace-prep discussions \u0026ldquo;\u003cem\u003eyou always make sure that they feel comfortable\u003c/em\u003e.\u0026rdquo; Lina also linked this relational work to resisting stereotyped \u0026ldquo;culture\u0026rdquo; shortcuts in GenAI outputs, when prompts over-localize, \u0026ldquo;\u003cem\u003eyou immediately get the camel, the desert and Ramadan defaults, and that this is not the reality.\u003c/em\u003e\u0026rdquo;\u003c/p\u003e \u003cp\u003e\u003cb\u003e Theme 4: Institutional, ethical, and cultural risks/barriers.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAcross interviews, participants emphasized that GenAI use in culturally responsive teaching is constrained by institutional governance and resources, academic integrity pressures, and cultural/ethical risks that require careful judgment rather than automated adoption.\u003c/p\u003e \u003cp\u003eParticipants described uneven institutional guidance, ranging from permissive policies to discouraging stances, shaping how confidently faculty could integrate GenAI. Andreas noted, \u003cem\u003e\u0026ldquo;our policy is that the faculty members just have to be very clear about what their AI policies are, but it's a pretty inclusive policy,\u0026rdquo;\u003c/em\u003e while Fadi contrasted this with a more restrictive stance: \u003cem\u003e\u0026ldquo;So our policy so far in computing, we discourage students, from using, actually, generative AI.\u0026rdquo;\u003c/em\u003e Participants also pointed to the need for capacity-building. Yannis described institutional efforts: \u003cem\u003e\u0026ldquo;We have professional development program\u0026hellip; part of it about AI, a certificate about how to incorporate AI. \u0026ldquo;Yet\u003c/em\u003e Theresa observed that cultural dimensions were often missing: \u003cem\u003e\u0026ldquo;in the training. there wasn't much talk about culture, honestly.\u0026rdquo;\u003c/em\u003e Resource constraints further limited uptake; Yannis raised cost and access concerns: \u003cem\u003e\u0026ldquo;my university does not, pay for our subscription\u0026hellip; Who's gonna pay for it?\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003cp\u003eA dominant concern was how GenAI complicates authorship, effort, and detection. Rana described the dilemma of \u0026ldquo;\u003cem\u003etoo perfect\u003c/em\u003e\u0026rdquo; submissions: \u003cem\u003e\u0026ldquo;You know that you have a level D or F students, and then his assignment is perfect. We are all suffering from this.\u0026rdquo;\u003c/em\u003e Others highlighted the limits of detection tools and the ambiguity of what \u0026ldquo;counts\u0026rdquo; as AI-assisted work; Saad captured this tension: \u003cem\u003e\u0026ldquo;we are promoting it; students are using it\u0026hellip; and sometimes AI is also not detected.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003cp\u003eParticipants emphasized that culturally responsive practice requires boundaries around sensitive content, active editing, and skepticism toward outputs. Yannis stated plainly, \u003cem\u003e\u0026ldquo;We avoid to discuss politics, we avoid to discuss religion, we avoid to discuss sensitive issues,\u0026rdquo;\u003c/em\u003e while Rana described human-in-the-loop screening as a safeguard: \u003cem\u003e\u0026ldquo;if there are certain phrases that I feel like it's culturally sensitive\u0026hellip; I change it.\u0026rdquo;\u003c/em\u003e Several participants warned that GenAI can amplify stereotypes; Khalid noted \u003cem\u003e\u0026ldquo;a kind of bias\u0026hellip; describing women\u0026hellip; that all of them wear abaya\u0026hellip; and Emirati, all of them rich,\u0026rdquo;\u003c/em\u003e and Dana cautioned that it can be \u003cem\u003e\u0026ldquo;fostering more stereotypes.\u0026rdquo;\u003c/em\u003e Privacy and data sensitivity were also salient; Rami drew a clear boundary: \u003cem\u003e\u0026ldquo;I cannot give Real-life data with them.\u0026rdquo;\u003c/em\u003e Finally, trust and quality problems were framed as an ever-present risk; Rana warned, \u003cem\u003e\u0026ldquo;Whatever you say, he will agree with you,\u0026rdquo;\u003c/em\u003e and Andreas advised, \u003cem\u003e\u0026ldquo;double-check everything\u0026hellip; never do something with generative AI that then you submit without really\u0026hellip; re-reading.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eThematic Framework and Code Tree GenAI and CRP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCodes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 1: Culturally responsive pedagogical strategies using GenAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1A. Localizing content to UAE contexts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1.1 UAE case/scenario prompting\u003c/p\u003e \u003cp\u003eT1.2 UAE policy/law anchoring\u003c/p\u003e \u003cp\u003eT1.3 Culturally familiar names/examples\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\u003e1B. Language \u0026amp; communication scaffolding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1.5 Translation / bilingual support\u003c/p\u003e \u003cp\u003eT1.6 Tone-polishing for culturally acceptable communication\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\u003e1C. Designing culturally engaging learning experiences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1.7 Gamification \u0026amp; interactive activities\u003c/p\u003e \u003cp\u003eT1.8 AI-generated multimedia/visual explanation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 2: Faculty sensemaking of GenAI for cultural responsiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2A. Role of AI vs role of educator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2.1 \u0026ldquo;AI is a tool, not a replacement\u0026rdquo;\u003c/p\u003e \u003cp\u003eT2.2 Educator as facilitator/mentor (human relationship as core)\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\u003e2B. Cultural-fit decision rules (how they make AI \u0026ldquo;fit\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2.3 \u0026ldquo;What you feed it\u0026rdquo; (prompting for cultural fit)\u003c/p\u003e \u003cp\u003eT2.4 Human-in-the-loop editing for sensitive phrasing\u003c/p\u003e \u003cp\u003eT2.5 Verification/triangulation (don\u0026rsquo;t take outputs for granted)\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\u003e2C. Adoption stance \u0026amp; cultural reflexivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2.6 Openness/professional growth orientation\u003c/p\u003e \u003cp\u003eT2.7 Resistance/denial among some faculty\u003c/p\u003e \u003cp\u003eT2.8 Noticing stereotype patterns in AI outputs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 3: Cultural and relational benefits of GenAI-supported teaching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3A. Inclusive access \u0026amp; support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT3.1 Accessibility supports (subtitles/hearing/reading support)\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\u003e3B. Engagement \u0026amp; motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT3.2 Attention/retention in \u0026ldquo;fast-bored\u0026rdquo; student culture\u003c/p\u003e \u003cp\u003eT3.3 Positive climate (fun, rewards, energy shifts)\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\u003e3C. Cross-cultural connection \u0026amp; belonging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT3.4 Breaking stereotypes through structured interaction\u003c/p\u003e \u003cp\u003eT3.5 Faster localization help (especially for non-local faculty)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 4: Institutional, ethical, and cultural risks/barriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4A. Governance \u0026amp; resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT4.1 Institutional policy/guidelines shaping use\u003c/p\u003e \u003cp\u003eT4.2 Need for training/workshops to use GenAI well\u003c/p\u003e \u003cp\u003eT4.3 Access/cost constraints (tools not free / uneven availability)\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\u003e4B. Academic integrity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT4.4 Plagiarism/\u0026ldquo;too perfect\u0026rdquo; work / ethical use concerns\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\u003e4C. Cultural \u0026amp; ethical risks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT4.5 Sensitive topics/taboos (what cannot be said directly)\u003c/p\u003e \u003cp\u003eT4.6 Stereotype amplification risk\u003c/p\u003e \u003cp\u003eT4.7 Privacy/data sensitivity concerns\u003c/p\u003e \u003cp\u003eT4.8 Trust/quality problems (over-agreeing, need to check sources)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to examine how faculty in UAE higher education are making sense of and practically using GenAI to support culturally responsive teaching in culturally and linguistically diverse classrooms. Our findings contribute to emerging scholarship on GenAI adoption by showing that instructors largely position these tools as capacity-extenders for localization and communication, while also highlighting the conditions and tensions that shape whether GenAI-supported \u003cem\u003ecultural fit\u003c/em\u003e becomes meaningful pedagogical responsiveness or remains largely surface-level adaptation.\u003c/p\u003e \u003cp\u003eA core contribution of the current study was showing that faculty framed GenAI as a practical accelerator for CRT in the UAE, especially when their materials were “\u003cem\u003eembedded in Western knowledge and examples\u003c/em\u003e” that felt distant from local cultural references and institutional realities. Rather than “innovating,” participants described using GenAI to quickly generate locally situated examples, cases, and materials that would otherwise take substantial time. A latent risk, however, is that some faculty may treat GenAI as a substitute for, rather than support to, the ongoing development of their own intercultural competence, producing surface-level relevance while deferring deeper cultural learning. Echoing this concern, Naidu and Sevnarayan (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) warn that unmediated GenAI can reinforce stereotypes and marginalize cultural perspectives, and Nyaaba (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) notes that even context-aware systems may hallucinate, especially in underrepresented languages. Thus, technical localization is helpful but insufficient: culturally responsive GenAI use ultimately depends on faculty cultural expertise and critical judgment, aligning with Gay’s (\u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) argument that CRT rests on educators’ deep, group-specific knowledge, not just localized examples or language.\u003c/p\u003e \u003cp\u003eDespite enthusiasm for GenAI, across Theme 2, faculty repeatedly positioned responsiveness in mentoring, facilitation, emotional attunement, and judgment as the human characteristics they believe GenAI cannot replicate. GenAI was described as capacity-extenders but not replacing the relational work through which students feel seen, supported, and guided: augmentation within relational pedagogy. This finding is coherent with the UAE’s relational culture (Eppard et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Moore-Jones, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shahin et al., \u003cspan class=\"CitationRef\"\u003e2025a\u003c/span\u003e) and CRT’s emphasis on cultural caring and community-building, where responsiveness is enacted through how instructors structure interaction, communicate respect, and sustain a learning climate that supports engagement without violating local norms around face, authority, and participation (Gay, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). These findings are also in line with the broader GenAI-in-higher-education literature cautioning that responsibility for meaning-making and pedagogical judgement remains with the human instructor, even when GenAI assists with drafting and ideation (Henadirage et al., \u003cspan class=\"CitationRef\"\u003e2026\u003c/span\u003e; Liu et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bearman et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticipants described benefits that went beyond saving time, linking GenAI to inclusive access, engagement, and classroom belonging when paired with purposeful instructional design. These outcomes were tied to specific practices, subtitles, multilingual supports, interactive activities, and structured collaboration, rather than assumed as automatic effects of the technology. This supports the argument that AI systems can scaffold instruction around learners’ linguistic and cultural realities when educators intentionally design for accessibility and participation (Mackey \u0026amp; Evans, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zawacki-Richter et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such inclusivity-oriented instructional practices may be understood as indirectly supporting academic success within Gay’s (\u003cspan class=\"CitationRef\"\u003e2010a\u003c/span\u003e) CRT framework. At the same time, it reinforces that inclusive outcomes are contingent on human judgement and design choices, rather than inherent properties of the tool, particularly in culturally specific contexts where communication style and appropriateness shape whether students experience instruction as respectful or misaligned (Gay, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Williams et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFaculty emphasized that GenAI-supported culturally responsive teaching is bounded by institutional conditions, including uneven policies, limited training, and tool access disparities. Academic integrity pressures, privacy concerns, and sensitive-topic boundaries further shaped what instructors felt they could do in practice (Zawacki-Richter et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). This finding is consistent with scholarship noting that GenAI adoption is occurring rapidly, often ahead of coherent governance, pedagogical guidance, or culturally grounded professional development (Damiano et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; O’Dea, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yusuf et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the UAE context, these constraints may be amplified because local norms strongly regulate what counts as appropriate classroom discourse and because faculty already navigate high-stakes expectations around respectful communication, sensitive topics, and institutional risk (Aydogan et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Singh et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Williams et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticipants spoke at length about localizing examples, polishing tone, translating language, and achieving \u003cem\u003efit\u003c/em\u003e, which can be interpreted primarily as cultural competence at the level of materials and communication (Gay, \u003cspan class=\"CitationRef\"\u003e2010a\u003c/span\u003e). Yet their accounts were comparatively thin on questions of power and knowledge politics, who is represented, whose knowledge counts, and how curricula may reproduce hierarchy through what is taught and what is left unspoken (Shahjahan et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). In that sense, culturally responsive practice risks being framed primarily as \u003cem\u003eadaptation\u003c/em\u003e (making Western materials more palatable or locally recognizable) rather than \u003cem\u003etransformation\u003c/em\u003e (re-centering local epistemologies and interrogating dominant frames). As Gay (\u003cspan class=\"CitationRef\"\u003e2010b\u003c/span\u003e) emphasizes, it is the educator’s culturally informed interpretation of students’ actions, needs, and strengths, not merely the presence of culturally familiar content that ultimately determines whether teaching is genuinely responsive. Participants’ emphasis on localization without sustained attention to power, representation, or epistemic authority reflects Gay’s (\u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) distinction between adding multicultural content and redesigning curriculum through a culturally responsive lens. This aligns with Smith’s (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) critique and, in the UAE context, with Shahin et al.’s (\u003cspan class=\"CitationRef\"\u003e2025b\u003c/span\u003e) findings on localization as “cultural substitution without epistemic transformation (p. 1),” where representational tweaks (e.g., local names, places, and culturally “safe” examples) may increase surface relevance while leaving Western authority structures intact, and can even edge toward symbolic epistemicide when non-Western ways of knowing remain structurally absent (Dutta, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shahjahan et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recent literature suggests GenAI may inadvertently accelerate this biased pattern (Tao et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e): it makes it easier to swap in UAE examples, remove idioms, and generate “acceptable” language, but it does not automatically bring power, positionality, or epistemic justice into the pedagogical work unless faculty explicitly pursue that agenda.\u003c/p\u003e \u003cp\u003e Many participants said they “check,” “filter,” and “adapt” GenAI outputs, especially around sensitive phrasing, yet the criteria for doing so often remain vague (e.g., what counts as culturally sensitive, what is removed, and which ethical/CRT commitments guide revisions). This points to a gap in critical consciousness, a key element of Gay’s (\u003cspan class=\"CitationRef\"\u003e2010a\u003c/span\u003e) framework. It matters because evaluative judgment in human–GenAI collaboration is not only technical or cognitive, but also cultural and epistemic (Bearman et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Liu et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Watson et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). When educators’ cultural judgment is thin or deficit-framed, “human-in-the-loop” can simply reproduce superficial localization, polished tone, localized names, and “safe” topics, without deeper engagement with inequity or local knowledge systems (Barana et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This concern aligns with work on the UAE’s Western-oriented higher education workforce, where expatriate-led localization may not become culturally embedded without meaningful local involvement (Austin et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), and with Shahin et al.’s (\u003cspan class=\"CitationRef\"\u003e2025b\u003c/span\u003e) argument that localization without shared authorship tends to default to substitution rather than epistemic co-production. A productive next step, therefore, is to frame “human-in-the-loop” not as a safeguard by itself, but as a practice requiring explicit decision rules grounded in CRT/decolonial commitments, transparent editing rationales, and capacity-building that centers culture, not just the tool (Dutta, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mignolo \u0026amp; Walsh, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Research\u003c/h2\u003e \u003cp\u003eWhile this study makes a timely contribution to understanding GenAI use for cultural relevance in a rapidly evolving landscape, the findings should be interpreted considering several limitations. First, the sample is likely affected by selection bias. Although qualitative work does not aim for statistical generalizability, volunteer participation can skew the dataset toward faculty who already use GenAI or who are more invested in culturally responsive teaching. Faculty with less confidence or competence in either GenAI or culturally responsive pedagogy may hold different views that are not adequately represented here.\u003c/p\u003e \u003cp\u003eSecond, the study relies on faculty self-reports, which are inherently vulnerable to social desirability effects, particularly within UAE higher education, where faculty mobility is high and job security can feel uncertain. Additionally, because the first two authors are faculty members at a public university in the UAE, our institutional proximity may have shaped the interview context and participants’ responses. Participants may have framed their practices in ways they perceived as professionally acceptable within local policy, cultural norms, or institutional expectations. Another limitation of the study was that participants’ accounts offered limited evidence of measurable learning outcomes (e.g., gains in critical thinking), changes in belonging, or student voice that would confirm whether these practices are experienced by learners as genuinely culturally responsive.\u003c/p\u003e \u003cp\u003eFuture research should, therefore, move beyond faculty perceptions alone by incorporating student perspectives and, where possible, learning evidence. Mixed or multi-perspective designs could examine not only how faculty use GenAI, but also whether students experience these GenAI-supported pedagogical choices as culturally respectful, engaging, or alternatively, patronizing or surveillant. More broadly, future studies could attend to both ends of the process: the instructor’s GenAI-supported design decisions and students’ lived experience of those decisions in the classroom.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion and Implications","content":"\u003cp\u003eTaken together, the findings point to a central paradox: GenAI can reduce cultural mismatch while simultaneously reproducing cultural harm through stereotyping, overconfidence, or culturally inappropriate content. This duality positions culturally responsive GenAI use as a high-judgment practice where ethical boundaries and critical review are not optional but foundational. As one participant noted, \u0026ldquo;\u003cem\u003eGenAI should be treated like a fallible GPS: Even when it sounds confident, it can still direct you to drive your car straight into the river,\u0026rdquo;\u003c/em\u003e so instructors must remain in control and verify outputs with informed judgment. In practical terms, GenAI currently functions more as a pedagogical capacity-extender in UAE higher education, supporting faster localization, tone-polishing, and language scaffolding, than as a transformative driver of CRT. To translate this potential into more consistently responsible practice, UAE institutions and faculty can act on several immediate, actionable implications:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMove from AI allowed to AI guidance faculty can actually use\u003c/em\u003e: Universities should publish a short, UAE-specific GenAI teaching guide that gives faculty clear \u0026ldquo;do/don\u0026rsquo;t\u0026rdquo; boundaries, what to avoid (sensitive topics, stereotypical depictions, culturally risky scenarios), what to verify (facts, cultural assumptions, tone), and what to protect (privacy, student data, identifiable cases).\u003c/p\u003e\n\u003cp\u003eProvide a standard prompting/revision template: Rather than leaving faculty to improvise, institutions can offer a small set of approved prompt frames (e.g., \u0026ldquo;rewrite for UAE higher education context,\u0026rdquo; \u0026ldquo;remove idioms,\u0026rdquo; \u0026ldquo;adjust for mixed-gender classroom dynamics,\u0026rdquo; \u0026ldquo;ensure respectful communication\u0026rdquo;) plus a required final step: \u0026ldquo;state what you changed and why.\u0026rdquo; This builds consistent practice across colleges.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCreate a shared, vetted UAE case bank to reduce ad-hoc localization:\u003c/em\u003e A practical step is to develop a repository of vetted UAE-relevant cases, examples, and bilingual resources (with periodic review). This reduces dependence on one-off GenAI outputs and helps prevent \u0026ldquo;name swaps\u0026rdquo; from becoming the default definition of localization.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eReorient professional development toward evaluation, not only tool use\u003c/em\u003e: Universtities should provide workshops prioritizing \u003cem\u003everification routines\u003c/em\u003e (fact-checking, bias spotting, cultural appropriateness review), not only prompting skills. A learning outcome of these workshops would be that faculty can demonstrate how they detect hallucinations, remove stereotypes, and justify edits using UAE classroom norms.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eReduce inequity in access and capacity across colleges:\u003c/em\u003e Institutions should ensure consistent access (subscriptions, approved tools) and baseline training across departments. Otherwise, culturally responsive GenAI use will remain uneven, limited to faculty who are already confident, well-resourced, or tech-forward.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTreat GenAI as support for communication, but keep relationship work human-led:\u003c/em\u003e Faculty can use GenAI for drafting announcements, feedback tone, and simplifying language, but should avoid outsourcing culturally sensitive relational decisions (conflict, advising, performance concerns). A good practice is: GenAI drafts; the instructor revises with local norms in mind.\u003c/p\u003e\n\u003cp\u003eIn conclusion, GenAI can support culturally responsive teaching in UAE higher education when it is treated as a tool that faculty actively govern, through explicit decision rules, careful verification, and culturally grounded revision. The most useful institutional move is to shift from broad encouragement or restriction toward shared standards, practical training, and vetted resources that help faculty produce culturally appropriate teaching at scale without reducing cultural responsiveness to surface-level localization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.O. and M.A. conceptualized and designed the study, developed the interview protocol, and conducted the interviews. M.A. and E.I. led the qualitative analysis (coding, theme development, and refinement) and drafted the Method and Results sections. A.O. served as an analytic auditor, reviewed and challenged coding decisions, and strengthened theme coherence. All authors contributed to interpretation of findings, revised the manuscript critically for intellectual content, and approved the final version for submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdam, I. O., Alhassan, M. D., \u0026amp; Diack, A. (2024). \u003cem\u003eExploring students\u0026rsquo; foundational skills in integrating generative AI in Ghanaian higher education: A constructive learning perspective\u003c/em\u003e. In \u003cem\u003eAMCIS 2024 Proceedings\u003c/em\u003e (Paper 23). Association for Information Systems. https://aisel.aisnet.org/amcis2024/ai_aa/ai_aa/23/\u003c/li\u003e\n\u003cli\u003eAgarwal, D., Naaman, M., \u0026amp; Vashistha, A. (2025). AI suggestions homogenize writing toward Western styles and diminish cultural nuances. In \u003cem\u003eProceedings of the 2025 CHI Conference on Human Factors in Computing Systems\u003c/em\u003e (Article 1117). Association for Computing Machinery. https://doi.org/10.1145/3706598.3713564\u003c/li\u003e\n\u003cli\u003eAustin, A. E., Chapman, D. W., Farah, S., Wilson, E., \u0026amp; Ridge, N. (2014). Expatriate academic staff in the United Arab Emirates: The nature of their work experiences in higher education institutions. \u003cem\u003eHigher Education, 68\u003c/em\u003e(4), 541\u0026ndash;557.\u003c/li\u003e\n\u003cli\u003eAydogan, M., İzmir, E., \u0026amp; Shahin, H. (2025). \u0026ldquo;Like a 6-year-old dropped on Mars without parents\u0026rdquo;: Faculty members\u0026rsquo; intercultural competence and cultural adaptation in the UAE. \u003cem\u003eStudies in Higher Education\u003c/em\u003e. Advance online publication. https://doi.org/10.1080/03075079.2025.2495713\u003c/li\u003e\n\u003cli\u003eBarana, A., Marchisio, M., \u0026amp; Roman, F. (2023). Fostering problem solving and critical thinking in mathematics through generative artificial intelligence. In \u003cem\u003eProceedings of the 20th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2023)\u003c/em\u003e (pp. 377\u0026ndash;385).\u003c/li\u003e\n\u003cli\u003eBearman, M., Tai, J., Dawson, P., Boud, D., \u0026amp; Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. \u003cem\u003eAssessment \u0026amp; Evaluation in Higher Education, 49\u003c/em\u003e(6), 893\u0026ndash;905.\u003c/li\u003e\n\u003cli\u003eCano, Y. M., Venuti, F., \u0026amp; Martinez, R. H. (2023). \u003cem\u003eChatGPT and AI text generators: Should academia adapt or resist?\u003c/em\u003e Harvard Business Publishing Education. https://hbsp.harvard.edu/inspiring-minds/chatgpt-and-ai-text-generators-should-academia-adapt-or-resist\u003c/li\u003e\n\u003cli\u003eCreswell, J. W. (2012). \u003cem\u003eQualitative inquiry and research design: Choosing among five approaches\u003c/em\u003e (3rd ed.). SAGE Publications.\u003c/li\u003e\n\u003cli\u003eDamiano, A. D., Laur\u0026iacute;a, E. J. M., Sarmiento, C., \u0026amp; Zhao, N. (2024). Early perceptions of teaching and learning using generative AI in higher education. \u003cem\u003eJournal of Educational Technology Systems, 52\u003c/em\u003e(3), 346\u0026ndash;375. https://doi.org/10.1177/00472395241233290\u003c/li\u003e\n\u003cli\u003eDenzin, N. K., \u0026amp; Lincoln, Y. S. (Eds.). (2011). \u003cem\u003eThe SAGE handbook of qualitative research\u003c/em\u003e (4th ed.). SAGE.\u003c/li\u003e\n\u003cli\u003eDutta, U. (2018). Decolonizing \u0026ldquo;community\u0026rdquo; in community psychology. \u003cem\u003eAmerican Journal of Community Psychology, 62\u003c/em\u003e(3\u0026ndash;4), 272\u0026ndash;282. https://doi.org/10.1002/ajcp.12281\u003c/li\u003e\n\u003cli\u003eEppard, J., Bailey, F., McKeown, K., \u0026amp; Singh, H. (2021). Expatriate faculty and student perspectives on teaching and learning in a United Arab Emirates university. \u003cem\u003eIssues in Educational Research, 31\u003c/em\u003e(2), 458\u0026ndash;475.\u003c/li\u003e\n\u003cli\u003eEssien, A., Bukoye, O. T., O\u0026rsquo;Dea, X., \u0026amp; Kremantzis, M. (2024). The influence of AI text generators on critical thinking skills in UK business schools. \u003cem\u003eStudies in Higher Education, 49\u003c/em\u003e(5), 865\u0026ndash;882. https://doi.org/10.1080/03075079.2024.2316881\u003c/li\u003e\n\u003cli\u003eGay, G. (2000). \u003cem\u003eCulturally responsive teaching: Theory, practice, and research\u003c/em\u003e. Teachers College Press.\u003c/li\u003e\n\u003cli\u003eGay, G. (2002). Preparing for culturally responsive teaching. \u003cem\u003eJournal of Teacher Education, 53\u003c/em\u003e(2), 106\u0026ndash;116. https://doi.org/10.1177/0022487102053002003\u003c/li\u003e\n\u003cli\u003eGay, G. (2010a). Acting on beliefs in teacher education for cultural diversity. \u003cem\u003eJournal of Teacher Education, 61\u003c/em\u003e(1\u0026ndash;2), 143\u0026ndash;152. https://doi.org/10.1177/0022487109347320\u003c/li\u003e\n\u003cli\u003eGay, G. (2010b). \u003cem\u003eCulturally responsive teaching: Theory, research, and practice\u003c/em\u003e (2nd ed.). Teachers College Press.\u003c/li\u003e\n\u003cli\u003eGay, G. (2015). The what, why, and how of culturally responsive teaching: International mandates, challenges, and opportunities. \u003cem\u003eMulticultural Education Review, 7\u003c/em\u003e(3), 123\u0026ndash;139. https://doi.org/10.1080/2005615X.2015.1072079\u003c/li\u003e\n\u003cli\u003eGlobal Media Insight. (2025). \u003cem\u003eUnited Arab Emirates (UAE) population statistics 2025\u003c/em\u003e. https://www.globalmediainsight.com/blog/uae-population-statistics/\u003c/li\u003e\n\u003cli\u003eGrab, M. O. (2025). Teaching for equity: An exploration of AI\u0026rsquo;s role in culturally responsive teaching in higher education settings. \u003cem\u003eInnovative Higher Education\u003c/em\u003e, 1\u0026ndash;22. https://doi.org/10.1007/s10755-025-09801-4\u003c/li\u003e\n\u003cli\u003eHenadirage, A., Chathuranga, B.T.K. \u0026amp; Gunarathne, N. (2026). Changing assessment landscape in management education with AI-driven technologies: Impacts and drivers. \u003cem\u003eInternational Journal of Educational Technology in Higher Education, \u003c/em\u003e\u003cem\u003e23\u003c/em\u003e, Article 4. https://doi.org/10.1186/s41239-026-00578-w\u003c/li\u003e\n\u003cli\u003eLincoln, Y. S., \u0026amp; Guba, E. G. (1985). \u003cem\u003eNaturalistic inquiry\u003c/em\u003e. SAGE.\u003c/li\u003e\n\u003cli\u003eLiu, J., Li, S., \u0026amp; Dong, Q. (2024). Collaboration with generative artificial intelligence: An exploratory study based on learning analytics. \u003cem\u003eJournal of Educational Computing Research, 62\u003c/em\u003e(5), 1014\u0026ndash;1046.\u003c/li\u003e\n\u003cli\u003eMackey, J., \u0026amp; Evans, C. (2020). The role of AI in promoting inclusive education practices. \u003cem\u003eInternational Journal of Inclusive Education, 24\u003c/em\u003e(10), 1061\u0026ndash;1077. https://doi.org/10.1080/13603116.2019.1622172\u003c/li\u003e\n\u003cli\u003eMerriam, S. B., \u0026amp; Tisdell, E. J. (2016). \u003cem\u003eQualitative research: A guide to design and implementation\u003c/em\u003e (4th ed.). Jossey-Bass.\u003c/li\u003e\n\u003cli\u003eMignolo, W. D., \u0026amp; Walsh, C. E. (2018). \u003cem\u003eOn decoloniality: Concepts, analytics, praxis\u003c/em\u003e. Duke University Press.\u003c/li\u003e\n\u003cli\u003eMonib, W. K., Qazi, A., \u0026amp; Mahmud, M. M. (2025). Exploring learners\u0026rsquo; experiences and perceptions of ChatGPT as a learning tool in higher education. \u003cem\u003eEducation and Information Technologies, 30\u003c/em\u003e(1), 917\u0026ndash;939. https://doi.org/10.1007/s10639-024-13065-4\u003c/li\u003e\n\u003cli\u003eMoore-Jones, P. J. 2015. The benefits and pitfalls of a multicultural teaching faculty and a monocultural student population: An interpretive analysis of tertiary teachers\u0026rsquo; and students\u0026rsquo; perceptions in the United Arab Emirates. \u003cem\u003eJournal of Language and Cultural Education 3\u003c/em\u003e(3), 69\u0026ndash;84. https://doi.org/10.1515/jolace-2015-0021.\u003c/li\u003e\n\u003cli\u003eNaidu, K., \u0026amp; Sevnarayan, K. (2025). Culturally relevant assessment and generative AI: A co-creation framework for pre-service teachers. \u003cem\u003eJournal of Applied Learning \u0026amp; Teaching, 8\u003c/em\u003e(2), 132\u0026ndash;142.\u003c/li\u003e\n\u003cli\u003eNorris, S. (2013). \u003cem\u003eAnalyzing multimodal interaction: A methodological framework\u003c/em\u003e. Routledge.\u003c/li\u003e\n\u003cli\u003eNyaaba, M. (2025). \u003cem\u003eGlocalizing generative AI in education for the Global South: The design case of 21st century teacher educator AI for Ghana\u003c/em\u003e [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2504.07149\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Dea, X. (2024). Generative AI: Is it a paradigm shift for higher education? \u003cem\u003eStudies in Higher Education, 49\u003c/em\u003e(5), 811\u0026ndash;816. https://doi.org/10.1080/03075079.2024.2332944\u003c/li\u003e\n\u003cli\u003eQiu, W., Thway, M., Lai, J. W., \u0026amp; Lim, F. S. (2025, March). GenAI for teaching and learning: A human-in-the-loop approach. In \u003cem\u003eProceedings of the Companion Proceedings of the 15th International Conference on Learning Analytics \u0026amp; Knowledge (LAK25)\u003c/em\u003e (pp. 3\u0026ndash;7). Dublin, Ireland.\u003c/li\u003e\n\u003cli\u003eSalda\u0026ntilde;a, J. (2016). \u003cem\u003eThe coding manual for qualitative researchers\u003c/em\u003e (3rd ed.). SAGE.\u003c/li\u003e\n\u003cli\u003eSchwandt, T. A. (1994). Constructivist, interpretivist approaches to human inquiry. In N. K. Denzin \u0026amp; Y. S. Lincoln (Eds.), \u003cem\u003eHandbook of qualitative research\u003c/em\u003e (pp. 118\u0026ndash;137). SAGE.\u003c/li\u003e\n\u003cli\u003eShahin, H., Patka, M., \u0026amp; Smail, L. (2025b). Balancing the local-international dialectic in community psychology pedagogy: Lessons from adapting American curricula in the United Arab Emirates. \u003cem\u003eAmerican Journal of Community Psychology\u003c/em\u003e, 1\u0026ndash;11. https://doi.org/10.1002/ajcp.70014\u003c/li\u003e\n\u003cli\u003eShahin, H., Patka, M., Aydogan, M., Al Ali, A., Bin Thalith, M., \u0026amp; Alhemeiri, S. (2025a). Relationality and learning: Insights from undergraduate student research assistant experiences. \u003cem\u003eLEARNing Landscapes, 29\u003c/em\u003e, 245\u0026ndash;262.\u003c/li\u003e\n\u003cli\u003eShahjahan, R. A., Estera, A. L., Surla, K. L., \u0026amp; Edwards, K. T. (2022). \u0026ldquo;Decolonizing\u0026rdquo; curriculum and pedagogy: A comparative review across disciplines and global higher education contexts. \u003cem\u003eReview of Educational Research, 92\u003c/em\u003e(1), 73\u0026ndash;113. https://doi.org/10.3102/00346543211042423\u003c/li\u003e\n\u003cli\u003eSingh, H., Bailey, F., J. Eppard, \u0026amp; McKeown, K. (2021). Partners in learning: An exploration of multi-cultural faculty and Emirati students\u0026rsquo; perspectives of university learning experiences. \u003cem\u003eLearning, Culture and Social Interaction, 31\u003c/em\u003e, Article 100564. https://doi.org/10.1016/j.lcsi.2021.100564\u003c/li\u003e\n\u003cli\u003eSmith, L. T. (1999). \u003cem\u003eDecolonizing methodologies: Research and Indigenous peoples\u003c/em\u003e. Zed Books.\u003c/li\u003e\n\u003cli\u003eTao, Y., Viberg, O., Baker, R. S., \u0026amp; Kizilcec, R. F. (2024). Cultural bias and cultural alignment of large language models. \u003cem\u003ePNAS Nexus, 3\u003c/em\u003e(9), Article 346. https://doi.org/10.1093/pnasnexus/pgae346\u003c/li\u003e\n\u003cli\u003eTu, Y.-F. (2024). Roles and functionalities of ChatGPT for students with different growth mindsets: Findings of drawing analysis. \u003cem\u003eEducational Technology \u0026amp; Society, 27\u003c/em\u003e(1), 198\u0026ndash;214. https://doi.org/10.30191/ETS.202401_27(1).TP01\u003c/li\u003e\n\u003cli\u003eWatson, E., Viana, T., Zhang, S., Sturgeon, B., \u0026amp; Petersson, L. (2024). Towards an end-to-end personal fine-tuning framework for AI value alignment. \u003cem\u003eElectronics, 13\u003c/em\u003e(20), Article 4044.\u003c/li\u003e\n\u003cli\u003eWilliams, C. D., Hojeij, Z., Johnson, J. D., Eppard, J., \u0026amp; McKeown, K. (2025). Navigating educational mistakes in learning: Cultural perspectives of Emirati students and expatriate instructors in higher education. \u003cem\u003eJournal of International Students, 15\u003c/em\u003e(12), 1\u0026ndash;20. https://doi.org/10.32674/5z34ya54\u003c/li\u003e\n\u003cli\u003eWu, Y. (2024). Revolutionizing learning and teaching: Crafting personalized, culturally responsive curriculum in the AI era. \u003cem\u003eCreative Education, 15\u003c/em\u003e, 1642\u0026ndash;1651. https://doi.org/10.4236/ce.2024.158098\u003c/li\u003e\n\u003cli\u003eYusuf, A., Pervin, N., \u0026amp; Rom\u0026aacute;n-Gonz\u0026aacute;lez, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. \u003cem\u003eInternational Journal of Educational Technology in Higher Education, 21\u003c/em\u003e, Article 21. https://doi.org/10.1186/s41239-024-00453-6\u003c/li\u003e\n\u003cli\u003eZawacki-Richter, O., Bai, J. Y., Lee, K., van Slagter Tryon, P. J., \u0026amp; Prinsloo, P. (2024). New advances in artificial intelligence applications in higher education? \u003cem\u003eInternational Journal of Educational Technology in Higher Education, 21\u003c/em\u003e(1), Article 32. https://doi.org/10.1186/s41239-024-00464-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GenAI, Culturally Responsive Teaching, Higher Education, UAE, Faculty Perspectives","lastPublishedDoi":"10.21203/rs.3.rs-8722631/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8722631/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenerative artificial intelligence (GenAI) is rapidly reshaping higher education teaching practices, yet we still know little about how faculty in culturally specific, non-Western contexts evaluate and adapt GenAI outputs for culturally responsive teaching (CRT). This qualitative interpretivist study examines how faculty members in UAE higher education use GenAI to support cultural fit in multicultural classrooms while navigating local norms, institutional expectations, and ethical constraints. Drawing on Gay\u0026rsquo;s CRT framework, we conducted semi-structured interviews with 20 faculty members across disciplines and national backgrounds who actively use GenAI in their teaching. Using reflexive thematic analysis, we identified four themes: (1) Culturally responsive pedagogical strategies using GenAI; (2) Faculty sensemaking of GenAI for cultural responsiveness; (3) Cultural and relational benefits of GenAI-supported teaching; and (4) Institutional, ethical, and cultural risks/barriers. Findings suggest that GenAI primarily functions as an accelerator of \u003cem\u003esurface localization\u003c/em\u003e (e.g., examples, tone, translation) rather than a driver of deeper CRT transformation unless faculty apply explicit cultural decision rules and critical consciousness to interrogate bias, representation, and epistemic authority. Implications highlight the need for UAE-specific guidance, culturally grounded professional development focused on evaluative judgment, and shared vetted case resources to support responsible GenAI use at scale.\u003c/p\u003e","manuscriptTitle":"Beyond “Camel, Desert, Ramadan Defaults”: Faculty Use of GenAI for Culturally Responsive Teaching in UAE","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 09:30:39","doi":"10.21203/rs.3.rs-8722631/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"583d9472-fcab-4fff-8dfb-72fc30fde817","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-28T19:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 09:30:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8722631","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8722631","identity":"rs-8722631","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}