Equity and inclusion in generative AI use in higher education: A qualitative study of Palestinian students’ experiences

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Using a qualitative design, data were collected through 21 semi-structured interviews and three online focus group sessions with students who had experience using generative AI for academic purposes. The findings showed that students perceived generative AI as both an opportunity and a risk. On the one hand, participants valued its role in supporting personalized learning, accessibility, productivity, and academic confidence. On the other hand, they raised concerns about unequal access to advanced tools, biased outputs, privacy, overreliance, and unclear institutional policies. The study also found that students’ sense of belonging was shaped by whether they felt represented, heard, and fairly treated in AI-enabled learning environments. The study concludes that equitable AI integration requires student-centered policies, AI literacy support, inclusive pedagogy, and attention to fairness, recognition, and meaningful participation in higher education. Generative artificial intelligence higher education equity inclusion belonging Figures Figure 1 Introduction Generative artificial intelligence (GenAI) has entered higher education rapidly and is increasingly presented as a tool for personalization, efficiency, academic support, and pedagogical innovation (Baidoo-Anu & Owusu Ansah, 2023 ; Farrelly & Baker, 2023 ; Ouyang et al., 2022 ; Wollny et al., 2021 ). Recent scholarship has highlighted its potential to assist students with writing, feedback, idea generation, and self-directed learning, thereby positioning GenAI as a promising educational resource. Yet this optimism can obscure a central issue: educational benefit is not automatically distributed fairly simply because a tool is available. Access to GenAI does not guarantee meaningful participation, equal advantage, or inclusive learning conditions. This is particularly important because GenAI is introduced into higher education systems that are already shaped by unequal access to technology, varied levels of digital and AI literacy, linguistic hierarchies, different disciplinary expectations, and uneven institutional support. As a result, students do not encounter GenAI under the same conditions, nor do they benefit from it in the same ways. Student-focused research already suggests that experiences with GenAI vary according to confidence, self-efficacy, language background, and the quality of institutional guidance rather than the technology alone (Chan & Hu, 2023 ; Gayed et al., 2022 ; Hmoud et al., 2024 ; Lee et al., 2022 ; Ngo, 2023 ). Accordingly, the key issue is no longer whether GenAI can support learning in principle, but whether it does so in ways that are equitable, inclusive, and responsive to students’ diverse realities. Although AI in education research increasingly addresses ethics, governance, and policy, much of this discussion remains abstract and insufficiently grounded in students’ lived experiences (Baker & Hawn, 2021; Chan, 2023 ; Holmes et al., 2022 ; Klimova et al., 2022 ). In practice, inequity in GenAI use may emerge through multiple interconnected factors, including unequal access to advanced tools, weak prompting skills, language barriers, biased outputs, accessibility challenges, and unclear rules governing legitimate use. These conditions shape not only whether students use GenAI, but also whether they perceive it as fair, trustworthy, and supportive within their academic environment (Calderwood, 2024; Perdana et al., 2025; Pretorius et al., 2025 ). Without attention to these issues, institutions may adopt AI practices that appear innovative while unintentionally reproducing exclusion and educational disadvantage. The Palestinian context makes these questions especially urgent. Recent Palestine-focused studies show that GenAI in universities is associated simultaneously with opportunity, dependency, technostress, and uncertainty rather than straightforward educational progress (Alhur et al., 2025 ; Hamamra, Khlaif, & Mayaleh, 2025 ; Hamamra, Khlaif, Mayaleh, & Baker, 2025 ; Khlaif et al., 2024 ; Khlaif et al., 2025 ; Khlaif, Alkouk, Salama, & Abu Eideh, 2025 ). This body of work is valuable because it challenges universal assumptions about AI adoption and shows that technological integration is always mediated by local structural conditions. However, much of this emerging research has focused on educators, institutional response, and assessment redesign, while students’ own experiences remain underexplored. In response to this gap, the present study explores how Palestinian university students experience equity, inclusion, and belonging in relation to the use of GenAI in higher education. Specifically, it examines how students perceive the opportunities and limitations of GenAI, what forms of exclusion or inequality they associate with its use, and what kinds of institutional, pedagogical, and policy support they believe are necessary for more just and inclusive AI-enabled learning environments. By centering students’ voices, the study contributes to a more socially grounded understanding of GenAI, not merely as a technological aid, but as a site where questions of fairness, participation, recognition, and belonging are actively negotiated. Problem Statement Generative artificial intelligence (GenAI) is increasingly promoted in higher education as a tool for personalization, efficiency, and academic support (Baidoo-Anu & Owusu Ansah, 2023 ; Farrelly & Baker, 2023 ; Ouyang et al., 2022 ). However, these promises do not automatically translate into equitable or inclusive educational experiences. Students encounter GenAI within higher education systems already shaped by unequal access to technology, varied levels of AI literacy, linguistic differences, uneven institutional guidance, and broader social inequalities (Baker & Hawn, 2021; Chan, 2023 ; Holmes et al., 2022 ; Klimova et al., 2022 ). As a result, the benefits of GenAI are not experienced uniformly, and its use may reproduce or intensify existing forms of exclusion rather than reduce them. Although previous research has explored the pedagogical potential, ethical concerns, and governance challenges of AI in education, less attention has been paid to how students themselves experience GenAI in relation to equity, inclusion, and belonging , particularly in underrepresented contexts such as Palestinian higher education (Alhur et al., 2025 ; Hamamra, Khlaif, & Mayaleh, 2025 ; Khlaif et al., 2024 ; Perdana et al., 2025). This creates an important gap in understanding how GenAI is actually lived, negotiated, and interpreted by students within unequal educational environments. Research Purpose This study aims to explore how Palestinian university students experience equity, inclusion, and belonging in relation to the use of generative artificial intelligence in higher education. Specifically, the study seeks to examine how students perceive the opportunities and limitations of GenAI in their academic lives, how they interpret the risks of inequality and exclusion associated with its use, and what forms of institutional, pedagogical, and policy support they believe are necessary for more equitable and inclusive AI-enabled learning environments. By centering students’ perspectives, the study aims to develop a deeper and more context-sensitive understanding of GenAI as a sociotechnical and educational phenomenon rather than merely as a digital tool. Research Contribution This study contributes to the emerging literature on GenAI in higher education in three main ways. First, it shifts the focus from general discussions of adoption, usefulness, and academic integrity toward a more socially grounded analysis of equity, inclusion, and belonging. Second, it foregrounds students’ voices in a field where much of the existing research has emphasized institutional perspectives, policy debates, or educators’ concerns. Third, it contributes evidence from the Palestinian higher education context, where GenAI use is shaped by specific structural, technological, and educational conditions that remain underrepresented in the international literature. Through this focus, the study offers both an empirical and conceptual contribution by showing that the educational significance of GenAI depends not only on its technical capabilities, but also on the conditions of access, recognition, support, and participation through which students engage with it. Research questions How do students perceive the role of generative AI in supporting or hindering equity and inclusion in higher education? What challenges, risks, and exclusionary experiences do students associate with the use of generative AI in higher education? What forms of institutional, pedagogical, and policy support do students believe are necessary to promote equity, inclusion, and belonging in AI-enabled higher education? Literature Review A substantial portion of the literature on GenAI in higher education frames it as a transformative instructional tool. Reviews and conceptual papers emphasize its capacity to provide immediate feedback, support drafting and revision, personalize learning, and enhance teaching and learning processes (Baidoo-Anu & Owusu Ansah, 2023 ; Farrelly & Baker, 2023 ; Jahic et al., 2023 ; Ouyang et al., 2022 ; Wollny et al., 2021 ). Related studies on chatbots and AI-supported learning likewise suggest benefits for self-efficacy, after-class review, and language support (Gayed et al., 2022 ; Lee et al., 2022 ). These contributions are useful in demonstrating why GenAI has gained rapid attention in higher education. However, much of this literature remains overly instrumental: it foregrounds what the technology can do while paying less attention to the uneven conditions under which students can actually use it effectively. Student-focused studies offer a more nuanced picture. Research indicates that students often perceive GenAI as both enabling and problematic. They value its support for brainstorming, writing, feedback, and academic efficiency, yet they also express concerns about accuracy, overreliance, plagiarism, and ethics (Chan & Hu, 2023 ; Hmoud et al., 2024 ; Ngo, 2023 ; Sousa et al., 2025). This suggests that GenAI is not experienced simply as a neutral aid, but as a tool whose usefulness is shaped by trust, skill, and academic context. Moreover, these mixed perceptions indicate that educational advantage is not inherent in GenAI itself. Rather, benefit depends on whether students possess the literacy, critical judgment, and institutional support needed to use AI meaningfully and responsibly. A second body of literature highlights the relationship between GenAI and inequality. Scholars have raised concerns about algorithmic bias, exclusionary design, and the risk that AI systems may reproduce dominant linguistic, cultural, and epistemic norms (Baker & Hawn, 2021; Fitzgerald et al., 2025 ). These concerns are particularly relevant in higher education, where students’ backgrounds, languages, abilities, and levels of technological familiarity differ considerably. In this sense, the issue is not only whether students can access GenAI, but whether the technology recognizes and responds to diverse learners fairly. Broader work on equity and inclusion in education similarly suggests that technological innovation can deepen rather than reduce inequality when it is detached from questions of access, representation, and participation (OECD, 2024 ; Miao & Holmes, 2023 ). The literature also points to the importance of institutional mediation. Researchers have emphasized the need for clear AI policies, ethical guidance, and structured support for students and faculty navigating this rapidly changing environment (Chan, 2023 ; Holmes et al., 2022 ; Jin et al., 2025 ). Yet policy alone is insufficient if it functions only as regulation rather than support. Students’ experiences of AI are also shaped by the availability of AI literacy training, prompting skills, guidance on critical evaluation, and assessment practices that define legitimate use. Recent studies suggest that uncertainty about policy, weak guidance, and inconsistent teaching expectations can increase anxiety and produce uneven patterns of AI adoption (Kim et al., 2025 ; Yusuf et al., 2024 ). Thus, institutional context is central to whether GenAI becomes a source of inclusion or exclusion in practice. Another important dimension that remains underdeveloped in the literature is belonging. Much of the current scholarship focuses on adoption, usefulness, governance, and academic integrity. Less attention has been paid to whether students feel represented, heard, trusted, and recognized in AI-enabled learning environments. Insights from disability and inclusion scholarship are relevant here, particularly the argument that meaningful inclusion requires not only access, but voice and participation in institutional decision-making (Shpigelman et al., 2022). Applied to GenAI, this suggests that students’ experiences cannot be fully understood through access and usage alone. It is also necessary to examine whether AI-related policies, platforms, and pedagogical practices foster trust, recognition, and a sense of legitimacy among students. Within the Palestinian context, this gap is especially significant. Recent research has shown that GenAI use in Palestinian higher education is shaped by uncertainty, dependency, assessment challenges, and uneven institutional readiness (Alhur et al., 2025 ; Khlaif et al., 2024 ; Khlaif et al., 2025 ). However, these studies have largely centered educators and institutional concerns. The student perspective remains insufficiently explored, despite the fact that students are the ones most directly negotiating the everyday boundaries between support and dependency, fairness and unfair advantage, and participation and exclusion. Taken together, the literature suggests that GenAI in higher education should not be understood only as a question of adoption or effectiveness. It must also be examined as a social and pedagogical issue shaped by distribution, recognition, and institutional response. This study addresses that need by exploring how Palestinian university students experience GenAI through the lenses of equity, inclusion, and belonging. In doing so, it responds to calls for more student-centered and context-sensitive research on the implications of AI in higher education. Methodology Research Design This study adopted a qualitative research design to explore students’ experiences of equity, inclusion, and belonging in relation to the use of generative AI in higher education. A qualitative approach was appropriate because the study sought to understand students’ perceptions, meanings, and lived experiences in depth. Participants and Recruitment Criteria Participants were recruited through purposive sampling to include Palestinian higher education students with direct experience using generative AI for academic purposes. Eligibility required current enrollment in a higher education institution and prior academic use of tools such as ChatGPT or similar applications. Students without prior academic use of generative AI were excluded. To capture diverse perspectives, variation was sought across discipline, gender, place of residence, frequency of use, type of access, and level of familiarity with generative AI. Participation was voluntary, and informed consent was obtained from all participants. The study included 46 participants: 21 students who took part in individual interviews and 25 different students who participated in three focus groups. Using both interviews and focus groups strengthened the study by combining detailed personal narratives with interactional insights into how students collectively discussed, compared, and negotiated their experiences of generative AI in higher education. Table 1 presents a cross-tabulated summary of participants’ demographic characteristics by gender, while Table 2 summarizes participants’ patterns of generative AI use for academic purposes. This organization was used to improve the clarity of the sample description and to show variation across key participant characteristics. Table 1 Cross-tabulation of participants’ demographic characteristics by gender (N = 46) Characteristic Category Female n (%) Male n (%) Total n (%) Field of study Humanities and Social Sciences 9 4 13 (28.3) Business and Economics 4 3 7 (15.2) Engineering and Information Technology 6 4 10 (21.7) Health and Medical Sciences 5 2 7 (15.2) Natural Sciences 6 3 9 (19.6) Place of residence City 15 6 21 (45.7) Village 11 8 19 (41.3) Camp 4 2 6 (13.0) Table 2 Generative AI use characteristics of participants (N = 46) Characteristic Category n % Frequency of generative AI use for academic purposes Daily 8 17.4 Several times a week 19 41.3 Once a week 11 23.9 Occasionally 8 17.4 Main access to generative AI tools Free version only 33 71.7 Paid subscription 5 10.9 Both free and paid tools 8 17.4 Self-reported familiarity with generative AI Beginner 10 21.7 Intermediate 25 54.3 Advanced 11 23.9 Main reason for using generative AI Brainstorming and idea generation 11 23.9 Writing and editing support 12 26.1 Summarizing and understanding content 9 19.6 Translation and language support 7 15.2 Research and information search 7 15.2 Research Instruments Data were collected using semi-structured interviews and focus group discussions. These two instruments were used to generate complementary forms of qualitative data, with interviews enabling in-depth exploration of individual experiences and focus groups capturing shared views, differences, and interactional perspectives. The interview and focus group guides were developed from the research questions and informed by relevant literature on generative AI in higher education. Open-ended questions were used to elicit detailed reflections on students’ experiences, perceived opportunities, challenges, and support needs. Data Collection Data were collected in two sequential phases using semi-structured interviews and focus groups. First, 21 in-person interviews were conducted, each lasting 45–60 minutes at a time and place chosen by the participant. This was followed by three online focus groups conducted via a video-conferencing platform with different participants. Participation was voluntary, written informed consent was obtained before participation, and all sessions were audio-recorded with permission. Data Analysis Data Analysis Data were analyzed using reflexive thematic analysis following the procedures outlined by Virginia Braun and Victoria Clarke (2006, 2019). The analysis followed an inductive approach, allowing themes to emerge from the data rather than being imposed from predetermined theoretical categories. This approach was appropriate because the study sought to explore students’ experiences and interpretations of generative AI in higher education. The analysis proceeded through several iterative stages. First, all interview and focus group recordings were transcribed verbatim and read multiple times to achieve familiarity with the data. During this stage, the researchers documented initial observations and analytic memos. Second, initial codes were generated from the transcripts by identifying meaningful segments of text related to students’ experiences of equity, inclusion, and belonging in relation to generative AI use. Coding was conducted collaboratively by the three researchers to enhance analytical rigor. Third, related codes were grouped into potential categories and themes through an iterative process of comparison across transcripts. Fourth, the research team reviewed and refined the themes by examining their internal coherence and distinctiveness, ensuring that each theme captured a meaningful pattern across the dataset. Finally, themes were defined, named, and supported with illustrative quotations drawn directly from the participants’ accounts. Throughout the analysis process, the researchers engaged in regular discussions to compare interpretations and resolve coding differences, which helped enhance the credibility and consistency of the thematic interpretation. Ethical Considerations Ethical approval for this study was obtained from the Institutional Review Board at An-Najah National University (Approval No. Edu/Hum. Dec. 2025/70). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. All participants were informed about the purpose of the study, and written informed consent was obtained prior to participation. Participants were informed about the purpose of the study, the voluntary nature of participation, and their right to withdraw at any stage without penalty. Written informed consent was obtained prior to participation. To ensure confidentiality, identifying information was removed from the transcripts, data were stored securely, and participant codes were used in reporting the findings. Trustworthiness Several strategies were used to enhance the trustworthiness of the study. All transcripts were returned to participants for review, giving them the opportunity to edit, clarify, or delete any part of their responses. In addition, the involvement of three researchers in the coding and interpretation process supported credibility through collaborative analysis. To strengthen analytic rigor, a subset of transcripts (30%) was independently coded by two researchers and intercoder agreement was assessed, yielding an agreement rate of 89%. Any discrepancies were discussed until consensus was reached. Trustworthiness was further strengthened through verbatim transcription, systematic coding, and the use of direct quotations to ground the findings in participants’ accounts. Findings The analysis generated ten interrelated themes that explain how students experience generative AI in relation to equity, inclusion, and belonging in higher education. Across the interviews and focus groups, participants described generative AI as both enabling and problematic. They valued its capacity to support learning, improve access, and increase academic efficiency. At the same time, they raised concerns about fairness, bias, reliability, privacy, and unequal access. Overall, students framed generative AI as a resource whose educational value depends on how it is accessed, governed, and integrated into teaching and learning. Theme 1: Generative AI as a Tool for Educational Opportunity Participants described generative AI as creating new opportunities for learning by offering timely, flexible, and personalized academic support. Personalized learning support. Many participants viewed generative AI as a personalized learning aid that could simplify complex ideas, explain content at different levels, and respond to individual academic needs. As one participant noted, “It explains the lesson in a simpler way and according to what I need, so it feels like having a tutor available all the time” (P4). Accessibility for diverse learners. Participants also linked AI to greater accessibility. Some, especially those who experienced difficulty with academic English or complex writing tasks, said AI helped them organize ideas and communicate more clearly. One participant explained, “As a student who sometimes struggles with academic English, AI helps me organize my ideas and improve my writing” (P11). Reduced cognitive load. Several participants reported that AI reduced the burden of repetitive or time-consuming academic tasks, such as summarizing content or structuring assignments, allowing them to focus more on higher-order thinking. As one focus group participant stated, “It saves me time on basic tasks, so I can spend more energy thinking about the actual content” (FGS1P3). Academic confidence and capacity building. Participants further described AI as helping them check understanding, refine drafts, and strengthen foundational knowledge, particularly when they felt less academically prepared. One participant reflected, “Sometimes I use it to check if I am on the right track, and that gives me more confidence in my work” (P15). Theme 2: Risks of Inequality and Exclusion in GenAI Use Although participants acknowledged the benefits of AI, they also emphasized that its use is shaped by unequal conditions that may reproduce exclusion. Unequal access to advanced AI tools. Many participants raised fairness concerns regarding differences between free and paid tools. They argued that students who could afford premium versions benefited from stronger outputs and more reliable support. As one participant stated, “Students who can pay for the better version definitely get better help than those using the free one” (P7). Bias in AI-generated outputs. Some participants highlighted that AI outputs often reflected assumptions or examples that felt culturally distant from their realities. One participant explained, “Sometimes the examples it gives do not reflect our context at all, as if education only happens in Western settings” (FGS2P8). This suggests that exclusion may arise not only through access, but also through representation. Exclusion through AI detection practices. Concerns about unfairness were also evident in discussions of AI detection tools. Some students feared that authentic work could be incorrectly labeled as AI-generated, particularly when their writing style did not fit institutional expectations. As one participant noted, “What worries me is that even if I write by myself, the detector may still accuse me, and that feels unfair” (P19). Digital and technical barriers. Participants also pointed to broader structural barriers, including unstable internet access, limited devices, and weak infrastructure. These shaped not only whether AI could be used, but how effectively it could support learning. One focus group participant stated, “Sometimes access itself is the problem, because not everyone has stable internet or devices to use these tools properly” (FGS1P6). Theme 3: Academic Benefits of GenAI in Learning Practices Students described using generative AI in practical and increasingly normalized ways to support academic work. Enhanced productivity and efficiency. Many participants said AI improved efficiency by helping with summarizing, organizing, editing, and managing academic tasks. One participant remarked, “It helps me summarize long readings and structure my assignments much faster” (P2). Creativity and brainstorming support. Several students also used AI to generate ideas and overcome difficulty starting assignments or presentations. As one participant said, “When I do not know how to start, it gives me ideas that help me move forward” (FGS2P11). Support for discipline-specific tasks. Some participants, especially in scientific and technical fields, highlighted more specialized uses such as coding support, problem solving, and data-related work. One participant explained, “In programming tasks, it helps me find errors and understand what is wrong in the code” (P10). Theme 4: Limitations and Challenges of GenAI in Higher Education Despite its usefulness, participants consistently pointed to limitations that complicated their engagement with AI. Inaccuracy and hallucination. Many participants questioned the reliability of AI-generated content, especially when responses sounded convincing but were later found to be incorrect. One participant stated, “Sometimes it gives an answer that sounds correct, but later I discover that the information is wrong” (P9). Overreliance and weakened critical engagement. Participants also worried that excessive dependence on AI could reduce independent thinking and weaken deeper engagement with learning materials. As one participant noted, “If students depend on it too much, they may stop thinking deeply and just accept the answer” (FGS1P2). Ethical ambiguity and academic integrity concerns. Several students described uncertainty about the boundary between legitimate support and misconduct. This ambiguity created anxiety, especially when institutional guidance was unclear. One participant explained, “The problem is that nobody clearly explains where support ends and cheating begins” (P13). Limited depth of understanding. Some participants argued that AI often produces responses that are useful for general understanding but insufficient for deeper or specialized learning. One participant observed, “It is useful for general ideas, but not always for deep or specialized understanding” (FGS2P14). Limited prompt literacy. Participants further recognized that effective AI use depends on the ability to formulate clear and precise prompts. Students who lacked this skill often reported frustration and poor-quality outputs. As one participant stated, “If you do not know how to ask properly, the answer becomes too general and not useful” (P5). Privacy and data protection concerns. Privacy also emerged as a concern, especially when students uploaded personal or academic material into AI systems. One participant said, “I am not always comfortable uploading my work because I do not know what happens to the data” (P21). Theme 5: Patterns of Student Adoption and Engagement Participants did not engage with AI in the same way. Instead, their accounts revealed different patterns of adoption and non-adoption. Active adoption for academic support. Many participants described AI as becoming a regular part of their study practices. One participant noted, “It has become part of how I study, especially when I need quick explanations or feedback” (P1). Selective or cautious use. Some participants reported using AI in a limited and deliberate way, balancing its benefits against concerns about reliability, ethics, and dependency. As one participant explained, “I use it, but only in certain tasks, because I do not trust it completely” (FGS1P5). Deliberate non-use to preserve human learning. A smaller number of participants intentionally avoided AI in some situations because they wanted to preserve direct engagement with learning and strengthen their own abilities. One participant stated, “Sometimes I avoid using it because I want to challenge myself and learn by doing the work on my own” (P17). Theme 6: Institutional Conditions for Equity and Inclusion Participants emphasized that the effects of AI cannot be separated from the institutional conditions in which it is introduced. Structured AI literacy training. Many participants called for formal training that would help students use AI critically, responsibly, and effectively. One participant noted, “The university should train students on how to use AI responsibly, not just warn them about it” (FGS2P4). Prompting and evaluative skills development. Students also stressed the need to learn how to prompt effectively, verify outputs, and identify bias or inaccuracy. As one participant stated, “We need to learn how to ask good questions and how to check whether the answers are accurate” (P8). Tiered and inclusive training opportunities. Some participants argued that training should be differentiated, since students begin from different levels of experience. One focus group participant explained, “Not all students start from the same level, so training should be designed for beginners and advanced users separately” (FGS1P7). Theme 7: Equitable Support and Resource Provision Participants linked fairness in AI use to the distribution of institutional resources and support. Subsidized or institutionally provided AI tools. Many participants believed universities should provide access to premium or licensed AI tools rather than leaving students to pay individually. One participant remarked, “If the university wants fairness, it should provide equal access instead of leaving students to pay on their own” (P6). Accessibility-oriented AI supports. Some participants emphasized the value of specialized tools that support students with disabilities or diverse learning needs, such as speech-to-text and audio-based supports. One participant stated, “These tools can be very helpful for students who need different ways to read, write, or organize learning” (FGS2P17). Safe institutional AI platforms. Participants also expressed greater trust in university-approved or university-developed systems that could better protect privacy and academic integrity. As one participant explained, “I would trust AI more if it was provided and monitored by the university itself” (P14). Theme 8: Policy, Governance, and Ethical Guidance Policy and governance emerged as central to how students understood fairness and legitimacy in AI use. Clear institutional guidelines. Many participants emphasized the need for clear, assignment-specific guidance regarding acceptable AI use. One participant stated, “What we need most is clear policy, because right now students are confused about what is allowed” (P3). Transparency in AI use. Some participants supported policies based on disclosure rather than prohibition. They preferred transparent reporting of AI use over punitive or overly restrictive approaches. As one participant noted, “I think students should be allowed to use it, but they should clearly explain how they used it” (FGS1P4). Student involvement in policy development. Participants also argued that students should be involved in shaping AI-related policies. One participant remarked, “Students should have a voice in these policies because we are the ones directly affected by them” (FGS2P10). Theme 9: Pedagogical Transformation in the AI Era Participants suggested that AI requires not only new rules, but also new pedagogical approaches. Authentic and process-based assessment. Many participants argued that assessment should shift toward more authentic and process-oriented approaches that cannot be reduced to easily generated outputs. One participant explained, “Assessment should focus more on what students can explain, defend, and apply, not only what they submit” (P12). AI as a supportive learning partner. Students did not generally advocate removing AI from education. Instead, they described it as most valuable when positioned as a learning support rather than a replacement for thinking. As one focus group participant stated, “AI should support learning, not do the whole learning process for the student” (FGS1P1). Inclusive teaching through AI. Some participants also saw AI as a resource for more inclusive teaching, especially when used to simplify concepts, translate materials, and create more flexible learning pathways. One participant noted, “It can help instructors make learning more flexible for students with different needs” (FGS2P6). Theme 10: Belonging, Voice, and Recognition in AI-Enabled Higher Education Belonging emerged as an important dimension of students’ experiences, particularly in relation to recognition, trust, and voice. Feeling represented in AI systems and content. Some participants felt distanced from AI outputs that did not reflect their social, cultural, or educational realities. One participant explained, “When AI gives examples far from our reality, it feels like students like us are not really seen” (P18). Having a voice in AI-related decisions. Participants linked belonging to being heard in institutional decision-making about AI. As one participant stated, “We should not only follow AI rules; we should also be asked what we think about them” (FGS3P4). Recognition of diverse learning needs and identities. Some participants defined belonging in terms of whether universities recognized that students learn differently and require different forms of support. One participant observed, “Belonging means that the system understands that students learn differently and need different kinds of support” (FGS2P5). Trust, safety, and legitimacy in AI use. Others described belonging as weakened by fear of unfair judgment, particularly through AI detection and surveillance. One participant remarked, “It is hard to feel comfortable using AI when you are always afraid that your work will be questioned” (P20). Inclusion in institutional AI dialogue and community. Finally, participants stressed the importance of open dialogue with faculty and peers. One focus group participant stated, “I would feel more included if there were honest conversations about AI between students and instructors” (FGS1P8). Overall Interpretation of the Findings Taken together, the findings show that students do not experience generative AI as inherently inclusive or inherently harmful. Rather, they understand it as a contested educational resource shaped by access, digital conditions, pedagogy, policy, and recognition. While students valued its capacity to support learning and expand opportunity, they also emphasized that these benefits are unevenly distributed and may be undermined by economic inequality, bias, weak guidance, limited literacy, and exclusionary institutional practices. The findings therefore suggest that the central issue is not simply whether AI is used in higher education, but under what conditions it becomes equitable, inclusive, and conducive to belonging. Conceptual Framework of Students’ Experiences of Generative AI in Higher Education Building on these themes, a conceptual framework was developed to illustrate how students’ experiences of AI-enabled opportunities are intertwined with risks of inequality and exclusion, and how both are mediated by institutional and pedagogical conditions (Fig. 1 ). The framework (Fig. 1 )shows that students’ experiences of equity, inclusion, and belonging are shaped not by generative AI alone, but by the broader educational, social, and policy contexts in which these tools are accessed and used. It therefore offers an interpretive lens for understanding how generative AI may function either as a resource for learning and participation or as a mechanism that reinforces exclusion and unequal opportunity. Discussion This study explored how Palestinian university students experience equity, inclusion, and belonging in relation to generative AI in higher education. The findings show that students view generative AI as an ambivalent educational resource that can expand learning opportunities while also deepening inequality. This shifts the discussion from whether AI is useful in general to the more important question of for whom, under what conditions, and with what consequences it is educationally beneficial. The findings therefore reinforce the argument that the value of generative AI is shaped by material, linguistic, pedagogical, and institutional conditions rather than by the technology alone. A key contribution of the study is that it complicates the dominant instrumental view of generative AI in the literature. Previous scholarship has emphasized its potential for personalization, efficiency, feedback, and academic support (Baidoo-Anu & Owusu Ansah, 2023 ; Farrelly & Baker, 2023 ; Ouyang et al., 2022 ). The present findings support these claims, as many participants described AI as a flexible tutor, writing aid, and accessible study companion. Their accounts also align with studies showing that students often value AI for academic support, writing, and motivation (Chan & Hu, 2023 ; Gayed et al., 2022 ; Hmoud et al., 2024 ; Lee et al., 2022 ; Ngo, 2023 ). However, this study shows that educational usefulness does not automatically translate into equity. AI often functioned as a way to compensate for gaps in formal academic support, suggesting that its perceived benefits may reflect institutional insufficiencies as much as technological affordances. The findings further show that access to benefit is uneven. Students with stronger prompting skills, better internet connectivity, greater linguistic confidence, or access to advanced tools were better positioned to use AI effectively. This supports the view that AI-related gains are conditional rather than evenly distributed (Chan & Hu, 2023 ). It also extends the idea of the digital divide beyond devices and connectivity to include AI literacy, evaluative skills, and institutional guidance. Inequity in AI-enabled higher education, therefore, is not simply about having access to technology, but about having the resources and capacities needed to turn that access into meaningful educational advantage. Participants also interpreted generative AI through the lens of fairness. Many drew attention to the difference between free and paid tools, arguing that premium access creates unfair academic advantages. Although the literature has increasingly recognized governance and ethics concerns (Chan, 2023 ; Holmes et al., 2022 ; Klimova et al., 2022 ), this study shows that students themselves frame unequal access as a fairness issue rather than a minor technical inconvenience. At the same time, they did not call only for equal access to the same tools. They also emphasized the need for differentiated and accessibility-oriented support. Their accounts suggest that equity is better understood as responsiveness to different needs rather than simple sameness. The study also deepens discussions of bias and inclusion. Several participants described AI outputs as culturally distant, Western-centered, or insufficiently aligned with their realities. This supports broader concerns that AI systems may privilege dominant languages, epistemologies, and representational norms (Baker & Hawn, 2021; Fitzgerald et al., 2025 ; Mack et al., 2024 ). More importantly, the findings show that students experienced this not only as a problem of relevance, but also as a problem of recognition. When their realities were missing from AI outputs, students felt peripheral to the knowledge structures embedded in the technology. In this way, the findings move the discussion from inclusion to belonging. Belonging emerged as one of the study’s most significant contributions. Much of the literature on generative AI in higher education focuses on adoption, usefulness, ethics, or academic integrity. By contrast, this study shows that belonging is central to students’ experiences. Participants linked belonging to being represented in AI-generated content, having their learning needs recognized, feeling safe from unfair suspicion, and being included in AI-related institutional dialogue. This extends current scholarship by showing that access and formal inclusion are not sufficient on their own. Belonging concerns whether students feel seen, trusted, and heard in AI-mediated learning environments, which aligns with broader scholarship showing that participation without recognition may remain exclusionary in practice (Shpigelman et al., 2022). Another important finding is that exclusion may also be produced by the systems used to regulate AI. Participants’ concerns about AI detection tools reveal a clear paradox. While institutions may frame detection systems as necessary for fairness and academic integrity, students often experience them as sources of anxiety, mistrust, and misrecognition. This supports Sullivan et al.’s ( 2023 ) argument that generative AI poses serious integrity challenges, but it also suggests that punitive responses can themselves become inequitable when they ignore differences in writing style, learning needs, and technological understanding. Fairness, in this sense, requires more than preventing misuse; it also requires avoiding systems that position students as pre-emptively suspect. The findings further show that students are not passive users of AI. Many were critically aware of problems such as hallucination, superficiality, overreliance, privacy risks, and weakened critical engagement. Their accounts reveal a negotiated relationship with the technology: some adopted it actively, others used it cautiously, and some deliberately limited or rejected it to preserve independent learning. This complicates the assumption that more AI use is inherently better. Instead, student engagement appears shaped by moral, pedagogical, and practical judgment, which is consistent with prior work showing mixed student perceptions of generative AI (Chan & Hu, 2023 ; Ngo, 2023 ). At the same time, the findings make clear that responsible AI use cannot be treated as an individual matter alone. Participants repeatedly called for institutional and pedagogical support, particularly AI literacy training, prompting skills, critical evaluation, and clear guidance. This supports calls in the literature for stronger institutional frameworks for AI integration (Chan, 2023 ; Holmes et al., 2022 ; Jin et al., 2025 ). However, students wanted more than rules; they wanted practical guidance, differentiated support, and opportunities to learn how to use AI critically. This suggests that policy should function not only as regulation, but also as support. It also indicates that AI literacy should not be treated as a generic competence, since differences in discipline, experience, language, and confidence shape how students engage with AI. The pedagogical implications are equally important. Participants did not advocate removing AI from education. Rather, they imagined a more balanced relationship in which AI serves as a study partner rather than a substitute for learning. Their support for authentic, process-based assessment echoes recent Palestine-focused work on assessment redesign in the generative AI era (Khlaif et al., 2024 ; Khlaif, Alkouk, Salama, & Abu Eideh, 2025 ). However, this study adds a student-centered rationale for such redesign: students were concerned not only with validity, but also with fairness, trust, and meaningful learning. At the same time, they saw AI as potentially strengthening inclusive teaching when used to simplify concepts, translate materials, and support diverse learning pathways. The challenge, therefore, is not whether to use AI, but how to integrate it without reducing learning to efficiency or dependency. The Palestinian context further sharpens these findings. Previous Palestine-focused research has shown that AI integration in higher education is marked by tension, technostress, dependency, and uneven institutional readiness (Alhur et al., 2025 ; Hamamra et al., 2025 ; Khlaif et al., 2025 ). This study extends that literature by centering students rather than educators. Students’ accounts reveal dimensions that educator-focused studies capture less clearly, particularly belonging, fear of misrecognition, and the everyday moral ambiguity of AI use. Their experiences show that AI integration is not a neutral modernization process, but a socially situated reorganization of support, power, and legitimacy. Taken together, the findings support the study’s conceptual framework, which shows that AI-enabled opportunities and risks are mediated by institutional and pedagogical conditions to shape students’ experiences of equity, inclusion, and belonging. Generative AI does not produce equitable outcomes on its own; its effects are filtered through access, representation, literacy, pedagogy, and voice. The central issue, therefore, is not simply whether AI should be used in higher education, but whether institutions are willing to create the conditions under which it can function as a genuinely supportive and just educational resource. Theoretical Implications This study contributes theoretically by extending discussions of generative AI in higher education beyond adoption, utility, and academic integrity toward a more socially grounded understanding of equity, inclusion, and belonging. The findings show that generative AI should be understood not simply as an instructional tool, but as a sociotechnical space in which fairness, recognition, access, and participation are negotiated. In particular, the study adds belonging as a key interpretive dimension, showing that students’ experiences are shaped not only by access to AI, but also by whether they feel represented, heard, and fairly treated in AI-mediated learning environments (Baker & Hawn, 2021; Chan, 2023 ; Fitzgerald et al., 2025 ). Practical Implications The study suggests that universities should move beyond general enthusiasm for AI integration and adopt more equity-oriented responses. This includes structured AI literacy training, support for prompting and critical evaluation skills, equitable access to high-quality AI tools, and clear guidelines for responsible use. The findings also highlight the need for inclusive pedagogy and authentic assessment that position AI as a support for learning rather than a substitute for student thinking. In addition, involving students in AI-related decision-making may strengthen trust, fairness, and belonging in higher education (Chan, 2023 ; Holmes et al., 2022 ; Khlaif, Alkouk, Salama, & Abu Eideh, 2025 ). Limitations This study has several limitations. First, it was conducted within the Palestinian higher education context, so the findings reflect a specific social, institutional, and technological environment and should not be generalized uncritically to other settings. Second, the study included only students with prior experience using generative AI for academic purposes, which means the perspectives of students who avoid or reject AI were less visible. Third, as a qualitative study based on interviews and focus groups, it offers depth and contextual insight rather than statistically generalizable findings. Future Research Future research could examine how experiences of equity, inclusion, and belonging vary across countries, institutions, and disciplines. Quantitative or mixed-methods studies could test the prevalence of the patterns identified here, particularly regarding access, AI literacy, and perceptions of fairness. Further work is also needed on groups that may face greater exclusion in AI-enabled higher education, including students with disabilities, linguistically diverse learners, and those with limited digital access. Longitudinal studies would be especially valuable for examining how students’ relationships with generative AI evolve as institutional policies and pedagogical practices continue to develop. Conclusion This study explored how university students experience equity, inclusion, and belonging in relation to the use of generative AI in higher education. The findings show that students perceive generative AI as both a source of educational opportunity and a potential site of inequality and exclusion. While many participants valued its capacity to support personalized learning, accessibility, efficiency, and academic confidence, they also highlighted important concerns related to unequal access, bias, privacy, overreliance, and unclear institutional guidance. These findings demonstrate that the educational value of generative AI is not determined by the technology alone, but by the social, pedagogical, and institutional conditions in which it is used. The study concludes that equitable and inclusive AI integration in higher education requires more than access to digital tools. It requires structured AI literacy, fair institutional policies, inclusive pedagogical design, and meaningful recognition of students’ diverse needs, identities, and voices. In this sense, belonging emerged as a critical dimension of students’ experiences, reflecting the importance of representation, trust, safety, and participation in AI-related educational environments. Overall, the study argues that generative AI should not be approached merely as a technological innovation, but as a sociotechnical and ethical issue that must be addressed through student-centered and justice-oriented higher education practices. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Institutional Review Board at An-Najah National University (Approval No. Edu/Hum. Dec. 2025/70). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki . All participants were informed about the purpose of the study, and written informed consent was obtained prior to participation. Funding NA Author Contribution Z.N.K. conceived and designed the study, supervised the research process, contributed to data analysis, and led the writing of the manuscript. B.H. contributed to data collection, data analysis, and drafting and revising the manuscript. H.F. contributed to data collection, data analysis, and manuscript revision. All authors reviewed and approved the final manuscript. Data Availability The datasets generated and/or analyzed during the current study are not publicly available in order to protect participants’ confidentiality, but anonymized data may be available from the corresponding author on reasonable request. 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Supplementary Files AppendixA.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 04 May, 2026 Submission checks completed at journal 27 Apr, 2026 First submitted to journal 26 Apr, 2026 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-9192785","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635735780,"identity":"5e2109cc-52e7-4280-bff4-7a731dd0a3f2","order_by":0,"name":"Zuheir N Khlaif","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIie3OMQrCMBTG8VcEXZ64CtX2Cp9kVDxLpFCXFhwdPUB01lt0E7eUroqr4KK7m4tgQdPNLXUTzH8IGd4veUQu1y/G7GlCEJirbJjDW9QgZIgQ3xISk2qyHgnVQevHDNNtS1/vMxr1M20h3nIlcwWkOyUjf02xsJJGh6EZZZppKX2mYmIlTUPyEpjieImeTC874bZCwYDEScbmF20nXd6j6AGD7HSJh4xIbGwkVMngeisR4phEZ56P+ysb+dxREqH+eFXri+ddLpfrr3oDJqlDmwsR2isAAAAASUVORK5CYII=","orcid":"","institution":"An-Najah National University","correspondingAuthor":true,"prefix":"","firstName":"Zuheir","middleName":"N","lastName":"Khlaif","suffix":""},{"id":635735781,"identity":"38204ece-82f8-4e97-9ec0-f6ab352323f1","order_by":1,"name":"Bilal Hamamra","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Bilal","middleName":"","lastName":"Hamamra","suffix":""},{"id":635735782,"identity":"2e6fed48-abda-4251-8daa-e854badd9cba","order_by":2,"name":"Hani Farran","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Hani","middleName":"","lastName":"Farran","suffix":""}],"badges":[],"createdAt":"2026-03-22 17:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9192785/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9192785/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109150360,"identity":"cb7d3796-1a33-4424-959c-0f543235c536","added_by":"auto","created_at":"2026-05-13 05:28:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70709,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudent-centered conceptual framework for equity, inclusion, and belonging in Gen AI enabled higher education\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9192785/v1/13086105bad9cda4e265e5b7.png"},{"id":109206673,"identity":"e952f117-5225-4147-a09b-53d8214871e6","added_by":"auto","created_at":"2026-05-13 15:15:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":345837,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9192785/v1/476fad3b-b379-4dbf-82d3-26db59d1e1b6.pdf"},{"id":109205349,"identity":"c4360869-f19b-49bc-8632-6965a5cb675b","added_by":"auto","created_at":"2026-05-13 15:04:22","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20359,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-9192785/v1/9e9f20d76e9448aa2684f606.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Equity and inclusion in generative AI use in higher education: A qualitative study of Palestinian students’ experiences","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGenerative artificial intelligence (GenAI) has entered higher education rapidly and is increasingly presented as a tool for personalization, efficiency, academic support, and pedagogical innovation (Baidoo-Anu \u0026amp; Owusu Ansah, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Farrelly \u0026amp; Baker, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ouyang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wollny et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recent scholarship has highlighted its potential to assist students with writing, feedback, idea generation, and self-directed learning, thereby positioning GenAI as a promising educational resource. Yet this optimism can obscure a central issue: educational benefit is not automatically distributed fairly simply because a tool is available. Access to GenAI does not guarantee meaningful participation, equal advantage, or inclusive learning conditions.\u003c/p\u003e \u003cp\u003eThis is particularly important because GenAI is introduced into higher education systems that are already shaped by unequal access to technology, varied levels of digital and AI literacy, linguistic hierarchies, different disciplinary expectations, and uneven institutional support. As a result, students do not encounter GenAI under the same conditions, nor do they benefit from it in the same ways. Student-focused research already suggests that experiences with GenAI vary according to confidence, self-efficacy, language background, and the quality of institutional guidance rather than the technology alone (Chan \u0026amp; Hu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gayed et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hmoud et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ngo, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Accordingly, the key issue is no longer whether GenAI can support learning in principle, but whether it does so in ways that are equitable, inclusive, and responsive to students\u0026rsquo; diverse realities.\u003c/p\u003e \u003cp\u003eAlthough AI in education research increasingly addresses ethics, governance, and policy, much of this discussion remains abstract and insufficiently grounded in students\u0026rsquo; lived experiences (Baker \u0026amp; Hawn, 2021; Chan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Holmes et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Klimova et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In practice, inequity in GenAI use may emerge through multiple interconnected factors, including unequal access to advanced tools, weak prompting skills, language barriers, biased outputs, accessibility challenges, and unclear rules governing legitimate use. These conditions shape not only whether students use GenAI, but also whether they perceive it as fair, trustworthy, and supportive within their academic environment (Calderwood, 2024; Perdana et al., 2025; Pretorius et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Without attention to these issues, institutions may adopt AI practices that appear innovative while unintentionally reproducing exclusion and educational disadvantage.\u003c/p\u003e \u003cp\u003eThe Palestinian context makes these questions especially urgent. Recent Palestine-focused studies show that GenAI in universities is associated simultaneously with opportunity, dependency, technostress, and uncertainty rather than straightforward educational progress (Alhur et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hamamra, Khlaif, \u0026amp; Mayaleh, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hamamra, Khlaif, Mayaleh, \u0026amp; Baker, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Khlaif et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khlaif et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Khlaif, Alkouk, Salama, \u0026amp; Abu Eideh, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This body of work is valuable because it challenges universal assumptions about AI adoption and shows that technological integration is always mediated by local structural conditions. However, much of this emerging research has focused on educators, institutional response, and assessment redesign, while students\u0026rsquo; own experiences remain underexplored.\u003c/p\u003e \u003cp\u003eIn response to this gap, the present study explores how Palestinian university students experience equity, inclusion, and belonging in relation to the use of GenAI in higher education. Specifically, it examines how students perceive the opportunities and limitations of GenAI, what forms of exclusion or inequality they associate with its use, and what kinds of institutional, pedagogical, and policy support they believe are necessary for more just and inclusive AI-enabled learning environments. By centering students\u0026rsquo; voices, the study contributes to a more socially grounded understanding of GenAI, not merely as a technological aid, but as a site where questions of fairness, participation, recognition, and belonging are actively negotiated.\u003c/p\u003e\n\u003ch3\u003eProblem Statement\u003c/h3\u003e\n\u003cp\u003eGenerative artificial intelligence (GenAI) is increasingly promoted in higher education as a tool for personalization, efficiency, and academic support (Baidoo-Anu \u0026amp; Owusu Ansah, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Farrelly \u0026amp; Baker, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ouyang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, these promises do not automatically translate into equitable or inclusive educational experiences. Students encounter GenAI within higher education systems already shaped by unequal access to technology, varied levels of AI literacy, linguistic differences, uneven institutional guidance, and broader social inequalities (Baker \u0026amp; Hawn, 2021; Chan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Holmes et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Klimova et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, the benefits of GenAI are not experienced uniformly, and its use may reproduce or intensify existing forms of exclusion rather than reduce them. Although previous research has explored the pedagogical potential, ethical concerns, and governance challenges of AI in education, less attention has been paid to how students themselves experience GenAI in relation to \u003cb\u003eequity, inclusion, and belonging\u003c/b\u003e, particularly in underrepresented contexts such as Palestinian higher education (Alhur et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hamamra, Khlaif, \u0026amp; Mayaleh, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Khlaif et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Perdana et al., 2025). This creates an important gap in understanding how GenAI is actually lived, negotiated, and interpreted by students within unequal educational environments.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Purpose\u003c/h2\u003e \u003cp\u003eThis study aims to explore how Palestinian university students experience equity, inclusion, and belonging in relation to the use of generative artificial intelligence in higher education. Specifically, the study seeks to examine how students perceive the opportunities and limitations of GenAI in their academic lives, how they interpret the risks of inequality and exclusion associated with its use, and what forms of institutional, pedagogical, and policy support they believe are necessary for more equitable and inclusive AI-enabled learning environments. By centering students\u0026rsquo; perspectives, the study aims to develop a deeper and more context-sensitive understanding of GenAI as a sociotechnical and educational phenomenon rather than merely as a digital tool.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Contribution\u003c/h3\u003e\n\u003cp\u003eThis study contributes to the emerging literature on GenAI in higher education in three main ways. First, it shifts the focus from general discussions of adoption, usefulness, and academic integrity toward a more socially grounded analysis of equity, inclusion, and belonging. Second, it foregrounds students\u0026rsquo; voices in a field where much of the existing research has emphasized institutional perspectives, policy debates, or educators\u0026rsquo; concerns. Third, it contributes evidence from the Palestinian higher education context, where GenAI use is shaped by specific structural, technological, and educational conditions that remain underrepresented in the international literature. Through this focus, the study offers both an empirical and conceptual contribution by showing that the educational significance of GenAI depends not only on its technical capabilities, but also on the conditions of access, recognition, support, and participation through which students engage with it.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch questions\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHow do students perceive the role of generative AI in supporting or hindering equity and inclusion in higher education?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat challenges, risks, and exclusionary experiences do students associate with the use of generative AI in higher education?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat forms of institutional, pedagogical, and policy support do students believe are necessary to promote equity, inclusion, and belonging in AI-enabled higher education?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eA substantial portion of the literature on GenAI in higher education frames it as a transformative instructional tool. Reviews and conceptual papers emphasize its capacity to provide immediate feedback, support drafting and revision, personalize learning, and enhance teaching and learning processes (Baidoo-Anu \u0026amp; Owusu Ansah, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Farrelly \u0026amp; Baker, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jahic et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ouyang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wollny et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Related studies on chatbots and AI-supported learning likewise suggest benefits for self-efficacy, after-class review, and language support (Gayed et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These contributions are useful in demonstrating why GenAI has gained rapid attention in higher education. However, much of this literature remains overly instrumental: it foregrounds what the technology can do while paying less attention to the uneven conditions under which students can actually use it effectively.\u003c/p\u003e \u003cp\u003eStudent-focused studies offer a more nuanced picture. Research indicates that students often perceive GenAI as both enabling and problematic. They value its support for brainstorming, writing, feedback, and academic efficiency, yet they also express concerns about accuracy, overreliance, plagiarism, and ethics (Chan \u0026amp; Hu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hmoud et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ngo, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sousa et al., 2025). This suggests that GenAI is not experienced simply as a neutral aid, but as a tool whose usefulness is shaped by trust, skill, and academic context. Moreover, these mixed perceptions indicate that educational advantage is not inherent in GenAI itself. Rather, benefit depends on whether students possess the literacy, critical judgment, and institutional support needed to use AI meaningfully and responsibly.\u003c/p\u003e \u003cp\u003eA second body of literature highlights the relationship between GenAI and inequality. Scholars have raised concerns about algorithmic bias, exclusionary design, and the risk that AI systems may reproduce dominant linguistic, cultural, and epistemic norms (Baker \u0026amp; Hawn, 2021; Fitzgerald et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These concerns are particularly relevant in higher education, where students\u0026rsquo; backgrounds, languages, abilities, and levels of technological familiarity differ considerably. In this sense, the issue is not only whether students can access GenAI, but whether the technology recognizes and responds to diverse learners fairly. Broader work on equity and inclusion in education similarly suggests that technological innovation can deepen rather than reduce inequality when it is detached from questions of access, representation, and participation (OECD, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Miao \u0026amp; Holmes, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe literature also points to the importance of institutional mediation. Researchers have emphasized the need for clear AI policies, ethical guidance, and structured support for students and faculty navigating this rapidly changing environment (Chan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Holmes et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Yet policy alone is insufficient if it functions only as regulation rather than support. Students\u0026rsquo; experiences of AI are also shaped by the availability of AI literacy training, prompting skills, guidance on critical evaluation, and assessment practices that define legitimate use. Recent studies suggest that uncertainty about policy, weak guidance, and inconsistent teaching expectations can increase anxiety and produce uneven patterns of AI adoption (Kim et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yusuf et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, institutional context is central to whether GenAI becomes a source of inclusion or exclusion in practice.\u003c/p\u003e \u003cp\u003eAnother important dimension that remains underdeveloped in the literature is belonging. Much of the current scholarship focuses on adoption, usefulness, governance, and academic integrity. Less attention has been paid to whether students feel represented, heard, trusted, and recognized in AI-enabled learning environments. Insights from disability and inclusion scholarship are relevant here, particularly the argument that meaningful inclusion requires not only access, but voice and participation in institutional decision-making (Shpigelman et al., 2022). Applied to GenAI, this suggests that students\u0026rsquo; experiences cannot be fully understood through access and usage alone. It is also necessary to examine whether AI-related policies, platforms, and pedagogical practices foster trust, recognition, and a sense of legitimacy among students.\u003c/p\u003e \u003cp\u003eWithin the Palestinian context, this gap is especially significant. Recent research has shown that GenAI use in Palestinian higher education is shaped by uncertainty, dependency, assessment challenges, and uneven institutional readiness (Alhur et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Khlaif et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khlaif et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, these studies have largely centered educators and institutional concerns. The student perspective remains insufficiently explored, despite the fact that students are the ones most directly negotiating the everyday boundaries between support and dependency, fairness and unfair advantage, and participation and exclusion.\u003c/p\u003e \u003cp\u003eTaken together, the literature suggests that GenAI in higher education should not be understood only as a question of adoption or effectiveness. It must also be examined as a social and pedagogical issue shaped by distribution, recognition, and institutional response. This study addresses that need by exploring how Palestinian university students experience GenAI through the lenses of equity, inclusion, and belonging. In doing so, it responds to calls for more student-centered and context-sensitive research on the implications of AI in higher education.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003eThis study adopted a qualitative research design to explore students\u0026rsquo; experiences of equity, inclusion, and belonging in relation to the use of generative AI in higher education. A qualitative approach was appropriate because the study sought to understand students\u0026rsquo; perceptions, meanings, and lived experiences in depth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Recruitment Criteria\u003c/h2\u003e \u003cp\u003e Participants were recruited through purposive sampling to include Palestinian higher education students with direct experience using generative AI for academic purposes. Eligibility required current enrollment in a higher education institution and prior academic use of tools such as ChatGPT or similar applications. Students without prior academic use of generative AI were excluded. To capture diverse perspectives, variation was sought across discipline, gender, place of residence, frequency of use, type of access, and level of familiarity with generative AI. Participation was voluntary, and informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003eThe study included 46 participants: 21 students who took part in individual interviews and 25 different students who participated in three focus groups. Using both interviews and focus groups strengthened the study by combining detailed personal narratives with interactional insights into how students collectively discussed, compared, and negotiated their experiences of generative AI in higher education.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a cross-tabulated summary of participants\u0026rsquo; demographic characteristics by gender, while Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes participants\u0026rsquo; patterns of generative AI use for academic purposes. This organization was used to improve the clarity of the sample description and to show variation across key participant characteristics.\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\u003eCross-tabulation of participants\u0026rsquo; demographic characteristics by gender (N\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\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\u003eFemale n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eField of study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumanities and Social Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (28.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness and Economics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (15.2)\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\u003eEngineering and Information Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (21.7)\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\u003eHealth and Medical Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (15.2)\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\u003eNatural Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9 (19.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21 (45.7)\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\u003eVillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (41.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\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\u003eGenerative AI use characteristics of participants (N\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\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\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of generative AI use for academic purposes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.4\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\u003eSeveral times a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.9\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\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMain access to generative AI tools\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFree version only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.7\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\u003ePaid subscription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.9\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\u003eBoth free and paid tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-reported familiarity with generative AI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeginner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.7\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\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMain reason for using generative AI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrainstorming and idea generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.9\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\u003eWriting and editing support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.1\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\u003eSummarizing and understanding content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.6\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\u003eTranslation and language support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.2\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\u003eResearch and information search\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eResearch Instruments\u003c/h2\u003e \u003cp\u003eData were collected using semi-structured interviews and focus group discussions. These two instruments were used to generate complementary forms of qualitative data, with interviews enabling in-depth exploration of individual experiences and focus groups capturing shared views, differences, and interactional perspectives. The interview and focus group guides were developed from the research questions and informed by relevant literature on generative AI in higher education. Open-ended questions were used to elicit detailed reflections on students\u0026rsquo; experiences, perceived opportunities, challenges, and support needs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eData were collected in two sequential phases using semi-structured interviews and focus groups. First, 21 in-person interviews were conducted, each lasting 45\u0026ndash;60 minutes at a time and place chosen by the participant. This was followed by three online focus groups conducted via a video-conferencing platform with different participants. Participation was voluntary, written informed consent was obtained before participation, and all sessions were audio-recorded with permission.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using reflexive thematic analysis following the procedures outlined by Virginia Braun and Victoria Clarke (2006, 2019). The analysis followed an inductive approach, allowing themes to emerge from the data rather than being imposed from predetermined theoretical categories. This approach was appropriate because the study sought to explore students\u0026rsquo; experiences and interpretations of generative AI in higher education.\u003c/p\u003e \u003cp\u003eThe analysis proceeded through several iterative stages. First, all interview and focus group recordings were transcribed verbatim and read multiple times to achieve familiarity with the data. During this stage, the researchers documented initial observations and analytic memos. Second, initial codes were generated from the transcripts by identifying meaningful segments of text related to students\u0026rsquo; experiences of equity, inclusion, and belonging in relation to generative AI use. Coding was conducted collaboratively by the three researchers to enhance analytical rigor.\u003c/p\u003e \u003cp\u003eThird, related codes were grouped into potential categories and themes through an iterative process of comparison across transcripts. Fourth, the research team reviewed and refined the themes by examining their internal coherence and distinctiveness, ensuring that each theme captured a meaningful pattern across the dataset. Finally, themes were defined, named, and supported with illustrative quotations drawn directly from the participants\u0026rsquo; accounts.\u003c/p\u003e \u003cp\u003eThroughout the analysis process, the researchers engaged in regular discussions to compare interpretations and resolve coding differences, which helped enhance the credibility and consistency of the thematic interpretation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003efor this study was obtained from the Institutional Review Board at An-Najah National University (Approval No. Edu/Hum. Dec. 2025/70). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. All participants were informed about the purpose of the study, and written informed consent was obtained prior to participation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eParticipants were informed about the purpose of the study, the voluntary nature of participation, and their right to withdraw at any stage without penalty. Written informed consent was obtained prior to participation. To ensure confidentiality, identifying information was removed from the transcripts, data were stored securely, and participant codes were used in reporting the findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTrustworthiness\u003c/h2\u003e \u003cp\u003eSeveral strategies were used to enhance the trustworthiness of the study. All transcripts were returned to participants for review, giving them the opportunity to edit, clarify, or delete any part of their responses. In addition, the involvement of three researchers in the coding and interpretation process supported credibility through collaborative analysis. To strengthen analytic rigor, a subset of transcripts (30%) was independently coded by two researchers and intercoder agreement was assessed, yielding an agreement rate of 89%. Any discrepancies were discussed until consensus was reached. Trustworthiness was further strengthened through verbatim transcription, systematic coding, and the use of direct quotations to ground the findings in participants\u0026rsquo; accounts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Findings","content":" \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003cp\u003eThe analysis generated ten interrelated themes that explain how students experience generative AI in relation to equity, inclusion, and belonging in higher education. Across the interviews and focus groups, participants described generative AI as both enabling and problematic. They valued its capacity to support learning, improve access, and increase academic efficiency. At the same time, they raised concerns about fairness, bias, reliability, privacy, and unequal access. Overall, students framed generative AI as a resource whose educational value depends on how it is accessed, governed, and integrated into teaching and learning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTheme 1: Generative AI as a Tool for Educational Opportunity\u003c/h2\u003e \u003cp\u003eParticipants described generative AI as creating new opportunities for learning by offering timely, flexible, and personalized academic support.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePersonalized learning support.\u003c/b\u003e Many participants viewed generative AI as a personalized learning aid that could simplify complex ideas, explain content at different levels, and respond to individual academic needs. As one participant noted, \u0026ldquo;It explains the lesson in a simpler way and according to what I need, so it feels like having a tutor available all the time\u0026rdquo; (P4).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAccessibility for diverse learners.\u003c/b\u003e Participants also linked AI to greater accessibility. Some, especially those who experienced difficulty with academic English or complex writing tasks, said AI helped them organize ideas and communicate more clearly. One participant explained, \u0026ldquo;As a student who sometimes struggles with academic English, AI helps me organize my ideas and improve my writing\u0026rdquo; (P11).\u003c/p\u003e \u003cp\u003e \u003cb\u003eReduced cognitive load.\u003c/b\u003e Several participants reported that AI reduced the burden of repetitive or time-consuming academic tasks, such as summarizing content or structuring assignments, allowing them to focus more on higher-order thinking. As one focus group participant stated, \u0026ldquo;It saves me time on basic tasks, so I can spend more energy thinking about the actual content\u0026rdquo; (FGS1P3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcademic confidence and capacity building.\u003c/b\u003e Participants further described AI as helping them check understanding, refine drafts, and strengthen foundational knowledge, particularly when they felt less academically prepared. One participant reflected, \u0026ldquo;Sometimes I use it to check if I am on the right track, and that gives me more confidence in my work\u0026rdquo; (P15).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTheme 2: Risks of Inequality and Exclusion in GenAI Use\u003c/h2\u003e \u003cp\u003eAlthough participants acknowledged the benefits of AI, they also emphasized that its use is shaped by unequal conditions that may reproduce exclusion.\u003c/p\u003e \u003cp\u003e \u003cb\u003eUnequal access to advanced AI tools.\u003c/b\u003e Many participants raised fairness concerns regarding differences between free and paid tools. They argued that students who could afford premium versions benefited from stronger outputs and more reliable support. As one participant stated, \u0026ldquo;Students who can pay for the better version definitely get better help than those using the free one\u0026rdquo; (P7).\u003c/p\u003e \u003cp\u003e \u003cb\u003eBias in AI-generated outputs.\u003c/b\u003e Some participants highlighted that AI outputs often reflected assumptions or examples that felt culturally distant from their realities. One participant explained, \u0026ldquo;Sometimes the examples it gives do not reflect our context at all, as if education only happens in Western settings\u0026rdquo; (FGS2P8). This suggests that exclusion may arise not only through access, but also through representation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion through AI detection practices.\u003c/b\u003e Concerns about unfairness were also evident in discussions of AI detection tools. Some students feared that authentic work could be incorrectly labeled as AI-generated, particularly when their writing style did not fit institutional expectations. As one participant noted, \u0026ldquo;What worries me is that even if I write by myself, the detector may still accuse me, and that feels unfair\u0026rdquo; (P19).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDigital and technical barriers.\u003c/b\u003e Participants also pointed to broader structural barriers, including unstable internet access, limited devices, and weak infrastructure. These shaped not only whether AI could be used, but how effectively it could support learning. One focus group participant stated, \u0026ldquo;Sometimes access itself is the problem, because not everyone has stable internet or devices to use these tools properly\u0026rdquo; (FGS1P6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTheme 3: Academic Benefits of GenAI in Learning Practices\u003c/h2\u003e \u003cp\u003eStudents described using generative AI in practical and increasingly normalized ways to support academic work.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEnhanced productivity and efficiency.\u003c/b\u003e Many participants said AI improved efficiency by helping with summarizing, organizing, editing, and managing academic tasks. One participant remarked, \u0026ldquo;It helps me summarize long readings and structure my assignments much faster\u0026rdquo; (P2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCreativity and brainstorming support.\u003c/b\u003e Several students also used AI to generate ideas and overcome difficulty starting assignments or presentations. As one participant said, \u0026ldquo;When I do not know how to start, it gives me ideas that help me move forward\u0026rdquo; (FGS2P11).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupport for discipline-specific tasks.\u003c/b\u003e Some participants, especially in scientific and technical fields, highlighted more specialized uses such as coding support, problem solving, and data-related work. One participant explained, \u0026ldquo;In programming tasks, it helps me find errors and understand what is wrong in the code\u0026rdquo; (P10).\u003c/p\u003e \u003cp\u003eTheme 4: Limitations and Challenges of GenAI in Higher Education\u003c/p\u003e \u003cp\u003eDespite its usefulness, participants consistently pointed to limitations that complicated their engagement with AI.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInaccuracy and hallucination.\u003c/b\u003e Many participants questioned the reliability of AI-generated content, especially when responses sounded convincing but were later found to be incorrect. One participant stated, \u0026ldquo;Sometimes it gives an answer that sounds correct, but later I discover that the information is wrong\u0026rdquo; (P9).\u003c/p\u003e \u003cp\u003e \u003cb\u003eOverreliance and weakened critical engagement.\u003c/b\u003e Participants also worried that excessive dependence on AI could reduce independent thinking and weaken deeper engagement with learning materials. As one participant noted, \u0026ldquo;If students depend on it too much, they may stop thinking deeply and just accept the answer\u0026rdquo; (FGS1P2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEthical ambiguity and academic integrity concerns.\u003c/b\u003e Several students described uncertainty about the boundary between legitimate support and misconduct. This ambiguity created anxiety, especially when institutional guidance was unclear. One participant explained, \u0026ldquo;The problem is that nobody clearly explains where support ends and cheating begins\u0026rdquo; (P13).\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimited depth of understanding.\u003c/b\u003e Some participants argued that AI often produces responses that are useful for general understanding but insufficient for deeper or specialized learning. One participant observed, \u0026ldquo;It is useful for general ideas, but not always for deep or specialized understanding\u0026rdquo; (FGS2P14).\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimited prompt literacy.\u003c/b\u003e Participants further recognized that effective AI use depends on the ability to formulate clear and precise prompts. Students who lacked this skill often reported frustration and poor-quality outputs. As one participant stated, \u0026ldquo;If you do not know how to ask properly, the answer becomes too general and not useful\u0026rdquo; (P5).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrivacy and data protection concerns.\u003c/b\u003e Privacy also emerged as a concern, especially when students uploaded personal or academic material into AI systems. One participant said, \u0026ldquo;I am not always comfortable uploading my work because I do not know what happens to the data\u0026rdquo; (P21).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTheme 5: Patterns of Student Adoption and Engagement\u003c/h2\u003e \u003cp\u003eParticipants did not engage with AI in the same way. Instead, their accounts revealed different patterns of adoption and non-adoption.\u003c/p\u003e \u003cp\u003e \u003cb\u003eActive adoption for academic support.\u003c/b\u003e Many participants described AI as becoming a regular part of their study practices. One participant noted, \u0026ldquo;It has become part of how I study, especially when I need quick explanations or feedback\u0026rdquo; (P1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSelective or cautious use.\u003c/b\u003e Some participants reported using AI in a limited and deliberate way, balancing its benefits against concerns about reliability, ethics, and dependency. As one participant explained, \u0026ldquo;I use it, but only in certain tasks, because I do not trust it completely\u0026rdquo; (FGS1P5).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDeliberate non-use to preserve human learning.\u003c/b\u003e A smaller number of participants intentionally avoided AI in some situations because they wanted to preserve direct engagement with learning and strengthen their own abilities. One participant stated, \u0026ldquo;Sometimes I avoid using it because I want to challenge myself and learn by doing the work on my own\u0026rdquo; (P17).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTheme 6: Institutional Conditions for Equity and Inclusion\u003c/h2\u003e \u003cp\u003eParticipants emphasized that the effects of AI cannot be separated from the institutional conditions in which it is introduced.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStructured AI literacy training.\u003c/b\u003e Many participants called for formal training that would help students use AI critically, responsibly, and effectively. One participant noted, \u0026ldquo;The university should train students on how to use AI responsibly, not just warn them about it\u0026rdquo; (FGS2P4).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrompting and evaluative skills development.\u003c/b\u003e Students also stressed the need to learn how to prompt effectively, verify outputs, and identify bias or inaccuracy. As one participant stated, \u0026ldquo;We need to learn how to ask good questions and how to check whether the answers are accurate\u0026rdquo; (P8).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTiered and inclusive training opportunities.\u003c/b\u003e Some participants argued that training should be differentiated, since students begin from different levels of experience. One focus group participant explained, \u0026ldquo;Not all students start from the same level, so training should be designed for beginners and advanced users separately\u0026rdquo; (FGS1P7).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eTheme 7: Equitable Support and Resource Provision\u003c/h2\u003e \u003cp\u003eParticipants linked fairness in AI use to the distribution of institutional resources and support.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSubsidized or institutionally provided AI tools.\u003c/b\u003e Many participants believed universities should provide access to premium or licensed AI tools rather than leaving students to pay individually. One participant remarked, \u0026ldquo;If the university wants fairness, it should provide equal access instead of leaving students to pay on their own\u0026rdquo; (P6).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAccessibility-oriented AI supports.\u003c/b\u003e Some participants emphasized the value of specialized tools that support students with disabilities or diverse learning needs, such as speech-to-text and audio-based supports. One participant stated, \u0026ldquo;These tools can be very helpful for students who need different ways to read, write, or organize learning\u0026rdquo; (FGS2P17).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSafe institutional AI platforms.\u003c/b\u003e Participants also expressed greater trust in university-approved or university-developed systems that could better protect privacy and academic integrity. As one participant explained, \u0026ldquo;I would trust AI more if it was provided and monitored by the university itself\u0026rdquo; (P14).\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eTheme 8: Policy, Governance, and Ethical Guidance\u003c/h2\u003e \u003cp\u003ePolicy and governance emerged as central to how students understood fairness and legitimacy in AI use.\u003c/p\u003e \u003cp\u003e\u003cb\u003e Clear institutional guidelines.\u003c/b\u003e Many participants emphasized the need for clear, assignment-specific guidance regarding acceptable AI use. One participant stated, \u0026ldquo;What we need most is clear policy, because right now students are confused about what is allowed\u0026rdquo; (P3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTransparency in AI use.\u003c/b\u003e Some participants supported policies based on disclosure rather than prohibition. They preferred transparent reporting of AI use over punitive or overly restrictive approaches. As one participant noted, \u0026ldquo;I think students should be allowed to use it, but they should clearly explain how they used it\u0026rdquo; (FGS1P4).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudent involvement in policy development.\u003c/b\u003e Participants also argued that students should be involved in shaping AI-related policies. One participant remarked, \u0026ldquo;Students should have a voice in these policies because we are the ones directly affected by them\u0026rdquo; (FGS2P10).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eTheme 9: Pedagogical Transformation in the AI Era\u003c/h2\u003e \u003cp\u003eParticipants suggested that AI requires not only new rules, but also new pedagogical approaches.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAuthentic and process-based assessment.\u003c/b\u003e Many participants argued that assessment should shift toward more authentic and process-oriented approaches that cannot be reduced to easily generated outputs. One participant explained, \u0026ldquo;Assessment should focus more on what students can explain, defend, and apply, not only what they submit\u0026rdquo; (P12).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAI as a supportive learning partner.\u003c/b\u003e Students did not generally advocate removing AI from education. Instead, they described it as most valuable when positioned as a learning support rather than a replacement for thinking. As one focus group participant stated, \u0026ldquo;AI should support learning, not do the whole learning process for the student\u0026rdquo; (FGS1P1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eInclusive teaching through AI.\u003c/b\u003e Some participants also saw AI as a resource for more inclusive teaching, especially when used to simplify concepts, translate materials, and create more flexible learning pathways. One participant noted, \u0026ldquo;It can help instructors make learning more flexible for students with different needs\u0026rdquo; (FGS2P6).\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003eTheme 10: Belonging, Voice, and Recognition in AI-Enabled Higher Education\u003c/h2\u003e \u003cp\u003eBelonging emerged as an important dimension of students\u0026rsquo; experiences, particularly in relation to recognition, trust, and voice.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFeeling represented in AI systems and content.\u003c/b\u003e Some participants felt distanced from AI outputs that did not reflect their social, cultural, or educational realities. One participant explained, \u0026ldquo;When AI gives examples far from our reality, it feels like students like us are not really seen\u0026rdquo; (P18).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHaving a voice in AI-related decisions.\u003c/b\u003e Participants linked belonging to being heard in institutional decision-making about AI. As one participant stated, \u0026ldquo;We should not only follow AI rules; we should also be asked what we think about them\u0026rdquo; (FGS3P4).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRecognition of diverse learning needs and identities.\u003c/b\u003e Some participants defined belonging in terms of whether universities recognized that students learn differently and require different forms of support. One participant observed, \u0026ldquo;Belonging means that the system understands that students learn differently and need different kinds of support\u0026rdquo; (FGS2P5).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTrust, safety, and legitimacy in AI use.\u003c/b\u003e Others described belonging as weakened by fear of unfair judgment, particularly through AI detection and surveillance. One participant remarked, \u0026ldquo;It is hard to feel comfortable using AI when you are always afraid that your work will be questioned\u0026rdquo; (P20).\u003c/p\u003e \u003cp\u003e\u003cb\u003eInclusion in institutional AI dialogue and community.\u003c/b\u003e Finally, participants stressed the importance of open dialogue with faculty and peers. One focus group participant stated, \u0026ldquo;I would feel more included if there were honest conversations about AI between students and instructors\u0026rdquo; (FGS1P8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eOverall Interpretation of the Findings\u003c/h2\u003e \u003cp\u003eTaken together, the findings show that students do not experience generative AI as inherently inclusive or inherently harmful. Rather, they understand it as a contested educational resource shaped by access, digital conditions, pedagogy, policy, and recognition. While students valued its capacity to support learning and expand opportunity, they also emphasized that these benefits are unevenly distributed and may be undermined by economic inequality, bias, weak guidance, limited literacy, and exclusionary institutional practices. The findings therefore suggest that the central issue is not simply whether AI is used in higher education, but under what conditions it becomes equitable, inclusive, and conducive to belonging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eConceptual Framework of Students\u0026rsquo; Experiences of Generative AI in Higher Education\u003c/h2\u003e \u003cp\u003eBuilding on these themes, a conceptual framework was developed to illustrate how students\u0026rsquo; experiences of AI-enabled opportunities are intertwined with risks of inequality and exclusion, and how both are mediated by institutional and pedagogical conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)shows that students\u0026rsquo; experiences of equity, inclusion, and belonging are shaped not by generative AI alone, but by the broader educational, social, and policy contexts in which these tools are accessed and used. It therefore offers an interpretive lens for understanding how generative AI may function either as a resource for learning and participation or as a mechanism that reinforces exclusion and unequal opportunity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored how Palestinian university students experience equity, inclusion, and belonging in relation to generative AI in higher education. The findings show that students view generative AI as an ambivalent educational resource that can expand learning opportunities while also deepening inequality. This shifts the discussion from whether AI is useful in general to the more important question of for whom, under what conditions, and with what consequences it is educationally beneficial. The findings therefore reinforce the argument that the value of generative AI is shaped by material, linguistic, pedagogical, and institutional conditions rather than by the technology alone.\u003c/p\u003e \u003cp\u003eA key contribution of the study is that it complicates the dominant instrumental view of generative AI in the literature. Previous scholarship has emphasized its potential for personalization, efficiency, feedback, and academic support (Baidoo-Anu \u0026amp; Owusu Ansah, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Farrelly \u0026amp; Baker, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ouyang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The present findings support these claims, as many participants described AI as a flexible tutor, writing aid, and accessible study companion. Their accounts also align with studies showing that students often value AI for academic support, writing, and motivation (Chan \u0026amp; Hu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gayed et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hmoud et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ngo, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, this study shows that educational usefulness does not automatically translate into equity. AI often functioned as a way to compensate for gaps in formal academic support, suggesting that its perceived benefits may reflect institutional insufficiencies as much as technological affordances.\u003c/p\u003e \u003cp\u003eThe findings further show that access to benefit is uneven. Students with stronger prompting skills, better internet connectivity, greater linguistic confidence, or access to advanced tools were better positioned to use AI effectively. This supports the view that AI-related gains are conditional rather than evenly distributed (Chan \u0026amp; Hu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It also extends the idea of the digital divide beyond devices and connectivity to include AI literacy, evaluative skills, and institutional guidance. Inequity in AI-enabled higher education, therefore, is not simply about having access to technology, but about having the resources and capacities needed to turn that access into meaningful educational advantage.\u003c/p\u003e \u003cp\u003eParticipants also interpreted generative AI through the lens of fairness. Many drew attention to the difference between free and paid tools, arguing that premium access creates unfair academic advantages. Although the literature has increasingly recognized governance and ethics concerns (Chan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Holmes et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Klimova et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), this study shows that students themselves frame unequal access as a fairness issue rather than a minor technical inconvenience. At the same time, they did not call only for equal access to the same tools. They also emphasized the need for differentiated and accessibility-oriented support. Their accounts suggest that equity is better understood as responsiveness to different needs rather than simple sameness.\u003c/p\u003e \u003cp\u003eThe study also deepens discussions of bias and inclusion. Several participants described AI outputs as culturally distant, Western-centered, or insufficiently aligned with their realities. This supports broader concerns that AI systems may privilege dominant languages, epistemologies, and representational norms (Baker \u0026amp; Hawn, 2021; Fitzgerald et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mack et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). More importantly, the findings show that students experienced this not only as a problem of relevance, but also as a problem of recognition. When their realities were missing from AI outputs, students felt peripheral to the knowledge structures embedded in the technology. In this way, the findings move the discussion from inclusion to belonging.\u003c/p\u003e \u003cp\u003eBelonging emerged as one of the study\u0026rsquo;s most significant contributions. Much of the literature on generative AI in higher education focuses on adoption, usefulness, ethics, or academic integrity. By contrast, this study shows that belonging is central to students\u0026rsquo; experiences. Participants linked belonging to being represented in AI-generated content, having their learning needs recognized, feeling safe from unfair suspicion, and being included in AI-related institutional dialogue. This extends current scholarship by showing that access and formal inclusion are not sufficient on their own. Belonging concerns whether students feel seen, trusted, and heard in AI-mediated learning environments, which aligns with broader scholarship showing that participation without recognition may remain exclusionary in practice (Shpigelman et al., 2022).\u003c/p\u003e \u003cp\u003eAnother important finding is that exclusion may also be produced by the systems used to regulate AI. Participants\u0026rsquo; concerns about AI detection tools reveal a clear paradox. While institutions may frame detection systems as necessary for fairness and academic integrity, students often experience them as sources of anxiety, mistrust, and misrecognition. This supports Sullivan et al.\u0026rsquo;s (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argument that generative AI poses serious integrity challenges, but it also suggests that punitive responses can themselves become inequitable when they ignore differences in writing style, learning needs, and technological understanding. Fairness, in this sense, requires more than preventing misuse; it also requires avoiding systems that position students as pre-emptively suspect.\u003c/p\u003e \u003cp\u003eThe findings further show that students are not passive users of AI. Many were critically aware of problems such as hallucination, superficiality, overreliance, privacy risks, and weakened critical engagement. Their accounts reveal a negotiated relationship with the technology: some adopted it actively, others used it cautiously, and some deliberately limited or rejected it to preserve independent learning. This complicates the assumption that more AI use is inherently better. Instead, student engagement appears shaped by moral, pedagogical, and practical judgment, which is consistent with prior work showing mixed student perceptions of generative AI (Chan \u0026amp; Hu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ngo, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the same time, the findings make clear that responsible AI use cannot be treated as an individual matter alone. Participants repeatedly called for institutional and pedagogical support, particularly AI literacy training, prompting skills, critical evaluation, and clear guidance. This supports calls in the literature for stronger institutional frameworks for AI integration (Chan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Holmes et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, students wanted more than rules; they wanted practical guidance, differentiated support, and opportunities to learn how to use AI critically. This suggests that policy should function not only as regulation, but also as support. It also indicates that AI literacy should not be treated as a generic competence, since differences in discipline, experience, language, and confidence shape how students engage with AI.\u003c/p\u003e \u003cp\u003eThe pedagogical implications are equally important. Participants did not advocate removing AI from education. Rather, they imagined a more balanced relationship in which AI serves as a study partner rather than a substitute for learning. Their support for authentic, process-based assessment echoes recent Palestine-focused work on assessment redesign in the generative AI era (Khlaif et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khlaif, Alkouk, Salama, \u0026amp; Abu Eideh, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, this study adds a student-centered rationale for such redesign: students were concerned not only with validity, but also with fairness, trust, and meaningful learning. At the same time, they saw AI as potentially strengthening inclusive teaching when used to simplify concepts, translate materials, and support diverse learning pathways. The challenge, therefore, is not whether to use AI, but how to integrate it without reducing learning to efficiency or dependency.\u003c/p\u003e \u003cp\u003eThe Palestinian context further sharpens these findings. Previous Palestine-focused research has shown that AI integration in higher education is marked by tension, technostress, dependency, and uneven institutional readiness (Alhur et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hamamra et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Khlaif et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This study extends that literature by centering students rather than educators. Students\u0026rsquo; accounts reveal dimensions that educator-focused studies capture less clearly, particularly belonging, fear of misrecognition, and the everyday moral ambiguity of AI use. Their experiences show that AI integration is not a neutral modernization process, but a socially situated reorganization of support, power, and legitimacy.\u003c/p\u003e \u003cp\u003eTaken together, the findings support the study\u0026rsquo;s conceptual framework, which shows that AI-enabled opportunities and risks are mediated by institutional and pedagogical conditions to shape students\u0026rsquo; experiences of equity, inclusion, and belonging. Generative AI does not produce equitable outcomes on its own; its effects are filtered through access, representation, literacy, pedagogy, and voice. The central issue, therefore, is not simply whether AI should be used in higher education, but whether institutions are willing to create the conditions under which it can function as a genuinely supportive and just educational resource.\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical Implications\u003c/h2\u003e \u003cp\u003eThis study contributes theoretically by extending discussions of generative AI in higher education beyond adoption, utility, and academic integrity toward a more socially grounded understanding of equity, inclusion, and belonging. The findings show that generative AI should be understood not simply as an instructional tool, but as a sociotechnical space in which fairness, recognition, access, and participation are negotiated. In particular, the study adds belonging as a key interpretive dimension, showing that students\u0026rsquo; experiences are shaped not only by access to AI, but also by whether they feel represented, heard, and fairly treated in AI-mediated learning environments (Baker \u0026amp; Hawn, 2021; Chan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fitzgerald et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePractical Implications\u003c/h3\u003e\n\u003cp\u003eThe study suggests that universities should move beyond general enthusiasm for AI integration and adopt more equity-oriented responses. This includes structured AI literacy training, support for prompting and critical evaluation skills, equitable access to high-quality AI tools, and clear guidelines for responsible use. The findings also highlight the need for inclusive pedagogy and authentic assessment that position AI as a support for learning rather than a substitute for student thinking. In addition, involving students in AI-related decision-making may strengthen trust, fairness, and belonging in higher education (Chan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Holmes et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khlaif, Alkouk, Salama, \u0026amp; Abu Eideh, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, it was conducted within the Palestinian higher education context, so the findings reflect a specific social, institutional, and technological environment and should not be generalized uncritically to other settings. Second, the study included only students with prior experience using generative AI for academic purposes, which means the perspectives of students who avoid or reject AI were less visible. Third, as a qualitative study based on interviews and focus groups, it offers depth and contextual insight rather than statistically generalizable findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eFuture Research\u003c/h2\u003e \u003cp\u003eFuture research could examine how experiences of equity, inclusion, and belonging vary across countries, institutions, and disciplines. Quantitative or mixed-methods studies could test the prevalence of the patterns identified here, particularly regarding access, AI literacy, and perceptions of fairness. Further work is also needed on groups that may face greater exclusion in AI-enabled higher education, including students with disabilities, linguistically diverse learners, and those with limited digital access. Longitudinal studies would be especially valuable for examining how students\u0026rsquo; relationships with generative AI evolve as institutional policies and pedagogical practices continue to develop.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study explored how university students experience equity, inclusion, and belonging in relation to the use of generative AI in higher education. The findings show that students perceive generative AI as both a source of educational opportunity and a potential site of inequality and exclusion. While many participants valued its capacity to support personalized learning, accessibility, efficiency, and academic confidence, they also highlighted important concerns related to unequal access, bias, privacy, overreliance, and unclear institutional guidance. These findings demonstrate that the educational value of generative AI is not determined by the technology alone, but by the social, pedagogical, and institutional conditions in which it is used.\u003c/p\u003e \u003cp\u003eThe study concludes that equitable and inclusive AI integration in higher education requires more than access to digital tools. It requires structured AI literacy, fair institutional policies, inclusive pedagogical design, and meaningful recognition of students\u0026rsquo; diverse needs, identities, and voices. In this sense, belonging emerged as a critical dimension of students\u0026rsquo; experiences, reflecting the importance of representation, trust, safety, and participation in AI-related educational environments. Overall, the study argues that generative AI should not be approached merely as a technological innovation, but as a sociotechnical and ethical issue that must be addressed through student-centered and justice-oriented higher education practices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e Ethical approval for this study was obtained from the Institutional Review Board at An-Najah National University (Approval No. Edu/Hum. Dec. 2025/70). The study was conducted in accordance with the ethical principles of the \u003cb\u003eDeclaration of Helsinki\u003c/b\u003e. All participants were informed about the purpose of the study, and written informed consent was obtained prior to participation.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNA\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.N.K. conceived and designed the study, supervised the research process, contributed to data analysis, and led the writing of the manuscript. B.H. contributed to data collection, data analysis, and drafting and revising the manuscript. H.F. contributed to data collection, data analysis, and manuscript revision. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available in order to protect participants\u0026rsquo; confidentiality, but anonymized data may be available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlhur AA, Khlaif ZN, Hamamra B, Hussein E (2025) Paradox of AI in higher education: Qualitative inquiry into AI dependency among educators in Palestine. 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Int J Educational Technol High Educ 21(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s41239-024-00453-6\u003c/span\u003e\u003cspan address=\"10.1186/s41239-024-00453-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-educational-integrity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijei","sideBox":"Learn more about [International Journal for Educational Integrity](https://edintegrity.biomedcentral.com/)","snPcode":"40979","submissionUrl":"https://submission.springernature.com/new-submission/40979/3","title":"International Journal for Educational Integrity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Generative artificial intelligence, higher education, equity, inclusion, belonging","lastPublishedDoi":"10.21203/rs.3.rs-9192785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9192785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explored university students\u0026rsquo; experiences of equity, inclusion, and belonging in relation to the use of generative artificial intelligence in higher education. Using a qualitative design, data were collected through 21 semi-structured interviews and three online focus group sessions with students who had experience using generative AI for academic purposes. The findings showed that students perceived generative AI as both an opportunity and a risk. On the one hand, participants valued its role in supporting personalized learning, accessibility, productivity, and academic confidence. On the other hand, they raised concerns about unequal access to advanced tools, biased outputs, privacy, overreliance, and unclear institutional policies. The study also found that students\u0026rsquo; sense of belonging was shaped by whether they felt represented, heard, and fairly treated in AI-enabled learning environments. The study concludes that equitable AI integration requires student-centered policies, AI literacy support, inclusive pedagogy, and attention to fairness, recognition, and meaningful participation in higher education.\u003c/p\u003e","manuscriptTitle":"Equity and inclusion in generative AI use in higher education: A qualitative study of Palestinian students’ experiences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 05:27:22","doi":"10.21203/rs.3.rs-9192785/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"313677783540369428535143375417475573698","date":"2026-05-06T19:43:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-04T05:54:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-27T06:15:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal for Educational Integrity","date":"2026-04-26T19:10:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-educational-integrity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijei","sideBox":"Learn more about [International Journal for Educational Integrity](https://edintegrity.biomedcentral.com/)","snPcode":"40979","submissionUrl":"https://submission.springernature.com/new-submission/40979/3","title":"International Journal for Educational Integrity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4dfff95b-5da6-46fb-adb4-8f61208675f9","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"313677783540369428535143375417475573698","date":"2026-05-06T19:43:00+00:00","index":16,"fulltext":""},{"type":"reviewersInvited","content":"2","date":"2026-05-04T05:54:22+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T05:27:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 05:27:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9192785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9192785","identity":"rs-9192785","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}