Generative AI in English Sixth Form Education: Student Use, Perceptions, and Literacy Gaps

Generative AI in English Sixth Form Education: Student Use, Perceptions, and Literacy Gaps | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Generative AI in English Sixth Form Education: Student Use, Perceptions, and Literacy Gaps Megan Ennion This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7122270/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This cross-sectional survey study investigates sixth form students’ engagement with generative artificial intelligence (GAI) tools in a large, urban, high-attaining, tech-friendly English college. The site was strategically selected to provide early insight into GAI adoption in a Further Education (FE) context where digital maturity, institutional support, and student demographics are conducive to advanced uptake. Survey responses from 543 students explore patterns of use, learning perceptions, and AI literacy needs - addressing a significant gap in empirical research on GAI use among 16–18-year-old learners in FE settings. Findings suggest notable - and often educationally productive - use of GAI, particularly for explaining complex concepts and generating ideas. Many students viewed GAI as a valuable learning partner, with a minority comparing it favourably to teacher support. Positive attitudes were more common among frequent users and male students, raising equity considerations in AI confidence and literacy. The use of GAI for fact-checking and solving maths problems - despite mixed views on accuracy - revealed important gaps in students’ understanding of the technology’s limitations. Applying Ng et al.’s ( 2021 ) AI Literacy Framework, the study found strong student interest in developing evaluative and ethical competencies. Grounded in dialogic and sociocultural learning theory, this study conceptualises GAI as both a cognitive tool and a source of epistemic risk. It draws attention to the role of students’ learning goals evidenced in their use - whether using GAI to complete tasks or to support learning - in shaping the cognitive value of their engagement. The study argues for structured, critical AI literacy in Further Education: enabling students to make meaning with , not just from , AI, and guiding institutional responses beyond restriction toward reflective and pedagogical support. Generative Artificial Intelligence ChatGPT Student Perceptions AI Literacy Academic Integrity Sixth Form Education Learning Further Education Introduction Since the release of ChatGPT in November 2022, generative artificial intelligence (GAI) technologies have drawn significant attention in education - offering immediate feedback, writing support, and concept clarification, while raising concerns around academic integrity, misinformation, skill development, and equity (Kasneci et al., 2023 ; Zhu et al., 2023 ). As educators and policymakers respond to these challenges, there is an urgent need for empirical research to inform responsible integration (Holmes, 2023 ; Mintz et al., 2023 ). Understanding how learners engage with GAI, perceive its impact, and identify their support needs is increasingly important (Thomson, Pickard-Jones, Baines, & Otermans, 2024 ). While much of the emerging research has focused on university students, context significantly shapes GAI use, and comparatively little is known about pre-university learners (Whyte, Kirby, & Sentance, 2024 ). The 16–18 phase - typically situated within the UK’s Further Education (FE) sector - represents a notable gap. This study addresses that gap by investigating how sixth form students are encountering GAI in practice. As a key group within Further Education preparing for high-stakes qualifications, these students have more autonomy than younger pupils but may lack the self-regulatory maturity to evaluate new technologies critically. This makes them especially susceptible to both the appeal and the risks of GAI - drawn by its fluency, yet lacking the metacognitive tools to assess its educational value. Such susceptibility reflects known dynamics of cognitive offloading and automation bias, where fluent outputs can obscure underlying epistemic limitations (Gerlich, 2025 ; Watson, 2025 ). Drawing on survey data from 543 students at a large, urban, high-aspiring tech-friendly English college with institutional support for GAI, it explores patterns of use, perceived educational value, and AI literacy needs. The site was strategically selected for its wealthier, urban context, digital maturity, and high student aspirations - conditions found to support early uptake. As such, it offers a valuable opportunity to examine not just whether students are engaging with GAI, but how its use is unfolding - and what this reveals about its evolving role in Further Education. To conceptualise student use and needs, the study draws on a multidimensional theoretical framework: Wegerif and Major’s (2025) emphasis on dialogic interaction, Vygotsky’s (1968) theory of social scaffolding, Watson’s ( 2025 ) notion of meaning mediation, and Ng et al.’s ( 2021 ) AI Literacy Framework. These perspectives are complemented by the distinction between learning and performance goals (Dweck & Leggett, 1988 ), which informs the interpretation of how different patterns of use may reflect underlying intentions. Together, this framework offers a richer lens for interpreting the pedagogical opportunities, challenges, and implications of GAI use in Further Education. Although situated in a UK sixth form context, the findings speak to wider international debates on how upper-secondary and FE learners engage with GAI. As access expands and institutional policies evolve, issues of digital confidence, ethical awareness, and educational purpose are likely to emerge across systems. This study contributes by examining the use and needs of students in Further Education as they prepare for higher education. 1. Literature Review 1.1. Student Use of Generative AI Generative artificial intelligence (GAI) tools have rapidly entered mainstream education, offering opportunities such as immediate feedback, idea generation, and concept explanation, while raising concerns about academic integrity, misinformation, overreliance, and equity (Kasneci et al., 2023 ; Zhu et al., 2023 ). Although research on student use of GAI is growing, it has predominantly focused on university populations. In the UK, Freeman ( 2025 ) recently found that 92% of undergraduate students had used AI in some form, with 88% using generative AI specifically to support assessment tasks. They reported the most common uses were explaining concepts, summarising texts, and generating research ideas. In this survey, students cited time-saving and improved work quality as key motivations, while concerns centred on academic misconduct, misinformation, and bias. Notably, use patterns also varied by gender, subject, and socio-economic background, with male, wealthier, and STEM students reporting greater enthusiasm and confidence. Despite this widespread uptake, only 36% of students had received institutional support to develop AI skills. These findings reflect broader patterns reported internationally (e.g., Almassaad et al., 2024 ; Chan & Hu, 2023 ), where use of ChatGPT is notable and students similarly value GAI’s efficiency and accessibility but express concern over ethical risks and limited policy guidance. Importantly, university studies also suggest a reciprocal relationship between use and perception, for example, Chan and Hu ( 2023 ) found that students who used GAI more frequently were more willing to adopt it in future study and work. Together, these studies highlight the growing embeddedness of GAI in university learning environments. However, emerging evidence suggests that GAI use and needs may differ significantly across educational levels (Gerlich, 2025 ; Hart, 2023; Impact Research, 2024 ), underscoring the importance of investigating how distinct age groups and contexts shape engagement with these tools. Among pre-university learners, survey research from the UK and US often treats secondary-age students (11–18) as a single group, masking potential variation. Reported usage remains inconsistent, with estimates ranging from 38–67% (Internet Matters, 2024 ; Pearson, 2024 ; Schiel, Bobek, & Schnieders, 2023 ), and some studies reporting rates as low as 8% (Michigan Virtual Learning Research Institute, 2024 ). ChatGPT is the most widely used tool, with engagement shaped by several factors. As in university populations (Freeman, 2025 ), male students (Hart Research Associates, 2024 ; Impact Research, 2024 ), students from wealthier backgrounds (Impact Research, 2024 ; Internet Matters, 2024 ), high attainers (Schiel et al., 2023 ), and those in urban areas (Hart Research Associates, 2024 ) are more likely to report regular use. However, few UK surveys disaggregate findings by these characteristics, limiting the ability to examine these patterns in detail. There is also evidence in this group of a reciprocal relationship between use and perception. Among secondary students, Michigan Virtual Learning Research Institute, ( 2024 ) found those who used GAI more frequently tended to view it more positively - and vice versa. This mirrors patterns in university contexts (Chan & Hu, 2023 ), and aligns with the Technology Acceptance Model (Davis, Bagozzi, & Warshaw, 1989 ), which links perceived usefulness and ease of use to adoption behaviour (see also Zou & Huang, 2023 ). While surveys are beginning to capture how pre-university students use generative AI (GAI), most treat the 11–18 age band as a single population. As a result, relatively little is known about the distinct experiences of students in Further Education (typically aged 16–18). In the UK, one major strand of Further Education is the sixth form college system, which serves students undertaking advanced academic qualifications (typically A-levels) required for university entry. Sixth formers occupy a unique position: granted more autonomy than younger pupils, yet still within structured environments. This phase is characterised by high-stakes assessments, compressed timelines, and pressure to demonstrate university readiness - conditions that may intensify both the appeal and the risks of using GAI, particularly for students still developing metacognitive and self-regulatory skills. However, despite these demands, research has yet to examine how sixth form students perceive the educational value of GAI or how well equipped they are to use it effectively. Understanding this requires not only empirical investigation, but also a conceptual framework for interpreting how students engage with GAI - not merely as a technical aid, but as a tool that may influence the depth, direction, and quality of their learning. 1.2. GAI and Learning A central question in evaluating GAI’s role in education is how it shapes student learning. While its appeal - through rapid feedback, personalised support, and conceptual clarification - is well documented (Kasneci et al., 2023 ), evidence on its actual impact remains mixed. Deng et al.’s ( 2024 ) meta-analysis reported positive effects of GAI use on academic outcomes, yet Kreijkes et al. (2025) found that GAI-supported notetaking reduced retention and comprehension compared to traditional methods, despite students reporting enhanced subjective experiences. These discrepancies suggest that outcomes may depend less on the presence of GAI than on how it is used. Recent research suggests that the benefits of GAI use depend on how - and why - students engage with it. Lehmann et al. ( 2024 ) found that positive outcomes were largely confined to students who engaged cognitively with GAI, highlighting the importance of intentional, effortful use. Wang and Fan ( 2025 ) similarly found that structured, role-defined applications - particularly when GAI was framed as an intelligent tutor - enhanced learning outcomes, suggesting that goal-aligned use plays a critical role. In contrast, passive, performance-oriented use - such as generating essays - risks what Kosmyna et al. ( 2025 , p. 141) term “cognitive debt,” where overreliance reduces neural activation and impairs recall. Gerlich ( 2025 ) and Darvishi et al. (2025) likewise caution that cognitive effort may be displaced when students outsource thinking to GAI. These findings echo broader research on learning versus performance goals (Dweck & Leggett, 1988 ; Elliot & McGregor, 2001 ), which shows that students’ intentions shape behaviour and learning outcomes. Extending this insight to GAI, this study interprets patterns of use as behavioural indicators of students’ underlying goals - whether to complete tasks or to support learning - which may influence the cognitive value of their engagement. From this perspective, it is not only the form of use, but the purpose it reflects that matters. GAI’s educational value is not intrinsic, but contingent on students’ goals, use, and the quality of their engagement. This theoretical distinction highlights the importance of examining the forms that GAI use takes in educational settings and what intentions they reflect - particularly where academic autonomy is high but metacognitive and self-regulatory skills are still developing. Behaviours like generating assignments or seeking clarification may reflect different learning orientations. Such inquiry is essential for understanding whether GAI is being used to support or substitute for learning, and for informing the design of AI literacy interventions that promote critical, reflective, and educationally purposeful engagement. To guide this investigation, the study draws on three complementary perspectives that help conceptualise what it means to learn with GAI. Vygotsky’s ( 1978 ) theory of social learning emphasises the role of social scaffolding - support provided through interaction with a more capable other - as a mechanism for extending understanding beyond what the learner can achieve independently. This is of particular relevance to GAI tools, which often present themselves as ever-available “more capable others,” offering tailored explanations, examples, and guidance in response to student queries. Wegerif and Major’s (2025) theory of dialogic learning further highlights the importance of learning as a process of meaning-making through dialogue. Rather than simply receiving information, students learn by engaging in conversations that challenge assumptions, prompt reflection, and open new conceptual spaces. Generative AI systems - particularly large language model chatbots - are explicitly designed to simulate such dialogic exchanges, making this theoretical lens especially useful for examining how students engage with AI in educational contexts. Watson’s ( 2025 ) framing of GAI as a mediator of meaning builds on these perspectives by drawing attention to the fact that AI systems do not merely transmit knowledge, but actively shape how knowledge is presented, interpreted, and internalised. This raises important questions not only about what students learn from GAI, but how their habits of thought are shaped by the tools themselves. Understanding how students experience and respond to GAI is therefore central to assessing its educational potential, risks, and support needs. Together, these frameworks reframe GAI not as a passive provider of answers, but as an interactive, responsive, and epistemically influential participant in the learning process. From this view, its educational value lies not in automation, but in supporting conceptual development through interaction. Aligned with this framing, the study draws on Marton and Säljö’s (1976) distinction between surface and deep learning - where surface learning is oriented toward memorisation and task completion, while deep learning involves conceptual understanding and critical engagement. In the context of GAI, this distinction helps illuminate whether students are using the technology with the intention of shortcutting tasks or supporting thinking. We use student perceptions as proxies for deeper engagement, examining whether they believe GAI enhances understanding, whether they use it to explain complex concepts, and whether they perceive it as offering teacher-comparable support - particularly where such support reflects scaffolded, dialogic learning. These usage patterns are read as indicative of underlying learning goals (Dweck & Leggett, 1988 ; Elliot & McGregor, 2001 ), which, alongside perceived depth, help interpret how students are engaging with GAI. Importantly, the depth of engagement is also shaped students’ capacity to evaluate, interpret, and manage it effectively. This brings the concept of AI literacy into focus. As Pratschke ( 2024 ) and Watson ( 2025 ) argue, enabling students to use GAI in ways that are pedagogically beneficial requires more than basic technical skill. Ng et al. ( 2021 ) offer a multidimensional framework comprising: (1) Know and Understand - grasping core concepts and recognising capabilities and limits; (2) Use and Apply - deploying GAI tools in academic tasks; (3) Evaluate and Create - critically assessing and refining outputs; and (4) Ethical Issues - addressing fairness, bias, and broader implications. Together, these dimensions frame AI literacy not as a checklist of competencies, but as a set of dispositions and evaluative capacities that shape how students approach, interpret, and integrate GAI into their learning. In this sense, literacy and learning are inseparable: students' ability to make meaning with GAI depends not only on the tool itself, but on their capacity to engage critically, reflectively, and strategically with its affordances. 2. Methodology 2.1. Context This study addresses a key gap through a cross-sectional survey examining the uses, attitudes, and support needs related to GAI among sixth form students (aged 16–18) in a high-performing, tech-friendly English college - a large, urban, non-fee-paying institution enrolling approximately 3,000 full-time A-Level students. This tech-friendly college promotes technology integration through a Bring Your Own Device policy, access to Microsoft Copilot for students and staff, and customised GAI chatbots for selected courses. It also employs dedicated staff to support digital initiatives. Institutional guidelines allow AI use in schoolwork, and teachers are encouraged to promote its responsible application. With a student body largely drawn from financially comfortable, professionally employed households, this urban and academically aspirational college represents a context in which both access to and attitudes toward GAI may be shaped by advantage. Research suggests wealthier, high-attaining, urban students are more likely to use GAI tools (Freeman, 2025 ; Hart Research Associates, 2024 ; Internet Matters, 2024 ; Impact Research, 2024 ; Schiel, Bobek, & Schnieders, 2023 ). Importantly, this is also an environment where GAI is not only permitted, but actively supported through staff expertise, institutional guidance, and platform access. As such, it was strategically selected as a likely site of early adoption - providing an opportunity to examine not just how students are using GAI, but what educational, ethical, and cognitive implications may arise in a setting where engagement is institutionally encouraged and where usage is likely to be higher due to the contextual factors outlined above. 2.2. Participants A total of 543 students responded anonymously: 277 in Year 12, 258 in Year 13, and 8 unspecified. In terms of gender, 268 identified as female, 227 as male, 24 as non-binary or other, and 23 preferred not to say. 2.3. Materials The questionnaire, developed collaboratively with college staff, featured multiple-choice and Likert-scale questions assessing GAI tools used, frequency and purpose of use, attitudes toward GAI, and interest in GAI-related training. Administered anonymously online via Qualtrics, it collected only year group and gender demographics. To explore students’ perceptions of educational benefit, the questionnaire distinguished between perceived impacts on understanding (as a proxy for deeper, conceptual learning, drawing on Wegerif and Major (2025)) and perceptions of ease and time-saving efficiency. To assess social learning impacts (informed by Vygotskian theory), students compared GAI support with human teacher assistance in terms of speed and ease of access and perceived equivalence. Drawing on Watson’s ( 2025 ) framing of GAI as a mediator of meaning and Dweck and Leggett’s ( 1988 ) distinction between learning and performance goals, the study also examined how students used, and what this revealed about both their underlying intentions and their understanding of its functions and educational value. Finally, AI literacy needs were assessed by presenting training options aligned with Ng et al.’s ( 2021 ) AI Literacy Framework, identifying which dimensions of AI literacy were recognised and where gaps remained. 2.4. Procedure Ethical approval for this study was obtained from the university conducting the survey. Guardians were notified in advance and given the option to opt their young person out. Informed consent was implied from respondents through voluntary completion of the survey. Those whose parent or guardian opted them out did not receive an invitation to take part or a survey link. Participation was voluntary and open to all full-time students aged 16–18. The response rate was approximately 18% of the college’s 3,000 students. Students were invited via email, with participation encouraged by staff. No incentives were offered. Anonymity was preserved through unlinked survey responses, participation was voluntary with no penalties for non-response, and data were stored securely, aligning with safeguarding protocols for research involving minors. Data was collected between January and March 2025 via the anonymous Qualtrics survey. 2.5. Data analysis Descriptive statistics summarised GAI tool use, purposes, attitudes, and training interest. Chi-square tests explored associations between gender, usage frequency, and attitudes, with male and female categories used for clearer gender analysis. Cramér’s V assessed effect sizes (0.1 = small, 0.3 = medium, 0.5 = large). Analyses were conducted using Qualtrics' StatsiQ platform, appropriate for the categorical data and exploratory aims of the study. Although logistic regression could offer further insights, modest subgroup sizes limited statistical power and stability. As multivariate controls were not applied, associations should be interpreted as indicative rather than explanatory. Future research with larger samples could explore predictive relationships through multivariate modelling. 3. Findings Findings from the survey are presented below, using descriptive and inferential statistics. 3.1. Descriptive Statistics 3.1.1. AI Tool Use Table 1 Frequency of GAI Tool Use for Schoolwork Among Students (N = 542) Frequency Count Percentage Very often 30 6% Often 81 15% Sometimes 125 23% Rarely 160 30% Table 2 AI Tools Used by Students to Support Schoolwork (N = 543) AI Tool % of Respondents Number (n) ChatGPT 61.14% 332 I don’t use any GAI tools for schoolwork 28.54% 155 Copilot 17.12% 93 Poe.com Bot 12.89% 70 Grammarly 10.68% 58 Quillbot 8.10% 44 Claude 4.41% 24 Other (please specify) 4.41% 24 Midjourney 3.31% 18 Bing GAI 3.13% 17 Google Gemini (formerly Bard) 1.10% 6 DALL·E 0.55% 3 Table 3 Self-Reported Frequency of GAI Tool Use by Gender Frequency Male (%) Female (%) Never 24 22 Rarely 25 34 Sometimes 27 24 Often 18 15 Very often 6 5 Table 4 Frequency of GAI Tool Use by Academic Task (N = 543) Academic Task Never (%) Rarely (%) Sometimes (%) Often (%) Very Often (%) Writing essays or reports 72 15 9 3 2 Generating ideas or brainstorming 34 21 22 16 6 Fact-checking or research 51 15 16 12 7 Solving maths problems 57 15 15 9 4 Explaining complex concepts 38 17 20 16 9 3.1.2. AI Attitudes Table 5 Student Agreement with GAI-Related Attitudinal Statements (N = 543) Statement Strongly Disagree (%) Disagree (%) Neither Agree Nor Disagree (%) Agree (%) Strongly Agree (%) Using GAI tools for schoolwork is cheating. 10 31 30 21 8 AI tools help you understand subjects better. 5 9 17 51 18 AI tools save time when doing schoolwork. 3 7 19 52 20 The information from GAI tools is often inaccurate. 2 19 29 32 17 Using GAI tools will better prepare me for my future career. 15 23 31 24 7 I believe that using a chatbot to help with schoolwork can be as good as getting guidance from a teacher. 29 32 14 18 6 If I am struggling with something in my schoolwork, I sometimes find it easier or quicker to first ask a chatbot, like Copilot or ChatGPT, than a teacher. 16 15 13 37 19 3.1.3. AI Training Interest Table 6 Student Interest in GAI Tools, Ethics, and Educational Support (N = 543) Statement Very Interested (%) Moderately Interested (%) Not at All Interested (%) Learning about different GAI tools and what they do 14% (n = 74) 54% (n = 293) 32% (n = 176) Understanding the ethical implications of using GAI 35% (n = 188) 41% (n = 222) 24% (n = 133) Training on how to use GAI tools for schoolwork in different subjects 26% (n = 139) 40% (n = 216) 35% (n = 188) Guidelines from teachers on acceptable GAI use in assignments 30% (n = 160) 46% (n = 250) 24% (n = 132) Learning how to assess whether GAI-generated information is true and think critically about it 45% (n = 243) 37% (n = 200) 18% (n = 99) School assignments where GAI is used to build skills and familiarity 15% (n = 79) 32% (n = 173) 53% (n = 288) Hands-on sessions exploring creative and fun projects using GAI 16% (n = 84) 32% (n = 172) 53% (n = 285) Discussions about the impact of GAI on future jobs and industries 38% (n = 207) 41% (n = 224) 20% (n = 110) Developing skills to work alongside GAI in future careers 26% (n = 140) 47% (n = 256) 27% (n = 145) 3.2. Statistical Analyses – Gender and Frequency Chi-square analyses were conducted to examine associations between students’ gender, year group, and frequency of GAI use and their attitudes toward GAI in education. Effect sizes were interpreted using Cramér’s V, where values around .10 indicate a small effect, .30 a medium effect, and .50 a large effect. 3.2.1. Gender and Attitudes Toward GAI There was no statistically significant relationship between gender and frequency of GAI use, χ²(4, N = 494) = 4.60, p = .33, Cramér’s V = .10, suggesting that male and female students in this context used it in almost equal amounts (Table 3 ). There was however a statistically significant gender difference was observed in students’ belief that GAI will support their future careers, χ²(4, N = 495) = 23.40, p < .001, Cramér’s V = .217, with a small-to-medium effect size. Male students were more likely to view GAI as professionally advantageous (Table 7 ). The largest gender-based difference concerned the belief that using GAI constitutes cheating, χ²(4, N = 495) = 27.20, p < .001, Cramér’s V = .235, also a small-to-medium effect. Male students were considerably less likely than females to view the use of GAI as academically dishonest (Table 7 ). 3.2.2. Frequency of GAI Use and Attitudes Toward GAI Further analyses explored the relationship between how frequently students used GAI tools and their perceptions of GAI’s educational value. A particularly strong association was observed between GAI use frequency and preference for GAI over teacher guidance, χ²(16, N = 542) = 320.00, p < .001, Cramér’s V = .384, indicating a medium-to-large effect. Students who used GAI most frequently were substantially more likely to prefer receiving help from a chatbot rather than from a teacher (Table 8 ). Frequency of use was also significantly associated with agreement that GAI helps with understanding, χ²(16, N = 542) = 242.00, p < .001, Cramér’s V = .334; that GAI is as good as teacher guidance, χ²(16, N = 542) = 195.00, p < .001, Cramér’s V = .300; and that GAI will benefit future careers, χ²(16, N = 542) = 161.00, p < .001, Cramér’s V = .273. These represent small-to-medium to medium effect sizes. Frequent users consistently expressed stronger confidence in GAI’s educational and professional value (see Table 8 ). A further association was found between GAI use and the belief that using GAI is cheating, χ²(16, N = 542) = 175.00, p < .001, Cramér’s V = .284, a small-to-medium effect. Frequent users were less likely to view GAI use as academically dishonest, while infrequent users were more likely to hold this belief (Table 8 ). Finally, frequency of GAI use was significantly associated with agreement that GAI tools save time, χ²(16, N = 542) = 151.00, p < .001, Cramér’s V = .264, and with perceptions of GAI inaccuracy, χ²(16, N = 542) = 119.00, p < .001, Cramér’s V = .234. Both represent small-to-medium effect sizes. Students who used GAI more frequently were more likely to believe that it saves time and less likely to view it as inaccurate (Table 8 ). Table 7 Chi-Square Tests of Association Between Gender and GAI Attitudes Comparison χ²(df) p-value Cramér’s V n Gender × GAI helps career χ²(4) = 23.40 < .001* .217 495 Gender × GAI use = cheating χ²(4) = 27.20 < .001* .235 495 Note . * p < .001. Table 8 Chi-Square Tests of Association Between Frequency of GAI Use and GAI Attitudes Comparison χ²(df) p-value Cramér’s V n Frequency × Prefer GAI to teacher χ²(16) = 320.00 < .001* .384 542 Frequency × GAI helps understanding χ²(16) = 242.00 < .001* .334 542 Frequency × GAI = Teacher guidance χ²(16) = 195.00 < .001* .300 542 Frequency × GAI use = cheating χ²(16) = 175.00 < .001* .284 542 Frequency × GAI helps career χ²(16) = 161.00 < .001* .273 542 Frequency × GAI saves time χ²(16) = 151.00 < .001* .264 542 Frequency × GAI is inaccurate χ²(16) = 119.00 < .001* .234 542 Note . * p < .001. 4. Discussion This section interprets the findings in relation to patterns of GAI use, learning behaviours, and literacy needs, drawing on the theoretical frameworks outlined earlier. It first examines how students are using GAI, then considers their perceptions of its learning value, before addressing emerging literacy gaps, attitudinal differences, and contextual implications. 4.1. How Much GAI Is Being Used In this study, 44% of students reported using GAI tools for schoolwork at least sometimes, while 27–29% reported never using GAI (Table 1 ). ChatGPT was the most commonly used tool, with 61.14% of those who had used GAI indicating they had used it to support their learning (Table 2 ). These findings suggest substantial, though not universal, GAI uptake among sixth form students. They broadly align with recent UK and US surveys of secondary-age students reporting usage rates between 38% and 67% (Impact Research, 2024 ; Pearson, 2024 ; Schiel, Bobek, & Schnieders, 2023 ), and sit well above the 8% figure reported by Michigan Virtual Learning Research Institute ( 2024 ), which focused on online learning environments. Findings contrast with UK university contexts, where uptake appears near-universal (Freeman, 2025 ). This likely reflects both institutional and developmental factors: sixth form students remain in more structured environments, governed by standardised curricula and high-stakes assessments. They are also younger, with less autonomy and fewer opportunities to independently decide how digital tools are used. It may also indicate that students in Further Education are still negotiating the boundaries of acceptable use - particularly in settings where GAI is permitted but not yet fully normalised. These findings underscore the need for institutional readiness and pedagogical support at the Further Education stage, where students are already engaging with GAI. This appears as a critical window for developing students’ capacity to use these tools wisely before use intensifies in higher education. 4.2. Student Use of GAI for Exploratory and Conceptual Engagement Findings indicate that many students in this context are using GAI in ways that reflect exploratory learning and conceptual engagement, rather than simply as a shortcut to completed work. Contrary to common concerns about academic dishonesty or dependency, patterns of use suggest that students were more likely to draw on GAI to support their thinking than to bypass it. For example, 72% of students reported never using GAI to write essays, and 57% said they never used it to solve maths problems. In contrast, 45% reported using GAI to explain complex concepts at least sometimes, and 44% used it to generate ideas. These activities suggest that students are engaging with GAI primarily as a tool for clarifying and expanding understanding, rather than for completing tasks. In contrast to more superficial uses associated with less productive learning (Kosmyna et al., 2025 ; Kreijkes et al., 2025), many students appear to be using GAI to support deeper learning (Marton & Säljö, 1976). This distinction is significant given concerns that passive or outcome-driven use can lead to cognitive offloading, reduced neural activation, and weaker recall (Kosmyna et al., 2025 ; Lehmann et al., 2024 ; Gerlich, 2025 ). As shown in studies such as Lehmann et al. ( 2024 ), the type of engagement observed in this study - characterised by cognitive effort and exploratory behaviour - is more likely to produce meaningful learning outcomes. This is further supported by the fact that these uses may reflect learning-oriented goals, where theories suggest that students’ underlying intentions - whether to learn or simply complete tasks - influence the cognitive value of their engagement (Dweck & Leggett, 1988 ). Such uses align with Watson’s ( 2025 ) framing of GAI as a mediator of meaning, and with Wegerif and Major’s (2025) emphasis on dialogue as a process of conceptual development. Rather than simply extracting answers, students appear to be using GAI as a responsive interlocutor - a means of exploring ideas and co-constructing understanding. These findings also align with prior research suggesting that students in wealthier, urban, high-attaining, digitally mature settings often use GAI confidently and purposefully (Freeman, 2025 ; Almassaad et al., 2024 ; Impact Research, 2024 ). That sixth form students report comparable patterns to university students suggests that GAI’s dialogic and exploratory appeal may cut across educational levels. Results challenge dominant narratives of misuse and point to its pedagogical potential in Further Education - particularly when students are supported to use it reflectively and critically. 4.3. Student Use of GAI as a Social Learning Partner Survey responses suggest that students in this context are not just using GAI tools for schoolwork, but are also beginning to position them as credible sources of academic support. While many reported efficiency gains − 72% said GAI saved time on schoolwork, and 56% found it quicker or easier to consult a chatbot than a teacher - the more notable finding was the perceived cognitive value. A substantial majority (69%) agreed that GAI helped them better understand subjects, and nearly a quarter (24%) felt that chatbot support could match teacher guidance. These perceptions suggest that students may be beginning to view GAI as a meaningful learning partner. They reflect more than a pragmatic interest in saving time, pointing instead to a growing perception of GAI as a socially responsive, low-barrier source of explanation, clarification, and feedback and reflect learning-oriented rather than task-driven intentions (Dweck & Leggett, 1988 ; Lehmann et al., 2023; Wang & Fan, 2024). Such interactions, where the GAI becomes an intelligent partner, may equate to the kind of cognitively engaged use that facilitates deeper learning and contributes to more productive educational outcomes (Lehmann et al., 2023; Wang & Fan, 2024). This aligns with recent work highlighting the dialogic potential of GAI systems (Ennion & McLellan, 2025 ; Gupta & Chen, 2022 ; Kasneci et al., 2023 ), and resonates with Vygotsky’s ( 1978 ) notion of the zone of proximal development, where learners benefit from scaffolded interaction with more capable others. In this context, GAI functions not simply as an informational resource but as a simulated interlocutor operating within the learner’s proximal zone. Wegerif and Major’s (2025) concept of “dialogic space” further helps explain how students may be experiencing GAI use as a form of mediated conceptual exploration - especially when the tool prompts reflection or generates new frames of reference. Likewise, Watson’s ( 2025 ) characterisation of GAI as a mediator of meaning positions these systems as active participants in shaping how learners interpret, engage with, and internalise knowledge. These sixth form perceptions mirror those reported in university contexts. For example, Almassaad et al. ( 2024 ) and Chan and Hu ( 2023 ) similarly documented students citing conceptual clarification and idea generation as primary benefits. That sixth form students report similar views suggests that the perceived value of GAI as a learning partner may not be limited to higher education, but is already shaping attitudes in earlier phases. While perceived value does not guarantee learning gains - Kreijkes et al. (2025) caution that students may feel supported by GAI without achieving deeper understanding - findings from this digitally mature, high-attaining context suggest that students increasingly view GAI as a partner for understanding rather than a shortcut or substitute for effort. This is particularly notable given their transitional stage. It suggests a readiness to engage meaningfully with GAI - provided students have the skills to do so effectively. 4.4. The Need for AI Literacy: Knowing and Understanding AI Limitations The findings reveal notable gaps in students’ understanding of GAI limitations. Despite well-documented risks around hallucinations (Khurma et al., 2024 ), under half of students (49%) acknowledged that GAI content is often inaccurate, and a substantial minority (21%) believed it to be accurate (Table 5 ). These perceptions are especially concerning given usage patterns: 35% reported using GAI for fact-checking at least sometimes, and 28% used it occasionally to solve maths problems (Table 4 ). This suggests that, despite generally constructive uses, many students are relying on GAI in contexts where it is fundamentally ill-suited. Such behaviour points to a foundational gap in AI literacy - particularly within the “Know and Understand” domain of Ng et al.’s ( 2021 ) framework, which emphasises conceptual awareness of how AI systems function, what their outputs represent, and where their limits lie. Students may trust fluent responses without recognising their potential to mislead. This misunderstanding carries real educational risks. As Holmes ( 2023 ) cautions, educational technologies can produce unintended consequences - particularly when adoption outpaces critical pedagogy. GAI systems generate language based on probabilistic patterns rather than verified fact and can produce false or fabricated content with a high degree of stylistic coherence. As Khurma et al. ( 2024 ) argue, the danger in educational contexts lies in the illusion of authority: ChatGPT’s confident, fluent style can obscure its lack of epistemic grounding, leading students to accept misleading outputs as credible. Students who rely on GAI for fact-checking or problem-solving without understanding its generative nature may internalise inaccuracies, ultimately undermining learning outcomes (Sun et al., 2024 ). This not only risks the spread of misinformation, but may also inhibit the development of independent critical thinking if students are not equipped to evaluate AI-generated responses. As UNESCO highlights, effective AI literacy begins with conceptual understanding of how generative systems produce content - and why their outputs require critical interpretation (Miao et al., 2024). Watson’s ( 2025 ) framing of GAI as a mediator of meaning reinforces this: students are not merely retrieving information, but shaping understanding in dialogue with a system whose strength lies not in conveying truth, but in producing plausible, context-sensitive responses. Without understanding, learners may conflate coherence with credibility. Yet when students recognise that GAI mediates meaning rather than delivers fact, they are better positioned to use it reflectively and productively. Findings from this study reinforce the urgent need to embed AI literacy into Further Education curricula - not only to build technical competence, but to equip students with the conceptual understanding needed to engage with generative systems in educationally productive ways. 4.5. The Need for AI Literacy: Critical and Ethical Reflection In addition to gaps in technical understanding, the findings point to a broader need for structured AI education that supports students’ ethical reasoning and critical reflection. Despite the college’s tech-friendly and relatively permissive environment, uncertainty and disagreement were evident in students’ views about the legitimacy of GAI use. While 41% disagreed that using GAI constituted academic dishonesty, 29% agreed (Table 5 ). This split suggests the absence of a shared normative framework - even in digitally mature settings - around what counts as appropriate or responsible use. In the context of a school-based survey, this variation may reflect more than personal opinion; students may have interpreted the question about cheating in light of institutional norms rather than individual beliefs - highlighting a lack of policy clarity and the need for transparent guidance around acceptable use. Alternatively, it may indicate genuine ethical disagreement among students about GAI’s role in learning. In either case, the findings point to the importance of creating space for critical dialogue - enabling students not just to follow rules, but to reason through complex issues and form reflective judgments. Student training preferences support this interpretation. When asked about learning needs, students expressed strong interest in developing evaluative and ethical competencies. A large majority wanted to learn how to assess the accuracy of GAI outputs (82%), understand its societal impacts (79%), and build skills to work with GAI in future careers (73%) (Table 6 ). By contrast, there was notably less enthusiasm for hands-on training in using GAI to complete assignments or creative tasks. Students appear less focused on functionality than on understanding GAI’s broader implications. Freeman ( 2025 ) similarly notes that institutional support for critical AI engagement has lagged behind student uptake. These findings map closely onto Ng et al.’s ( 2021 ) AI Literacy Framework, particularly the “Evaluate and Create” and “Ethical Issues” domains, which emphasise the importance of reasoning about fairness, boundaries, and social impact. They also reflect Watson’s ( 2025 ) argument that ethical GAI use requires understanding how AI shapes meaning and mediates social norms, and align with UNESCO’s call for AI literacy that includes ethical, societal, and cognitive dimensions (Miao et al., 2021). Taken together, these findings point to a need for AI literacy in Further Education that goes to ethical engagement and critical reasoning. As UNESCO (Miao et al., 2021) emphasises, effective AI education must address ethical, cognitive, and societal dimensions - not just technical skills. 4.6. Patterns in GAI Perceptions Analysis of the survey data revealed clear patterns in student perceptions of GAI, particularly in relation to gender and frequency of use. These patterns are important for understanding how individual and contextual factors shape students’ attitudes toward emerging educational technologies - and for designing AI literacy interventions that address disparities in confidence, engagement, and access. Gender Differences in Perceptions Although usage rates did not differ significantly by gender (Table 3 ), pronounced attitudinal differences emerged. Male students were significantly more likely than female students to view GAI as valuable for career development (p < .001, Cramér’s V = .217) and less likely to consider its use a form of academic dishonesty (p < .001, Cramér’s V = .235) (Table 7 ). These findings align with prior research reporting gender differences in technology adoption that favour males. For instance, Freeman ( 2025 ) found that male university students in the UK expressed greater enthusiasm for GAI and were less deterred by concerns around academic misconduct or misinformation. Similarly, Hart Research Associates ( 2024 ) and Impact Research ( 2024 ) reported greater digital confidence and AI-related self-efficacy among male students in secondary education. While this study did not detect gender-based differences in GAI usage frequency, the persistence of attitudinal differences across contexts points to deeper social and psychological dynamics - particularly around digital confidence, perceived legitimacy, and alignment with dominant tech cultures. As GAI becomes further embedded in academic and professional domains, such divides may compound, shaping students’ readiness, comfort, and opportunities for engagement. Addressing these gaps will require more than equitable access to tools; it calls for targeted, inclusive AI literacy initiatives that build digital self-trust and challenge gendered norms around technology use. Without this, underrepresented groups may be less likely to use GAI confidently or critically, further entrenching existing disparities in educational and career trajectories. Frequency of Use and Perceptions A consistent and statistically significant pattern emerged between students’ frequency of GAI use and their attitudes toward its value (Table 8 ). More frequent users expressed markedly more positive perceptions across multiple domains - including beliefs that GAI supports understanding, saves time, enhances future career prospects, and provides useful learning support. For example, only 38% of non-users agreed that GAI helped them better understand subjects, compared to 97% of very frequent users. Similarly, agreement that chatbots were easier to consult than teachers ranged from 11% among non-users to 94% among very frequent users. Confidence in the quality of GAI support also followed this gradient: just 12% of non-users believed chatbot support could rival teacher guidance, compared to 57% of very frequent users. All associations were statistically significant (Cramér’s V = .264 to .384). While the cross-sectional design precludes causal inference, these findings support prior research suggesting a reciprocal relationship between GAI use and positive perceptions. Students who use GAI more often may develop greater familiarity and confidence, which in turn increases their willingness to engage further. This aligns with the Technology Acceptance Model (Davis, Bagozzi, & Warshaw, 1989 ; Zou & Huang, 2023 ), which posits that perceived usefulness and ease of use drive technology adoption and are reinforced through user experience. Chan and Hu ( 2023 ) also report that frequent users are more likely to adopt GAI in future academic and professional settings. Parallel findings from Michigan Virtual Learning Research Institute ( 2024 ) among secondary students suggest that this feedback loop may be robust across age groups and institutional contexts. These results highlight the role of exposure in shaping attitudes: students who use GAI more frequently tend to report stronger beliefs in its usefulness, efficiency, and learning value. While the data do not permit causal inference or speak to critical awareness, frequent users appear more able to access its educational potential. This underscores the importance of ensuring that all students have equitable opportunities to engage meaningfully with GAI tools, supported by appropriate guidance. 4.7. Contextual Factors These findings emerge from a large, urban, tech-friendly sixth form college - a site selected for its distinctive characteristics. Rather than treating context as a limitation, this study focused intentionally on a setting where GAI adoption was likely to be relatively advanced. Prior research (Hart Research Associates, 2024 ; Internet Matters, 2024 ; Impact Research, 2024 ; Schiel, Bobek, & Schnieders, 2023 ) suggests that students in urban, high-attaining, and advantaged settings are more likely to engage with GAI. These studies highlight how access, aspiration, and infrastructure shape adoption. Studying GAI in a digitally mature, high-aspiration context allowed the research to move beyond access to examine how students are using GAI, navigating its limitations, and forming judgments about its role in learning. In this sense, the setting offers a window into a potential “next phase” of integration - where use is already normalised and questions of depth, discernment, and pedagogical value become more salient. Several results align with national surveys of secondary students (e.g., usage rates, perceived benefits), but others diverge. For instance, this study found no gender difference in GAI usage frequency, contrasting with prior research that reports higher male uptake. This suggests the environment may help mitigate - but not eliminate - gender disparities. The persistence of gender-based differences in perceived value and legitimacy reinforces the point that access alone does not ensure equitable engagement. More broadly, the findings suggest context shapes not only access, but also how students use and learn from GAI. In well-supported environments, learners may be more inclined to explore GAI's dialogic and conceptual affordances. In contrast, students in under-resourced or policy-restrictive settings may face limited opportunities and greater risks. Infrastructure, institutional guidance, policy clarity, and socio-economic background all likely shape the trajectory of GAI use and its educational outcomes. Future research should examine these dynamics across more varied Further Education contexts to determine which patterns observed here are replicable - and which are unique to early-adopting settings. Understanding how GAI’s pedagogical potential is shaped by local conditions will be key to supporting equitable and effective integration. 5. Limitations and Future Research Directions This study offers insight into sixth formers’ GAI engagement in a digitally supportive setting, but has several limitations. First, the cross-sectional design captures a single moment in time and cannot establish causality. As GAI evolves rapidly (Impact Research, 2024 ), patterns of use and perceptions are likely to shift. Longitudinal research is needed to track these changes in response to technological, institutional, and social developments. Second, the study was conducted in a single, urban, high-aspiration sixth form college. While this limits generalisability, it is also a strategic strength as the context enabled exploration not just of adoption, but of the ethical and educational dimensions of GAI use. However, students in less resourced, more restrictive, or rural settings may experience different opportunities and barriers. Prior research suggests digital engagement often correlates with affluence, aspiration, and institutional support (Impact Research, 2024 ; Internet Matters, 2024 ), underscoring the need for comparative work across diverse educational contexts. Third, the exclusive use of quantitative survey data limits insight into students’ motivations, reasoning, and situated experiences. Qualitative methods - such as interviews or focus groups - could offer a deeper understanding of how students interpret GAI’s role in learning. Finally, the voluntary nature of participation introduces possible self-selection bias. Students more positively disposed toward GAI may have been overrepresented. Additionally, as the survey was administered by the college, responses may have been influenced by social desirability, particularly in a setting where GAI is institutionally supported. Future studies should aim to mitigate this through independent administration and anonymised protocols. Further research should address these limitations through longitudinal, mixed-methods studies conducted across varied institutional and demographic contexts. International comparative research will also be essential to explore how national policies, student profiles, and institutional cultures shape the adoption and educational impact of GAI in Further Education. Such work is crucial to discerning which patterns are context-specific and which may hold more broadly. Conclusion This study offers insight into how sixth form students in Further Education are engaging with generative AI (GAI), drawing on survey data from an urban, tech-friendly, high-aspiration English college. GAI use was widespread, with 44% of students reporting at least occasional engagement, and ChatGPT the dominant tool. While patterns varied, students were more likely to use GAI to explain complex concepts or generate ideas than to fact-check or write essays - suggesting exploratory, conceptually oriented use rather than shortcutting. Students’ perceptions reflected this pattern, with many reporting that GAI supported their understanding and offered more accessible help than a teacher. Frequent users were more likely to hold positive views of its educational value, suggesting that familiarity may shape perceptions of usefulness. A notable proportion even viewed chatbot support as comparable to teacher guidance. Gender-based attitudinal differences were also evident: male students were more likely to view GAI as beneficial and less likely to consider its use as cheating. These divides echo national trends and underscore the need for AI literacy initiatives that attend to both equity and confidence. These findings contribute to growing research on how GAI is shaping student behaviours, expectations, and epistemic dynamics. Students’ use aligns with Watson’s ( 2025 ) framing of GAI as a mediator of meaning, and with dialogic and sociocultural theories of learning (Wegerif & Major, 2025; Vygotsky, 1968). Many appeared to use GAI not simply to retrieve information, but to support thinking and construct understanding - treating it as a learning partner. Such patterns may reflect learning-oriented goals - an interpretive lens that positions student intentions as central to shaping the cognitive value of GAI use. Applying Ng et al.’s ( 2021 ) AI Literacy Framework, the study also identified important gaps in students’ understanding of GAI’s limitations, accuracy, and ethical implications. Addressing these gaps will require AI literacy education that promotes not only technical skill but also critical, reflective engagement. These findings offer a grounded response to the dual promise and concern raised in prior work (Zhu et al., 2023 ), where GAI is seen as both a support for and a potential threat to skill development and integrity. While based on a single site, this study offers an early snapshot of GAI use in Further Education. Longitudinal and mixed-methods research is needed to track change over time and explore more diverse contexts - particularly rural, socioeconomically disadvantaged, or less digitally mature settings, where access and support may differ. Nonetheless, the findings indicate that many students are already using GAI in meaningful educational ways. Building AI literacy is now essential to ensure that such use is informed, ethical, and effective. Rather than policing use, educators should focus on helping staff and students engage critically and constructively. GAI is already shaping how sixth formers learn; the challenge is whether education will prepare them to use it wisely. Declarations Declaration of generative AI and AI-assisted technologies in the writing process: During the preparation of this work the author used ChatGPT Plus in order to assist with formatting and language editing, including improving readability and suggesting reductions in word count where appropriate. After using this tool/service, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication. Funding: This research was supported by funding from Hughes Hall and the Cambridge Trust at the University of Cambridge. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Ethics approval statement : This study was reviewed and received ethical approval from the University of Cambridge. Open data availability statement: The data that support the findings of this study are not publicly available due to participant privacy and ethical considerations. Author Contribution M.E. is the sole author and was responsible for the study design, data collection and analysis, and manuscript preparation. References Almassaad, A., Alajlan, H., & Alebaikan, R. (2024). Student Perceptions of Generative Artificial Intelligence: Investigating Utilization, Benefits, and Challenges in Higher Education. Systems , 12 (10), 385. https://doi.org/10.3390/systems12100385 Abbas, M., Jam, F.A. & Khan, T.I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students . 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How to harness the potential of ChatGPT in education? (2023). Knowledge Management & E-Learning: An International Journal, 133–152. https://doi.org/10.34105/j.kmel.2023.15.008 Zou, M., & Huang, L. (2023). To use or not to use? Understanding doctoral students’ acceptance of ChatGPT in writing through technology acceptance model. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1259531 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7122270","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485792273,"identity":"8567783d-7f1f-4521-aa5e-e4c09c2fafea","order_by":0,"name":"Megan Ennion","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACdgbmBxIVcG4CEVqYGdgMLM6QqIVBorKNFC26zcwHDG7Os5PXbWB++IGxLY2wFrPDbAkPZ25LNtx2gM1YgrEthxgtPAbGktsOMG47wGDGwNhWQYwW/g/Sf+ccsN92gP0bsVp4GCQkGw4kbjvAA7KFKIexmRlIHEtO3naYp1gi4Rwx3j/e/PiBRI2d7bbj7Rs/fChLJqwFAYARRFREjoJRMApGwSggAgAATKs2xhc2+PoAAAAASUVORK5CYII=","orcid":"","institution":"University of Cambridge","correspondingAuthor":true,"prefix":"","firstName":"Megan","middleName":"","lastName":"Ennion","suffix":""}],"badges":[],"createdAt":"2025-07-14 14:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7122270/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7122270/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104961463,"identity":"6c850082-7b72-4422-b81c-249b5c541623","added_by":"auto","created_at":"2026-03-19 08:59:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1246914,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7122270/v1/55923db1-2398-40e7-9890-e9b23ac9e4cf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Generative AI in English Sixth Form Education: Student Use, Perceptions, and Literacy Gaps","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the release of ChatGPT in November 2022, generative artificial intelligence (GAI) technologies have drawn significant attention in education - offering immediate feedback, writing support, and concept clarification, while raising concerns around academic integrity, misinformation, skill development, and equity (Kasneci et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As educators and policymakers respond to these challenges, there is an urgent need for empirical research to inform responsible integration (Holmes, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mintz et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnderstanding how learners engage with GAI, perceive its impact, and identify their support needs is increasingly important (Thomson, Pickard-Jones, Baines, \u0026amp; Otermans, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While much of the emerging research has focused on university students, context significantly shapes GAI use, and comparatively little is known about pre-university learners (Whyte, Kirby, \u0026amp; Sentance, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The 16\u0026ndash;18 phase - typically situated within the UK\u0026rsquo;s Further Education (FE) sector - represents a notable gap.\u003c/p\u003e\u003cp\u003eThis study addresses that gap by investigating how sixth form students are encountering GAI in practice. As a key group within Further Education preparing for high-stakes qualifications, these students have more autonomy than younger pupils but may lack the self-regulatory maturity to evaluate new technologies critically. This makes them especially susceptible to both the appeal and the risks of GAI - drawn by its fluency, yet lacking the metacognitive tools to assess its educational value. Such susceptibility reflects known dynamics of cognitive offloading and automation bias, where fluent outputs can obscure underlying epistemic limitations (Gerlich, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Watson, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDrawing on survey data from 543 students at a large, urban, high-aspiring tech-friendly English college with institutional support for GAI, it explores patterns of use, perceived educational value, and AI literacy needs. The site was strategically selected for its wealthier, urban context, digital maturity, and high student aspirations - conditions found to support early uptake. As such, it offers a valuable opportunity to examine not just whether students are engaging with GAI, but how its use is unfolding - and what this reveals about its evolving role in Further Education.\u003c/p\u003e\u003cp\u003eTo conceptualise student use and needs, the study draws on a multidimensional theoretical framework: Wegerif and Major\u0026rsquo;s (2025) emphasis on dialogic interaction, Vygotsky\u0026rsquo;s (1968) theory of social scaffolding, Watson\u0026rsquo;s (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) notion of meaning mediation, and Ng et al.\u0026rsquo;s (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) AI Literacy Framework. These perspectives are complemented by the distinction between learning and performance goals (Dweck \u0026amp; Leggett, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), which informs the interpretation of how different patterns of use may reflect underlying intentions. Together, this framework offers a richer lens for interpreting the pedagogical opportunities, challenges, and implications of GAI use in Further Education.\u003c/p\u003e\u003cp\u003eAlthough situated in a UK sixth form context, the findings speak to wider international debates on how upper-secondary and FE learners engage with GAI. As access expands and institutional policies evolve, issues of digital confidence, ethical awareness, and educational purpose are likely to emerge across systems. This study contributes by examining the use and needs of students in Further Education as they prepare for higher education.\u003c/p\u003e"},{"header":"1. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.1. Student Use of Generative AI\u003c/h2\u003e\u003cp\u003eGenerative artificial intelligence (GAI) tools have rapidly entered mainstream education, offering opportunities such as immediate feedback, idea generation, and concept explanation, while raising concerns about academic integrity, misinformation, overreliance, and equity (Kasneci et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough research on student use of GAI is growing, it has predominantly focused on university populations. In the UK, Freeman (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) recently found that 92% of undergraduate students had used AI in some form, with 88% using generative AI specifically to support assessment tasks. They reported the most common uses were explaining concepts, summarising texts, and generating research ideas. In this survey, students cited time-saving and improved work quality as key motivations, while concerns centred on academic misconduct, misinformation, and bias. Notably, use patterns also varied by gender, subject, and socio-economic background, with male, wealthier, and STEM students reporting greater enthusiasm and confidence. Despite this widespread uptake, only 36% of students had received institutional support to develop AI skills.\u003c/p\u003e\u003cp\u003eThese findings reflect broader patterns reported internationally (e.g., Almassaad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chan \u0026amp; Hu, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), where use of ChatGPT is notable and students similarly value GAI\u0026rsquo;s efficiency and accessibility but express concern over ethical risks and limited policy guidance. Importantly, university studies also suggest a reciprocal relationship between use and perception, for example, Chan and Hu (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that students who used GAI more frequently were more willing to adopt it in future study and work.\u003c/p\u003e\u003cp\u003eTogether, these studies highlight the growing embeddedness of GAI in university learning environments. However, emerging evidence suggests that GAI use and needs may differ significantly across educational levels (Gerlich, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hart, 2023; Impact Research, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), underscoring the importance of investigating how distinct age groups and contexts shape engagement with these tools.\u003c/p\u003e\u003cp\u003eAmong pre-university learners, survey research from the UK and US often treats secondary-age students (11\u0026ndash;18) as a single group, masking potential variation. Reported usage remains inconsistent, with estimates ranging from 38\u0026ndash;67% (Internet Matters, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pearson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Schiel, Bobek, \u0026amp; Schnieders, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and some studies reporting rates as low as 8% (Michigan Virtual Learning Research Institute, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eChatGPT is the most widely used tool, with engagement shaped by several factors. As in university populations (Freeman, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), male students (Hart Research Associates, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Impact Research, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), students from wealthier backgrounds (Impact Research, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Internet Matters, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), high attainers (Schiel et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and those in urban areas (Hart Research Associates, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) are more likely to report regular use. However, few UK surveys disaggregate findings by these characteristics, limiting the ability to examine these patterns in detail.\u003c/p\u003e\u003cp\u003eThere is also evidence in this group of a reciprocal relationship between use and perception. Among secondary students, Michigan Virtual Learning Research Institute, (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found those who used GAI more frequently tended to view it more positively - and vice versa. This mirrors patterns in university contexts (Chan \u0026amp; Hu, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and aligns with the Technology Acceptance Model (Davis, Bagozzi, \u0026amp; Warshaw, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), which links perceived usefulness and ease of use to adoption behaviour (see also Zou \u0026amp; Huang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile surveys are beginning to capture how pre-university students use generative AI (GAI), most treat the 11\u0026ndash;18 age band as a single population. As a result, relatively little is known about the distinct experiences of students in Further Education (typically aged 16\u0026ndash;18). In the UK, one major strand of Further Education is the sixth form college system, which serves students undertaking advanced academic qualifications (typically A-levels) required for university entry. Sixth formers occupy a unique position: granted more autonomy than younger pupils, yet still within structured environments. This phase is characterised by high-stakes assessments, compressed timelines, and pressure to demonstrate university readiness - conditions that may intensify both the appeal and the risks of using GAI, particularly for students still developing metacognitive and self-regulatory skills.\u003c/p\u003e\u003cp\u003eHowever, despite these demands, research has yet to examine how sixth form students perceive the educational value of GAI or how well equipped they are to use it effectively. Understanding this requires not only empirical investigation, but also a conceptual framework for interpreting how students engage with GAI - not merely as a technical aid, but as a tool that may influence the depth, direction, and quality of their learning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.2. GAI and Learning\u003c/h2\u003e\u003cp\u003eA central question in evaluating GAI\u0026rsquo;s role in education is how it shapes student learning. While its appeal - through rapid feedback, personalised support, and conceptual clarification - is well documented (Kasneci et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), evidence on its actual impact remains mixed. Deng et al.\u0026rsquo;s (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) meta-analysis reported positive effects of GAI use on academic outcomes, yet Kreijkes et al. (2025) found that GAI-supported notetaking reduced retention and comprehension compared to traditional methods, despite students reporting enhanced subjective experiences. These discrepancies suggest that outcomes may depend less on the presence of GAI than on how it is used.\u003c/p\u003e\u003cp\u003eRecent research suggests that the benefits of GAI use depend on how - and why - students engage with it. Lehmann et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that positive outcomes were largely confined to students who engaged cognitively with GAI, highlighting the importance of intentional, effortful use. Wang and Fan (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) similarly found that structured, role-defined applications - particularly when GAI was framed as an intelligent tutor - enhanced learning outcomes, suggesting that goal-aligned use plays a critical role. In contrast, passive, performance-oriented use - such as generating essays - risks what Kosmyna et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, p. 141) term \u0026ldquo;cognitive debt,\u0026rdquo; where overreliance reduces neural activation and impairs recall. Gerlich (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Darvishi et al. (2025) likewise caution that cognitive effort may be displaced when students outsource thinking to GAI.\u003c/p\u003e\u003cp\u003eThese findings echo broader research on learning versus performance goals (Dweck \u0026amp; Leggett, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Elliot \u0026amp; McGregor, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), which shows that students\u0026rsquo; intentions shape behaviour and learning outcomes. Extending this insight to GAI, this study interprets patterns of use as behavioural indicators of students\u0026rsquo; underlying goals - whether to complete tasks or to support learning - which may influence the cognitive value of their engagement. From this perspective, it is not only the form of use, but the purpose it reflects that matters. GAI\u0026rsquo;s educational value is not intrinsic, but contingent on students\u0026rsquo; goals, use, and the quality of their engagement.\u003c/p\u003e\u003cp\u003eThis theoretical distinction highlights the importance of examining the forms that GAI use takes in educational settings and what intentions they reflect - particularly where academic autonomy is high but metacognitive and self-regulatory skills are still developing. Behaviours like generating assignments or seeking clarification may reflect different learning orientations. Such inquiry is essential for understanding whether GAI is being used to support or substitute for learning, and for informing the design of AI literacy interventions that promote critical, reflective, and educationally purposeful engagement.\u003c/p\u003e\u003cp\u003eTo guide this investigation, the study draws on three complementary perspectives that help conceptualise what it means to learn \u003cem\u003ewith\u003c/em\u003e GAI. Vygotsky\u0026rsquo;s (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) theory of social learning emphasises the role of social scaffolding - support provided through interaction with a more capable other - as a mechanism for extending understanding beyond what the learner can achieve independently. This is of particular relevance to GAI tools, which often present themselves as ever-available \u0026ldquo;more capable others,\u0026rdquo; offering tailored explanations, examples, and guidance in response to student queries.\u003c/p\u003e\u003cp\u003eWegerif and Major\u0026rsquo;s (2025) theory of dialogic learning further highlights the importance of learning as a process of meaning-making through dialogue. Rather than simply receiving information, students learn by engaging in conversations that challenge assumptions, prompt reflection, and open new conceptual spaces. Generative AI systems - particularly large language model chatbots - are explicitly designed to simulate such dialogic exchanges, making this theoretical lens especially useful for examining how students engage with AI in educational contexts.\u003c/p\u003e\u003cp\u003eWatson\u0026rsquo;s (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) framing of GAI as a mediator of meaning builds on these perspectives by drawing attention to the fact that AI systems do not merely transmit knowledge, but actively shape how knowledge is presented, interpreted, and internalised. This raises important questions not only about \u003cem\u003ewhat\u003c/em\u003e students learn from GAI, but \u003cem\u003ehow\u003c/em\u003e their habits of thought are shaped by the tools themselves. Understanding how students experience and respond to GAI is therefore central to assessing its educational potential, risks, and support needs.\u003c/p\u003e\u003cp\u003eTogether, these frameworks reframe GAI not as a passive provider of answers, but as an interactive, responsive, and epistemically influential participant in the learning process. From this view, its educational value lies not in automation, but in supporting conceptual development through interaction.\u003c/p\u003e\u003cp\u003eAligned with this framing, the study draws on Marton and S\u0026auml;lj\u0026ouml;\u0026rsquo;s (1976) distinction between surface and deep learning - where surface learning is oriented toward memorisation and task completion, while deep learning involves conceptual understanding and critical engagement. In the context of GAI, this distinction helps illuminate whether students are using the technology with the intention of shortcutting tasks or supporting thinking. We use student perceptions as proxies for deeper engagement, examining whether they believe GAI enhances understanding, whether they use it to explain complex concepts, and whether they perceive it as offering teacher-comparable support - particularly where such support reflects scaffolded, dialogic learning. These usage patterns are read as indicative of underlying learning goals (Dweck \u0026amp; Leggett, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Elliot \u0026amp; McGregor, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), which, alongside perceived depth, help interpret how students are engaging with GAI.\u003c/p\u003e\u003cp\u003eImportantly, the depth of engagement is also shaped students\u0026rsquo; capacity to evaluate, interpret, and manage it effectively. This brings the concept of AI literacy into focus. As Pratschke (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Watson (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) argue, enabling students to use GAI in ways that are pedagogically beneficial requires more than basic technical skill. Ng et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) offer a multidimensional framework comprising: (1) Know and Understand - grasping core concepts and recognising capabilities and limits; (2) Use and Apply - deploying GAI tools in academic tasks; (3) Evaluate and Create - critically assessing and refining outputs; and (4) Ethical Issues - addressing fairness, bias, and broader implications. Together, these dimensions frame AI literacy not as a checklist of competencies, but as a set of dispositions and evaluative capacities that shape \u003cem\u003ehow\u003c/em\u003e students approach, interpret, and integrate GAI into their learning. In this sense, literacy and learning are inseparable: students' ability to make meaning with GAI depends not only on the tool itself, but on their capacity to engage critically, reflectively, and strategically with its affordances.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Context\u003c/h2\u003e\u003cp\u003eThis study addresses a key gap through a cross-sectional survey examining the uses, attitudes, and support needs related to GAI among sixth form students (aged 16\u0026ndash;18) in a high-performing, tech-friendly English college - a large, urban, non-fee-paying institution enrolling approximately 3,000 full-time A-Level students.\u003c/p\u003e\u003cp\u003eThis tech-friendly college promotes technology integration through a Bring Your Own Device policy, access to Microsoft Copilot for students and staff, and customised GAI chatbots for selected courses. It also employs dedicated staff to support digital initiatives. Institutional guidelines allow AI use in schoolwork, and teachers are encouraged to promote its responsible application.\u003c/p\u003e\u003cp\u003eWith a student body largely drawn from financially comfortable, professionally employed households, this urban and academically aspirational college represents a context in which both access to and attitudes toward GAI may be shaped by advantage. Research suggests wealthier, high-attaining, urban students are more likely to use GAI tools (Freeman, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hart Research Associates, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Internet Matters, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Impact Research, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Schiel, Bobek, \u0026amp; Schnieders, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Importantly, this is also an environment where GAI is not only permitted, but actively supported through staff expertise, institutional guidance, and platform access. As such, it was strategically selected as a likely site of early adoption - providing an opportunity to examine not just how students are using GAI, but what educational, ethical, and cognitive implications may arise in a setting where engagement is institutionally encouraged and where usage is likely to be higher due to the contextual factors outlined above.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Participants\u003c/h2\u003e\u003cp\u003eA total of 543 students responded anonymously: 277 in Year 12, 258 in Year 13, and 8 unspecified. In terms of gender, 268 identified as female, 227 as male, 24 as non-binary or other, and 23 preferred not to say.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Materials\u003c/h2\u003e\u003cp\u003eThe questionnaire, developed collaboratively with college staff, featured multiple-choice and Likert-scale questions assessing GAI tools used, frequency and purpose of use, attitudes toward GAI, and interest in GAI-related training. Administered anonymously online via Qualtrics, it collected only year group and gender demographics. To explore students\u0026rsquo; perceptions of educational benefit, the questionnaire distinguished between perceived impacts on understanding (as a proxy for deeper, conceptual learning, drawing on Wegerif and Major (2025)) and perceptions of ease and time-saving efficiency. To assess social learning impacts (informed by Vygotskian theory), students compared GAI support with human teacher assistance in terms of speed and ease of access and perceived equivalence. Drawing on Watson\u0026rsquo;s (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) framing of GAI as a mediator of meaning and Dweck and Leggett\u0026rsquo;s (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) distinction between learning and performance goals, the study also examined how students used, and what this revealed about both their underlying intentions and their understanding of its functions and educational value. Finally, AI literacy needs were assessed by presenting training options aligned with Ng et al.\u0026rsquo;s (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) AI Literacy Framework, identifying which dimensions of AI literacy were recognised and where gaps remained.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Procedure\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cp\u003efor this study was obtained from the university conducting the survey. Guardians were notified in advance and given the option to opt their young person out. Informed consent was implied from respondents through voluntary completion of the survey. Those whose parent or guardian opted them out did not receive an invitation to take part or a survey link.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eParticipation was voluntary and open to all full-time students aged 16\u0026ndash;18. The response rate was approximately 18% of the college\u0026rsquo;s 3,000 students. Students were invited via email, with participation encouraged by staff. No incentives were offered. Anonymity was preserved through unlinked survey responses, participation was voluntary with no penalties for non-response, and data were stored securely, aligning with safeguarding protocols for research involving minors.\u003c/p\u003e\u003cp\u003eData was collected between January and March 2025 via the anonymous Qualtrics survey.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Data analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics summarised GAI tool use, purposes, attitudes, and training interest. Chi-square tests explored associations between gender, usage frequency, and attitudes, with male and female categories used for clearer gender analysis. Cram\u0026eacute;r\u0026rsquo;s V assessed effect sizes (0.1\u0026thinsp;=\u0026thinsp;small, 0.3\u0026thinsp;=\u0026thinsp;medium, 0.5\u0026thinsp;=\u0026thinsp;large). Analyses were conducted using Qualtrics' StatsiQ platform, appropriate for the categorical data and exploratory aims of the study. Although logistic regression could offer further insights, modest subgroup sizes limited statistical power and stability. As multivariate controls were not applied, associations should be interpreted as indicative rather than explanatory. Future research with larger samples could explore predictive relationships through multivariate modelling.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Findings","content":"\u003cp\u003eFindings from the survey are presented below, using descriptive and inferential statistics.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Descriptive Statistics\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1. AI Tool Use\u003c/h2\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\u003e\u003cem\u003eFrequency of GAI Tool Use for Schoolwork Among Students (N\u0026thinsp;=\u0026thinsp;542)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery often\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOften\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRarely\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30%\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\u003e\u003cem\u003eAI Tools Used by Students to Support Schoolwork (N\u0026thinsp;=\u0026thinsp;543)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI Tool\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e% of Respondents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber (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\u003eChatGPT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.14%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e332\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI don\u0026rsquo;t use any GAI tools for schoolwork\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.54%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCopilot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.12%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoe.com Bot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.89%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrammarly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.68%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuillbot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClaude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.41%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther (please specify)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.41%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMidjourney\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.31%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBing GAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.13%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoogle Gemini (formerly Bard)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDALL\u0026middot;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\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=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eSelf-Reported Frequency of GAI Tool Use by Gender\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRarely\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOften\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery often\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\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=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eFrequency of GAI Tool Use by Academic Task (N\u0026thinsp;=\u0026thinsp;543)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademic Task\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRarely (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSometimes (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOften (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVery Often (%)\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\u003eWriting essays or reports\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72\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\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGenerating ideas or brainstorming\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFact-checking or research\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51\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\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSolving maths problems\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExplaining complex concepts\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\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=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2. AI Attitudes\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eStudent Agreement with GAI-Related Attitudinal Statements (N\u0026thinsp;=\u0026thinsp;543)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrongly Disagree (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDisagree (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNeither Agree Nor Disagree (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAgree (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrongly Agree (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUsing GAI tools for schoolwork is cheating.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAI tools help you understand subjects better.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\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\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAI tools save time when doing schoolwork.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\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\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eThe information from GAI tools is often inaccurate.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\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\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUsing GAI tools will better prepare me for my future career.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eI believe that using a chatbot to help with schoolwork can be as good as getting guidance from a teacher.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIf I am struggling with something in my schoolwork, I sometimes find it easier or quicker to first ask a chatbot, like Copilot or ChatGPT, than a teacher.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\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\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19\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=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3. AI Training Interest\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eStudent Interest in GAI Tools, Ethics, and Educational Support (N\u0026thinsp;=\u0026thinsp;543)\u003c/em\u003e\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery Interested (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerately Interested (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot at All Interested (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLearning about different GAI tools and what they do\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14% (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54% (n\u0026thinsp;=\u0026thinsp;293)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32% (n\u0026thinsp;=\u0026thinsp;176)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderstanding the ethical implications of using GAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35% (n\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41% (n\u0026thinsp;=\u0026thinsp;222)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24% (n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraining on how to use GAI tools for schoolwork in different subjects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26% (n\u0026thinsp;=\u0026thinsp;139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40% (n\u0026thinsp;=\u0026thinsp;216)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35% (n\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuidelines from teachers on acceptable GAI use in assignments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30% (n\u0026thinsp;=\u0026thinsp;160)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46% (n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24% (n\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLearning how to assess whether GAI-generated information is true and think critically about it\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45% (n\u0026thinsp;=\u0026thinsp;243)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37% (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18% (n\u0026thinsp;=\u0026thinsp;99)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool assignments where GAI is used to build skills and familiarity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15% (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32% (n\u0026thinsp;=\u0026thinsp;173)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53% (n\u0026thinsp;=\u0026thinsp;288)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHands-on sessions exploring creative and fun projects using GAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16% (n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32% (n\u0026thinsp;=\u0026thinsp;172)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53% (n\u0026thinsp;=\u0026thinsp;285)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscussions about the impact of GAI on future jobs and industries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38% (n\u0026thinsp;=\u0026thinsp;207)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41% (n\u0026thinsp;=\u0026thinsp;224)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20% (n\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeveloping skills to work alongside GAI in future careers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26% (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47% (n\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27% (n\u0026thinsp;=\u0026thinsp;145)\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\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Statistical Analyses \u0026ndash; Gender and Frequency\u003c/h2\u003e\u003cp\u003eChi-square analyses were conducted to examine associations between students\u0026rsquo; gender, year group, and frequency of GAI use and their attitudes toward GAI in education. Effect sizes were interpreted using Cram\u0026eacute;r\u0026rsquo;s V, where values around .10 indicate a small effect, .30 a medium effect, and .50 a large effect.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Gender and Attitudes Toward GAI\u003c/h2\u003e\u003cp\u003eThere was no statistically significant relationship between gender and frequency of GAI use, χ\u0026sup2;(4, N\u0026thinsp;=\u0026thinsp;494)\u0026thinsp;=\u0026thinsp;4.60, p\u0026thinsp;=\u0026thinsp;.33, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.10, suggesting that male and female students in this context used it in almost equal amounts (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThere was however a statistically significant gender difference was observed in students\u0026rsquo; belief that GAI will support their future careers, χ\u0026sup2;(4, N\u0026thinsp;=\u0026thinsp;495)\u0026thinsp;=\u0026thinsp;23.40, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.217, with a small-to-medium effect size. Male students were more likely to view GAI as professionally advantageous (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe largest gender-based difference concerned the belief that using GAI constitutes cheating, χ\u0026sup2;(4, N\u0026thinsp;=\u0026thinsp;495)\u0026thinsp;=\u0026thinsp;27.20, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.235, also a small-to-medium effect. Male students were considerably less likely than females to view the use of GAI as academically dishonest (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. Frequency of GAI Use and Attitudes Toward GAI\u003c/h2\u003e\u003cp\u003eFurther analyses explored the relationship between how frequently students used GAI tools and their perceptions of GAI\u0026rsquo;s educational value.\u003c/p\u003e\u003cp\u003eA particularly strong association was observed between GAI use frequency and preference for GAI over teacher guidance, χ\u0026sup2;(16, N\u0026thinsp;=\u0026thinsp;542)\u0026thinsp;=\u0026thinsp;320.00, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.384, indicating a medium-to-large effect. Students who used GAI most frequently were substantially more likely to prefer receiving help from a chatbot rather than from a teacher (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrequency of use was also significantly associated with agreement that GAI helps with understanding, χ\u0026sup2;(16, N\u0026thinsp;=\u0026thinsp;542)\u0026thinsp;=\u0026thinsp;242.00, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.334; that GAI is as good as teacher guidance, χ\u0026sup2;(16, N\u0026thinsp;=\u0026thinsp;542)\u0026thinsp;=\u0026thinsp;195.00, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.300; and that GAI will benefit future careers, χ\u0026sup2;(16, N\u0026thinsp;=\u0026thinsp;542)\u0026thinsp;=\u0026thinsp;161.00, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.273. These represent small-to-medium to medium effect sizes. Frequent users consistently expressed stronger confidence in GAI\u0026rsquo;s educational and professional value (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA further association was found between GAI use and the belief that using GAI is cheating, χ\u0026sup2;(16, N\u0026thinsp;=\u0026thinsp;542)\u0026thinsp;=\u0026thinsp;175.00, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.284, a small-to-medium effect. Frequent users were less likely to view GAI use as academically dishonest, while infrequent users were more likely to hold this belief (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFinally, frequency of GAI use was significantly associated with agreement that GAI tools save time, χ\u0026sup2;(16, N\u0026thinsp;=\u0026thinsp;542)\u0026thinsp;=\u0026thinsp;151.00, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.264, and with perceptions of GAI inaccuracy, χ\u0026sup2;(16, N\u0026thinsp;=\u0026thinsp;542)\u0026thinsp;=\u0026thinsp;119.00, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.234. Both represent small-to-medium effect sizes. Students who used GAI more frequently were more likely to believe that it saves time and less likely to view it as inaccurate (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eChi-Square Tests of Association Between Gender and GAI Attitudes\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;(df)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCram\u0026eacute;r\u0026rsquo;s V\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender \u0026times; GAI helps career\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;(4)\u0026thinsp;=\u0026thinsp;23.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e495\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender \u0026times; GAI use\u0026thinsp;=\u0026thinsp;cheating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;(4)\u0026thinsp;=\u0026thinsp;27.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e495\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eChi-Square Tests of Association Between Frequency of GAI Use and GAI Attitudes\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;(df)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCram\u0026eacute;r\u0026rsquo;s V\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency \u0026times; Prefer GAI to teacher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;(16)\u0026thinsp;=\u0026thinsp;320.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency \u0026times; GAI helps understanding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;(16)\u0026thinsp;=\u0026thinsp;242.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency \u0026times; GAI\u0026thinsp;=\u0026thinsp;Teacher guidance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;(16)\u0026thinsp;=\u0026thinsp;195.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency \u0026times; GAI use\u0026thinsp;=\u0026thinsp;cheating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;(16)\u0026thinsp;=\u0026thinsp;175.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency \u0026times; GAI helps career\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;(16)\u0026thinsp;=\u0026thinsp;161.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency \u0026times; GAI saves time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;(16)\u0026thinsp;=\u0026thinsp;151.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency \u0026times; GAI is inaccurate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;(16)\u0026thinsp;=\u0026thinsp;119.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis section interprets the findings in relation to patterns of GAI use, learning behaviours, and literacy needs, drawing on the theoretical frameworks outlined earlier. It first examines how students are using GAI, then considers their perceptions of its learning value, before addressing emerging literacy gaps, attitudinal differences, and contextual implications.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.1. How Much GAI Is Being Used\u003c/h2\u003e\u003cp\u003eIn this study, 44% of students reported using GAI tools for schoolwork at least sometimes, while 27\u0026ndash;29% reported never using GAI (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). ChatGPT was the most commonly used tool, with 61.14% of those who had used GAI indicating they had used it to support their learning (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings suggest substantial, though not universal, GAI uptake among sixth form students. They broadly align with recent UK and US surveys of secondary-age students reporting usage rates between 38% and 67% (Impact Research, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pearson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Schiel, Bobek, \u0026amp; Schnieders, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and sit well above the 8% figure reported by Michigan Virtual Learning Research Institute (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which focused on online learning environments.\u003c/p\u003e\u003cp\u003eFindings contrast with UK university contexts, where uptake appears near-universal (Freeman, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This likely reflects both institutional and developmental factors: sixth form students remain in more structured environments, governed by standardised curricula and high-stakes assessments. They are also younger, with less autonomy and fewer opportunities to independently decide how digital tools are used. It may also indicate that students in Further Education are still negotiating the boundaries of acceptable use - particularly in settings where GAI is permitted but not yet fully normalised. These findings underscore the need for institutional readiness and pedagogical support at the Further Education stage, where students are already engaging with GAI. This appears as a critical window for developing students\u0026rsquo; capacity to use these tools wisely before use intensifies in higher education.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Student Use of GAI for Exploratory and Conceptual Engagement\u003c/h2\u003e\u003cp\u003eFindings indicate that many students in this context are using GAI in ways that reflect exploratory learning and conceptual engagement, rather than simply as a shortcut to completed work. Contrary to common concerns about academic dishonesty or dependency, patterns of use suggest that students were more likely to draw on GAI to support their thinking than to bypass it.\u003c/p\u003e\u003cp\u003eFor example, 72% of students reported never using GAI to write essays, and 57% said they never used it to solve maths problems. In contrast, 45% reported using GAI to explain complex concepts at least sometimes, and 44% used it to generate ideas. These activities suggest that students are engaging with GAI primarily as a tool for clarifying and expanding understanding, rather than for completing tasks.\u003c/p\u003e\u003cp\u003eIn contrast to more superficial uses associated with less productive learning (Kosmyna et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kreijkes et al., 2025), many students appear to be using GAI to support deeper learning (Marton \u0026amp; S\u0026auml;lj\u0026ouml;, 1976). This distinction is significant given concerns that passive or outcome-driven use can lead to cognitive offloading, reduced neural activation, and weaker recall (Kosmyna et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lehmann et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gerlich, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As shown in studies such as Lehmann et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the type of engagement observed in this study - characterised by cognitive effort and exploratory behaviour - is more likely to produce meaningful learning outcomes. This is further supported by the fact that these uses may reflect learning-oriented goals, where theories suggest that students\u0026rsquo; underlying intentions - whether to learn or simply complete tasks - influence the cognitive value of their engagement (Dweck \u0026amp; Leggett, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Such uses align with Watson\u0026rsquo;s (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) framing of GAI as a mediator of meaning, and with Wegerif and Major\u0026rsquo;s (2025) emphasis on dialogue as a process of conceptual development. Rather than simply extracting answers, students appear to be using GAI as a responsive interlocutor - a means of exploring ideas and co-constructing understanding.\u003c/p\u003e\u003cp\u003eThese findings also align with prior research suggesting that students in wealthier, urban, high-attaining, digitally mature settings often use GAI confidently and purposefully (Freeman, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Almassaad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Impact Research, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). That sixth form students report comparable patterns to university students suggests that GAI\u0026rsquo;s dialogic and exploratory appeal may cut across educational levels. Results challenge dominant narratives of misuse and point to its pedagogical potential in Further Education - particularly when students are supported to use it reflectively and critically.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Student Use of GAI as a Social Learning Partner\u003c/h2\u003e\u003cp\u003eSurvey responses suggest that students in this context are not just using GAI tools for schoolwork, but are also beginning to position them as credible sources of academic support. While many reported efficiency gains \u0026minus;\u0026thinsp;72% said GAI saved time on schoolwork, and 56% found it quicker or easier to consult a chatbot than a teacher - the more notable finding was the perceived cognitive value. A substantial majority (69%) agreed that GAI helped them better understand subjects, and nearly a quarter (24%) felt that chatbot support could match teacher guidance. These perceptions suggest that students may be beginning to view GAI as a meaningful learning partner.\u003c/p\u003e\u003cp\u003eThey reflect more than a pragmatic interest in saving time, pointing instead to a growing perception of GAI as a socially responsive, low-barrier source of explanation, clarification, and feedback and reflect learning-oriented rather than task-driven intentions (Dweck \u0026amp; Leggett, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Lehmann et al., 2023; Wang \u0026amp; Fan, 2024).\u003c/p\u003e\u003cp\u003eSuch interactions, where the GAI becomes an intelligent partner, may equate to the kind of cognitively engaged use that facilitates deeper learning and contributes to more productive educational outcomes (Lehmann et al., 2023; Wang \u0026amp; Fan, 2024). This aligns with recent work highlighting the dialogic potential of GAI systems (Ennion \u0026amp; McLellan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gupta \u0026amp; Chen, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kasneci et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and resonates with Vygotsky\u0026rsquo;s (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) notion of the zone of proximal development, where learners benefit from scaffolded interaction with more capable others. In this context, GAI functions not simply as an informational resource but as a simulated interlocutor operating within the learner\u0026rsquo;s proximal zone. Wegerif and Major\u0026rsquo;s (2025) concept of \u0026ldquo;dialogic space\u0026rdquo; further helps explain how students may be experiencing GAI use as a form of mediated conceptual exploration - especially when the tool prompts reflection or generates new frames of reference. Likewise, Watson\u0026rsquo;s (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) characterisation of GAI as a mediator of meaning positions these systems as active participants in shaping how learners interpret, engage with, and internalise knowledge.\u003c/p\u003e\u003cp\u003eThese sixth form perceptions mirror those reported in university contexts. For example, Almassaad et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Chan and Hu (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) similarly documented students citing conceptual clarification and idea generation as primary benefits. That sixth form students report similar views suggests that the perceived value of GAI as a learning partner may not be limited to higher education, but is already shaping attitudes in earlier phases.\u003c/p\u003e\u003cp\u003eWhile perceived value does not guarantee learning gains - Kreijkes et al. (2025) caution that students may feel supported by GAI without achieving deeper understanding - findings from this digitally mature, high-attaining context suggest that students increasingly view GAI as a partner for understanding rather than a shortcut or substitute for effort. This is particularly notable given their transitional stage. It suggests a readiness to engage meaningfully with GAI - provided students have the skills to do so effectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.4. The Need for AI Literacy: Knowing and Understanding AI Limitations\u003c/h2\u003e\u003cp\u003eThe findings reveal notable gaps in students\u0026rsquo; understanding of GAI limitations. Despite well-documented risks around hallucinations (Khurma et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), under half of students (49%) acknowledged that GAI content is often inaccurate, and a substantial minority (21%) believed it to be accurate (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These perceptions are especially concerning given usage patterns: 35% reported using GAI for fact-checking at least sometimes, and 28% used it occasionally to solve maths problems (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This suggests that, despite generally constructive uses, many students are relying on GAI in contexts where it is fundamentally ill-suited.\u003c/p\u003e\u003cp\u003eSuch behaviour points to a foundational gap in AI literacy - particularly within the \u0026ldquo;Know and Understand\u0026rdquo; domain of Ng et al.\u0026rsquo;s (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) framework, which emphasises conceptual awareness of how AI systems function, what their outputs represent, and where their limits lie. Students may trust fluent responses without recognising their potential to mislead.\u003c/p\u003e\u003cp\u003eThis misunderstanding carries real educational risks. As Holmes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) cautions, educational technologies can produce unintended consequences - particularly when adoption outpaces critical pedagogy. GAI systems generate language based on probabilistic patterns rather than verified fact and can produce false or fabricated content with a high degree of stylistic coherence. As Khurma et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) argue, the danger in educational contexts lies in the illusion of authority: ChatGPT\u0026rsquo;s confident, fluent style can obscure its lack of epistemic grounding, leading students to accept misleading outputs as credible. Students who rely on GAI for fact-checking or problem-solving without understanding its generative nature may internalise inaccuracies, ultimately undermining learning outcomes (Sun et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This not only risks the spread of misinformation, but may also inhibit the development of independent critical thinking if students are not equipped to evaluate AI-generated responses.\u003c/p\u003e\u003cp\u003eAs UNESCO highlights, effective AI literacy begins with conceptual understanding of how generative systems produce content - and why their outputs require critical interpretation (Miao et al., 2024). Watson\u0026rsquo;s (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) framing of GAI as a mediator of meaning reinforces this: students are not merely retrieving information, but shaping understanding in dialogue with a system whose strength lies not in conveying truth, but in producing plausible, context-sensitive responses. Without understanding, learners may conflate coherence with credibility. Yet when students recognise that GAI mediates meaning rather than delivers fact, they are better positioned to use it reflectively and productively. Findings from this study reinforce the urgent need to embed AI literacy into Further Education curricula - not only to build technical competence, but to equip students with the conceptual understanding needed to engage with generative systems in educationally productive ways.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.5. The Need for AI Literacy: Critical and Ethical Reflection\u003c/h2\u003e\u003cp\u003eIn addition to gaps in technical understanding, the findings point to a broader need for structured AI education that supports students\u0026rsquo; ethical reasoning and critical reflection. Despite the college\u0026rsquo;s tech-friendly and relatively permissive environment, uncertainty and disagreement were evident in students\u0026rsquo; views about the legitimacy of GAI use. While 41% disagreed that using GAI constituted academic dishonesty, 29% agreed (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This split suggests the absence of a shared normative framework - even in digitally mature settings - around what counts as appropriate or responsible use.\u003c/p\u003e\u003cp\u003eIn the context of a school-based survey, this variation may reflect more than personal opinion; students may have interpreted the question about cheating in light of institutional norms rather than individual beliefs - highlighting a lack of policy clarity and the need for transparent guidance around acceptable use. Alternatively, it may indicate genuine ethical disagreement among students about GAI\u0026rsquo;s role in learning. In either case, the findings point to the importance of creating space for critical dialogue - enabling students not just to follow rules, but to reason through complex issues and form reflective judgments.\u003c/p\u003e\u003cp\u003eStudent training preferences support this interpretation. When asked about learning needs, students expressed strong interest in developing evaluative and ethical competencies. A large majority wanted to learn how to assess the accuracy of GAI outputs (82%), understand its societal impacts (79%), and build skills to work with GAI in future careers (73%) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). By contrast, there was notably less enthusiasm for hands-on training in using GAI to complete assignments or creative tasks. Students appear less focused on functionality than on understanding GAI\u0026rsquo;s broader implications.\u003c/p\u003e\u003cp\u003eFreeman (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) similarly notes that institutional support for critical AI engagement has lagged behind student uptake. These findings map closely onto Ng et al.\u0026rsquo;s (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) AI Literacy Framework, particularly the \u0026ldquo;Evaluate and Create\u0026rdquo; and \u0026ldquo;Ethical Issues\u0026rdquo; domains, which emphasise the importance of reasoning about fairness, boundaries, and social impact. They also reflect Watson\u0026rsquo;s (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) argument that ethical GAI use requires understanding how AI shapes meaning and mediates social norms, and align with UNESCO\u0026rsquo;s call for AI literacy that includes ethical, societal, and cognitive dimensions (Miao et al., 2021).\u003c/p\u003e\u003cp\u003eTaken together, these findings point to a need for AI literacy in Further Education that goes to ethical engagement and critical reasoning. As UNESCO (Miao et al., 2021) emphasises, effective AI education must address ethical, cognitive, and societal dimensions - not just technical skills.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.6. Patterns in GAI Perceptions\u003c/h2\u003e\u003cp\u003eAnalysis of the survey data revealed clear patterns in student perceptions of GAI, particularly in relation to gender and frequency of use. These patterns are important for understanding how individual and contextual factors shape students\u0026rsquo; attitudes toward emerging educational technologies - and for designing AI literacy interventions that address disparities in confidence, engagement, and access.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGender Differences in Perceptions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlthough usage rates did not differ significantly by gender (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), pronounced attitudinal differences emerged. Male students were significantly more likely than female students to view GAI as valuable for career development (p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.217) and less likely to consider its use a form of academic dishonesty (p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.235) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These findings align with prior research reporting gender differences in technology adoption that favour males. For instance, Freeman (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that male university students in the UK expressed greater enthusiasm for GAI and were less deterred by concerns around academic misconduct or misinformation. Similarly, Hart Research Associates (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Impact Research (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported greater digital confidence and AI-related self-efficacy among male students in secondary education.\u003c/p\u003e\u003cp\u003eWhile this study did not detect gender-based differences in GAI usage frequency, the persistence of attitudinal differences across contexts points to deeper social and psychological dynamics - particularly around digital confidence, perceived legitimacy, and alignment with dominant tech cultures. As GAI becomes further embedded in academic and professional domains, such divides may compound, shaping students\u0026rsquo; readiness, comfort, and opportunities for engagement. Addressing these gaps will require more than equitable access to tools; it calls for targeted, inclusive AI literacy initiatives that build digital self-trust and challenge gendered norms around technology use. Without this, underrepresented groups may be less likely to use GAI confidently or critically, further entrenching existing disparities in educational and career trajectories.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFrequency of Use and Perceptions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA consistent and statistically significant pattern emerged between students\u0026rsquo; frequency of GAI use and their attitudes toward its value (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). More frequent users expressed markedly more positive perceptions across multiple domains - including beliefs that GAI supports understanding, saves time, enhances future career prospects, and provides useful learning support.\u003c/p\u003e\u003cp\u003eFor example, only 38% of non-users agreed that GAI helped them better understand subjects, compared to 97% of very frequent users. Similarly, agreement that chatbots were easier to consult than teachers ranged from 11% among non-users to 94% among very frequent users. Confidence in the quality of GAI support also followed this gradient: just 12% of non-users believed chatbot support could rival teacher guidance, compared to 57% of very frequent users. All associations were statistically significant (Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;.264 to .384).\u003c/p\u003e\u003cp\u003eWhile the cross-sectional design precludes causal inference, these findings support prior research suggesting a reciprocal relationship between GAI use and positive perceptions. Students who use GAI more often may develop greater familiarity and confidence, which in turn increases their willingness to engage further. This aligns with the Technology Acceptance Model (Davis, Bagozzi, \u0026amp; Warshaw, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Zou \u0026amp; Huang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which posits that perceived usefulness and ease of use drive technology adoption and are reinforced through user experience. Chan and Hu (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) also report that frequent users are more likely to adopt GAI in future academic and professional settings. Parallel findings from Michigan Virtual Learning Research Institute (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) among secondary students suggest that this feedback loop may be robust across age groups and institutional contexts.\u003c/p\u003e\u003cp\u003eThese results highlight the role of exposure in shaping attitudes: students who use GAI more frequently tend to report stronger beliefs in its usefulness, efficiency, and learning value. While the data do not permit causal inference or speak to critical awareness, frequent users appear more able to access its educational potential. This underscores the importance of ensuring that all students have equitable opportunities to engage meaningfully with GAI tools, supported by appropriate guidance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.7. Contextual Factors\u003c/h2\u003e\u003cp\u003eThese findings emerge from a large, urban, tech-friendly sixth form college - a site selected for its distinctive characteristics. Rather than treating context as a limitation, this study focused intentionally on a setting where GAI adoption was likely to be relatively advanced. Prior research (Hart Research Associates, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Internet Matters, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Impact Research, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Schiel, Bobek, \u0026amp; Schnieders, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) suggests that students in urban, high-attaining, and advantaged settings are more likely to engage with GAI. These studies highlight how access, aspiration, and infrastructure shape adoption.\u003c/p\u003e\u003cp\u003eStudying GAI in a digitally mature, high-aspiration context allowed the research to move beyond access to examine how students are using GAI, navigating its limitations, and forming judgments about its role in learning. In this sense, the setting offers a window into a potential \u0026ldquo;next phase\u0026rdquo; of integration - where use is already normalised and questions of depth, discernment, and pedagogical value become more salient.\u003c/p\u003e\u003cp\u003eSeveral results align with national surveys of secondary students (e.g., usage rates, perceived benefits), but others diverge. For instance, this study found no gender difference in GAI usage frequency, contrasting with prior research that reports higher male uptake. This suggests the environment may help mitigate - but not eliminate - gender disparities. The persistence of gender-based differences in perceived value and legitimacy reinforces the point that access alone does not ensure equitable engagement.\u003c/p\u003e\u003cp\u003eMore broadly, the findings suggest context shapes not only access, but also how students use and learn from GAI. In well-supported environments, learners may be more inclined to explore GAI's dialogic and conceptual affordances. In contrast, students in under-resourced or policy-restrictive settings may face limited opportunities and greater risks. Infrastructure, institutional guidance, policy clarity, and socio-economic background all likely shape the trajectory of GAI use and its educational outcomes.\u003c/p\u003e\u003cp\u003eFuture research should examine these dynamics across more varied Further Education contexts to determine which patterns observed here are replicable - and which are unique to early-adopting settings. Understanding how GAI\u0026rsquo;s pedagogical potential is shaped by local conditions will be key to supporting equitable and effective integration.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Limitations and Future Research Directions","content":"\u003cp\u003eThis study offers insight into sixth formers\u0026rsquo; GAI engagement in a digitally supportive setting, but has several limitations.\u003c/p\u003e\u003cp\u003eFirst, the cross-sectional design captures a single moment in time and cannot establish causality. As GAI evolves rapidly (Impact Research, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), patterns of use and perceptions are likely to shift. Longitudinal research is needed to track these changes in response to technological, institutional, and social developments.\u003c/p\u003e\u003cp\u003eSecond, the study was conducted in a single, urban, high-aspiration sixth form college. While this limits generalisability, it is also a strategic strength as the context enabled exploration not just of adoption, but of the ethical and educational dimensions of GAI use. However, students in less resourced, more restrictive, or rural settings may experience different opportunities and barriers. Prior research suggests digital engagement often correlates with affluence, aspiration, and institutional support (Impact Research, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Internet Matters, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), underscoring the need for comparative work across diverse educational contexts.\u003c/p\u003e\u003cp\u003eThird, the exclusive use of quantitative survey data limits insight into students\u0026rsquo; motivations, reasoning, and situated experiences. Qualitative methods - such as interviews or focus groups - could offer a deeper understanding of how students interpret GAI\u0026rsquo;s role in learning.\u003c/p\u003e\u003cp\u003eFinally, the voluntary nature of participation introduces possible self-selection bias. Students more positively disposed toward GAI may have been overrepresented. Additionally, as the survey was administered by the college, responses may have been influenced by social desirability, particularly in a setting where GAI is institutionally supported. Future studies should aim to mitigate this through independent administration and anonymised protocols.\u003c/p\u003e\u003cp\u003eFurther research should address these limitations through longitudinal, mixed-methods studies conducted across varied institutional and demographic contexts. International comparative research will also be essential to explore how national policies, student profiles, and institutional cultures shape the adoption and educational impact of GAI in Further Education. Such work is crucial to discerning which patterns are context-specific and which may hold more broadly.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study offers insight into how sixth form students in Further Education are engaging with generative AI (GAI), drawing on survey data from an urban, tech-friendly, high-aspiration English college. GAI use was widespread, with 44% of students reporting at least occasional engagement, and ChatGPT the dominant tool. While patterns varied, students were more likely to use GAI to explain complex concepts or generate ideas than to fact-check or write essays - suggesting exploratory, conceptually oriented use rather than shortcutting.\u003c/p\u003e\u003cp\u003eStudents\u0026rsquo; perceptions reflected this pattern, with many reporting that GAI supported their understanding and offered more accessible help than a teacher. Frequent users were more likely to hold positive views of its educational value, suggesting that familiarity may shape perceptions of usefulness. A notable proportion even viewed chatbot support as comparable to teacher guidance. Gender-based attitudinal differences were also evident: male students were more likely to view GAI as beneficial and less likely to consider its use as cheating. These divides echo national trends and underscore the need for AI literacy initiatives that attend to both equity and confidence.\u003c/p\u003e\u003cp\u003eThese findings contribute to growing research on how GAI is shaping student behaviours, expectations, and epistemic dynamics. Students\u0026rsquo; use aligns with Watson\u0026rsquo;s (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) framing of GAI as a mediator of meaning, and with dialogic and sociocultural theories of learning (Wegerif \u0026amp; Major, 2025; Vygotsky, 1968). Many appeared to use GAI not simply to retrieve information, but to support thinking and construct understanding - treating it as a learning partner. Such patterns may reflect learning-oriented goals - an interpretive lens that positions student intentions as central to shaping the cognitive value of GAI use.\u003c/p\u003e\u003cp\u003eApplying Ng et al.\u0026rsquo;s (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) AI Literacy Framework, the study also identified important gaps in students\u0026rsquo; understanding of GAI\u0026rsquo;s limitations, accuracy, and ethical implications. Addressing these gaps will require AI literacy education that promotes not only technical skill but also critical, reflective engagement.\u003c/p\u003e\u003cp\u003eThese findings offer a grounded response to the dual promise and concern raised in prior work (Zhu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), where GAI is seen as both a support for and a potential threat to skill development and integrity. While based on a single site, this study offers an early snapshot of GAI use in Further Education. Longitudinal and mixed-methods research is needed to track change over time and explore more diverse contexts - particularly rural, socioeconomically disadvantaged, or less digitally mature settings, where access and support may differ. Nonetheless, the findings indicate that many students are already using GAI in meaningful educational ways. Building AI literacy is now essential to ensure that such use is informed, ethical, and effective. Rather than policing use, educators should focus on helping staff and students engage critically and constructively. GAI is already shaping how sixth formers learn; the challenge is whether education will prepare them to use it wisely.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process:\u003c/strong\u003e During the preparation of this work the author used ChatGPT Plus in order to assist with formatting and language editing, including improving readability and suggesting reductions in word count where appropriate. After using this tool/service, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis research was supported by funding from Hughes Hall and the Cambridge Trust at the University of Cambridge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThis study was reviewed and received ethical approval from the University of Cambridge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen data availability statement:\u003c/strong\u003eThe data that support the findings of this study are not publicly available due to participant privacy and ethical considerations.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.E. is the sole author and was responsible for the study design, data collection and analysis, and manuscript preparation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmassaad, A., Alajlan, H., \u0026amp; Alebaikan, R. 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Secondary Students\u0026rsquo; Emerging Conceptions of AI: Understanding AI Applications, Models, Engines and Implications. Proceedings of the 2024 Conference on United Kingdom \u0026amp;amp; Ireland Computing Education Research, 1\u0026ndash;7. https://doi.org/10.1145/3689535.3689552\u003c/li\u003e\n\u003cli\u003eZhu, C., Sun, M., Luo, J., Li, T., \u0026amp; Wang, M. (2023). How to harness the potential of ChatGPT in education? (2023). Knowledge Management \u0026amp;amp; E-Learning: An International Journal, 133\u0026ndash;152. https://doi.org/10.34105/j.kmel.2023.15.008\u003c/li\u003e\n\u003cli\u003eZou, M., \u0026amp; Huang, L. (2023). To use or not to use? Understanding doctoral students\u0026rsquo; acceptance of ChatGPT in writing through technology acceptance model. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1259531 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Generative Artificial Intelligence, ChatGPT, Student Perceptions, AI Literacy, Academic Integrity, Sixth Form Education, Learning, Further Education","lastPublishedDoi":"10.21203/rs.3.rs-7122270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7122270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis cross-sectional survey study investigates sixth form students\u0026rsquo; engagement with generative artificial intelligence (GAI) tools in a large, urban, high-attaining, tech-friendly English college. The site was strategically selected to provide early insight into GAI adoption in a Further Education (FE) context where digital maturity, institutional support, and student demographics are conducive to advanced uptake.\u003c/p\u003e\u003cp\u003eSurvey responses from 543 students explore patterns of use, learning perceptions, and AI literacy needs - addressing a significant gap in empirical research on GAI use among 16\u0026ndash;18-year-old learners in FE settings.\u003c/p\u003e\u003cp\u003eFindings suggest notable - and often educationally productive - use of GAI, particularly for explaining complex concepts and generating ideas. Many students viewed GAI as a valuable learning partner, with a minority comparing it favourably to teacher support. Positive attitudes were more common among frequent users and male students, raising equity considerations in AI confidence and literacy.\u003c/p\u003e\u003cp\u003eThe use of GAI for fact-checking and solving maths problems - despite mixed views on accuracy - revealed important gaps in students\u0026rsquo; understanding of the technology\u0026rsquo;s limitations. Applying Ng et al.\u0026rsquo;s (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) AI Literacy Framework, the study found strong student interest in developing evaluative and ethical competencies.\u003c/p\u003e\u003cp\u003eGrounded in dialogic and sociocultural learning theory, this study conceptualises GAI as both a cognitive tool and a source of epistemic risk. It draws attention to the role of students\u0026rsquo; learning goals evidenced in their use - whether using GAI to complete tasks or to support learning - in shaping the cognitive value of their engagement. The study argues for structured, critical AI literacy in Further Education: enabling students to make meaning \u003cem\u003ewith\u003c/em\u003e, not just \u003cem\u003efrom\u003c/em\u003e, AI, and guiding institutional responses beyond restriction toward reflective and pedagogical support.\u003c/p\u003e","manuscriptTitle":"Generative AI in English Sixth Form Education: Student Use, Perceptions, and Literacy Gaps","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 10:24:41","doi":"10.21203/rs.3.rs-7122270/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f438b2f2-91f1-4a28-8028-59ffd456dbac","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T08:58:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 10:24:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7122270","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7122270","identity":"rs-7122270","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}