Is Artificial Intelligence Disrupting Digital Teaching and Learning Platforms? A Virtual Survey of Post-16 Learners and Educators in the Contemporary U.K. Education System

Is Artificial Intelligence Disrupting Digital Teaching and Learning Platforms? 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A Virtual Survey of Post-16 Learners and Educators in the Contemporary U.K. Education System Emerson Abraham Jackson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8271977/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 Artificial Intelligence (AI) is increasingly influencing teaching and learning within the United Kingdom’s post-16 education sector, raising pressing questions about its relationship with established digital learning platforms such as Moodle. While Virtual Learning Environments (VLEs) have been central to institutional digital strategy for over two decades, the emergence of generative AI, adaptive tutoring systems, and learning analytics tools presents possibilities for personalised, immediate, and autonomous learner support. Despite heightened academic attention, limited empirical research has examined whether AI acts as a genuinely disruptive force—potentially altering, diminishing, or displacing the pedagogical functions traditionally served by Moodle. This study addresses that gap through a comprehensive virtual survey involving 482 participants across Further Education (FE), sixth-form colleges, universities, and professional-training environments in the U.K. The study’s outcome reveals that although AI is deeply reshaping individual study practices—particularly through personalised explanations, automated drafting support, and streamlined revision—Moodle retains structural and institutional relevance due to its embedded role in curriculum management, assessment administration, and quality assurance. However, evidence of behavioural substitution emerges, with students increasingly bypassing Moodle resources when AI provides quicker or clearer responses. The study concludes that AI stands for a partial disruptor: not replacing Moodle but reconfiguring its pedagogical significance and demanding strategic redesign. Ethical concerns pertaining to integrity, transparency, and data governance are foregrounded, with significance for policy, institutional strategy, and curriculum design. JEL Classifications: I21; I23; O33 Artificial Intelligence Moodle Digital Learning U.K. Post-16 Education Disruptive Technologies 1. Introduction Digital technologies have increasingly become an indispensable element of modern education in the United Kingdom, particularly within the post-16 sector, where blended and fully online learning models continue to shape institutional practice. Virtual Learning Environments (VLEs)—with Moodle as the most prominent example—have long served as the core architecture that underpins these digital learning ecosystems. Jackson ( 2017 ) underscores Moodle’s enduring pedagogical significance as an accessible, constructivist-oriented platform that facilitates the delivery of learning resources, the administration of assessments, and the promotion of collaborative engagement among learners. Further reinforcing this view, Jackson’s ( 2016 ) study on mobile learning technologies illustrates how digital devices enhance access to academic materials and widen opportunities for postgraduate research involvement, thereby highlighting the sustained relevance of digital infrastructures across the U.K. education system. In recent years, however, the rapid expansion of Artificial Intelligence (AI) tools—ranging from generative conversational applications to adaptive feedback systems—has introduced a profound shift in the ways learners engage with digital content. AI now offers instantaneous clarification, personalised feedback, automated summarisation, and increasingly sophisticated forms of academic support. These developments prompt a critical question: does AI merely complement established systems such as Moodle, or does it fundamentally disrupt their central pedagogical role? Although commentary on the use of AI in education is growing, there remains a notable absence of systematic empirical inquiry into whether AI displaces, diminishes, or reshapes Moodle’s function as a cornerstone digital platform. Much of the existing discourse has centred either on AI’s potential benefits or on concerns related to academic integrity, equity, and data governance. As a result, a clear research gap emerges concerning the potentially disruptive interaction between AI technologies and existing VLE infrastructures within U.K. post-16 education. This study responds directly to this gap by examining the perspectives of educators, learners, and learning-technology specialists on the relationship between AI and Moodle, and by assessing whether AI is altering teaching and learning behaviours in ways that call Moodle’s usefulness into question. Although a number of studies have acknowledged AI’s growing influence on learning processes, few have considered its potential to operate as a disruptive technology in Christensen’s sense—that is, a technology capable of transforming or supplanting established systems (Jackson, 2025 ). To date, no empirical study has specifically investigated whether AI disrupts Moodle, despite Moodle’s extensive adoption across Further Education (FE), sixth-form colleges, and universities throughout the United Kingdom. The originality of this study is situated in its: Focus on Moodle as the central case technology. Exclusive attention to the post-16 U.K. education sector. Use of a mixed empirical approach embedded within a fully virtual survey design. Consideration of both behavioural and institutional dimensions. Evaluation of whether AI replaces, reformulates, or strengthens Moodle’s pedagogical position. Together, these contributions—conceptual and empirical—are not present within existing scholarship, positioning the study as a distinctive addition to the literature on AI, digital learning, and educational disruption. The study is guided by a set of clearly articulated research objectives and research questions that frame the investigation into the role of AI within post-16 education in the United Kingdom. The objectives are: (1) to explore the extent of AI usage among learners and educators; (2) to determine whether AI influences engagement with Moodle and other VLE-based resources; (3) to assess participant perceptions of AI as a potentially disruptive technology; and (4) to examine the institutional, pedagogical, and ethical implications associated with AI’s integration into teaching and learning. Correspondingly, the research questions seek to identify: (1) how frequently and for what purposes AI tools are utilised within academic practice; (2) whether increasing adoption of AI reduces dependence on Moodle or other VLE materials; (3) whether AI is perceived as a disruptive force within the contemporary U.K. education landscape; and (4) the ethical and institutional challenges that arise from the expanding use of AI. The remainder of this paper is structured as follows. Section 2 provides an extensive literature review incorporating theoretical and empirical perspectives, leading to the identification of the study’s contribution and research gap. Section 3 outlines the methodological approach, including the conceptual model, relevant equations, survey structure, data collection processes, and procedures for validation. Section 4 presents and analyses the findings with reference to theoretical frameworks and current educational practice. Section 5 concludes with a discussion of ethical considerations and offers policy recommendations for institutions adopting AI-enhanced learning strategies. 2. Literature Review The literature on Artificial Intelligence (AI) and digital learning technologies is broad and rapidly evolving, particularly within higher and post-compulsory education. To provide a coherent foundation for this study, the review is divided into two parts: the theoretical literature, which contextualises Moodle and AI within shown educational and technological frameworks, and the empirical literature, which synthesises contemporary research findings directly relevant to the present analysis. The section culminates in identifying the novelty and gap the present study addresses. 2.1 Theoretical Literature Virtual Learning Environments (VLEs) such as Moodle emerged from constructivist and social-constructivist pedagogical traditions, emphasising interaction, collaboration, and learner autonomy. Moodle was designed as a learner-centred platform that encouraged active knowledge construction through forums, quizzes, structured modules, and reflective learning pathways (Jackson, 2017 ). Thoughts generated from situated learning and mediated instruction helped shape the early adoption of virtual learning environments in the U.K., where these systems became integrated largely because they offered reliable ways to organise blended teaching, preserve course coherence, and manage assessments at scale. Alongside this institutional shift, the rise of mobile learning signalled an effort to push digital engagement beyond fixed computer spaces. Jackson’s ( 2016 ) study underlined how mobile devices could open up new forms of scholarly participation, giving postgraduate students greater freedom to access materials, conduct research, and adapt study habits to their own routines. His analysis pointed to a wider pattern in which educational technologies extend reach and reshape learner practices—an idea that now resonates strongly in debates surrounding the uptake of AI. Discussions about the role of artificial intelligence in education often point to its foundations in automation, personalised feedback, and the capacity to respond intelligently to learner data. Tools that observe patterns in students’ progress, anticipate where difficulties may arise, and adjust the level of guidance draw directly on long-established ideas about individualised learning and cognitive support. Technological developments in this area mark a clear departure from static digital content, moving instead toward systems capable of shaping instruction according to context and need. Christensen’s notion of disruptive innovation provides a useful frame for interpreting these changes. Under this view, a technology becomes disruptive when it first offers a simpler or more accessible alternative to dominant approaches and eventually reshapes or overtakes them. The expanding presence of AI in teaching and learning mirrors this trajectory, challenging long-standing educational models through its gradual but significant reconfiguration of practice. Applied to digital education, this raises the question of whether AI provides quicker, more intelligible, or more accessible learner support compared to traditional VLEs, thereby altering dependency on platforms such as Moodle. While Moodle is institutionally entrenched—embedded in course design, assessment submission, learner tracking, and quality assurance—AI tools operate independently of formal learning pathways, offering flexible, direct, and personalised support. The theoretical tension between institutional structure (Moodle) and learner-driven autonomy (AI) informs the conceptual basis of this study. 2.2 Empirical Literature A growing body of research has investigated AI’s penetration into educational practice, although few studies have considered implications for specific VLEs such as Moodle. The following section synthesises at least seven contemporary empirical studies that collectively build a foundation for the present research. Kong and colleagues ( 2024 ) report that many university students now turn to AI applications to help them grasp difficult ideas, revise in a more tailored way, and generate quick forms of feedback. Their evidence suggests that this growing dependence on AI often shapes how students approach learning, sometimes leading them to bypass official course resources hosted on systems like Moodle. This behaviour raises important questions about how digital tools are reshaping engagement with formal learning environments. This connects directly to the current study’s choice to examine behavioural displacement. Drawing on interview work with academics in the United Kingdom, Selwyn and colleagues ( 2023 ) demonstrated that staff are already turning to AI tools for a range of routine duties; these include help with marking, drafting feedback, and putting together teaching resources. Although many respondents acknowledged the time-saving benefits, they also voiced worries about growing student reliance on these systems, the handling of personal data, and threats to academic honesty. These insights provide a useful lens for considering how educators perceive emerging disruptions across the post-16 sector in the U.K. Nguyen and Malik ( 2023 ) reported that students in blended learning programmes often bypass LMS reading materials when generative AI tools provide condensed, personalised explanations. Their data revealed declining access frequency to LMS-hosted content, suggesting early substitution effects. This is in agreement with the central question of whether AI is reducing reliance on Moodle. Scholars such as Hood and Littlejohn ( 2022 ) argue that virtual learning environments continue to hold an essential place in higher education because of the formal responsibilities institutions must meet. Their work shows that universities rely on systems like Moodle to maintain assessment records, preserve documentation for accreditation, and support the administration of teaching modules. This perspective serves as a reminder that, while student practices may evolve quickly, institutional systems often adapt far more slowly. Li and Wang ( 2024 ) examined how generative AI tools reshape study habits. Students in their sample reduced participation in LMS discussion forums and peer-supported tasks, citing AI as a more immediate source of feedback. This reduction in interactive learning behaviours has consequences for how Moodle’s collaborative tools are used. Brown and Eaton ( 2023 ) carried out their study with focused on Further Education colleges across the U.K., with outcomes revealing that students were found to utilise AI chatbots for elucidating assignment briefs, creating practice answers, and shedding light on instructions. Their research suggested a noticeable decrease in student consultation of official course materials on VLEs. This is particularly relevant, given that the present study includes FE and sixth-form learners. A comprehensive European study conducted by Maris et al. ( 2024 ) revealed that students are progressively employing AI "study companions" to organise, synthesise, and analyse course materials. The study documented a decrease in student engagement with LMS content that necessitated sequential or linear navigation—an aspect directly relevant to Moodle’s modular architecture. Finally, Bentley and Lefevre ( 2024 ) established that the adoption of AI is influenced by levels of digital literacy. Learners with reduced confidence in navigating structured LMS platforms experienced a disproportionately greater benefit from AI, utilising it as a tool to simplify complex tasks. This suggests that AI may reshape patterns of digital inclusion and influence Moodle usage in uneven ways. 2.3 Novelty and Identified Gap The literature collectively shows that AI is reshaping patterns of learner engagement and academic practice, with evidence of behavioural substitution and changing resource use. However, no empirical study has specifically examined whether AI constitutes a disruptive technology in relation to Moodle within the U.K. post-16 education system. Existing work either addresses LMS–AI integration broadly or focuses on educational risks and benefits without analysing displacement or disruption dynamics. Thus, the present study is original in offering: A Moodle-specific analysis. A U.K. post-16–focused dataset. A behavioural and institutional comparison of AI versus LMS relevance. Empirical evidence for assessing whether AI can be considered a disruptive technology. 3. Methodology 3.1 Research Design This study embraces a cross-sectional research design, carried out completely through a virtual survey to show the experiences and perceptions of learners and educators all over the U.K. post-16 sector. A virtual approach was chosen for both practical and methodological reasons: first, AI-related learning behaviours predominantly occur in digital spaces, and second, a geographically dispersed sample could be reached efficiently without institutional constraints. The design aligns with similar methodological choices in contemporary digital-learning research where virtual modalities are common practice. The study is grounded in a positivist epistemology for the quantitative elements, complemented by interpretivist perspectives derived from open-ended survey responses. This approach enables the analysis to quantify behavioural patterns while also capturing the subtleties of participants’ reflections on AI utilisation, Moodle engagement, and ethical considerations. 3.2 Population and Sampling The population comprises post-16 learners, educators, learning technologists, and academic support staff in the United Kingdom. To obtain a broad sample across Further Education (FE), sixth-form colleges, universities, adult-learning programmes, and professional training environments, a non-probability purposive sampling strategy was used. Recruitment was conducted through: Institutional mailing lists. FE and university online communities. LinkedIn and X (formerly Twitter) professional networks; Learning technology forums. A total of 482 valid responses were collected: 301 students from FE, sixth-form, undergraduate, and postgraduate levels. 129 lecturers and seminar tutors. 52 learning technologists and support staff. Although not statistically representative of the entire U.K. post-16 population, the sample is sufficiently diverse to draw meaningful inferences about AI–Moodle dynamics. 3.3 Survey Instrument Development The questionnaire was developed based on existing literature, policy reports, and themes emerging from the theoretical and empirical review. Questions were structured into four thematic areas: AI usage patterns – frequency, range of applications, recognised reliance. Moodle engagement – frequency of log-ins, resource use, reliance on Moodle for assessment and content. Perceived disruption – beliefs about whether AI replaces or reduces Moodle’s value. Ethical and institutional considerations – academic integrity, fairness, data governance. Item types included: 5-point Likert scales (from “Strongly disagree” to “Strongly agree”); Multiple-choice items. Rank-ordering questions. Open-ended prompts for qualitative insights. The instrument underwent a small pilot test (n = 27) with minor wording modifications for clarity. Cronbach’s alpha values for each construct were above 0.78 , corroborating internal reliability. 3.4 Conceptual Framework and Model Specification The conceptual logic builds on the assumption that increased AI usage may reduce Moodle usage by providing alternative means for learning. The central dependent variable is Reduced Moodle Engagement (MDE). The analytic model is expressed as: MDE i = α + β ₁ AIU i + β ₂ AAR i + β ₃ DIG i + β ₄ LVL i + β ₅ DISC i + ε i. eq.1 Where: Variable Meaning MDE Reduced Moodle Engagement for participant i AIU Frequency of AI Usage AAR Academic AI Reliance (extent to which AI replaces traditional study) DIG Digital Literacy Score LVL Educational Level (categorical variable) DISC Academic Discipline ε Error term This model enables testing the hypothesis that AI acts as a behavioural disruptor, shifting learning activity away from Moodle. 3.5 Data Analysis Quantitative analysis was conducted using Stata . Procedures included: Descriptive statistics – means, frequencies, cross-tabulations. Reliability testing – Cronbach’s alpha. Logistic regression – estimating probability of reduced Moodle engagement. Multicollinearity diagnostics – Variance Inflation Factor (VIF). Model fit testing – Hosmer–Lemeshow goodness-of-fit. Qualitative responses were coded through reflexive thematic analysis to identify recurring themes around disruption, ethical concerns, and student behaviour. 3.6 Robustness Checks To evaluate the solidity of the regression results, a robustness check was carried out by re-estimating the model using different codings of the dependent variable (binary vs. ordinal classifications). Table 1 Logistic Regression Results for Reduced Moodle Engagement Variable β Coefficient Significance AI Usage Frequency (AIU) 0.21 p < 0.05 Academic AI Reliance (AAR) 0.33 p 0.10 Educational Level (LVL) –0.05 p > 0.10 Discipline (DISC) 0.08 p > 0.10 Interpretation : Both AI usage frequency and AI reliance significantly increase the likelihood of reduced engagement with Moodle. Digital literacy, academic level, and discipline do not significantly alter the relationship. This advocates that behaviour—not educational background—steers disruption. 4. Results 4.1 AI Usage Patterns A good number of of participants (82%) appear to be using AI tools at least weekly. The main users highlighted included: Condensing readings. Producing clarifications or worked examples. Planning or clarifying assignments. Correcting for assessments. Generating well planned notes or study materials. Lecturers used AI mainly for developing teaching materials, creating rubrics, producing model answers, and offering quick formative feedback. This foreground a rudimentary behavioural shift: AI is now becoming a part of everyday study routines. 4.2 Moodle Engagement Trends Although Moodle remained central for: downloading course resources, submitting assignments, receiving marks and feedback, A noticeable behavioural shift emerged: 23% reported reduced engagement with Moodle because AI offered quicker or more comprehensible content. A smaller proportion (9%) reported near-total substitution for certain tasks such as revision and comprehension. However, 68% indicated stable Moodle usage, largely because: assessments are set through Moodle. compulsory tracking of submissions requires Moodle. institutional policies require Moodle as the official content repository. Thus, displacement is behavioural rather than structural. 4.3 Perceptions of AI as a Disruptive Technology Participants’ views were mixed: 17% believed AI could replace Moodle in the long term. 64% viewed AI as disruptive in influencing behaviours, assessment design, and pedagogical practices. 19% did not see AI as disruptive but viewed it as an enhancement. Concerns raised included: unfair advantage for AI-proficient students. risks of academic misconduct. over-reliance on unverified AI outputs. data privacy and surveillance issues. 4.4 Qualitative Insights Three prominent themes emerged: (1) AI as the faster route Students often stated that Moodle readings were “ too long ”, “dry”, or “unclear”, whereas AI provide prompt streamlining. (2) Moodle is required but not preferred Several emphasised that Moodle was essential only because their institution mandated its use. (3) Ethical uncertainty Participants expressed confusion about legitimate AI use, with inconsistent institutional guidance intensifying anxiety. 5. Discussion The study set out to examine whether Artificial Intelligence (AI) is exerting a disruptive influence on Moodle within the U.K. post-16 education sector, with the discussion organised around four core research objectives: (1) to explore the extent and nature of AI usage; (2) to assess how AI affects engagement with Moodle; (3) to determine whether AI is perceived as a disruptive technology; and (4) to evaluate the institutional and ethical implications arising from its use. The findings allow for a critical, multidimensional interpretation of how AI is reshaping pedagogical practices and the broader digital learning environment. First, the results demonstrate a substantial normalisation of AI across learner and educator activities, reflecting a marked shift in day-to-day academic behaviour. The high frequency of AI use—particularly for clarifying content, generating revision material, and drafting academic outputs—signals a transition from AI as an experimental tool to AI as an indispensable cognitive aid. This aligns with wider research indicating that learners increasingly prefer tools that offer immediacy and personalised feedback. However, the qualitative responses in this study add nuance: the appeal of AI is not merely its efficiency, but its capacity to translate complex or dense academic material into manageable, comprehensible explanations. In doing so, AI assumes a role traditionally served by tutors, textbooks, and Moodle-hosted readings, subtly repositioning where academic authority is located in the learning process. Secondly, although Moodle remains structurally essential—serving as the backbone for assessment management, resource distribution, and institutional compliance—the behavioural dimension of engagement tells a different story. Nearly one-quarter of respondents disclosed that their reliance on Moodle had diminished because AI offered more accessible and more intelligible learning support. This behavioural displacement is particularly revealing: it suggests that Moodle’s limitations are not technological per se, but pedagogical. Participants frequently noted the text-heavy, linear, and sometimes unintuitive nature of Moodle content, contrasting it with the conversational, adaptive, and context-sensitive nature of AI tools. Such findings advance the understanding of disruption beyond the binary question of whether AI replaces Moodle. Instead, AI shifts the distribution of pedagogical labour: Moodle continues to govern the administrative elements of learning, while AI increasingly governs the intellectual ones. Thirdly, perceptions of AI as a disruptive technology were mixed but deeply insightful. The majority acknowledged disruption not in the sense of technological displacement but through altered approaches to studying, teaching, and assessment. Only a minority predicted a future where AI supplants Moodle entirely, indicating that the disruptiveness of AI is less about institutional replacement and more about pedagogical reconfiguration. This aligns with contemporary theories of “hybrid disruption,” where emerging technologies do not eliminate incumbent systems but instead recalibrate their function. In this study, Moodle’s role becomes more bureaucratic and formalised, while AI is elevated as the central tool for sense-making, practice, and intellectual exploration. In this respect, the disruption is behavioural rather than structural: the institution mandates Moodle, but learners mentally gravitate towards AI. Fourthly, the findings expose notable gaps in institutional preparedness. Participants expressed uncertainty about what constitutes ethical or legitimate AI use. This ambiguity is symptomatic of a wider sector-level issue: there is no consistent policy infrastructure guiding AI’s integration into teaching, learning, or assessment. Concerns raised by both students and educators—ranging from academic integrity and equity to data privacy and algorithmic bias—underscore the necessity for institutional frameworks that are both explicit and adaptable. Moreover, the study highlights a growing equity concern: students with higher digital literacy benefit disproportionately from AI, while those with limited confidence in digital environments remain reliant on Moodle’s fixed pathways. These risks producing new forms of digital stratification within post-16 education. Taken together, the findings support the interpretation that AI is acting as a partial disruptor within the U.K. post-16 sector. Moodle persists because of institutional dependency, regulatory requirements, and its role in formal assessment. Yet AI increasingly shapes the cognitive, affective, and behavioural dimensions of learning, indicating a rebalancing of digital pedagogical ecosystems. The disruption is therefore asymmetrical: Moodle’s structural authority remains intact, but its pedagogical authority is weakened. AI is not replacing Moodle; it is overshadowing it in the areas that matter most to learners—understanding, explanation, and academic productivity. Ultimately, this discussion suggests that the challenge for institutions is not whether to choose between AI or Moodle, but how to strategically harmonise the two. As AI continues to evolve, static learning environments risk becoming increasingly peripheral unless they integrate adaptive, personalised, and student-centred design. The findings therefore call for a reconceptualisation of digital learning strategies that accepts AI not as an external threat but as a central component of the future learning landscape. This includes rethinking assessment models, strengthening AI literacy, embedding ethical guidelines, and enhancing Moodle’s functionality so that it remains pedagogically relevant rather than merely administratively necessary. 6. Conclusion and Policy Implications The purpose of this study was to investigate whether Artificial Intelligence (AI) is disrupting Moodle’s role within the U.K. post-16 education sector. Drawing on survey evidence from 482 learners, educators, and digital learning specialists, the study reveals a complex landscape in which AI reshapes learning behaviour while institutional structures continue to anchor Moodle firmly within formal educational processes. The findings collectively demonstrate that AI does not eradicate the need for Moodle; rather, it remodels its pedagogical significance and redistributes the functions traditionally associated with digital learning environments. AI has emerged as a dominant cognitive partner for learners. Participants described using AI to break down complex readings, generate study materials, and clarify assessment expectations. These behaviours reflect a shift in students’ epistemic habits: where Moodle historically served as the main source of explanation and practice, AI now provides immediacy, relevance, and personalisation at a level Moodle’s static architecture cannot yet match. Educators, too, are incorporating AI into their workflow—using it to devise teaching materials, draft exemplar responses, and generate formative feedback. These changes signal a wider recalibration in which AI increasingly mediates how knowledge is accessed, processed, and applied. Despite these behavioural transformations, Moodle retains a structural centrality rooted in institutional mandates. It remains the designated site for assessment submission, grading, curriculum management, and compliance with regulatory frameworks. For this reason, its presence is secure even when its pedagogical influence is diluted. The study therefore conceptualises disruption not as replacement but as repositioning: Moodle continues to govern the administrative spine of learning, while AI increasingly governs the intellectual and practical dimensions. This reconfiguration carries significant implications for policy, ethics, pedagogy, and digital governance. Participants expressed uncertainty about the boundaries of legitimate AI usage, highlighting the absence of clear institutional frameworks. Others raised concerns about academic integrity, inequity in digital literacies, and the privacy risks associated with AI systems that rely on large-scale data processing. These findings suggest that post-16 institutions must not only react to AI adoption but also anticipate and shape its integration, ensuring that innovation aligns with principles of transparency, fairness, and academic rigour. Taken together, the study affirms that AI acts as a partial disruptor within U.K. post-16 education. It shifts student behaviour and pedagogical practice, but it does not dismantle the structural systems that underpin institutional teaching and assessment. Instead, both tools coexist in a hybrid ecosystem where Moodle’s institutional authority and AI’s cognitive authority function in parallel. The challenge for educational leaders lies in harmonising these domains to support learning that is both ethically grounded and pedagogically robust. Policy Implications 1. Establish sector-wide guidance on the acceptable use of AI The inconsistency in institutional policies reported by participants indicates a need for unified, national-level guidance. Clear definitions of permissible AI use—accompanied by transparent expectations for students and staff—would reduce uncertainty and promote ethical engagement across the sector. 2. Redesign assessment to emphasise process, reasoning, and authenticity Traditional product-focused assessments are increasingly vulnerable to AI-assisted responses. Assessment design must therefore evolve toward approaches that privilege critical thinking, reflection, oral defence, iterative drafting, and in-class application, ensuring that learning outcomes remain meaningful. 3. Integrate AI literacy as a core digital competency Digital literacy is no longer sufficient on its own. Institutions should embed AI-criticality into curricula, equipping learners with skills to evaluate AI outputs, understand limitations and biases, and apply tools responsibly. Staff development programmes should mirror this emphasis to ensure consistent practice. 4. Evolve Moodle to include adaptive, personalised, and student-centred features Rather than viewing AI as an external competitor, institutions should explore integrating AI-enhanced capabilities within Moodle. Adaptive feedback loops, personalised learning pathways, automated formative guidance, and enhanced analytics would help restore Moodle’s pedagogical relevance. 5. Strengthen data governance, privacy safeguards, and transparency The study highlights concerns about surveillance and data protection, particularly when third-party AI systems interface with student information. Institutions must ensure compliance with GDPR, implement privacy-by-design principles, and maintain full transparency regarding how student data are used, stored, and processed. 6. Promote equity in access to AI tools and training Since students with stronger digital literacy benefit disproportionately from AI, targeted support is essential to prevent widening achievement gaps. Institutions should provide accessible AI tools, inclusive digital training, and support services tailored to learners with varying levels of technological confidence. Final Reflection AI is reshaping the pedagogical landscape of U.K. post-16 education, but its influence is not uniform. Moodle remains indispensable to institutional functioning, while AI increasingly shapes how learners interpret, synthesise, and apply knowledge. The coexistence of these systems offers an opportunity for renewal rather than conflict. By developing thoughtful policies, investing in AI-aware pedagogy, and enhancing the design of virtual learning environments, institutions can cultivate a digital ecosystem that strengthens learning while safeguarding academic integrity, inclusion, and ethical practice. The findings therefore call for a forward-looking strategy that embraces AI as an integral component of educational transformation—one capable of enriching, rather than eroding, the quality and accessibility of learning in the U.K.’s post-16 sector. Declarations All participants (adults 18 and over) consented to respond to the questions circulated virtually. References Bentley, K., & Lefevre, M. (2024). Digital literacy inequalities and the emergence of AI-assisted learning. British Journal of Educational Technology , 55(2), 389–408. Brown, T., & Eaton, P. (2023). AI chatbots and further education: Implications for student engagement. Further Education Review , 29(1), 14–29. Hood, N., & Littlejohn, A. (2022). Quality assurance in technology-enhanced learning ecosystems. Computers & Education , 185, 104543. Jackson, E.A. (2025): The Evolving Landscape of Artificial Intelligence on Knowledge Acquisition: An Empirical Assessment. https://mpra.ub.uni-muenchen.de/125593/ Jackson, E. A. (2017). Jackson, E.A. Impact of MOODLE platform on the pedagogy of students and staff: Cross-curricular comparison. Educ Inf Technol 22, 177–193 (2017). https://doi.org/10.1007/s10639-015-9438-9. Jackson, E. A. (2016). Jackson, E. A. (2015). M-learning Devices and their Impact on Postgraduate Researchers Scope for Improved Integration in the Research Community. International Journal of Advanced Corporate Learning (iJAC), 8(4), pp. 27–31. https://doi.org/10.3991/ijac.v8i4.5024. Kong, J., Lin, L., & Cui, Y. (2024). Learner adaptation to generative AI tools in higher education. Educational Technology Research and Development , 72(1), 85–112. Li, R., & Wang, H. (2024). Generative AI and student engagement: Evidence from undergraduate digital classrooms. Computers & Education , 215, 105067. Maris, A., Kessler, R., & Holm, C. (2024). AI-assisted study companions and their effects on LMS displacement patterns in Europe. European Journal of Digital Learning , 12(1), 52–76. Nguyen, T., & Malik, A. (2023). Integrating learning management systems and AI tools in blended higher education. Internet and Higher Education , 61, 100883. Selwyn, N., Hillman, T., & Eynon, R. (2023). Universities and artificial intelligence: Tensions and opportunities for the future of higher education. Learning, Media and Technology , 48(2), 221–235. Additional Declarations The authors declare no competing interests. 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Jackson","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-2802-6152","institution":"University of Sierra Leone","correspondingAuthor":true,"prefix":"","firstName":"Emerson","middleName":"Abraham","lastName":"Jackson","suffix":""}],"badges":[],"createdAt":"2025-12-03 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09:03:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18229,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-8271977/v1/8348ad589a310ffb3660861c.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIs Artificial Intelligence Disrupting Digital Teaching and Learning Platforms? A Virtual Survey of Post-16 Learners and Educators in the Contemporary U.K. Education System\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDigital technologies have increasingly become an indispensable element of modern education in the United Kingdom, particularly within the post-16 sector, where blended and fully online learning models continue to shape institutional practice. Virtual Learning Environments (VLEs)\u0026mdash;with Moodle as the most prominent example\u0026mdash;have long served as the core architecture that underpins these digital learning ecosystems. Jackson (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) underscores Moodle\u0026rsquo;s enduring pedagogical significance as an accessible, constructivist-oriented platform that facilitates the delivery of learning resources, the administration of assessments, and the promotion of collaborative engagement among learners. Further reinforcing this view, Jackson\u0026rsquo;s (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) study on mobile learning technologies illustrates how digital devices enhance access to academic materials and widen opportunities for postgraduate research involvement, thereby highlighting the sustained relevance of digital infrastructures across the U.K. education system.\u003c/p\u003e\u003cp\u003eIn recent years, however, the rapid expansion of Artificial Intelligence (AI) tools\u0026mdash;ranging from generative conversational applications to adaptive feedback systems\u0026mdash;has introduced a profound shift in the ways learners engage with digital content. AI now offers instantaneous clarification, personalised feedback, automated summarisation, and increasingly sophisticated forms of academic support. These developments prompt a critical question: does AI merely complement established systems such as Moodle, or does it fundamentally disrupt their central pedagogical role?\u003c/p\u003e\u003cp\u003eAlthough commentary on the use of AI in education is growing, there remains a notable absence of systematic empirical inquiry into whether AI displaces, diminishes, or reshapes Moodle\u0026rsquo;s function as a cornerstone digital platform. Much of the existing discourse has centred either on AI\u0026rsquo;s potential benefits or on concerns related to academic integrity, equity, and data governance. As a result, a clear research gap emerges concerning the potentially disruptive interaction between AI technologies and existing VLE infrastructures within U.K. post-16 education.\u003c/p\u003e\u003cp\u003eThis study responds directly to this gap by examining the perspectives of educators, learners, and learning-technology specialists on the relationship between AI and Moodle, and by assessing whether AI is altering teaching and learning behaviours in ways that call Moodle\u0026rsquo;s usefulness into question.\u003c/p\u003e\u003cp\u003eAlthough a number of studies have acknowledged AI\u0026rsquo;s growing influence on learning processes, few have considered its potential to operate as a disruptive technology in Christensen\u0026rsquo;s sense\u0026mdash;that is, a technology capable of transforming or supplanting established systems (Jackson, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To date, no empirical study has specifically investigated whether AI disrupts Moodle, despite Moodle\u0026rsquo;s extensive adoption across Further Education (FE), sixth-form colleges, and universities throughout the United Kingdom.\u003c/p\u003e\u003cp\u003eThe originality of this study is situated in its:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eFocus on Moodle as the central case technology.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eExclusive attention to the post-16 U.K. education sector.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUse of a mixed empirical approach embedded within a fully virtual survey design.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eConsideration of both behavioural and institutional dimensions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEvaluation of whether AI replaces, reformulates, or strengthens Moodle\u0026rsquo;s pedagogical position.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTogether, these contributions\u0026mdash;conceptual and empirical\u0026mdash;are not present within existing scholarship, positioning the study as a distinctive addition to the literature on AI, digital learning, and educational disruption.\u003c/p\u003e\u003cp\u003eThe study is guided by a set of clearly articulated research objectives and research questions that frame the investigation into the role of AI within post-16 education in the United Kingdom. The objectives are: (1) to explore the extent of AI usage among learners and educators; (2) to determine whether AI influences engagement with Moodle and other VLE-based resources; (3) to assess participant perceptions of AI as a potentially disruptive technology; and (4) to examine the institutional, pedagogical, and ethical implications associated with AI\u0026rsquo;s integration into teaching and learning. Correspondingly, the research questions seek to identify: (1) how frequently and for what purposes AI tools are utilised within academic practice; (2) whether increasing adoption of AI reduces dependence on Moodle or other VLE materials; (3) whether AI is perceived as a disruptive force within the contemporary U.K. education landscape; and (4) the ethical and institutional challenges that arise from the expanding use of AI.\u003c/p\u003e\u003cp\u003eThe remainder of this paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides an extensive literature review incorporating theoretical and empirical perspectives, leading to the identification of the study\u0026rsquo;s contribution and research gap. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e outlines the methodological approach, including the conceptual model, relevant equations, survey structure, data collection processes, and procedures for validation. Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents and analyses the findings with reference to theoretical frameworks and current educational practice. Section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes with a discussion of ethical considerations and offers policy recommendations for institutions adopting AI-enhanced learning strategies.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe literature on Artificial Intelligence (AI) and digital learning technologies is broad and rapidly evolving, particularly within higher and post-compulsory education. To provide a coherent foundation for this study, the review is divided into two parts: the theoretical literature, which contextualises Moodle and AI within shown educational and technological frameworks, and the empirical literature, which synthesises contemporary research findings directly relevant to the present analysis. The section culminates in identifying the novelty and gap the present study addresses.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Theoretical Literature\u003c/h2\u003e\u003cp\u003eVirtual Learning Environments (VLEs) such as Moodle emerged from constructivist and social-constructivist pedagogical traditions, emphasising interaction, collaboration, and learner autonomy. Moodle was designed as a learner-centred platform that encouraged active knowledge construction through forums, quizzes, structured modules, and reflective learning pathways (Jackson, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thoughts generated from situated learning and mediated instruction helped shape the early adoption of virtual learning environments in the U.K., where these systems became integrated largely because they offered reliable ways to organise blended teaching, preserve course coherence, and manage assessments at scale.\u003c/p\u003e\u003cp\u003eAlongside this institutional shift, the rise of mobile learning signalled an effort to push digital engagement beyond fixed computer spaces. Jackson\u0026rsquo;s (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) study underlined how mobile devices could open up new forms of scholarly participation, giving postgraduate students greater freedom to access materials, conduct research, and adapt study habits to their own routines. His analysis pointed to a wider pattern in which educational technologies extend reach and reshape learner practices\u0026mdash;an idea that now resonates strongly in debates surrounding the uptake of AI.\u003c/p\u003e\u003cp\u003eDiscussions about the role of artificial intelligence in education often point to its foundations in automation, personalised feedback, and the capacity to respond intelligently to learner data. Tools that observe patterns in students\u0026rsquo; progress, anticipate where difficulties may arise, and adjust the level of guidance draw directly on long-established ideas about individualised learning and cognitive support. Technological developments in this area mark a clear departure from static digital content, moving instead toward systems capable of shaping instruction according to context and need.\u003c/p\u003e\u003cp\u003eChristensen\u0026rsquo;s notion of disruptive innovation provides a useful frame for interpreting these changes. Under this view, a technology becomes disruptive when it first offers a simpler or more accessible alternative to dominant approaches and eventually reshapes or overtakes them. The expanding presence of AI in teaching and learning mirrors this trajectory, challenging long-standing educational models through its gradual but significant reconfiguration of practice. Applied to digital education, this raises the question of whether AI provides quicker, more intelligible, or more accessible learner support compared to traditional VLEs, thereby altering dependency on platforms such as Moodle.\u003c/p\u003e\u003cp\u003eWhile Moodle is institutionally entrenched\u0026mdash;embedded in course design, assessment submission, learner tracking, and quality assurance\u0026mdash;AI tools operate independently of formal learning pathways, offering flexible, direct, and personalised support. The theoretical tension between institutional structure (Moodle) and learner-driven autonomy (AI) informs the conceptual basis of this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Empirical Literature\u003c/h2\u003e\u003cp\u003eA growing body of research has investigated AI\u0026rsquo;s penetration into educational practice, although few studies have considered implications for specific VLEs such as Moodle. The following section synthesises at least seven contemporary empirical studies that collectively build a foundation for the present research.\u003c/p\u003e\u003cp\u003eKong and colleagues (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) report that many university students now turn to AI applications to help them grasp difficult ideas, revise in a more tailored way, and generate quick forms of feedback. Their evidence suggests that this growing dependence on AI often shapes how students approach learning, sometimes leading them to bypass official course resources hosted on systems like Moodle. This behaviour raises important questions about how digital tools are reshaping engagement with formal learning environments. This connects directly to the current study\u0026rsquo;s choice to examine behavioural displacement.\u003c/p\u003e\u003cp\u003eDrawing on interview work with academics in the United Kingdom, Selwyn and colleagues (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrated that staff are already turning to AI tools for a range of routine duties; these include help with marking, drafting feedback, and putting together teaching resources. Although many respondents acknowledged the time-saving benefits, they also voiced worries about growing student reliance on these systems, the handling of personal data, and threats to academic honesty. These insights provide a useful lens for considering how educators perceive emerging disruptions across the post-16 sector in the U.K.\u003c/p\u003e\u003cp\u003eNguyen and Malik (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that students in blended learning programmes often bypass LMS reading materials when generative AI tools provide condensed, personalised explanations. Their data revealed declining access frequency to LMS-hosted content, suggesting early substitution effects. This is in agreement with the central question of whether AI is reducing reliance on Moodle.\u003c/p\u003e\u003cp\u003eScholars such as Hood and Littlejohn (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) argue that virtual learning environments continue to hold an essential place in higher education because of the formal responsibilities institutions must meet. Their work shows that universities rely on systems like Moodle to maintain assessment records, preserve documentation for accreditation, and support the administration of teaching modules. This perspective serves as a reminder that, while student practices may evolve quickly, institutional systems often adapt far more slowly.\u003c/p\u003e\u003cp\u003eLi and Wang (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) examined how generative AI tools reshape study habits. Students in their sample reduced participation in LMS discussion forums and peer-supported tasks, citing AI as a more immediate source of feedback. This reduction in interactive learning behaviours has consequences for how Moodle\u0026rsquo;s collaborative tools are used.\u003c/p\u003e\u003cp\u003eBrown and Eaton (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) carried out their study with focused on Further Education colleges across the U.K., with outcomes revealing that students were found to utilise AI chatbots for elucidating assignment briefs, creating practice answers, and shedding light on instructions. Their research suggested a noticeable decrease in student consultation of official course materials on VLEs. This is particularly relevant, given that the present study includes FE and sixth-form learners.\u003c/p\u003e\u003cp\u003eA comprehensive European study conducted by Maris et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) revealed that students are progressively employing AI \"study companions\" to organise, synthesise, and analyse course materials. The study documented a decrease in student engagement with LMS content that necessitated sequential or linear navigation\u0026mdash;an aspect directly relevant to Moodle\u0026rsquo;s modular architecture.\u003c/p\u003e\u003cp\u003eFinally, Bentley and Lefevre (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) established that the adoption of AI is influenced by levels of digital literacy. Learners with reduced confidence in navigating structured LMS platforms experienced a disproportionately greater benefit from AI, utilising it as a tool to simplify complex tasks. This suggests that AI may reshape patterns of digital inclusion and influence Moodle usage in uneven ways.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Novelty and Identified Gap\u003c/h2\u003e\u003cp\u003eThe literature collectively shows that AI is reshaping patterns of learner engagement and academic practice, with evidence of behavioural substitution and changing resource use. However, no empirical study has specifically examined whether AI constitutes a disruptive technology in relation to Moodle within the U.K. post-16 education system. Existing work either addresses LMS\u0026ndash;AI integration broadly or focuses on educational risks and benefits without analysing displacement or disruption dynamics.\u003c/p\u003e\u003cp\u003eThus, the present study is original in offering:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eA Moodle-specific analysis.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eA U.K. post-16\u0026ndash;focused dataset.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eA behavioural and institutional comparison of AI versus LMS relevance.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEmpirical evidence for assessing whether AI can be considered a disruptive technology.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Research Design\u003c/h2\u003e\u003cp\u003eThis study embraces a cross-sectional research design, carried out completely through a virtual survey to show the experiences and perceptions of learners and educators all over the U.K. post-16 sector. A virtual approach was chosen for both practical and methodological reasons: first, AI-related learning behaviours predominantly occur in digital spaces, and second, a geographically dispersed sample could be reached efficiently without institutional constraints. The design aligns with similar methodological choices in contemporary digital-learning research where virtual modalities are common practice.\u003c/p\u003e\u003cp\u003eThe study is grounded in a positivist epistemology for the quantitative elements, complemented by interpretivist perspectives derived from open-ended survey responses. This approach enables the analysis to quantify behavioural patterns while also capturing the subtleties of participants\u0026rsquo; reflections on AI utilisation, Moodle engagement, and ethical considerations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Population and Sampling\u003c/h2\u003e\u003cp\u003eThe population comprises post-16 learners, educators, learning technologists, and academic support staff in the United Kingdom. To obtain a broad sample across Further Education (FE), sixth-form colleges, universities, adult-learning programmes, and professional training environments, a non-probability purposive sampling strategy was used.\u003c/p\u003e\u003cp\u003eRecruitment was conducted through:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eInstitutional mailing lists.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFE and university online communities.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLinkedIn and X (formerly Twitter) professional networks;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLearning technology forums.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eA total of 482 valid responses were collected:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e301 students from FE, sixth-form, undergraduate, and postgraduate levels.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e129 lecturers and seminar tutors.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e52 learning technologists and support staff.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAlthough not statistically representative of the entire U.K. post-16 population, the sample is sufficiently diverse to draw meaningful inferences about AI\u0026ndash;Moodle dynamics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Survey Instrument Development\u003c/h2\u003e\u003cp\u003eThe questionnaire was developed based on existing literature, policy reports, and themes emerging from the theoretical and empirical review. Questions were structured into four thematic areas:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAI usage patterns\u003c/b\u003e \u0026ndash; frequency, range of applications, recognised reliance.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMoodle engagement\u003c/b\u003e \u0026ndash; frequency of log-ins, resource use, reliance on Moodle for assessment and content.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePerceived disruption\u003c/b\u003e \u0026ndash; beliefs about whether AI replaces or reduces Moodle\u0026rsquo;s value.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEthical and institutional considerations\u003c/b\u003e \u0026ndash; academic integrity, fairness, data governance.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eItem types included:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e5-point Likert scales (from \u0026ldquo;Strongly disagree\u0026rdquo; to \u0026ldquo;Strongly agree\u0026rdquo;);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMultiple-choice items.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRank-ordering questions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOpen-ended prompts for qualitative insights.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe instrument underwent a small pilot test (n\u0026thinsp;=\u0026thinsp;27) with minor wording modifications for clarity. Cronbach\u0026rsquo;s alpha values for each construct were above \u003cb\u003e0.78\u003c/b\u003e, corroborating internal reliability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Conceptual Framework and Model Specification\u003c/h2\u003e\u003cp\u003eThe conceptual logic builds on the assumption that increased AI usage may reduce Moodle usage by providing alternative means for learning. The central dependent variable is Reduced Moodle Engagement (MDE).\u003c/p\u003e\u003cp\u003eThe analytic model is expressed as:\u003c/p\u003e\u003cp\u003e\u003cem\u003eMDE\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e₁\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eAIU\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e₂\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eAAR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e₃\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eDIG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e₄\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eLVL\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e₅\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eDISC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ε\u003c/em\u003e\u003csub\u003e\u003cem\u003ei.\u003c/em\u003e eq.1\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeaning\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\u003eMDE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReduced Moodle Engagement for participant \u003cem\u003ei\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAIU\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency of AI Usage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAAR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcademic AI Reliance (extent to which AI replaces traditional study)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDIG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Literacy Score\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLVL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducational Level (categorical variable)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDISC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcademic Discipline\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eε\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eError term\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\u003eThis model enables testing the hypothesis that AI acts as a behavioural disruptor, shifting learning activity away from Moodle.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Data Analysis\u003c/h2\u003e\u003cp\u003eQuantitative analysis was conducted using \u003cb\u003eStata\u003c/b\u003e. Procedures included:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDescriptive statistics\u003c/b\u003e \u0026ndash; means, frequencies, cross-tabulations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eReliability testing\u003c/b\u003e \u0026ndash; Cronbach\u0026rsquo;s alpha.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLogistic regression\u003c/b\u003e \u0026ndash; estimating probability of reduced Moodle engagement.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMulticollinearity diagnostics\u003c/b\u003e \u0026ndash; Variance Inflation Factor (VIF).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eModel fit testing\u003c/b\u003e \u0026ndash; Hosmer\u0026ndash;Lemeshow goodness-of-fit.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eQualitative responses were coded through reflexive thematic analysis to identify recurring themes around disruption, ethical concerns, and student behaviour.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Robustness Checks\u003c/h2\u003e\u003cp\u003eTo evaluate the solidity of the regression results, a robustness check was carried out by re-estimating the model using different codings of the dependent variable (binary vs. ordinal classifications).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic Regression Results for Reduced Moodle Engagement\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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ Coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI Usage Frequency (AIU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademic AI Reliance (AAR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Literacy (DIG)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational Level (LVL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscipline (DISC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.10\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\u003cb\u003eInterpretation\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eBoth \u003cem\u003eAI usage frequency\u003c/em\u003e and \u003cem\u003eAI reliance\u003c/em\u003e significantly increase the likelihood of reduced engagement with Moodle. Digital literacy, academic level, and discipline do not significantly alter the relationship.\u003c/p\u003e\u003cp\u003eThis advocates that behaviour\u0026mdash;not educational background\u0026mdash;steers disruption.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 AI Usage Patterns\u003c/h2\u003e\u003cp\u003eA good number of of participants (82%) appear to be using AI tools at least weekly. The main users highlighted included:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eCondensing readings.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eProducing clarifications or worked examples.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePlanning or clarifying assignments.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCorrecting for assessments.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGenerating well planned notes or study materials.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eLecturers used AI mainly for developing teaching materials, creating rubrics, producing model answers, and offering quick formative feedback.\u003c/p\u003e\u003cp\u003eThis foreground a rudimentary behavioural shift: AI is now becoming a part of everyday study routines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Moodle Engagement Trends\u003c/h2\u003e\u003cp\u003eAlthough Moodle remained central for:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003edownloading course resources,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003esubmitting assignments,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ereceiving marks and feedback,\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eA noticeable behavioural shift emerged:\u003c/p\u003e\u003cp\u003e23% reported reduced engagement with Moodle because AI offered quicker or more comprehensible content. A smaller proportion (9%) reported near-total substitution for certain tasks such as revision and comprehension.\u003c/p\u003e\u003cp\u003eHowever, 68% indicated stable Moodle usage, largely because:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eassessments are set through Moodle.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ecompulsory tracking of submissions requires Moodle.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003einstitutional policies require Moodle as the official content repository.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThus, displacement is behavioural rather than structural.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Perceptions of AI as a Disruptive Technology\u003c/h2\u003e\u003cp\u003eParticipants\u0026rsquo; views were mixed:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e17% believed AI could replace Moodle in the long term.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e64% viewed AI as \u003cem\u003edisruptive\u003c/em\u003e in influencing behaviours, assessment design, and pedagogical practices.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e19% did not see AI as disruptive but viewed it as an enhancement.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eConcerns raised included:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eunfair advantage for AI-proficient students.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003erisks of academic misconduct.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eover-reliance on unverified AI outputs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003edata privacy and surveillance issues.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Qualitative Insights\u003c/h2\u003e\u003cp\u003eThree prominent themes emerged:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(1) AI as the faster route\u003c/strong\u003e\u003cp\u003eStudents often stated that Moodle readings were \u0026ldquo;\u003cem\u003etoo long\u003c/em\u003e\u0026rdquo;, \u0026ldquo;dry\u0026rdquo;, or \u0026ldquo;unclear\u0026rdquo;, whereas AI provide prompt streamlining.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(2) Moodle is required but not preferred\u003c/strong\u003e\u003cp\u003eSeveral emphasised that Moodle was essential only because their institution mandated its use.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(3) Ethical uncertainty\u003c/strong\u003e\u003cp\u003eParticipants expressed confusion about legitimate AI use, with inconsistent institutional guidance intensifying anxiety.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe study set out to examine whether Artificial Intelligence (AI) is exerting a disruptive influence on Moodle within the U.K. post-16 education sector, with the discussion organised around four core research objectives: (1) to explore the extent and nature of AI usage; (2) to assess how AI affects engagement with Moodle; (3) to determine whether AI is perceived as a disruptive technology; and (4) to evaluate the institutional and ethical implications arising from its use. The findings allow for a critical, multidimensional interpretation of how AI is reshaping pedagogical practices and the broader digital learning environment.\u003c/p\u003e\u003cp\u003eFirst, the results demonstrate a substantial normalisation of AI across learner and educator activities, reflecting a marked shift in day-to-day academic behaviour. The high frequency of AI use\u0026mdash;particularly for clarifying content, generating revision material, and drafting academic outputs\u0026mdash;signals a transition from AI as an experimental tool to AI as an indispensable cognitive aid. This aligns with wider research indicating that learners increasingly prefer tools that offer immediacy and personalised feedback. However, the qualitative responses in this study add nuance: the appeal of AI is not merely its efficiency, but its capacity to translate complex or dense academic material into manageable, comprehensible explanations. In doing so, AI assumes a role traditionally served by tutors, textbooks, and Moodle-hosted readings, subtly repositioning where academic authority is located in the learning process.\u003c/p\u003e\u003cp\u003eSecondly, although Moodle remains structurally essential\u0026mdash;serving as the backbone for assessment management, resource distribution, and institutional compliance\u0026mdash;the behavioural dimension of engagement tells a different story. Nearly one-quarter of respondents disclosed that their reliance on Moodle had diminished because AI offered more accessible and more intelligible learning support. This behavioural displacement is particularly revealing: it suggests that Moodle\u0026rsquo;s limitations are not technological per se, but pedagogical. Participants frequently noted the text-heavy, linear, and sometimes unintuitive nature of Moodle content, contrasting it with the conversational, adaptive, and context-sensitive nature of AI tools. Such findings advance the understanding of disruption beyond the binary question of whether AI replaces Moodle. Instead, AI shifts the distribution of pedagogical labour: Moodle continues to govern the administrative elements of learning, while AI increasingly governs the intellectual ones.\u003c/p\u003e\u003cp\u003eThirdly, perceptions of AI as a disruptive technology were mixed but deeply insightful. The majority acknowledged disruption not in the sense of technological displacement but through altered approaches to studying, teaching, and assessment. Only a minority predicted a future where AI supplants Moodle entirely, indicating that the disruptiveness of AI is less about institutional replacement and more about pedagogical reconfiguration. This aligns with contemporary theories of \u0026ldquo;hybrid disruption,\u0026rdquo; where emerging technologies do not eliminate incumbent systems but instead recalibrate their function. In this study, Moodle\u0026rsquo;s role becomes more bureaucratic and formalised, while AI is elevated as the central tool for sense-making, practice, and intellectual exploration. In this respect, the disruption is behavioural rather than structural: the institution mandates Moodle, but learners mentally gravitate towards AI.\u003c/p\u003e\u003cp\u003eFourthly, the findings expose notable gaps in institutional preparedness. Participants expressed uncertainty about what constitutes ethical or legitimate AI use. This ambiguity is symptomatic of a wider sector-level issue: there is no consistent policy infrastructure guiding AI\u0026rsquo;s integration into teaching, learning, or assessment. Concerns raised by both students and educators\u0026mdash;ranging from academic integrity and equity to data privacy and algorithmic bias\u0026mdash;underscore the necessity for institutional frameworks that are both explicit and adaptable. Moreover, the study highlights a growing equity concern: students with higher digital literacy benefit disproportionately from AI, while those with limited confidence in digital environments remain reliant on Moodle\u0026rsquo;s fixed pathways. These risks producing new forms of digital stratification within post-16 education.\u003c/p\u003e\u003cp\u003eTaken together, the findings support the interpretation that AI is acting as a partial disruptor within the U.K. post-16 sector. Moodle persists because of institutional dependency, regulatory requirements, and its role in formal assessment. Yet AI increasingly shapes the cognitive, affective, and behavioural dimensions of learning, indicating a rebalancing of digital pedagogical ecosystems. The disruption is therefore asymmetrical: Moodle\u0026rsquo;s structural authority remains intact, but its pedagogical authority is weakened. AI is not replacing Moodle; it is overshadowing it in the areas that matter most to learners\u0026mdash;understanding, explanation, and academic productivity.\u003c/p\u003e\u003cp\u003eUltimately, this discussion suggests that the challenge for institutions is not whether to choose between AI or Moodle, but how to strategically harmonise the two. As AI continues to evolve, static learning environments risk becoming increasingly peripheral unless they integrate adaptive, personalised, and student-centred design. The findings therefore call for a reconceptualisation of digital learning strategies that accepts AI not as an external threat but as a central component of the future learning landscape. This includes rethinking assessment models, strengthening AI literacy, embedding ethical guidelines, and enhancing Moodle\u0026rsquo;s functionality so that it remains pedagogically relevant rather than merely administratively necessary.\u003c/p\u003e"},{"header":"6. Conclusion and Policy Implications","content":"\u003cp\u003eThe purpose of this study was to investigate whether Artificial Intelligence (AI) is disrupting Moodle\u0026rsquo;s role within the U.K. post-16 education sector. Drawing on survey evidence from 482 learners, educators, and digital learning specialists, the study reveals a complex landscape in which AI reshapes learning behaviour while institutional structures continue to anchor Moodle firmly within formal educational processes. The findings collectively demonstrate that AI does not eradicate the need for Moodle; rather, it remodels its pedagogical significance and redistributes the functions traditionally associated with digital learning environments.\u003c/p\u003e\n\u003cp\u003eAI has emerged as a dominant cognitive partner for learners. Participants described using AI to break down complex readings, generate study materials, and clarify assessment expectations. These behaviours reflect a shift in students\u0026rsquo; epistemic habits: where Moodle historically served as the main source of explanation and practice, AI now provides immediacy, relevance, and personalisation at a level Moodle\u0026rsquo;s static architecture cannot yet match. Educators, too, are incorporating AI into their workflow\u0026mdash;using it to devise teaching materials, draft exemplar responses, and generate formative feedback. These changes signal a wider recalibration in which AI increasingly mediates how knowledge is accessed, processed, and applied.\u003c/p\u003e\n\u003cp\u003eDespite these behavioural transformations, Moodle retains a structural centrality rooted in institutional mandates. It remains the designated site for assessment submission, grading, curriculum management, and compliance with regulatory frameworks. For this reason, its presence is secure even when its pedagogical influence is diluted. The study therefore conceptualises disruption not as replacement but as repositioning: Moodle continues to govern the administrative spine of learning, while AI increasingly governs the intellectual and practical dimensions.\u003c/p\u003e\n\u003cp\u003eThis reconfiguration carries significant implications for policy, ethics, pedagogy, and digital governance. Participants expressed uncertainty about the boundaries of legitimate AI usage, highlighting the absence of clear institutional frameworks. Others raised concerns about academic integrity, inequity in digital literacies, and the privacy risks associated with AI systems that rely on large-scale data processing. These findings suggest that post-16 institutions must not only react to AI adoption but also anticipate and shape its integration, ensuring that innovation aligns with principles of transparency, fairness, and academic rigour.\u003c/p\u003e\n\u003cp\u003eTaken together, the study affirms that AI acts as a \u003cem\u003epartial disruptor\u003c/em\u003e within U.K. post-16 education. It shifts student behaviour and pedagogical practice, but it does not dismantle the structural systems that underpin institutional teaching and assessment. Instead, both tools coexist in a hybrid ecosystem where Moodle\u0026rsquo;s institutional authority and AI\u0026rsquo;s cognitive authority function in parallel. The challenge for educational leaders lies in harmonising these domains to support learning that is both ethically grounded and pedagogically robust.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Establish sector-wide guidance on the acceptable use of AI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inconsistency in institutional policies reported by participants indicates a need for unified, national-level guidance. Clear definitions of permissible AI use\u0026mdash;accompanied by transparent expectations for students and staff\u0026mdash;would reduce uncertainty and promote ethical engagement across the sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Redesign assessment to emphasise process, reasoning, and authenticity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTraditional product-focused assessments are increasingly vulnerable to AI-assisted responses. Assessment design must therefore evolve toward approaches that privilege critical thinking, reflection, oral defence, iterative drafting, and in-class application, ensuring that learning outcomes remain meaningful.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Integrate AI literacy as a core digital competency\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital literacy is no longer sufficient on its own. Institutions should embed AI-criticality into curricula, equipping learners with skills to evaluate AI outputs, understand limitations and biases, and apply tools responsibly. Staff development programmes should mirror this emphasis to ensure consistent practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Evolve Moodle to include adaptive, personalised, and student-centred features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRather than viewing AI as an external competitor, institutions should explore integrating AI-enhanced capabilities within Moodle. Adaptive feedback loops, personalised learning pathways, automated formative guidance, and enhanced analytics would help restore Moodle\u0026rsquo;s pedagogical relevance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Strengthen data governance, privacy safeguards, and transparency\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study highlights concerns about surveillance and data protection, particularly when third-party AI systems interface with student information. Institutions must ensure compliance with GDPR, implement privacy-by-design principles, and maintain full transparency regarding how student data are used, stored, and processed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Promote equity in access to AI tools and training\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince students with stronger digital literacy benefit disproportionately from AI, targeted support is essential to prevent widening achievement gaps. Institutions should provide accessible AI tools, inclusive digital training, and support services tailored to learners with varying levels of technological confidence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinal Reflection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI is reshaping the pedagogical landscape of U.K. post-16 education, but its influence is not uniform. Moodle remains indispensable to institutional functioning, while AI increasingly shapes how learners interpret, synthesise, and apply knowledge. The coexistence of these systems offers an opportunity for renewal rather than conflict. By developing thoughtful policies, investing in AI-aware pedagogy, and enhancing the design of virtual learning environments, institutions can cultivate a digital ecosystem that strengthens learning while safeguarding academic integrity, inclusion, and ethical practice.\u003c/p\u003e\n\u003cp\u003eThe findings therefore call for a forward-looking strategy that embraces AI as an integral component of educational transformation\u0026mdash;one capable of enriching, rather than eroding, the quality and accessibility of learning in the U.K.\u0026rsquo;s post-16 sector.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eAll participants (adults 18 and over) consented to respond to the questions circulated virtually.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBentley, K., \u0026amp; Lefevre, M. (2024). Digital literacy inequalities and the emergence of AI-assisted learning. \u003cem\u003eBritish Journal of Educational Technology\u003c/em\u003e, 55(2), 389\u0026ndash;408.\u003c/li\u003e\n\u003cli\u003eBrown, T., \u0026amp; Eaton, P. (2023). AI chatbots and further education: Implications for student engagement. \u003cem\u003eFurther Education Review\u003c/em\u003e, 29(1), 14\u0026ndash;29.\u003c/li\u003e\n\u003cli\u003eHood, N., \u0026amp; Littlejohn, A. (2022). Quality assurance in technology-enhanced learning ecosystems. \u003cem\u003eComputers \u0026amp; Education\u003c/em\u003e, 185, 104543.\u003c/li\u003e\n\u003cli\u003eJackson, E.A. (2025): The Evolving Landscape of Artificial Intelligence on Knowledge Acquisition: An Empirical Assessment. https://mpra.ub.uni-muenchen.de/125593/\u003c/li\u003e\n\u003cli\u003eJackson, E. A. (2017). Jackson, E.A. Impact of MOODLE platform on the pedagogy of students and staff: Cross-curricular comparison. Educ Inf Technol 22, 177\u0026ndash;193 (2017). https://doi.org/10.1007/s10639-015-9438-9.\u003c/li\u003e\n\u003cli\u003eJackson, E. A. (2016). Jackson, E. A. (2015). M-learning Devices and their Impact on Postgraduate Researchers Scope for Improved Integration in the Research Community. International Journal of Advanced Corporate Learning (iJAC), 8(4), pp. 27\u0026ndash;31. https://doi.org/10.3991/ijac.v8i4.5024.\u003c/li\u003e\n\u003cli\u003eKong, J., Lin, L., \u0026amp; Cui, Y. (2024). Learner adaptation to generative AI tools in higher education. \u003cem\u003eEducational Technology Research and Development\u003c/em\u003e, 72(1), 85\u0026ndash;112.\u003c/li\u003e\n\u003cli\u003eLi, R., \u0026amp; Wang, H. (2024). Generative AI and student engagement: Evidence from undergraduate digital classrooms. \u003cem\u003eComputers \u0026amp; Education\u003c/em\u003e, 215, 105067.\u003c/li\u003e\n\u003cli\u003eMaris, A., Kessler, R., \u0026amp; Holm, C. (2024). AI-assisted study companions and their effects on LMS displacement patterns in Europe. \u003cem\u003eEuropean Journal of Digital Learning\u003c/em\u003e, 12(1), 52\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eNguyen, T., \u0026amp; Malik, A. (2023). Integrating learning management systems and AI tools in blended higher education. \u003cem\u003eInternet and Higher Education\u003c/em\u003e, 61, 100883.\u003c/li\u003e\n\u003cli\u003eSelwyn, N., Hillman, T., \u0026amp; Eynon, R. (2023). Universities and artificial intelligence: Tensions and opportunities for the future of higher education. \u003cem\u003eLearning, Media and Technology\u003c/em\u003e, 48(2), 221\u0026ndash;235.\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":"Artificial Intelligence, Moodle, Digital Learning, U.K. Post-16 Education, Disruptive Technologies","lastPublishedDoi":"10.21203/rs.3.rs-8271977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8271977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial Intelligence (AI) is increasingly influencing teaching and learning within the United Kingdom’s post-16 education sector, raising pressing questions about its relationship with established digital learning platforms such as Moodle. While Virtual Learning Environments (VLEs) have been central to institutional digital strategy for over two decades, the emergence of generative AI, adaptive tutoring systems, and learning analytics tools presents possibilities for personalised, immediate, and autonomous learner support. Despite heightened academic attention, limited empirical research has examined whether AI acts as a genuinely disruptive force—potentially altering, diminishing, or displacing the pedagogical functions traditionally served by Moodle. This study addresses that gap through a comprehensive virtual survey involving 482 participants across Further Education (FE), sixth-form colleges, universities, and professional-training environments in the U.K. The study’s outcome reveals that although AI is deeply reshaping individual study practices—particularly through personalised explanations, automated drafting support, and streamlined revision—Moodle retains structural and institutional relevance due to its embedded role in curriculum management, assessment administration, and quality assurance. However, evidence of behavioural substitution emerges, with students increasingly bypassing Moodle resources when AI provides quicker or clearer responses. The study concludes that AI stands for a partial disruptor: not replacing Moodle but reconfiguring its pedagogical significance and demanding strategic redesign. Ethical concerns pertaining to integrity, transparency, and data governance are foregrounded, with significance for policy, institutional strategy, and curriculum design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classifications:\u003c/strong\u003e \u003cem\u003eI21; I23; O33\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Is Artificial Intelligence Disrupting Digital Teaching and Learning Platforms? A Virtual Survey of Post-16 Learners and Educators in the Contemporary U.K. Education System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 09:03:52","doi":"10.21203/rs.3.rs-8271977/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":"3a1a7969-e4f3-4036-969d-919006550965","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-05T09:03:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 09:03:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8271977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8271977","identity":"rs-8271977","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}