Tag: widen

  • How educators can use Gen AI to promote inclusion and widen access

    How educators can use Gen AI to promote inclusion and widen access

    by Eleni Meletiadou

    Introduction

    Higher education faces a pivotal moment as Generative AI becomes increasingly embedded within academic practice. While AI technologies offer the potential to personalize learning, streamline processes, and expand access, they also risk exacerbating existing inequalities if not intentionally aligned with inclusive values. Building on our QAA-funded project outputs, this blog outlines a strategic framework for deploying AI to foster inclusion, equity, and ethical responsibility in higher education.

    The digital divide and GenAI

    Extensive research shows that students from marginalized backgrounds often face barriers in accessing digital tools, digital literacy training, and peer networks essential for technological confidence. GenAI exacerbates this divide, demanding not only infrastructure (devices, subscriptions, internet access) but also critical AI literacy. According to previous research, students with higher AI competence outperform peers academically, deepening outcome disparities.

    However, the challenge is not merely technological; it is social and structural. WP (Widening Participation) students often remain outside informal digital learning communities where GenAI tools are introduced and shared. Without intervention, GenAI risks becoming a “hidden curriculum” advantage for already-privileged groups.

    A framework for inclusive GenAI adoption

    Our QAA-funded “Framework for Educators” proposes five interrelated principles to guide ethical, inclusive AI integration:

    • Understanding and Awareness Foundational AI literacy must be prioritized. Awareness campaigns showcasing real-world inclusive uses of AI (eg Otter.ai for students with hearing impairments) and tiered learning tracks from beginner to advanced levels ensure all students can access, understand, and critically engage with GenAI tools.
    • Inclusive Collaboration GenAI should be used to foster diverse collaboration, not reinforce existing hierarchies. Tools like Miro and DeepL can support multilingual and neurodiverse team interactions, while AI-powered task management (eg Notion AI) ensures equitable participation. Embedding AI-driven teamwork protocols into coursework can normalize inclusive digital collaboration.
    • Skill Development Higher-order cognitive skills must remain at the heart of AI use. Assignments that require evaluating AI outputs for bias, simulating ethical dilemmas, and creatively applying AI for social good nurture critical thinking, problem-solving, and ethical awareness.
    • Access to Resources Infrastructure equity is critical. Universities must provide free or subsidized access to key AI tools (eg Grammarly, ReadSpeaker), establish Digital Accessibility Centers, and proactively support economically disadvantaged students.
    • Ethical Responsibility Critical AI literacy must include an ethical dimension. Courses on AI ethics, student-led policy drafting workshops, and institutional AI Ethics Committees empower students to engage responsibly with AI technologies.

    Implementation strategies

    To operationalize the framework, a phased implementation plan is recommended:

    • Phase 1: Needs assessment and foundational AI workshops (0–3 months).
    • Phase 2: Pilot inclusive collaboration models and adaptive learning environments (3–9 months).
    • Phase 3: Scale successful practices, establish Ethics and Accessibility Hubs (9–24 months).

    Key success metrics include increased AI literacy rates, participation from underrepresented groups, enhanced group project equity, and demonstrated critical thinking skill growth.

    Discussion: opportunities and risks

    Without inclusive design, GenAI could deepen educational inequalities, as recent research warns. Students without access to GenAI resources or social capital will be disadvantaged both academically and professionally. Furthermore, impersonal AI-driven learning environments may weaken students’ sense of belonging, exacerbating mental health challenges.

    Conversely, intentional GenAI integration offers powerful opportunities. AI can personalize support for students with diverse learning needs, extend access to remote or rural learners, and reduce administrative burdens on staff – freeing them to focus on high-impact, relational work such as mentoring.

    Conclusion

    The future of inclusive higher education depends on whether GenAI is adopted with a clear commitment to equity and social justice. As our QAA project outputs demonstrate, the challenge is not merely technological but ethical and pedagogical. Institutions must move beyond access alone, embedding critical AI literacy, equitable resource distribution, community-building, and ethical responsibility into every stage of AI adoption.

    Generative AI will not close the digital divide on its own. It is our pedagogical choices, strategic designs, and values-driven implementations that will determine whether the AI-driven university of the future is one of exclusion – or transformation.

    This blog is based on the recent outputs from our QAA-funded project entitled: “Using AI to promote education for sustainable development and widen access to digital skills”

    Dr Eleni Meletiadou is an Associate Professor (Teaching) at London Metropolitan University  specialising in Equity, Diversity, and Inclusion (EDI), AI, inclusive digital pedagogy, and multilingual education. She leads the Education for Social Justice and Sustainable Learning and Development (RILEAS) and the Gender Equity, Diversity, and Inclusion (GEDI) Research Groups. Dr Meletiadou’s work, recognised with the British Academy of Management Education Practice Award (2023), focuses on transforming higher education curricula to promote equitable access, sustainability, and wellbeing. With over 15 years of international experience across 35 countries, she has led numerous projects in inclusive assessment and AI-enhanced learning. She is a Principal Fellow of the Higher Education Academy and serves on several editorial boards. Her research interests include organisational change, intercultural communication, gender equity, and Education for Sustainable Development (ESD). She actively contributes to global efforts in making education more inclusive and future-ready. LinkedIn: https://www.linkedin.com/in/dr-eleni-meletiadou/

    Author: SRHE News Blog

    An international learned society, concerned with supporting research and researchers into Higher Education

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  • Will GenAI narrow or widen the digital divide in higher education?

    Will GenAI narrow or widen the digital divide in higher education?

    by Lei Fang and Xue Zhou

    This blog is based on our recent publication: Zhou, X, Fang, L, & Rajaram, K (2025) ‘Exploring the digital divide among students of diverse demographic backgrounds: a survey of UK undergraduates’ Journal of Applied Learning and Teaching, 8(1).

    Introduction – the widening digital divide

    Our recent study (Zhou et al, 2025) surveyed 595 undergraduate students across the UK to examine the evolving digital divide across all forms of digital technologies. Although higher education is expected to narrow this divide and build students’ digital confidence, our findings revealed the opposite. We found that the gap in digital confidence and skills between widening participation (WP) and non-WP students widened progressively throughout the undergraduate journey. While students reported peak confidence in Year 2, this was followed by a notable decline in Year 3, when the digital divide became most pronounced. This drop coincides with a critical period when students begin applying their digital skills in real-world contexts, such as job applications and final-year projects.

    Based on our study (Zhou et al, 2025), while universities offer a wide range of support such as laptop loans, free access to remote systems, extracurricular digital skills training, and targeted funding to WP students, WP students often do not make use of these resources. The core issue lies not in the absence of support, but in its uptake. WP students are often excluded from the peer networks and digital communities where emerging technologies are introduced, shared, and discussed. From a Connectivist perspective (Siemens, 2005), this lack of connection to digital, social, and institutional networks limits their awareness, confidence, and ability to engage meaningfully with available digital tools.

    Building on these findings, this blog asks a timely question: as Generative Artificial Intelligence (GenAI) becomes embedded in higher education, will it help bridge this divide or deepen it further?

    GenAI may widen the digital divide — without proper strategies

    While the digital divide in higher education is already well-documented in relation to general technologies, the emergence of GenAI introduces new risks that may further widen this gap (Cachat-Rosset & Klarsfeld, 2023). This matters because students who are GenAI-literate often experience better academic performance (Sun & Zhou, 2024), making the divide not just about access but also about academic outcomes.

    Unlike traditional digital tools, GenAI often demands more advanced infrastructure — including powerful devices, high-speed internet, and in many cases, paid subscriptions to unlock full functionality. WP students, who already face barriers to accessing basic digital infrastructure, are likely to be disproportionately excluded. This divide is not only student-level but also institutional. A few well-funded universities are able to subscribe to GenAI platforms such as ChatGPT, invest in specialised GenAI tools, and secure campus-wide licenses. In contrast, many institutions, particularly those under financial pressure, cannot afford such investments. These disparities risk creating a new cross-sector digital divide, where students’ access to emerging technologies depends not only on their background, but also on the resources of the university they attend.

    In addition, the adoption of GenAI currently occurs primarily through informal channels via peers, online communities, or individual experimentation rather than structured teaching (Shailendra et al, 2024). WP students, who may lack access to these digital and social learning networks (Krstić et al, 2021), are therefore less likely to become aware of new GenAI tools, let alone develop the confidence and skills to use them effectively. Even when they do engage with GenAI, students may experience uncertainty, confusion, or fear about using it appropriately especially in the absence of clear guidance around academic integrity, ethical use, or institutional policy. This ambiguity can lead to increased anxiety and stress, contributing to wider concerns around mental health in GenAI learning environments.

    Another concern is the risk of impersonal learning environments (Berei & Pusztai, 2022). When GenAI are implemented without inclusive design, the experience can feel detached and isolating, particularly for WP students, who often already feel marginalised. While GenAI tools may streamline administrative and learning processes, they can also weaken the sense of connection and belonging that is essential for student engagement and success.

    GenAI can narrow the divide — with the right strategies

    Although WP students are often excluded from digital networks, which Connectivism highlights as essential for learning (Goldie, 2016), GenAI, if used thoughtfully, can help reconnect them by offering personalised support, reducing geographic barriers, and expanding access to educational resources.

    To achieve this, we propose five key strategies:

    • Invest in infrastructure and access: Universities must ensure that all students have the tools to participate in the AI-enabled classroom including access to devices, core software, and free versions of widely used GenAI platforms. While there is a growing variety of GenAI tools on the market, institutions facing financial pressures must prioritise tools that are both widely used and demonstrably effective. The goal is not to adopt everything, but to ensure that all students have equitable access to the essentials.
    • Rethink training with inclusion in mind: GenAI literacy training must go beyond traditional models. It should reflect Equality, Diversity and Inclusion principles recognising the different starting points students bring and offering flexible, practical formats. Micro-credentials on platforms like LinkedIn Learning or university-branded short courses can provide just-in-time, accessible learning opportunities. These resources are available anytime and from anywhere, enabling students who were previously excluded such as those in rural or under-resourced areas to access learning on their own terms.
    • Build digital communities and peer networks: Social connection is a key enabler of learning (Siemens, 2005). Institutions should foster GenAI learning communities where students can exchange ideas, offer peer support, and normalise experimentation. Mental readiness is just as important as technical skill and being part of a supportive network can reduce anxiety and stigma around GenAI use.
    • Design inclusive GenAI policies and ensure ongoing evaluation: Institutions must establish clear, inclusive policies around GenAI use that balance innovation with ethics (Schofield & Zhang, 2024). These policies should be communicated transparently and reviewed regularly, informed by diverse student feedback and ongoing evaluation of impact.
    • Adopt a human-centred approach to GenAI integration: Following UNESCO’s human-centred approach to AI in education (UNESCO, 2024; 2025), GenAI should be used to enhance, not replace the human elements of teaching and learning. While GenAI can support personalisation and reduce administrative burdens, the presence of academic and pastoral staff remains essential. By freeing staff from routine tasks, GenAI can enable them to focus more fully on this high-impact, relational work, such as mentoring, guidance, and personalised support that WP students often benefit from most.

    Conclusion

    Generative AI alone will not determine the future of equity in higher education, our actions will. Without intentional, inclusive strategies, GenAI risks amplifying existing digital inequalities, further disadvantaging WP students. However, by proactively addressing access barriers, delivering inclusive and flexible training, building supportive digital communities, embedding ethical policies, and preserving meaningful human interaction, GenAI can become a powerful tool for inclusion. The digital divide doesn’t close itself; institutions must embed equity into every stage of GenAI adoption. The time to act is not once systems are already in place, it is now.

    Dr Lei Fang is a Senior Lecturer in Digital Transformation at Queen Mary University of London. Her research interests include AI literacy, digital technology adoption, the application of AI in higher education, and risk management. lei.fang@qmul.ac.uk

    Professor Xue Zhou is a Professor in AI in Business Education at the University of Leicester. Her research interests fall in the areas of digital literacy, digital technology adoption, cross-cultural adjustment and online professionalism. xue.zhou@le.ac.uk

    Author: SRHE News Blog

    An international learned society, concerned with supporting research and researchers into Higher Education

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