Nurun Nahar

This paper investigates the relationship between artificial intelligence (AI) technology and educational policy in higher education, highlighting key research and implementation. The paper focuses on critical considerations for AI policy development with a view to producing a roadmap focused on contextual higher education AI policies. The rapid development of AI presents both significant opportunities and challenges for higher education institutions in Europe and globally. As AI technologies become ubiquitous, integrated into teaching, learning, and administrative functions, it is essential to identify critical considerations at the core of the AI integration process, namely: (1) regulatory framework, (2) stakeholder-specific guidelines, (3) AIED research, and (4) AI literacy. As a starting point, the paper presents a review of existing AI policy frameworks within higher education, drawing on recent empirical research, identifying four design and implementation priorities for higher education stakeholders aiming to create responsible AI governance frameworks. As a result, we propose a roadmap designed to be used as strategic planning instrument for higher education stakeholders developing AI policies and guidance. In proposing a strategic roadmap for AI policy development, the work offers valuable insight into how higher education can effectively leverage the potential of AI whilst ensuring ethical considerations, equity, and maintaining academic integrity. Additionally, the paper contributes to the ongoing discourse regarding AI’s role in higher education in proposing research pathways that will benefit all stakeholders involved in the academic ecosystem.

The integration of Artificial Intelligence (AI) in educational settings has opened new avenues for enhancing student learning. This study investigated the use of a generative AI chatbot, trained to provide feedback using the Socratic Method, in a Business Management programme. Recent literature highlights the transformative potential of AI in education, particularly in fostering personalised learning experiences and supporting critical thinking (Gökçearslan et al., 2024; Lee and Moore, 2024; Mustafa et al., 2024). Understanding student perspectives on AI-generated feedback is crucial for optimising its use in learning development. This study aimed to evaluate the effectiveness of AI feedback in promoting critical thinking and its acceptance among students. Previous research has shown that AI chatbots can enhance learning by providing timely and relevant feedback, though challenges such as limited interaction and potential for misleading guidance remain (Banihashem et al., 2024; Gökçearslan et al., 2024; Guo et al., 2024). A qualitative approach was employed, utilising a focus group with n=14 final-year undergraduate students on a Business Management pathway. The generative AI tool was piloted to provide feedback on student drafts for summative coursework. The quality of feedback was assessed based on its accuracy, relevance, timeliness, and effectiveness in fostering critical thinking. Data was analysed using thematic analysis, a method well-suited for identifying and interpreting patterns within qualitative data (Nowell et al., 2017; Braun and Clarke, 2022). The Socratic Method, known for its effectiveness in promoting critical thinking through questioning, was employed as the feedback mechanism (Buckingham Shum, 2024). The study revealed that students found AI-generated feedback useful and relevant for improving their work and identifying knowledge gaps, thereby promoting deep learning. The Socratic Method used by the AI encouraged deeper engagement with their work, unlike the straightforward answers typically provided by other chatbots. However, students preferred tutor feedback.

What does it truly mean to learn with a machine, and are machines capable of engaging in dialogic learning? Generative AI models—capable of producing text, images, or other content in response to prompts—are rapidly reshaping educational discourse by introducing 'scalable' forms of personalised learning, while also raising challenges around academic integrity and the need to redefine what critical thinking entails in the context of learning with AI. More recently, features within popular Generative AI models like ChatGPT's " Study and Learn mode " (which guides learners with questions instead of just giving answers) and Google Gemini's ''Learn Your Way'' (which transforms textbooks into interactive, AI-driven study guides) are being marketised on the promise of more conversational, personalised learning experiences that are fine tuned for learning based on education research and principles. Within higher education, the growing presence of these systems demands deeper exploration. Are they genuinely expanding the possibilities for dialogue and feedback, or quietly reshaping the conditions of academic exchange? As practitioners, we must ask not only how these systems work, but also why they are used—and for whom? Do they stimulate enquiry, or do they replace the productive discomfort of genuine dialogue with frictionless interactions that risk remaining superficial (Tang et al, 2024; Wu et al., 2025)?

This study addresses the impact of Artificial Intelligence (AI) in Higher Education Institutions (HEIs) and the whole institutional approach undertaken to develop an AI literacy framework for students in a UK HEI. Drawing upon established models of AI literacy developed for higher education, informed by Leavitt’s People, Process, and Technology model (1965), and grounded in pedagogic theories, the study synthesises principles that address the interplay between emerging technologies, institutional resources, and learner needs. An analysis of existing AI literacy frameworks revealed a gap in terms of aligning institutional readiness, AI literacy and learner needs. To bridge this gap, this study proposes a conceptual AI literacy framework that is learner centred. It integrates practical knowledge and competencies to enable students to
confidently collaborate with AI systems for academic and professional development. Using a mixed-methods study approach and a convergent parallel design, primary data was collected using a survey (n = 110) and two focus groups (combined n = 63) involving multi-ethnic students to capture their perspectives on AI usage. A thematic analysis of secondary literature on AI literacy frameworks was undertaken along with a survey of academic staff (n = 66) to understand the relevance of incorporating AI literacy within curriculum. The resulting framework takes a four-step approach to cultivating AI literacy in higher education students: (i) prepare (ii) understand (iii) apply and (iv) responsible use. This conceptual framework is anticipated to offer a scalable, robust model that will help guide any HEI to cultivate adaptive learners prepared to responsibly navigate and innovate in an AI-driven world.

The increasing integration of Artificial Intelligence (AI) in education and business necessitates the development of critical AI literacy among management graduates. This paper explores how institutional policies, pedagogical strategies, and primary stakeholder engagement can support the responsible use of Generative Artificial Intelligence (GAI) in management education. By analysing existing AI literacy frameworks and institutional practices, the study identifies key gaps and proposes a co-designed AI literacy model tailored for institution ‘A’. Using co-design as a central tenet, the study advocates for AI literacy initiatives that align institutional resources with learner needs and professional domains.

This paper analyses 15 AI policies for higher education from eight European countries, drawn from individual universities, from consortia of universities and from government agencies. Based on an overview of current research findings, it focuses the comparison of different aspects among the selected AI policies. The analysis distinguishes between four potential target groups, namely students, teachers, education managers and policy makers. The paper aims at contributing to the further development and improvement of AI policies for higher education through the identification of commonalities and gaps within the existing AI policies. Moreover, it calls for further and in particular evidence-based research to identify the potential and practical impact of AI in higher education and highlights the need to combine AI use in (higher) education with education about AI, often called as AI literacy.