Artificial Intelligence Policies for Higher Education: Manifesto for Critical Considerations and a Roadmap

______________________________________________________________________ Please cite as: Stracke, C. M. et al. (2025). Artificial Intelligence Policies for Higher Education: Manifesto for Critical Considerations and a Roadmap (submitted, in peer-review). Preprint: https://doi.org/10.5281/zenodo.17280557 Artificial Intelligence Policies for Higher Education: Manifesto for Critical Considerations and a Roadmap Christian M. Stracke 1 * [https://orcid.org/0000-0001-9656-8298], Nurun Nahar 2, Veronica Punzo 3, Stefania Massaro 4, Dimitra Pappa 5, Annamari Di Grassi 6, Senad Bećirović 7, Paul Hollins 2, Xenia Ziouvelou 5, Marjana Prifti Skenduli 8 and Daniel Burgos 9 1 University of Bonn, Germany 2 University of Bolton, Bolton (UK) 3 Scuola Superiore Sant'Anna, Pisa (Italy) 4 University of Bari, Bari (Italy) 5 National Centre for Scientific Research “Demokritos” (Greece) 6 University of Foggia, Foggia (Italy) 7 University College of Teacher Education Lower Austria, Baden (Austria) 8 University of New York Tirana, Tirana (Albania) 9 Universidad Internacional de La Rioja (Spain) * [email protected] Abstract. 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 2 research pathways that will benefit all stakeholders involved in the academic ecosystem. Keywords: Artificial Intelligence in Education (AIED), AI policy development, higher education, framework for strategic planning, design and implementation roadmap 1 Introduction As rapid integration of Artificial Intelligence (AI) technologies becomes ubiquitous in Higher Education teaching and learning, research and administrative processes, it raises important ethical and policy questions to ensure equitable, safe and effective implementation. Society's need to guide the development of Artificial Intelligence (AI) technologies is becoming more widely acknowledged. Guidance is crucial for maximizing AI benefits and managing risks, ensuring that AI systems are designed to serve the common good, align with human values and ethical principles, and preventing misuse (Stracke, 2025). Recent studies have shown a marked increase in the use of AI in higher education, with applications ranging from intelligent tutoring systems to predictive analytics for student success (Bećirović & Mattoš, 2024; Crompton & Burke, 2023). The adoption of AI tools in Higher Education has been driven by their affordances, to personalise learning experiences, provide real-time feedback and automate routine tasks, thereby allowing 3 educators to focus on more complex instructional activities (Slimi, 2023). However, as these technologies continue to evolve, ethical considerations become of paramount significance as robust measures are required to protect individual human rights such as data privacy and compliance with regulations whilst ensuring transparency and fairness in use. Developing flexible regulatory frameworks that can adapt to rapid technological advancements is a complex task if measures to be undertaken prioritise equitable distribution of benefits of AI across all societal segments. This paper will provide a comprehensive overview of the current state of AI policies in Higher Education, drawing on recent empirical studies. It will to present a roadmap for developing AI policies for Higher Education by examining the intersection of AI technology and educational policy and contribute to the ongoing discourse on how to harness AI’s potential best so that all stakeholders in the higher education ecosystem benefit. 2 Background AI is a transformative force in education with the potential to revolutionise learning experiences and create new opportunities for 4 personalised education (Holmes & Tuomi, 2022; Zheng, Niu, Zhong & Gyasi, 2023). The integration of AI in education is part of a global context of rapid technological innovation, where AI-based tools such as intelligent tutoring systems, predictive analytics and personalised learning platforms are redefining the way students and teachers interact with knowledge. Artificial intelligence has become a priority issue for governments and international organisations (Educause, 2022; UNESCO, 2021a, 2021b, 2023; OECD, 2024), as it now impacts on all areas of human activity. Existing literature indicates significant progress in the development of AI applications for education (Zheng, Niu, Zhong & Gyasi, 2023). Recent studies have shown that AI-based tools can improve the personalisation of learning by adapting content to the specific needs of students through machine learning systems. In addition, the use of predictive algorithms provides institutions with the ability to identify students at risk of dropping out early and thereby improve course completion rates (Tlili et al. 2024; Bozkurt, et al. 2023). However, there is no shortage of criticism (Crawford, Allen, Pani & Cowling, 2024). Research suggests that the indiscriminate adoption of these technology risks creating new forms of inequality, particularly in contexts where 5 technological resources and digital skills are limited (Baker & Haw 2022). Other concerns relate to issues of informed consent, invasion of privacy, biased data collection, fairness and accountability (Nguyen et al. 2023). Although AI systems are designed to be unbiased, they may perpetuate or even exacerbate existing biases if the original data on which they are trained and their proxies are not accurate and free from bias and incorrect assumptions (Miao, Holmes, Huang & Zhang, 2021). There are also concerns about the impact of AI on the exercise of democracy and active citizenship (ECAP, 2023; Burr, Taddeo & Floridi, 2020; Dignum, 2021). Many educational institutions are adopting AI tools without a clear regulatory framework, risking ethical issues related to privacy, data security and transparency of algorithms (Stracke, 2024; Stracke et al., 2024). The current widespread adoption of unregulated AI applications in schools poses a serious threat to democratic civil society and individual freedom and liberty (Williamson, Molnar & Boninger, 2024). To understand the challenges, we face in education and to increase trust in AI systems, the concept of Explainable AI has recently emerged. This term refers to movements, initiatives and efforts to ensure that algorithmic decisions and the data that drive these decisions can be explained in a clear and understandable way to end users and other 6 stakeholders (Adadi, & Berrada, 2018). All these findings highlight the need for guidelines for the responsible use of AI in education. A detailed study was conducted by Stracke et al. (in press) to analyze and compare AI policies for higher education. 15 AI policies were selected from governments and universities of eight European countries. Their evaluation compared four potential target groups (students, teachers, education managers, and policymakers) emphasizing their commonalities and gaps within the selected AI policies. The final conclusion is that unique ethical and social challenges are caused by AI, including data security, algorithm transparency, social impact and educational quality, and ethical responsibility (Stracke et al., in press). There is still no clear consensus on the ethical dimensions of AI as a technological practice, meaning its development is primarily shaped by the principles of those who create and implement it. As a result, the ethical considerations reflected in policies and declarations are often personal and subjective perspectives put forward by those involved. This complexity is further heightened by the interplay between regulatory adaptations and the rapid pace of AI advancement. To ensure that AI in higher education is deployed ethically, transparently, and with respect for human rights, regulatory frameworks are essential at all levels— 7 nationally, internationally, and institutionally. While a broad framework can provide a theoretical foundation, practical guidelines are necessary to offer targeted, context-specific responses categorized by topic, sector, and audience. Currently, the development of regulations and ethical frameworks for AI use in universities remains in its early stages. Although European governments are making strides in establishing regulatory standards for AI in the public sector, comprehensive national policies specifically addressing the ethical and responsible use of AI in education are still lacking. At present, regulatory efforts in higher education are largely fragmented, with most initiatives emerging from grassroots, bottom-up approaches. Universities and academic institutions are only beginning to implement structured frameworks for AI ethics and governance. Regulations tend to lag behind technological advancements, making it both inevitable and potentially beneficial for institutions to take the lead in shaping AI policies and practices—ensuring that regulations are informed by the real-world implications of the technology. While existing AI and higher education policies provide a foundational framework for integrating AI technologies into educational institutions, several critical gaps remain that could undermine their 8 effectiveness and equity. Ethical considerations such as bias and fairness, are often addressed in guidelines but lack comprehensive policies that ensure accountability and transparency in AI operations (Lowe 2023; European Commission, 2020). This oversight could lead to perpetuation of existing inequalities and the introduction of new forms of discrimination that could compromise the ethical deployment of AI in Higher Education. The current policies on data privacy and security measures often fallback on compliance with regulations but lack clear robust frameworks that would safeguard data against breaches and misuse which could erode public trust in AI technologies and institutions using them. Moreover, as AI technologies proliferate, it could exacerbate educational inequities by further widening the digital divide, potentially leaving marginalised and under-represented groups at a disadvantage (Imbrie, 2024; UNESCO, 2021) if existing policies do not focus on a roadmap of comprehensive guidelines for promoting critical AI literacy in higher education stakeholders. Recent research provides groundwork for developing comprehensive and relevant guidelines to ensure the ethical use of AI in higher education, enabling all stakeholders to navigate its complexities responsibly. From this perspective, we have identified several key 9 elements necessary for creating effective guidelines on AI ethics and responsible use in higher education. These guidelines should be tailored to different target groups, clarify roles in AI interactions, encompass various application areas, and establish a well-defined scope of guidance. Our findings emphasize the need for further, particularly evidencebased, research to assess both the potential and practical impact of AI in higher education. It is crucial to integrate AI use in education with education about AI—commonly referred to as AI literacy—to ensure that all stakeholders, including students, educators, education administrators, and policymakers, understand both the opportunities and risks associated with AI in higher education. Ultimately, AI itself is neither ethical nor moral; rather, it is people who bear this responsibility. Therefore, AI policies in education should be designed to support institutions and individuals in upholding ethical responsibilities. Furthermore, our research reiterates the necessity of continued evidence-based inquiry into the impact of AI in higher education while reinforcing the importance of combining AI implementation with AI literacy initiatives. Policies are being developed to inform the ethical, safe and effective integration of AI into educational practices. International agencies 16 3 Methodology of Narrative Review Our study examined “Artificial Intelligence Policies for Higher Education” using a critical narrative review methodology with the objective to develop a “Manifesto for Critical Considerations and a Roadmap”. In contrast to a systematic review, which often concentrates on a specific subject within a particular context and utilises a predetermined process to synthesise results from related studies, a narrative review can incorporate a wide range of literature and offer a comprehensive view along with interpretations and discussion (Sukhera, 2022). Further, a narrative review approach allows for the comprehensive and meticulous determination of the primary research on the subject, enabling the drawing of inferences based on the researchers' professional experiences and pre-existing theories (Demiris et al., 2019). Topics that need an effective synthesis of research evidence, which may be broad or complex, and that call for in-depth, sophisticated analysis and interpretation are frequently well-suited for narrative reviews (Greenhalgh et al., 2018). Likewise, this approach enables researchers to describe what is already known about the topic and perform subjective evaluation and critique of reviewed studies (Sukhera, 2022). In our study, an extensive searching technique was implemented 17 across numerous internet-based databases including Web of Science as the most restrictive indexing service of peer-reviewed journal publications. The sources of information for the analysis were chosen based on its timeline (2020-2025), its connection with the research subject, and dissemination in quality publications. By employing narrative reviews in the process of reviewing the literature within the topic, scholars are able to first describe what is already known and the current issues with the topic, then advance the body of knowledge by generating new insights from different perspectives as well as a new theory (Rumrill & Fitzgerald, 2001). Therefore, this method enabled to investigate the current status of AI policies for higher education, as represented in recent publications, in a comprehensive thematic manner. By choosing and collecting relevant information from previous publications and addressing inconsistencies using a consensus decision-making procedure, the researchers carried out data extraction. Likewise, researchers were able to thoroughly identify and arrange common themes pertaining to artificial intelligence policies for higher education by analysing and synthesizing the records from selected publications using a thematic analysis approach (Naeem et al., 2023). Thus, these studies can be helpful in examining under- 18 researched subjects as well as in providing fresh perspectives on established, thoroughly studied domains (Sukhera, 2022) in our case artificial intelligence policies for higher education and proposing the new insights on these policies and advancing this field. The foundation for this narrative review was our research objectives as well as the socio-technical system theory, which aims to illustrate and address the theoretical and practical challenges of integrating technology into educational systems (Ropohl, 1999). This conceptual framework has been also successfully used in numerous prior studies (Onesi-Ozigagun et al., 2024; Vinay & Surendra, 2024) which explained the reciprocal interactions between individuals and the integration of AI technology and its implications for organisational transformation (Dervić et al., 2025). The critical narrative review approach, employed in this study and which proposes a narrative synthesis of literature through an interpretative lens, implies the interpretation which “combines the reviewer's theoretical premise with existing theories and models to allow for synthesis and interpretation of diverse studies” (Sukhera, 2022, p. 416). In order to gather data and gain thorough and deep insights into 19 different facets of policies for artificial intelligence for higher education, we examined studies that used a variety of methodological techniques. 4 Results and Discussion: Manifesto and Roadmap AI policy development in higher education should be informed by various critical considerations, including overarching regulations and guidelines, operational guidance (implementation), and individual AI literacy. Firstly, there needs to be an understanding of the overall regulations and guidelines that govern the ethical use of AI. Additionally, operational guidance is essential for implementing effective strategies. Furthermore, promoting AI literacy among students and staff is imperative. This ensures that everyone is equipped with the knowledge and skills needed to responsibly and effectively navigate the complexities of artificial intelligence. Aiming to facilitate the strategic policy planning processes for the use of AIED systems across countries, we propose a policy priority framework. This framework is intended to be used as strategic planning instrument for higher education stakeholders developing AI policies and guidance taking into account the cultural diversities and context of each country (i.e., digital education readiness, AI readiness, etc.). This is 20 followed by a strategic roadmap for AI policy development, aiming to offer valuable insights into how higher education can effectively leverage the AI potential while ensuring ethical considerations, promoting equity and maintaining academic integrity. Aiming to enhance strategic policy planning for the ethical and responsible use of AI in higher education institutions across countries, we investigated the relationship between AI technology and educational policy in higher education, concentrating on critical considerations for AI policy contextualisation. Our resulting manifesto proposes a roadmap that could serve as an instrument for practical implementation in multiple given specific situations and contexts. Manifesto: Critical considerations for AI policy development The emerging critical considerations for stakeholders developing and designing an own AI policy in their own institution include: Critical consideration 1: Regulatory framework An overarching framework can provide a theoretical approach to the topic that will guide the ethical and responsible use of AI in higher 21 education. At the same time, such framework should encompass practical guidelines that can offer contextualized answers to questions clustered by topic, sector, target group, etc. Ensuring this way consistency and coherence across different levels of education. In addition, it is important to promote collaborative and co-creation approach, involving all stakeholder segments, including students, teachers, parents, administrators, and policy makers; that instead of limiting policy development initiatives to specific educational levels or target groups. Such an inclusive approach will ensure that the proposed frameworks are comprehensive and reflect the direct needs and interests of all the involved stakeholders of the higher education community. To ensure the effectiveness of such regulatory frameworks, it is important to adopt a risk-based approach, aligned with the EU Artificial Intelligence Regulation (usually known as AI Act (EU AI Act 2024/1689) and the Framework Convention by the Council of Europe (CoE, 2024). The AI Act addresses AI providers and classifies AI systems based on their potential risks, which shapes the regulations accordingly. The Framework Convention focuses individual and global rights and in particular the values of human rights, democracy and rules 22 of law for the deployment of AI systems and services. Both frameworks however do not explicitly address education. In addition, the contextualisation of the practical guidelines for the implementation of the regulatory frameworks will provide a set of clear directions linked with the use of AI systems in specific educational contexts adapting to the cultural and pedagogical needs. Acknowledging that children and education, constitute unique cases, there is a need for a legal framework aimed at regulating AI systems within educational environments, as highlighted in the Council of Europe Preparatory Study for the Development of a Legal Instrument on Regulating the Use of AI systems in Education (CoE, 2024). This proposal for a comprehensive legislation aims to address the distinct challenges of the use of AI in education while ensuring the protection and promotion of human rights, democracy, and the rule of law. Critical consideration 2: Stakeholder-specific guidelines Customized guidelines are needed to meet the specific needs of each group involved: it is important to ensure that all stakeholders (educators, institutions, children, parents) play an active role in ethical AI application in Education. Therefore, guidelines and policies should be 23 tailored to their needs and roles of the different stakeholders taking into account their distinct needs for understanding and utilising AI system in education as well as evaluating their effectiveness. In addition, they should ensure that all stakeholders can deal responsibly with the complexities of AI. These guidelines should address different target groups, define roles in AI interaction, cover diverse application areas, and provide a clear scope for their guidance. An agile approach for the development of guidelines should be adopted in order to ensure alliance with the evolving aspects of the use of AI in education. Furthermore, the country cultural and digital education readiness level should be taken into account. Critical consideration 3: AI&ED Research Institutional AI policy development should be guided by and aligned with AI and Education (AI&ED) research. AI&ED research is required to analyse and evaluate the impact of AI use in education (AIED) and the need for AI literacy. Such research should be based on evidences to determine the potential and practical impact of AI in higher education. 24 In particular, there is the need for evidence-based research to analyse precise conditions and long-term effects. The monitoring and evaluating of the use of AI systems in Education is crucial to identify potential impact and gaps of related AI policies. Critical consideration 4: AI literacy There is an urgent need to combine AI use in (higher) education with education about AI, often called AI literacy, to ensure that all stakeholders and target groups (students, teachers, education managers and policy makers) are aware of the potential opportunities and risks of AI use in (higher) education. In the final analysis, AI is not ethical nor moral; people are. AI literacy must encompass the ethical use of artificial intelligence as it grows in education. Students and teachers need skills to evaluate and use AI responsibly, balancing technical abilities with ethical considerations (Zimmerman, 2018). Literacy programs should involve the whole school community, including parents, focusing on evaluating AI-generated content and recognizing bias to uphold academic integrity. The AI Act (EU AI Act 2024/1689) recently formalised the concept of AI literacy as the obligation for AI system vendors and those who deploy 25 systems to devise appropriate measures to ensure a sufficient level of understanding of AI's functioning, potentialities, limitations, and risks. Even though AI is a long-standing field, most of the research on how to develop non-expert literacy has been published in recent years, and discussions on how to improve it are ongoing, in part because it must be funded on other types of competences, such as digital literacy (European Union, 2024). Issues raised in the AI literacy debate revolve around the importance of ethical AI use, which summarises the ethical concerns and challenges associated with the regulation and governance of AI technologies for a sustainable development that balances the undeniable benefits with the need to protect universally recognised values through a risk-anchored approach (Jobin et al., 2019). Integrating ethics into AI literacy programs is essential for responsible AI use that benefits society (Ng et al., 2021). Educating developers, users, and policymakers fosters a technological culture that balances innovation with respect for fundamental rights (Microsoft, 2021). Understanding AI's social and moral implications is vital to prevent discrimination and ensure equitable distribution of technology's benefits in education (Burgsteiner et al., 2016; Ghallab, 2019). 32 establishing guidelines and policies that ensure the responsible and sustainable use of AI. Governance must involve a plurality of actors, including educators, students, administrators, ethicists and civil society representatives, to ensure that each decision takes into account different perspectives and potential impacts. It is also crucial to promote transparency in decision-making processes, ensuring that algorithms are understandable and that the criteria for using AI are clear and shared. 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