Key points are not available for this paper at this time.
Soon AI may move beyond its role as a mere tool to funcBon as a creaBve agent-potenBally even as a virtual student or professor-capable of generaBng original artworks and contribuBng to research leadership. However, it remains unclear whether educaBonal insBtuBons are adequately prepared for such a rapid integraBon of AI into the educaBonal and research processes. This quesBon becomes parBcularly relevant in the context of rapid advancement of large language models (LLMs) and generaBve arBficial intelligence (GenAI) and their potenBal to transform both the landscape of scienBfic research and educaBonal methodologies. This work therefore examines how educaBonal insBtuBons are responding to the integraBon of AI into research and educaBon. Specifically, we analyzed policies and guidelines regulaBng the use of GenAI in both general universiBes and art-focused insBtuBons, and conducted a strategic review of insBtuBonal approaches, along with a content analysis of some curricula related to GenAI implementaBon. Based on the analysis, we posit that current GenAI policies in higher educaBon are largely reacBve, unevenly implemented across regions and disciplines, and oWen fail to address research-specific use cases and the disBnct challenges faced by art-focused insBtuBons. This finding aligns with recent work showing that insBtuBons tend to conform to external regulatory, normaBve, and mimeBc pressures in their adopBon of GenAI, oWen prioriBzing legiBmacy and compliance over proacBve strategic vision 14. From an educaBonal perspecBve, researcher further argue that Bloom's Taxonomy requires revision to address the cogniBve, affecBve, and metacogniBve demands of AI-assisted learning, underscoring the need for insBtuBonal policies that not only regulate GenAI but also foster criBcal thinking, ethical reasoning, and iteraBve learning processes in higher educaBon 15.UniversiBes worldwide face the dual nature of using these technologies 5,6, navigaBng the potenBal for innovaBon alongside the need to address ethical and pracBcal concerns 2 that may disproporBonately affect students and faculty 8. Notably, current guidelines for the use of GenAI in higher educaBon tend to represent a reacBve response, oWen embedded within modern technology programs. The discourse surrounding GenAI in higher educaBon is mulBfaceted, encompassing discussions on academic integrity, innovaBve teaching programs, and the potenBal for GenAI to enhance student outcomes 2,3. With the release of ChatGPT, these issues have gained a parBcular relevance. While many universiBes have experienced challenges associated with plagiarism, data confidenBality and privacy 1,2,10, art-focused universiBes have encountered issues related to copyright protecBon and assessment of the originality of creaBvity 4.In this paper, we comprehensively reviewed publicly available policies and guidelines regarding the use of GenAI in educaBon and research from higher educaBon insBtuBons in the US, Europe and Central Asia. General universiBes from the US and Europe were selected from among the leaders of the QS World University Rankings 2024. For the US, we focused on the top 20 universiBes according to the QS World University Rankings 2024 (general category). The list of European universiBes was compiled with explicit consideraBon for geographical diversity, ensuring representaBon from different European countries. StarBng from the top of the QS rankings, we applied an addiBonal rule: if several consecuBve insBtuBons belonged to the same country (e.g., mulBple from the UK), we selected the highest-ranked one and then moved to the next university from another European country. This procedure helped avoid overrepresentaBon of a single country while sBll capturing leading insBtuBons. If a university appeared in the ranking but did not provide publicly available GenAI-related policies or guidelines (i.e., official documents explicitly outlining recommendaBons or regulaBons), it was excluded from the sample. In such cases, we conBnued down the QS list unBl a suitable insBtuBon was idenBfied. References to projects, pilot iniBaBves, or informal menBons of GenAI were not considered sufficient for inclusion. It is important to note that although many insBtuBons arBculate strategies (e.g., vision statements, curricular integraBon plans), these do not always translate into concrete policies (e.g., official guidelines for faculty and students). Throughout the analysis we therefore treat strategies as indicaBve of insBtuBonal intent, while policies are considered evidence of operaBonalized regulaBon.The list of art universiBes was compiled based on the QS World University Rankings by Subject (Art however, policies regulaBng the use of generaBve AI are less developed in art-focused universiBes. Policies addressing students and faculty are more prevalent than those for researchers, however, 40% of the analyzed universiBes have separate guidelines for researchers with detailed recommendaBons. In Central Asia universiBes, there is a notable absence of policies and guidelines regulaBng the use of GenAI. Only a few general universiBes, including Nazarbayev University in Kazakhstan and Westminster InternaBonal University in Uzbekistan, have published relevant documents. No policies or recommendaBons regarding the use of GenAI are found on the official websites of art universiBes in the region.To assess insBtuBonal readiness and policies regarding GenAI across different regions, we applied the theoreBcal framework proposed by Lim et al. 11, which encompasses seven dimensions of strategic planning. Building on this framework, we constructed a structured analyBcal matrix covering each dimension (e.g., vision and narraBve, curriculum integraBon, technology-centric support, human-centric support, stakeholder engagement), and mapped the content of insBtuBonal policies to these categories. Three researchers independently described the policies of each university across the seven dimensions of the Lim et al. framework. Their individual descripBons were then compared and consolidated into a unified account through discussion, which helped to ensure consistency of interpretaBon. While this approach provided a structured basis for cross-regional comparison, we acknowledge certain limitaBons: in several cases, insBtuBons did not provide comprehensive or publicly accessible documentaBon, and therefore only parBal evidence could be included, which may underrepresent the actual extent of insBtuBonal readiness. Separately, a raBng system inspired by the methodology of 7 was developed to evaluate the leniency of university policies. Table 1 presents the universiBes from US and Europe included in the analysis, along with their leniency raBngs based on a 5-point Likert scale. UniversiBes from Central Asia are excluded as most did not have GenAI policies yet. Three authors independently evaluated the policies aWer agreeing on raBng criteria. Final raBngs were determined by majority voBng. Nevertheless, we acknowledge the potenBal influence of subjecBve factors on the assessment results, which is primarily due to implicit ambiguiBes in the policy formulaBons.Most universiBes showed a moderate degree of leniency (raBngs of '3' or '4') to the integraBon of GenAI into the educaBonal process and scienBfic research, while all educaBonal insBtuBons recognize the importance of using new technologies and the need to adapt to modern realiBes. For instance, The University of Chicago's policy received a '3' for educaBon because the university allows GenAI in selected tasks, from brainstorming ideas to clarifying complex concepts, but also sets limits on its use in some academic work. The University of Edinburgh was rated '4' as they permit the use of GenAI flexibly provided it is disclosed. Only two universiBes received a raBng of '2' for educaBon: the University of Amsterdam and Ludwig Maximilian University of Munich, because they allow only limited use for narrowly defined purposes.Takeaway: These iniBal observaBons highlight a fundamental gap: while many insBtuBons recognize the transformaBve potenBal of GenAI, policy responses remain largely reacBve and fragmented, lacking a unified framework to support both educaBonal and research pracBces.Table 1. Leniency raDngs on a 5-pt Likert scale for universiDes in USA and Europe, where "1" = "Extremely restricDve" (restricts almost all types of GeneraDve AI use), "2" = "Somewhat restricDve" (allows the limited use of GeneraDve AI for narrowly defined purposes, such as grammar correcDons, text simplificaDon, or non-sensiDve data analysis. Transparency and consultaDon are required before adopDng AI tools), "3" = "Neither lenient nor restricDve" (balanced with restricDons and allowances, permiTng selected tasks such as ediDng, summarizing, and idea generaDon under clear guidelines that prioriDze transparency, data protecDon, and accuracy. However, tasks such as examinaDons, discussion-based assessments, class tests, laboratories, and pracDcals are prohibited), "4" = "Somewhat lenient" (permits most applicaDons of GeneraDve AI in academic, research, and creaDve contexts, provided ethical guidelines are adhered to and usage is disclosed. Supports tasks such as academic content creaDon, data analysis, and creaDve exploraDon, while maintaining awareness of copyright and intellectual property concerns), "5" = "Extremely lenient" (allows the use of all types of GeneraDve AI). UniversiDes with no policies are marked as '-'. University policies were iniDally evaluated between December 9-18, 2024, and subsequently reviewed and revised between April 8-14, 2025.We analyzed a total of 30 general universiBes from the United States and Europe. However, selecBng art-focused universiBes based on rankings has been challenging due to the absence of publicly available policies and guidelines. As a result of an extensive review of exisBng policies and guidelines available online, at the end, we selected 6 US and 5 European art-focused universiBes, as shown in Table 1.As previously noted, the approaches of general universiBes and art-related insBtuBons are very similar, but there are slight differences in the universiBes' strategies. Table 2 shows the key differences and common strategies for GenAI between general and art-focused universiBes.That is, in general universiBes, the focus is placed on academic ethics and the educaBonal process, whereas in art-focused insBtuBons, the focus shiWs to authorship, creaBvity and cultural implicaBons of using GenAI. We further analyzed how general universiBes and art-related insBtuBons implement GenAI across insBtuBonal pracBces by applying the framework proposed by Lim et al. 11, which idenBfies seven core areas: vision and policy, curriculum, infrastructure and resources, professional development, student support, partnerships, and research. General universiBes such as MIT, Harvard, Stanford, Oxford, and KU Leuven have adopted a structured and mulBdimensional approach to GenAI integraBon, aligning closely with the seven areas outlined in Lim et al.'s (2019) framework. These insBtuBons typically begin with strong insBtuBonal visions that emphasize transparency, academic integrity, and ethical responsibility. Faculty are supported through detailed syllabus templates, training workshops, and ethical guidelines for GenAI use in both teaching and research. On the infrastructure side, many universiBes provide secure, licensed environments (e.g., PhoenixAI in UChicago, U-M GPT in UMich, AI Sandbox in Harvard) and riskassessment protocols to ensure data privacy. Human-centric support includes consultaBons, seminars, and instrucBonal design assistance to foster criBcal engagement with GenAI. Furthermore, policy development is collaboraBve and interdisciplinary, involving IT services, legal counsel, teaching centers, and ethics boards. This comprehensive model reflects a proacBve insBtuBonalizaBon of GenAI tools into academic, administraBve, and research processes.In contrast, art-related insBtuBons such as RISD, Ringling, MICA, Prat, and CCA are sBll in the early stages of GenAI policy development. While some of them have begun experimenBng with GenAI integraBon, their efforts remain largely decentralized and course-specific. Faculty are oWen granted autonomy to set GenAI-related rules, and insBtuBonal visions emphasize creaBvity, authorship, and reflecBve use of AI rather than systemic implementaBon. Curricular integraBon is typically limited to design and wriBng assignments, with an emphasis on process, iteraBon, and ethical exploraBon. Infrastructure is oWen limited to recommended external tools (e.g., Midjourney, ChatGPT, DALL-E), with safety and atribuBon guidelines provided through libraries and teaching centers. Human-centric support includes workshops, collaboraBve assignments, and criBcal dialogue around copyright, bias, and authenBcity. While some insBtuBons (e.g., RISD, Prat) are beginning to formalize policies through provost offices or AI task forces, engagement with stakeholders is typically informal and driven by teaching units, librarians, and design labs. This botom-up and discipline-driven model aligns only parBally with Lim et al.'s framework-primarily in the areas of curriculum, vision, and student support-highlighBng an ongoing transiBon toward more formalized insBtuBonal strategies.We analyzed the curricula of those universiBes to examine how they adapt to modern technological advancements. In recent years, they have been acBvely modernizing their educaBonal programs in response to the development of AI. In parBcular, the integraBon of specialized courses on GenAI highlights its growing role in creaBve work. Many art universiBes are integraBng courses on GenAI, AI-driven wriBng, and AI techniques in art in their curricula. In May 2024, for instance, Ringling College of Art and Design in the US announced the launch of a new AI undergraduate certificate program to equip students with the knowledge and skills needed to understand and use AI in creaBve industries of all kinds. In April 2024, in addiBon, Rhode Island School of Design in the US offered new courses on GenAI such as Designing with Emerging Technologies: GeneraBve AI and Text Transformed: WriBng in the Age of AI. However, many schools have no GenAI related policies for their students or faculty.In addiBon, Figures 2 and3 show the Bmeline starBng from the emergence of GPT-3.5, when policies for the use of GenAI began to be developed in the first a few general universiBes. The Bmeline figures were compiled based on the deadlines provided in the guidelines published on the official websites of the universiBes. In cases where date informaBon was not available, the date was esBmated using the Wayback Machine service, based on the Bme of the last update of the relevant material on the site. In general, universiBes first introduced GenAI policies for educaBonal acBviBes, and only a few subsequently extended these to research acBviBes. By and large, the primary focus has been on guiding students' learning and faculty's teaching pracBces involving GenAI.Takeaway: This contrast reinforces the core argument that while general universiBes are making structured efforts toward policy development, art-focused insBtuBons are lagging, exposing a disciplinary divide that leaves creaBve fields without coherent or comprehensive GenAI guidance. Figure 3 presents a Bmeline of policy publicaBons across regions. The US and Europe have been acBvely implemenBng guidelines and policies for the use of GenAI since 2023. In general, approaches to regulaBng this process show litle to no variaBon based on regional characterisBcs. Both areas place significant emphasis on academic integrity, transparency, confidenBal data protecBon and copyright compliance. Most universiBes were rated '3' or '4'. Notably, GenAI policies for research only began to appear in 2024, almost a year aWer the iniBal guidelines for educaBon were introduced.However, in Central Asia, the process of developing and adapBng either policy is sBll in its infancy. One of the primary challenges lies in limited technological access, insufficient resources, and inadequate insBtuBonal infrastructure 12, alongside the absence of centralized naBonal policies specifically addressing the use of GenAI in educaBon. Although many Central Asian universiBes have not yet developed formal guidelines, they are taking steps to integrate these technologies into the educaBonal process and research. In parBcular, universiBes organize various seminars, round tables, and discussions, while also acBvely integraBng GenAI courses into their curricula.Takeaway: The regional the policy in Central Asia, the in insBtuBonal the absence of centralized naBonal strategies and the need for GenAI in higher educaBon. Based on the analysis, the of higher educaBon insBtuBons clear policies or on the use of GenAI. The first includes universiBes, both general and that do not have policies or guidelines the for the use of GenAI in when or whether students use GenAI to research or faculty use GenAI to in wriBng of universiBes, only have guidelines in research. The of art-focused universiBes around the only a of which have developed relevant guidelines or policies. aWer an extensive search, we have only 6 GenAI policies from art universiBes in the USA and 5 in Europe. The is by universiBes in Central Asia countries. of 30 universiBes that we have only 2 universiBes have guidelines for the use of GenAI. Based on the we have the following recommendaBons for each of the of educaBonal analysis further the of applying Lim et al.'s framework to insBtuBonal policies. In leading and European universiBes, regarding GenAI (e.g., visions of and ethical are at parBally by concrete in other such as curriculum guidelines, of AI and faculty support By contrast, in art-focused insBtuBons, the between vision and is more While creaBvity, authorship, and ethical are at the (e.g., Ringling, RISD, MICA, Prat, these are only supported by curricular integraBon, or faculty and student engagement also to remain and this that although general universiBes oWen begin to translate their visions into art-focused insBtuBons sBll face significant between and the first insBtuBons researchers to the use of GenAI in research including the wriBng and of scienBfic publicaBons and is for ensuring the and of research. criBcal is the of parBcularly in cases where data may be used to AI UniversiBes also review to assess potenBal associated with the use of GenAI in research, including the of model and their on research universiBes are to AI among both researchers and This be through the development of on AI and the integraBon of AI into exisBng presents a comprehensive of recommendaBons for integraBng GenAI into higher educaBon. These educaBonal workshops, curriculum integraBon, ethical policy development, faculty support, the of criBcal thinking, and ensuring While many general universiBes have adopted these to art-focused universiBes sBll a coherent and unified for GenAI the need for guidelines and policy art universiBes challenges due to the of creaBve authorship, and Policies for these insBtuBons account for concerns such as privacy and data ethical and of as in parBcular be given to the and of creaBve in the of for the in Central is recommended that insBtuBons begin developing GenAI-related policies and guidelines by on the pracBces of leading and European universiBes, while at the same Bme adapBng them to the and insBtuBonal of the as well as to the concrete educaBonal of both faculty and efforts remain with and ethical by such as and the to to academic As although a suitable starBng for policy development, and are for its In this Table 2 a framework that highlights the of general and art-focused universiBes while also their which the of policies that are both and the same the of this analysis be in of several the only publicly available which may to an of the actual of insBtuBonal while to the limited of certain universiBes. the analysis was in nature and on although the descripBons were independently by mulBple researchers and subsequently the of subjecBve
Aristombayeva et al. (Mon,) studied this question.