The swift advancement of technology and the growing accessibility of artificial intelligence (AI) enriched tools have profoundly reshaped the landscape of higher education. The adoption of AI-enriched tools is driving a rapid transformation of study practices, presenting not only novel opportunities but also intricate challenges. These developments underscore the necessity for innovative pedagogical approaches and well-considered strategies for effective technology integration. UNESCO Guidance for generative AI in education and research (2023) emphasizes that AI has the potential to significantly change education systems, but its implementation must be ethically justified, fair and appropriately regulated. The literature highlights the potential of generative artificial intelligence tools (e.g., ChatGPT, Gemini) to personalize learning, improve student engagement and streamline administrative processes, while raising concerns about academic integrity, ethical use and the digital divide (Cai et al., 2024; Kurtz et al., 2024; Ali et al., 2024). Research by UNESCO highlights that Generative Artificial Intelligence tools have significant potential to promote learning and research, but their use must be based on a human-centred approach that respects ethics, inclusion, gender equality and cultural diversity (Miao and Holmes, 2023). In turn, the OECD research project “AI and the Future of Skills”, implemented at the Centre for Educational Research and Innovation, confirms that AI is fundamentally changing the work and education environment, requiring a thoughtful approach to assessing AI capabilities and comparing them with human skills (OECD, 2023). The impact of AI on education and employment is directly related to how the requirements for competencies – especially digital, problem-solving and collaborative skills – are changing, and how education systems are able to adapt to these changes. The OECD emphasizes that a multidimensional approach is needed, simultaneously encompassing both expert assessments and direct AI indicators, to understand the development of AI technologies and its interaction with human competencies in the context of education (OECD, 2023).

Strategic integration of AI-enhanced tools in higher education opens up opportunities for the transformation of the study process. Such technologies can enhance the learning experience by providing adaptive feedback, supporting self-directed learning and facilitating collaboration. Of particular importance are the possibilities for personalization, targeted feedback and structuring of study processes based on the development of student autonomy, critical thinking and reflection skills (Cai et al., 2024; Chang et al., 2023; Vorobyeva et al., 2025; Kakhkharova and Tuychieva, 2024). At the same time, integrating AI into education brings notable challenges. Key concerns include data privacy risks, algorithmic biases, and the widening digital divide all of which may impact both the accessibility and the quality of educational experiences (Alotaibi, 2024; Eden et al., 2024; Vorobyeva et al., 2025). The professional development of academic staff is also a significant issue – the effective use of AI tools requires not only digital literacy, but also the ability to integrate them into the study process in a pedagogically sound manner. Therefore, comprehensive professional development is needed to strengthen both technical and didactic competence. To address these challenges, well-thought-out educational policies and ethical regulations that set the boundaries of the use of AI and promote transparency are essential. At the same time, it is important to provide long-term support mechanisms for academic staff, especially those who are less confident in using AI (Chen et al., 2025; Sidorkin, 2025). A well-considered and strategic approach that aligns innovation potential with a commitment to academic integrity and equity can enable higher education institutions to leverage AI’s transformative capabilities for fostering inclusive and impactful learning experiences (Alotaibi, 2024; Alqahtani and Wafula, 2024; Cai et al., 2024; Eden et al., 2024).

These changes are occurring at a time when the education system must be able to adapt not only to technological challenges, but also to societal and labor market demands. The open innovation approach offers an effective way to integrate AI-enriched tools into the study process, promoting collaboration between educational institutions, technology developers, and students as active co-creators. This perspective aligns with broader frameworks: the Sustainable Development Goals, particularly Goal 4 on quality education, which emphasizes inclusive and equitable learning opportunities (United Nations, 2015), and technology acceptance models such as the Unified Theory of Acceptance and Use of Technology, which provides guidance on factors influencing the adoption of new digital tools in educational settings, including performance expectancy, effort expectancy, social influence, and facilitating conditions (Taiwo and Downe, 2013). In this context, it is essential to understand how the integration of AI-enriched tools affects the transformation of the study process, how this change is perceived by students and academic staff, as well as what competencies are needed to ensure the meaningful and sustainable use of AI in higher education.

The aim of the study is to analyze the perceptiveness and evaluation of students and academic staff regarding the transformation of the study process, considering the integration of AI-enriched tools in higher education within the framework of the open innovation approach. In line with the study’s aim, the following research questions have been formulated:

In order to respond to the research questions and reach the study’s goal, the authors have defined the following research tasks:

The study analyzed both quantitative and qualitative data to understand the perspectives of academic staff and students on the integration of AI-enriched tools in higher education. Data triangulation allowed the identification of both common and different aspects regarding perceptions, risks, benefits, and competence development needs.

Quantitative data were collected from 97 respondents to 45 students (mean age 30.93) and 52 academic staff (mean age 48.19). Among students, 14 were enrolled in first-cycle professional higher education programs, 23 in bachelor’s programs, 4 in master’s programs, and 4 in doctoral studies. This distribution helps explain the relatively high mean age of the student group, as it includes adult learners and students at advanced levels of study. The student group included 35 women and 10 men, whereas the academic staff group was composed of 38 women and 14 men. This distribution shows that the sample represents a relatively balanced gender structure, with slightly higher female participation, and reflects the diversity of international backgrounds in higher education. To assess the opinion on the integration of AI-enriched tools in higher education, the respondents’ (academic staff and students) ratings of the role of AI-enriched tools in the study process were analyzed. Since the data did not meet the assumptions of parametric statistics (p < 0.05), group differences were tested using the Mann–Whitney U test. Mean ranks are reported as recommended for non-parametric group comparisons, while mean values are provided for descriptive purposes only (see Table 2).

The analysis revealed several statistically significant differences between students and academic staff. Overall, academic staff consistently rated the role of AI-enriched tools in the study process higher than students. For example, academic staff rated the role of AI in the study process higher (mean = 5.12) compared to students (mean = 4.53), and this difference was statistically significant (p = 0.037). A similar trend was observed with regard to the development of transversal competencies: academic staff were much more confident in stating that AI improves both their own (mean = 5.37, p < 0.001) and students’ (mean = 4.92) competencies than students.

A significant difference was also found in the question of the need to supplement knowledge in the use of AI – the average rating of academic staff (mean = 5.60) was higher than that of students (mean = 4.73, p = 0.012), indicating their increased self-reflection and readiness to improve. Similarly, the opportunities created by AI for improving the study process were rated significantly higher in the academic staff group (mean = 5.69 vs. mean = 4.53, p < 0.001). In turn, a statistically significant difference (p = 0.005) was found regarding the threats posed by AI in the study process, where academic staff (mean = 5.33) perceive risks more strongly than students (mean = 4.38), which could indicate a deeper understanding of aspects of ethics, academic integrity, or privacy.

Overall, the results indicate differences in perceptions between academic staff and students, especially regarding the opportunities and challenges of AI in improving the quality of education. This indicates the need for coordinated awareness-raising and support mechanisms that would promote more active engagement of both students and academic staff in the integration of AI-enhanced tools into the study process.

Two focus group discussions were organized as part of the study. The discussions were structured according to the research questions. Thematic analysis (Braun and Clarke, 2006) was used for data analysis, which allowed for the identification and systematization of main themes and subthemes. The results were structured within the framework of SWOT analysis, dividing the responses into four thematic blocks: strengths, weaknesses, opportunities and threats. Each of these categories revealed distinctive patterns and insights, beginning with the strengths identified by both academic staff and students.

Strengths: In focus group discussions, participants from both groups acknowledged that AI provides significant advantages in the study process. The ability of AI-enhanced tools to quickly summarize and structure information, as well as to provide conceptual overviews that help to better understand complex topics, was emphasized. Several participants mentioned that AI can act as an “idea generator” or “first draft assistant,” especially when working on creative tasks, project ideas or preparing lecture content.

Academic staff members pointed to the usefulness of AI in saving time and facilitating routine tasks, such as checking grammar, creating references or selecting thematic information. The role of AI in supporting students’ self-directed learning was also positively assessed. Some participants compared AI to an “additional intellectual resource” that can help ask better questions and expand thinking.

While the strengths reveal considerable optimism and perceived benefits, the discussions also brought attention to a number of challenges and limitations associated with AI integration. These are reflected in the following weaknesses. Weaknesses: Both students and academic staff acknowledged that in practice there is still a lack of full knowledge and confidence in how to effectively and ethically integrate AI-enhanced tools into the study process. In particular, limited AI skills and a lack of critical thinking regarding the information generated by AI were highlighted. Students indicated that they often do not know how to evaluate the content offered by AI or how to interpret it, while academic staff highlighted the lack of methodological guidelines from institutions.

Several students in the discussion admitted that they do not use AI-enhanced tools themselves in the study process or do so very minimally. There was a certain caution and distrust of the information provided by AI, especially in relation to the correctness of the language or the quality of the information. At the same time, it was mentioned that academic staff lacks support, both technical and pedagogical, to ensure that the integration of AI is consistent and useful.

Despite the identified gaps and challenges, focus group participants also saw significant development directions and opportunities that the use of AI-enriched tools could open up in the transformation of the study process. Opportunities: Participants mentioned personalized learning opportunities, adaptive feedback, and individual progress monitoring as areas where AI can significantly improve the student study experience. Academic staff members expressed greater optimism about the possibilities of AI in the development of study materials. It was mentioned that AI can serve as a tool that supports interdisciplinary collaboration, helps prepare for labor market challenges, and promotes student autonomy. Several participants emphasized that the results provided by AI should be interpreted with critical thinking, and users should be able to assess the reliability of the information themselves.

However, alongside the opportunities, concerns were also expressed about the risks and potential negative consequences that could arise if AI-enriched tools are not used thoughtfully and responsibly. Threats: The tendency for students to use AI to avoid active engagement and reflection, thus undermining academic integrity, was highlighted. Concerns were expressed that AI-enriched tools are used for automated task completion rather than as a support for thinking and analysis. Unequal access to AI-enriched tools was also mentioned, which may exacerbate the digital divide between different user segments. Both students and lecturers pointed out the need to strengthen ethical guidelines and responsible use to prevent risks to information quality and AI-generated erroneous or unverified content.

The focus group findings provided qualitative support for several patterns identified in the survey data. For example, the quantitative results showed that academic staff consistently rated the role of AI-enriched tools higher than students, particularly with regard to opportunities for improving the study process and supporting competency development. This trend was reflected in the focus group discussions, where academic staff expressed greater optimism and confidence in the pedagogical potential of AI, while students more frequently emphasized uncertainty, limited experience, and the need for guidance. Similarly, the survey results indicating moderate confidence in AI-related skills among both groups were echoed in the focus group themes related to insufficient digital competence, the need for professional development, and concerns about ethical and responsible use. These qualitative insights helped to contextualize the quantitative findings and deepen the understanding of underlying perceptions and concerns.

The conceptual model presented in Figure 2 was developed through the integration of quantitative survey results, qualitative focus group insights, and the structured synthesis provided by the SWOT analysis. The survey findings revealed statistically significant differences between students and academic staff regarding the perceived role of AI-enriched tools in improving the study process, supporting the development of transversal competencies, and addressing challenges related to AI integration. These findings informed the model components related to stakeholder roles, competency development, and perceived opportunities and risks.

The focus group discussions further contextualized and expanded these results by highlighting concrete experiences related to institutional support mechanisms, leadership, ethical considerations, and professional development needs. These themes are reflected in the model as enabling and contextual conditions for meaningful AI integration. In turn, the SWOT analysis provided a systematic synthesis of strengths, weaknesses, opportunities, and threats associated with AI-enriched tools, directly informing the model’s dimensions related to institutional readiness, sustainability, and collaboration. Through this triangulation of data sources, the conceptual model represents an integrative framework that links empirical evidence with theoretical insights to illustrate the key conditions, stakeholders, and support mechanisms involved in the sustainable transformation of the study process through AI-enriched tools.

As illustrated in Figure 2, the proposed conceptual model identifies three interrelated support mechanisms that underpin the meaningful and sustainable integration of AI-enriched tools in higher education within an open innovation approach. The first mechanism, competence-based support, refers to the development of digital, research, and innovative competencies among both students and academic staff. The empirical findings indicate that moderate self-assessed confidence in using AI tools highlights the need to strengthen these competencies as a foundation for effective AI adoption. The second mechanism, professional and pedagogical development support, emphasizes continuous professional learning, methodological guidance, and capacity building for academic staff. This mechanism reflects the study’s findings that academic staff play a key role in driving AI integration, yet require ongoing support to align technological possibilities with pedagogical goals. The third mechanism, ethical and regulatory governance support, encompasses institutional policies, ethical guidelines, and regulatory frameworks that ensure responsible, transparent, and equitable use of AI. Both survey and focus group data underscored the importance of clear ethical boundaries and institutional leadership in mitigating risks related to academic integrity, data privacy, and bias. Together, these support mechanisms operate within a collaborative ecosystem involving students, academic staff, institutions, and industry, reinforcing the open innovation approach and enabling the pedagogical strategies necessary for sustainable educational transformation.

The results of the study allow us to draw conclusions about the views of students and academic staff on the integration of AI-enriched tools in higher education. The effective integration of AI-enriched tools in higher education requires a systematic approach that includes both professional development and institutional support for academic staff, as well as strengthening students’ digital competence and building ethical awareness. The results also highlight the importance of interdisciplinary collaboration and active learning as important aspects to achieve a modern and future-oriented learning environment. Integration is not a one-time process, but a continuous collaboration between stakeholders, policymakers, academic staff, students and industry representatives. It is precisely such an open, ethically based and collaborative approach that can ensure that the potential of AI is used to its full potential, responsibly and sustainably.

The literature reviewed in the study shows that integrating AI-enhanced tools into higher education can significantly improve teaching and learning outcomes if institutions adopt comprehensive strategies that address pedagogical, ethical, and technical aspects (Kurtz et al., 2024; Chang et al., 2023; Celik, 2022; Cordero et al., 2024; Chan, 2023; Chiu, 2024; Walter, 2024; Eden et al., 2024). The literature confirms the need for training academic staff, curriculum redesign, and AI literacy development among both educators and students (Kurtz et al., 2024; Chang et al., 2023; Celik, 2022; Ng et al., 2023; Iskandarova et al., 2024). However, the authors also highlight potential challenges, including the digital divide, ethical concerns, and resistance to change, that hinder the equitable and sustainable implementation of AI-enriched tools in educational settings (Ali et al., 2024; Alotaibi, 2024; Iskandarova et al., 2024; Eden et al., 2024).

The results of the study confirm the trends identified in the literature and reveal that both students and academic staff recognize the potential of AI in improving the study process. While both groups acknowledge AI’s value in education, academic staff consistently provided more positive evaluations. This discrepancy may be attributed to their deeper disciplinary knowledge, more extensive exposure to institutional initiatives, or a stronger sense of ownership in shaping educational innovation. This is consistent with previous findings that highlight the importance of faculty digital competence and proactive attitudes in the adoption of educational technologies (Mah et al., 2025; Titko et al., 2023). It is also noteworthy that several differences between students and academic staff were not statistically significant, particularly regarding self-reported confidence in using AI-enriched tools. The moderate mean scores reported by both groups suggest a shared perception of insufficient AI-related skills, indicating a broader competence gap that affects both learners and educators. This finding highlights that AI integration challenges are not limited to one stakeholder group but require institution-wide capacity-building initiatives.

Although the study involved participants from multiple countries, the findings are interpreted at an aggregated level. Differences in national education systems, institutional strategies, disciplinary cultures, and levels of digital maturity may influence how AI-enriched tools are perceived and integrated in practice. While these contextual factors could not be examined systematically due to sample size and distribution constraints, the observed patterns suggest that institutional roles, access to innovation processes, and professional responsibilities may play a more significant role in shaping perceptions than national context alone. Future research could build on these findings by adopting comparative or multi-level designs that explicitly examine how contextual and cultural factors mediate AI integration in higher education.

This study also explores the integration of AI-enriched tools through the lens of the open innovation approach, emphasizing the importance of stakeholder engagement and cross-sectoral collaboration. Theoretically, the findings reinforce the importance of open innovation as a framework for analyzing AI integration in higher education. These principles suggest that higher education institutions should not operate in isolation but instead create innovation ecosystems involving students, academic staff, administrators, industry, and policy makers. The focus group data support this view by revealing that academic staff are more actively involved in the implementation of AI-enriched tools and perceive themselves as contributors to institutional innovation. This aligns with prior research emphasizing the critical role of faculty in driving technological change (Titko et al., 2023). In contrast, students appear less engaged in these processes, which may signal a missed opportunity for participatory design and co-creation. This discrepancy points to the need for a more intentional inclusion of students as co-creators in AI-related educational reforms. Bridging this gap requires deliberate strategies to strengthen both technological and social collaboration across institutional roles. An inclusive culture of AI integration should be fostered through shared responsibility, open dialogue, and mechanisms that build trust, capacity, and a common vision.

AI-enriched tools hold considerable promise for reshaping educational practices and enhancing the learning experience. However, the realization of these benefits depends on how well the challenges related to technology, academic staff competence and ethics are addressed. Sustainable and meaningful integration demands not only access to tools, but also a clear ethical and pedagogical framework that guides their use. Strategic planning, professional development support and responsible leadership are critical factors for achieving effective and equitable integration of AI-enriched tools in higher education. From a practical and policy perspective, these insights underline the need for institutional strategies that balance innovation with equity and ethics.

In light of these findings, we recommend developing structured professional development initiatives that extend beyond technical training to encompass ethical reasoning, pedagogical innovation, and critical reflection. These efforts should be grounded in inter-institutional collaboration and aligned with the principles of open innovation, ensuring that AI integration contributes to both academic excellence and institutional transformation.

The authors acknowledge certain limitations of the study; the selection of model components was informed by triangulated data, yet their validation calls for further investigation. One limitation is the use of convenience sampling for the focus group discussions, which may limit the transferability of the qualitative findings, as participants were selected based on their availability and willingness to participate. While this approach allowed for valuable qualitative insights from respondents with direct experience using AI tools, it limits the generalizability of the results to a broader population of academic staff and students. In addition, due to the distribution of the assessment survey through multiple institutional and professional channels, a precise response rate could not be calculated. The use of voluntary participation and the absence of a documented response rate may have introduced self-selection bias, as individuals with a stronger interest in AI-related topics may have been more likely to participate. Although the study involved respondents from multiple countries and study levels, the uneven distribution across these categories limited the feasibility of subgroup analyses. Therefore, the findings should be interpreted as indicative of general trends rather than context-specific differences. Although the qualitative phase followed the quantitative phase, the focus group participants were separate and non-overlapping from the survey respondents. As a result, the qualitative data were used to contextualize and deepen the interpretation of overall quantitative trends rather than to explain individual survey responses, which limits the explanatory strength of the mixed methods design at the individual level. Another limitation is that the focus group discussions were conducted in Latvian, which may have influenced nuances of meaning during translation into English for analysis and reporting. Future studies would be recommended to use a more representative sample or expand the range of participants to strengthen the comparability of data across institutional and cultural contexts.

By conducting this study, the authors explored student and academic staff perspectives on how the integration of AI-enriched tools, within the framework of the open innovation approach, contributes to the transformation of the study process in higher education. Quantitative and qualitative methods complemented each other, providing a multidimensional perspective on the potential, challenges, and required competencies of integrating AI-enriched tools.

Regarding the first research question (What opportunities and challenges are seen by students and academic staff while integrating AI tools into the study process?), the authors concluded that the integration of AI-enriched tools is perceived as a potential driver for the transformation of the study process, offering benefits such as information structuring, personalized learning experiences, and time savings. The focus groups emphasized the support of AI in generating ideas, strengthening self-directed learning, and interdisciplinary thinking. However, several challenges exist, including insufficient user skills, lack of critical thinking, risks to academic integrity, and insufficient institutional guidelines that limit the meaningful and ethical use of AI.

Regarding the second research question (What competencies and prerequisites are considered essential for the effective use of AI-enriched tools in the study process?), the authors concluded that the effective integration of AI-enriched tools requires not only specific skills, but also a broader set of competencies. Among the most essential competencies, digital competence, ethical awareness, critical thinking and the ability to formulate questions precisely were identified. Innovation competence, the ability to generate new ideas, adapt technologies to pedagogical needs and create creative solutions, and research competence, which includes the ability to analyze AI-generated content, assess its reliability, as well as use AI as a tool for data acquisition, interpretation and formulation of conclusions, also play an important role. Both students and academic staff recognized that the use of AI is effective only if the user is able to evaluate the quality of information and adapt AI-generated content to the specific situation. The development of these competencies requires systematic institutional support and targeted professional development programs.

The results of the study show that the integration of AI-enriched tools offers significant transformational potential for both the study process and for institutional operations as a whole. However, this transformation cannot happen without a strategic approach that combines pedagogical, ethical, technological and organizational aspects. In summary, the study emphasizes that the transformation of the study process with AI-enriched tools requires a multi-level, systemically organized approach, in which the development of competencies, support mechanisms (methodological, technical, administrative), ethical regulation and an open innovation approach play a significant role—in cooperation between students, academic staff, management and industry representatives.

For sustainable and inclusive integration, it is also necessary to strengthen students participation, which is currently limited. Further research is recommended to investigate in depth the long-term impact, to develop strategies and competencies frameworks, and to evaluate the practical implementation of AI-enriched tools in higher education.