Leading AI-Driven Student Engagement: The Role of Digital Leadership in Higher Education

Abstract

This theoretical position paper explores the potential of artificial intelligence (AI) to enhance student engagement through the lens of academic leadership. To illustrate our argument, we include exploratory, mixed-methods evidence drawn from a descriptive survey of 95 undergraduate students and five semi-structured interviews with key academic leaders. These data are indicative only and not intended for statistical generalisation; however, they ground and inform the theoretical propositions of this paper. Focusing on how AI-driven tools can be used, the study examines the central role of academic leaders in guiding these innovations. By addressing key leadership decisions—including resource allocation, policy development, and faculty support—the study explores how AI can foster a more responsive and engaging learning environment and provides insights into how academic leadership can guide the integration of AI technologies to increase student motivation, participation and academic success in different educational settings.

1. Introduction

Over the last few decades, the process of globalisation has enhanced worldwide interconnectedness and facilitated the rapid exchange of information, culture, and technology in various areas across borders. Consequently, as a massive increase in technological progress has been evidenced, digitalisation has started to affect all human activities, including higher education (HE).

Although the first steps in digitalisation might be traced back to the 1950s, its influence on the global HE arena has significantly increased over the last two decades. Rapid technological developments coincided with an ongoing process of institutional change. This involved redefining organisational paradigms, institutional missions and goals, and organisational culture and processes at higher education institutions (HEIs) (Levin, 1998, p. 409), resulting in significant digitalisation in academia and altering established practices. As a new digital era emerged, the necessity to implement digital transformation processes became a focus of HEIs. Consequently, most universities have adopted strategies in this area, defining specific goals and activities.

While many university activities have been digitalised, the process of digital transformation has profoundly influenced teaching and learning areas. Previous practice that relied on the combination of classroom experience and individual tutoring, dialogue with teachers and self-study gradually began transforming in the new online modalities, consequently bringing change in course design, forms of instruction, assessment, learning analytics, and credentialing (Schuetze et al., 2024).

However, the peak for transformation came with the COVID-19 crisis. As it globally stopped and remodelled everyday life, universities needed to embrace new ways of functioning and switch classroom teaching to online surroundings. All academics, both those who were to a certain point resistant to change and those who were not well technically equipped for the implementation of specific e-learning tools, needed to embrace the transition and implement remote learning modalities as an emergency response to the global crisis (Kučina Softić et al., 2022).

Nevertheless, the main change occurred after the COVID-19 pandemic ended, when it became necessary to analyse outcomes and decide upon new strategies and policies. In addition, artificial intelligence (AI), which has been already significantly present for several years in specific areas, was not often overtly used in the field of HE. The launch of ChatGPT (version GPT-3.5), an artificial intelligence system which became available to the general public in November 2022, became a certain point of no return. In that moment, the phase in which the concept of AI in HE had to be reconsidered started (Pejić-Bach & Marić, 2025).

As AI, mostly through ChatGPT, became available to the broader population, it was not possible just to continue the ongoing process of digitalisation in HE. This new momentum prompted university leaders and the academic community to analyse and reconsider what AI means for HE, how it should be used in academic contexts, and whether universities should support its implementation. Ongoing discussions in academia on AI potential to deliver benefits for education, e.g. improving efficiency in curriculum design, student support services and research advancements automatization of administrative tasks, are being opposed to arguments on its potential risks, e.g. data privacy concerns, lack of transparency and academic integrity issues (European Commission, 2023). In such a context, digital leadership has become a new strategic imperative, influencing increased use of technology in education, research, and administration while fostering innovation and digital transformation in HE.

The concept of digital leadership entered the HE arena more profoundly in the last decade of the twentieth century. As a relatively new concept of research in the HE field, the understanding of digital leadership varies in the literature. While some authors define it as “a strategic approach to leveraging digital tools and resources to drive organizational success” (Z. Cheng et al., 2024, pp. 42–43), others say it is a process that seeks to “engage learners, teachers and all other stakeholders in the transformation” in order to achieve innovative change in education through joint efforts (Aldawood et al., 2019, p. 1). The literature describes digital leadership as networked, flexible, team-oriented, and innovative drive for change in the process of transition to a digital university (Sułkowski, 2023), a concept that in its essence involves the development of innovative teaching methods which lead to increased student engagement and improved learning performances (Tanniru & Peral, 2021).

After a brief presentation of the concept of digital leadership in the literature, this paper provides an overview of the findings from an online survey of students and semi-structured interviews with several experts at different levels. In the next part of the paper, we discuss AI’s potential from both student and teaching/learning perspectives and look at the institutional level in order to debate how academic leadership can guide the integration of AI technologies in different educational settings.

Digital Leadership

The term digital leadership emerged simultaneously with the process of digital transformation and rapid development of AI. “Changed structures, procedures and rules for the HEI’s core processes as well as in establishing commitment and acceptance for new, changed values and everyday practices… [in…] teaching, research, third mission and administration” are among the outcomes of the digital transformation process (Ehlers, 2021, p. 9).

At the institutional level, a process of digital transformation relies on three pillars, which include policies, processes and resources (Ehlers, 2021). While actors in the policy pillar set the strategies with the main institutional goals, in the process pillar, actions and activities are defined. These two pillars rely on available resources, which are limited for many HEIs. The entire process leads to changes in organisational culture, depending on leaders’ capacity to guide institutions towards new ways of functioning.

While scholars predominantly agree that digital leadership integrates new technologies, instruments, and tools in HE, some authors emphasise the role of technology in changing the attitudes, emotions, thoughts, behaviours, and performance of individuals, groups, and organisations (Z. Cheng et al., 2024). In addition, as it favours incremental change, digital leadership goes beyond securing infrastructure and implementing digital tools by creating a shared vision and fostering a collaborative culture among individuals (Moyle, 2006). By cultivating “an environment that is conducive to the meaningful implementation of technology in education” (E. C. K. Cheng & Wang, 2023, p. 4), digital leadership creates a new academic context in which cultural transformation is taking place and new modalities of skills development are being promoted (Rizki & Suwadi, 2024).

Rapid adaptation to digital changes is a precondition for digital transformation (Kokot et al., 2023) and the effective embedding of digital technologies into all university functions, particularly in teaching and learning processes (Rizki & Suwadi, 2024). Therefore, digital leaders need to have specific characteristics. E-communication, e-technological, e-team building and e-trustworthiness skills are only some of characteristics which enable digital leaders to engage with various actors and stakeholders.

The main goal of digital leadership is to create “a more inclusive and participatory decision-making processes that enhances the effectiveness of digital strategies” (Z. Cheng et al., 2024, p. 43). To achieve that goal, a division of certain leadership dimensions at the institutional level is necessary for the successful implementation of the digital transformation process (Antonopoulou et al., 2019). Accordingly, digital leadership should not be limited only to the top management at the university or faculty level. Rather, academics—as key actors in teaching and learning who introduce digital content into courses and study programmes—should be considered as another level of digital leadership. They should actively participate in discussions on university strategies and institutional shifts towards a community of digital scholars.

Although recent documents highlight the strategic importance of digital leadership for institutional transformation in higher education (Z. Cheng et al., 2024; Sułkowski, 2023), most empirical and conceptual work concentrates on system- or university-level policy, infrastructure and governance. Far less attention has been paid to how individual academics—those who design and deliver courses—translate these macro-strategies into AI-enabled practices that directly shape day-to-day student engagement. Likewise, reviews of AI use in higher education emphasise technical affordances and ethical risks (European Commission, 2023), but stop short of explaining which leadership behaviours at the classroom level foster responsible uptake. This imbalance leaves two important gaps: (i) a theoretically grounded account of academic leadership at the micro level, and (ii) evidence-informed guidance on how such leadership can harness AI to motivate and involve students. The present paper addresses both gaps by positioning individual academics as pivotal digital leaders and by offering exploratory insights into their role in AI-driven engagement.

2. Evidence-Based Analysis

Since the aim of this paper is to recognise the increasing role of AI in higher education and to explore AI’s potential for improving student engagement through the lens of academic leadership, its objective is therefore exploratory and conceptual rather than confirmatory. Guided by this objective, and in keeping with the paper’s theoretical stance (we develop research questions rather than testable hypotheses), our analysis addresses two main questions:

• What opportunities and challenges related to AI integration in higher education can be identified in fostering ethical and effective digital transformation?
• How can academic leadership guide and support the effective and ethical integration of AI technologies to enhance student engagement and educational quality?

Although this paper is primarily a theoretical position paper, we included exploratory qualitative and quantitative components in order to provide additional context and support for our theoretical arguments, rather than positioning it primarily as a research paper.

The quantitative component of the study involved 95 undergraduate students from various academic fields. Data were collected in winter semester 2025 at a large public university in Croatia within the teaching courses where teachers use Chat GPT as one of the additional tools in the teaching process. The survey was completed by all students enrolled in the selected courses.

The survey was carried out as an integral part of the regular teaching process with the aim of gathering preliminary insights into the use of AI in teaching and learning. Although the sample of 95 respondents is not representative and does not allow for broad generalisations, the findings nevertheless provide valuable support for the arguments advanced in this theoretical position paper.

Participants’ age ranged from 19 to 25 years, with an average age of 20.4 years (SD = 1.09), and the majority were between 20 and 21 years old. Participants responded to an ad hoc questionnaire developed to assess their attitudes towards using AI in an academic context. The questionnaire was developed specifically for this exploratory study based on the literature review, discussions among the research team, and preliminary insights from existing studies on AI integration in education. Items were designed to measure students’ perceptions of AI tools (specifically ChatGPT as a generic term) regarding usefulness, ethics, academic integrity, regulatory aspects, and educational impact. ChatGPT was used as an exemplary AI system, as it was the most commonly used and widely recognised by students at the time of data collection, thus reducing potential confusion with other types of AI tools. The instrument consisted of 15 Likert-scale items (1 = strongly disagree; 5 = strongly agree). The data collection occurred online through a standardised survey platform. Students were invited to participate voluntarily and anonymously. Statistical analyses were conducted using R 4.3.1 (R Core Team, 2023), employing tidyverse (Wickham et al., 2019) for data manipulation and reflexR (Rajter, 2022) for descriptive statistics. Consistent with the exploratory aim of this paper, analysis was restricted to descriptive statistics (means, standard deviations, and frequency distributions). No inferential tests (e.g., correlations or significance testing) were performed because of the exploratory purpose and sample size.

The qualitative component of the study consisted of five semi-structured interviews (Forsey, 2012; Brinkmann, 2014) with academics and experts that are involved in higher education policies at national and university levels, specifically those related to digital transformation and the use of modern technologies in teaching and research. These interviews, conducted in December 2024, formed a critical-case purposive sample: a national quality assurance officer, members of university and faculty leadership, and two departmental e-learning coordinators, each directly engaged in digital policy implementation. Four of the five interviewees were affiliated with the same large Croatian university that leads the national roll-out of AI-enhanced teaching, while the fifth represented the national quality assurance agency. This ‘critical-case’ purposive sampling strategy was adopted to gain an in-depth view of one institution’s AI integration rather than to obtain results generalizable to all Croatian HEIs. The interviews included representatives from various institutional levels, including the national institution responsible for quality assurance in higher education, universities, faculties, and departments, covering diverse leadership roles. Questions for semi-structured interviews were designed to obtain answers about the present policies related to digital transformation, the existence of necessary resources for the implementation of specific activities in this area and the information on processes that are being carried out at the institutional level. Each interview followed a common guide consisting of eight open-ended prompts (e.g., “Which institutional barriers most hinder AI adoption in teaching?”, “How do you define digital leadership at your level of responsibility?”, “What concrete support do lecturers receive for integrating AI into their courses?”). Follow-up probes were used to elicit depth and examples. Interviews lasted 45 min and were audio-recorded, transcribed verbatim, and anonymized. In addition, interviewees discussed their definitions of digital leadership and its role in teaching, emphasising AI as a transformative educational tool. They also discussed whether universities adequately prepare students for AI use, the necessity of institutional policies, and ethical challenges associated with AI in academia. Transcripts were examined using a descriptive thematic analysis to identify recurring leadership and AI integration themes; given the illustrative purpose, no formal coding software or inter-coder reliability statistics were employed. After an initial round of coding, the authors jointly reviewed and discussed the coded segments, integrating the data into broader themes in light of the existing theoretical framework. This interpretative dialogue served as the primary validation step for credibility.

Although gathered within the same project, the survey and interview results were not formally triangulated; rather, each dataset offers complementary illustrative insight that informs—but does not statistically validate—the theoretical argument.

Taken together, these procedures offer a transparent methodological foundation that is proportionate to the paper’s exploratory, theory-building purpose while openly acknowledging inherent limitations.

3. Revealing Insights: Findings from Questionnaires and Semi-Structured Interviews

The majority of students (85.3%) reported no prior experience using ChatGPT in academic courses. A small proportion (12.6%) had used ChatGPT in one course, while only 2.1% had experience using it in multiple courses.

On average, students reported moderately positive attitudes1 towards ChatGPT’s ability to assist in understanding complex academic topics (M = 3.6, SD = 1.21), ease of writing academic papers (M = 3.6, SD = 1.16), and quick retrieval of relevant information (M = 3.7, SD = 1.12). However, ethical acceptance of using ChatGPT in academic writing was rated lower (M = 2.8, SD = 1.19). Students expressed concerns about academic integrity, indicating moderate agreement that ChatGPT usage could compromise integrity (M = 3.2, SD = 1.12). They also strongly supported the need for clear guidelines outlining acceptable use (M = 4.0, SD = 1.05).

Participants also recognised potential negative consequences of reliance on ChatGPT, agreeing that excessive use could impair independent problem-solving abilities (M = 4.0, SD = 1.17), raise plagiarism concerns (M = 3.9, SD = 1.16), and potentially reduce educational quality (M = 3.2, SD = 1.25). Nevertheless, they supported a regulated integration of ChatGPT into educational settings (M = 3.6, SD = 1.17).

Finally, students generally perceived a lack of faculty encouragement for using ChatGPT (M = 2.3, SD = 1.01), suggesting that academic leaders were cautious about integrating AI tools into the curriculum.

Insights from the interviews indicate that changes in institutional culture are needed for further progress toward adequately integrating AI into teaching and learning processes. While policies and strategies regarding digital transformation have been adopted at the university level (University of Zagreb, 2024)2, some interviewees argued that these strategies place greater emphasis on research rather than on teaching and learning processes. All interviewees agree that the process of digital transformation is one of the university management’s priority policy areas and that in the last year, the use of AI has been discussed by relevant university bodies. Still, this topic requires deeper analysis at both faculty and university levels, particularly regarding the ethical challenges associated with AI use.

The majority of interviewees linked digitalisation processes at the university with e-learning, hybrid learning, and distance learning. They confirmed that the necessary infrastructure, training opportunities and assistance for university teachers in course development and online teaching exist at the university level, particularly through the university e-learning platform. As stated by one interviewee, “the Merlin system is the most modern e-learning system in Croatia and it currently contains over 30,000 courses. It is free, various training courses are regularly organized for its use, and support is provided for university teachers”. Interviewees also agreed that further modernisation of the curriculum is needed, including the integration of AI into teaching and learning processes.

The modernisation of study programmes and courses, digitalisation, the integration of AI into teaching and learning, and changes in study modalities still largely depend on individual university professors, as stated in interviews. In addition, while some argue that top-down implementation of digital transformation policy has a positive impact, some interviewees prioritise a bottom-up approach and link it with quality assurance and evaluation processes.

Legal regulations are identified as obstacles in the digitalisation process of teaching and learning by a few interviewees. Still, while some say that the law allows universities and faculties to modernise and change present study programmes up to 40 percent without re-entering a new accreditation process, others highlight the crucial role of institutions for the motivation of university teachers to systematically modernise courses, which includes the digitalisation of teaching and learning as well.

Finally, the concept of digital leadership was not fully internalised or understood among the interviewees. Mostly, it was viewed as a category associated with university management and institutional governance. The individual or the university teacher level of digital leadership was not fully recognised or acknowledged in the university context.

4. Unlocking the Lessons: How Study Insights Can Enhance Institutional Policy

In order to better advocate for AI use in higher education and its potential in learning and teaching, our two research questions provide a foundation for further analysis. In what follows, every conceptual point is explicitly anchored either in our descriptive survey results (Appendix A) or in the leadership themes extracted from the interviews, and is cross-referenced to recent studies to ensure tangible support for each inference.

The analysis indicates that the global shift toward online modalities during the pandemic accelerated digital transformation at universities (see Survey Items 1–3 in Appendix A and Theme 1 from the interviews). Digital leadership, which increasingly integrates advanced technology and AI into teaching, research, and administrative processes, must permeate and be recognised in all aspects of university life. Following the idea that digital leadership represents much more than simply adopting new technologies (Z. Cheng et al., 2024; Rizki & Suwadi, 2024), interviewees stressed its importance in creating shared visions, promoting collaborative cultures, guiding comprehensive institutional change, and recognising its critical role at multiple levels within HEIs

Insights from the interviews indicate that institutional leaders generally demonstrate a strong commitment to digitalisation strategies, as reflected in existing policies on digital transformation. Furthermore, investments in infrastructural resources (e.g., specific e-learning platforms) are made at the institutional level, and specialised training programmes designed for faculty members are organised. This all confirms that at the institutional level, all three fundamental pillars (policy, processes and resources) exist. Nonetheless, resistance at the individual level, especially among university teachers, was evident and might be considered a potential ‘weak spot’ in this transformative process. The resistance highlights the critical distinction between ‘change’—the implementation of new technologies and practices—and ‘transition’, which refers to the internal psychological process that faculty members experience while adapting to these changes (Bridges & Bridges, 2017). The observed resistance thus indicates critical gaps between institutional strategies focused on digitalisation and individual faculty engagement requiring more profound support in transitioning.

Our qualitative findings further reveal a perceived imbalance between strategic documents and institutional priorities. Documents often disproportionately emphasise digital innovation in research activities, giving less attention to teaching and learning processes. This imbalance could marginalise key stakeholders, particularly academic staff, preventing their active participation in and benefiting from digital transformation initiatives.

Interviewees emphasised the need for balanced strategic planning, integrating both research and pedagogical innovation equally. Such an inclusive approach ensures faculty members, who act as primary digital leaders, effectively translate institutional goals into actionable educational practices.

Continuous professional development, clear strategic guidance, motivation, and institutional recognition emerged as crucial factors in addressing faculty resistance and fostering deeper engagement with digital tools. McKeown’s (2007) pencil metaphor categorises technology adopters into distinct groups—innovators, early adopters, early majority, late majority, and laggards—each with varying degrees of openness to technological changes. The accelerated pace of technological developments significantly widens the gap between these adopter types, making targeted intervention even more essential. This metaphor emphasises the necessity of targeted, structured, and motivational guidance from institutional leadership to effectively bridge the expanding gap among these groups. Digital leaders must clearly articulate expectations tailored to different adopter types, offer relevant training and resources to support adoption, and foster a supportive environment that acknowledges and rewards innovative teaching practices. Such a comprehensive approach can significantly reduce faculty resistance, creating a more cohesive and receptive culture that accelerates the digital transformation process.

Recent studies emphasise the importance of leadership styles in motivating academic staff to actively engage in leadership development programmes, especially in digital environments. Z. Cheng and Zhu (2025) in an article published in this Special Issue found that transformational leadership significantly enhances academics’ motivation to participate in leadership training through inspiring confidence and articulating compelling visions, whereas certain transactional leadership elements, like active management-by-exception, can effectively encourage participation by clarifying expectations and providing structured feedback. This highlights the necessity for institutions to tailor leadership programmes to diverse leadership styles and motivational needs, particularly within digital and hybrid learning settings.

Quantitative data from student surveys strongly support these findings, revealing moderately positive attitudes towards AI tools like ChatGPT in academic contexts. These illustrative results echo recent multi-institution studies reporting that AI chatbots can boost students’ perceived engagement when clear ethical guidance is in place (E. C. K. Cheng & Wang, 2023; Li et al., 2025). Nevertheless, they do not constitute statistical proof; rather, they serve as exploratory evidence that complements our theoretical reasoning. Students acknowledged AI’s potential to enhance educational experiences, particularly in understanding complex academic content and efficiently managing academic tasks. Nonetheless, the data also reflect significant ethical concerns and apprehensions regarding academic integrity, clearly indicating the need for explicit institutional guidelines on ethical AI usage. Students expressed a desire for faculty-led guidance and transparent institutional frameworks that define acceptable uses of AI tools in educational contexts, highlighting the ethical responsibilities of digital leadership. As stated by one interviewee, “digitalisation changed the role of university teachers. The introduction of new forms of teaching and new digital tools, especially artificial intelligence, must keep pace with technological developments. The pandemic situation has shown that the use of digital tools cannot be avoided, but it must be done in the right way, while respecting ethical principles”.

Interestingly, our research identified a notable lack of direct faculty encouragement regarding AI use in coursework, suggesting a cautious stance primarily driven by concerns about ethical integrity and educational quality. This caution highlights a significant issue: without clear guidelines, institutions risk either uncontrolled and potentially unethical use of AI tools, or conversely, a complete avoidance of these valuable technological resources—both outcomes being detrimental. Such challenges underscore the necessity for proactive, clearly defined, and ethically informed academic leadership. Beyond technological infrastructure, institutions must prioritise ethical leadership, providing transparent frameworks for AI integration to adequately address students’ ethical and academic concerns and optimise the educational potential of AI.

Additionally, interview findings revealed significant institutional challenges, including rigid regulatory frameworks and structural inertia that impede agile adaptation to educational innovations. Although existing legal provisions allow curriculum flexibility, bureaucratic barriers frequently hinder timely and effective curriculum updates. Addressing these barriers requires enhancing institutional adaptive capabilities through streamlined policy-making processes and fostering an organisational culture receptive to continuous change and innovation.

Ultimately, fostering effective digital leadership involves recognising university teachers as pivotal agents in implementing digital transformation strategies. As stated by one interviewee, “Investing in people and providing support for university teachers is crucial so that digital technologies and active forms of learning are continuously incorporated into the teaching and learning process”. Academic leaders at individual and departmental levels must actively participate in shaping digital strategies, aligning institutional strategic goals with practical educational methodologies. Institutions are responsible for developing effective leadership training programmes with structured, interactive designs to enhance engagement, as proposed by Li et al. (2025). By cultivating a collaborative and inclusive institutional culture and providing necessary resources and training, universities can effectively harness AI and other digital tools to comprehensively enhance educational experiences.

5. Conclusions

In this paper, which is framed as a theoretical position paper supported by exploratory evidence, we have analysed the critical role of digital leadership in facilitating an effective digital transformation within HEIs. Our findings clearly indicate that digital leadership extends beyond the mere adoption of new technologies; it encompasses fostering an institutional culture that embraces innovation, guiding strategic planning, and addressing the psychological transition experienced by faculty members adapting to change. Robust digital leadership is indispensable for successful digital transformation in universities.

Teaching models at universities must evolve and keep pace with the rapid development of new technologies. In this context, academic leadership becomes crucial for strategically linking decisions initiated at the top management level with each member of the academic community, including decisions related to digitalisation. This emphasises the necessity of acknowledging university teachers as digital leaders since they represent the primary level of academic leadership within universities.

Although the university community comprises individuals belonging to both analogue and digital generations, all university teachers should embrace the digitalisation process. To achieve this goal, continuous communication and motivation among all members of the academic community are essential.

Although strategies, policies, and resources have been secured at the institutional level, the success of digitalisation in teaching and learning still largely depends on the engagement of university teachers. Advancing digitalisation requires coordinated efforts across multiple levels. If we focus specifically on the use of AI, university leadership must establish clear institutional guidelines and a robust ethical framework for its application in teaching and learning; faculties should integrate AI into existing curricula while proactively addressing the associated ethical challenges; and policy-makers must ensure regulatory flexibility so that universities can adapt their curricula and pedagogical methods swiftly in response to technological change. Once these preconditions are met, university teachers need to be motivated and make additional effort required to continually improve and modernise the learning process, while institutions need mechanisms to support and incentivise them.

Our findings suggest that comprehensive strategic policies that equally prioritise research and teaching activities are essential for ensuring balanced digital innovation across all institutional domains. This interpretation aligns with recent reviews of digital leadership in higher education (Z. Cheng et al., 2024) and with European Commission (2023) guidance on AI in education, indicating that our conceptual claims are consistent with emerging empirical work. Continuous professional development, targeted motivational strategies, and institutional recognition emerged as key drivers in reducing faculty resistance and enhancing engagement with digital tools. These elements, combined with explicit ethical guidelines for AI integration, are crucial for addressing concerns about academic integrity and the ethical use of technology voiced by students and faculty alike.

Accordingly, academic staff should provide strong leadership by fostering critical thinking, creativity, and problem-solving methods that prepare students for future challenges. Recognising and empowering academic staff as primary digital leaders will create an innovative, resilient educational environment capable of rapidly adapting to ongoing technological advancements, ultimately benefiting all stakeholders within the academic community. If implemented, such measures are expected—in line with the literature cited above—to foster students’ critical thinking, creativity and future-oriented problem-solving; however, their actual effectiveness remains to be confirmed by larger, longitudinal studies.

However, while the interviews and students’ perceptions clearly indicate a need for guidance and leadership from their professors, the qualitative interviews indicate that the professors, as the most direct digital leaders, are not yet fully ready to take on the role that is expected of them. Thus, universities are responsible not only for developing policies and procedures, but also for educating and empowering professors in their role of digital leaders and creating programmes based on transformational leadership with clear expectations and structured feedback.

In summary, the analysis revealed that successful digital transformation depends not only on the existence of strategic documents, top-level digital transformation policies, or resources provided at the institutional level (e.g., infrastructure, specific tools, and professional development for academic staff). Ultimately, the willingness of university teachers to integrate digital teaching and learning elements remains a critical factor. Digitalisation needs to be embedded systematically in the academic context, with university teachers—as primary digital leaders—playing a crucial role in this process. Future research using representative samples and rigorous design is therefore needed to evaluate the impact of individual-level digital leadership on AI-driven student engagement.