Statement on AI and Digitalisation

1. Introduction

Artificial Intelligence (AI) and digitalisation are rapidly reshaping higher education
across the European Higher Education Area (EHEA), affecting teaching, learning,
assessment, research and institutional governance. These developments extend
beyond generative AI tools and include a wide range of technologies, such as
automated decision-making systems, learning analytics and digital credentials.

ESU stresses that these transformations must not be treated as purely
technological developments. They directly affect access to education, quality of
learning, academic freedom, data protection and student rights, and therefore
require coordinated, value-driven governance, including clear regulatory
frameworks and policy approaches at institutional, national and European levels,
as well as meaningful student participation. AI and digitalisation should therefore
be understood not as isolated tools, but as structural developments that reshape
how higher education functions, is governed and is experienced by students.

To inform this statement, ESU collected input from several National Unions of
Students (NUSes) across the EHEA on the implementation of AI, digitalisation and
interoperability across higher education systems. The findings highlight uneven
implementation across systems and gaps in regulation, resulting in differing
conditions for students across Europe. While AI technologies have rapidly evolved
in recent years, ESU notes that many of the structural challenges identified in the
previous Statement on Artificial Intelligence remain unresolved, reflecting a lack of
sufficiently coordinated and decisive action at institutional, national and European
levels.

For the purpose of this statement:

● Artificial intelligence refers to machine-based systems that, based on input
data, infer how to generate outputs such as predictions, content,
recommendations or decisions that may influence physical or virtual
environments. Different AI systems vary in their levels of autonomy and
adaptiveness after deployment.
● Digitalisation refers to the integration of digital technologies across
teaching, learning, assessment, research and governance.
● Interoperability refers to the ability of systems and data to function across
institutional and national contexts.

2. How AI and digitalisation are impacting Higher Education

Artificial intelligence and digitalisation are transforming higher education across
teaching, learning, assessment, research and institutional processes, reshaping
how knowledge is accessed, produced and organised. These developments are
progressing unevenly across systems and institutions. While some have introduced
dedicated strategies, policies and support mechanisms to address the integration
and use of AI, others rely on fragmented or ad hoc approaches. As a result, students
experience significantly different learning conditions depending on their institution,
programme or country. Many higher education institutions still lack clear,
comprehensive strategies on AI, often relying on fragmented guidelines or informal
practices. This absence of structured approaches contributes to uncertainty for
students and staff and limits the ability to ensure coherent and fair implementation.
This also reflects insufficient coordination and prioritisation at national and
European levels, where policy responses have not kept pace with the rapid
integration of AI in higher education, leaving institutions and students to navigate
complex transformations without adequate guidance or safeguards.

This unevenness reflects disparities in infrastructure, funding and institutional
capacity. As highlighted in the GUAF report “Similarities and Differences in the
Digital Transformation of Higher Education”, the digital transformation continues to
develop differently across institutions and regions, reflecting varying levels of
preparedness, resources and governance capacity. In practice, this means that the
benefits and risks of digitalisation are not distributed equally.

AI is increasingly integrated into both educational and institutional processes.
However, its implementation often develops faster than the frameworks guiding its
use, leading to inconsistencies in how AI is applied and experienced across higher
education. This includes its growing use in admissions and access to higher
education, where automated systems may be used to filter or assess
applications.Such practices compromise transparency, accountability, data
protection, protection of student rights, and have the potential to reinforce existing
biases, particularly where decisions affecting access to education are made
without meaningful oversight. This growing gap between technological adoption
and quality assurance risks prioritising speed of implementation over the quality,
fairness and integrity of higher education processes. According to the European
University Association, universities are increasingly expected to develop AI
strategies that balance experimentation with responsibility and align technological
adoption with institutional values, while differences in institutional capacity risk
deepening inequalities in how AI is adopted and governed.

ESU stresses that, due to the lack of coordinated and systemic approaches, AI and
digitalisation are already reinforcing inequalities and fragmenting the European
Higher Education Area. Addressing these developments requires coherent
strategies that ensure consistency, fairness and alignment with the core values of
higher education. Across systems, there is a growing gap between the widespread
use of AI by students and the lack of clear institutional policies governing its use.
This mismatch places students in situations where expectations are unclear and
responsibilities are inconsistently defined.

HEIs are continuing to respond to AI developments reactively rather than
proactively and strategically, relying on individual initiatives or short-term
measures instead of coherent governance approaches. This is shifting
responsibility onto students and staff, while leaving core questions of
accountability, consistency and fairness insufficiently addressed.

The rapid normalisation of AI in higher education also risks embedding its use
without sufficient reflection on long-term implications, governance and alignment
with educational values.

ESU calls for:

● coordinated national and European strategies and regulatory frameworks
on AI and digitalisation in higher education.
● institutional frameworks ensuring coherent and transparent approaches to
AI.
● public investment to reduce disparities in digital capacity and infrastructure
across higher education institutions.

3. Student participation, governance, and power asymmetries

Student participation in decision-making regarding artificial intelligence in higher
education must be ensured as a fundamental principle, not treated as an
afterthought or formal requirement. Decisions on the adoption, procurement and
use of AI tools, ranging from learning platforms to assessment technologies, have
direct and long-term consequences on students’ learning conditions, data rights,
and academic integrity.

In practice, these developments are already reshaping the day-to-day student
experience. Students are required to navigate unclear or inconsistent expectations
regarding the use of AI in coursework, often without adequate guidance. They may
be expected to use digital platforms or AI tools that they have not been properly
introduced to, while at the same time facing restrictions or penalties for their use.
The increasing integration of AI into learning environments also affects how
students access feedback, interact with teaching staff and organise their learning
processes. These shifts create uncertainty, increase pressure on students to self
regulate their use of technology and raise concerns regarding fairness,
transparency and equal treatment across programmes and institutions.

Therefore, students must be meaningfully involved at all stages of decision
making, including needs assessment, tool selection, implementation, and
evaluation processes. This includes decisions related to contracting, data
governance and the conditions under which AI systems are implemented, as well
as decisions by public authorities outside the education sector that may affect
students. The final report of the European University Association’s Task-and-Finish
Group on AI also emphasizes that AI integration must be a “genuinely shared
responsibility” between leadership, staff and students.

In many cases, student involvement remains limited to consultation without real
influence over outcomes, reinforcing power asymmetries between institutional
leadership, technology providers and students. Across higher education systems,
there is a clear gap between the widespread use of AI tools by students and their
awareness of institutional policies governing their use. This gap demonstrates that
consultation without meaningful involvement is insufficient, as students are often
expected to navigate AI use without clear guidance. Higher education institutions
must instead ensure inclusive governance structures where student
representatives participate as equal partners, with access to information and
genuine decision-making power.

Addressing power imbalances also requires transparency in procurement
processes and clarity on how AI systems are selected, funded, and integrated into
educational environments. Students must have the ability not only to voice
concerns but to shape national and institutional strategies on AI, ensuring that
these technologies serve pedagogical goals, uphold academic values, and protect
students’ rights.

ESU calls for:

● meaningful student participation in all stages of AI-related decision
making, including procurement and implementation.
● active involvement of students in the development of national and
institutional AI strategies and regulatory frameworks.
● regular and structured collection of student feedback on the use and impact
of AI and digital tools, including through surveys and consultations with the
results and follow-up actions clearly communicated.
● transparent governance structures with clear access to information for
student representatives including information regarding data-sharing
agreements.
● recognition of students as equal partners in the decision-making process,
auditing, governance and implementation of AI at institutional and national
levels.
● transparency in procurement and contracting processes related to AI
systems used in higher education.

4. Learning, teaching and assessment

AI and digital tools are increasingly embedded in learning, teaching and
assessment processes across higher education. They offer opportunities to support
feedback, accessibility, language inclusion and the organisation of learning, and,
when used responsibly, can enable more flexible and inclusive forms of learning. At
the same time, their rapid uptake challenges traditional approaches to pedagogy
and assessment.

ESU observes that institutional responses remain uneven and, in many cases,
insufficient to address the scale of change introduced by AI. While some institutions
adapt assessment methods and teaching practices to reflect AI-supported
learning environments, others continue to rely on models that no longer
correspond to current realities. This creates uncertainty for students and risks
inconsistent academic standards. The increasing integration of AI into learning
processes also challenges fundamental assumptions about assessment.
Traditional forms of evaluation may no longer accurately reflect students’
knowledge, skills or learning processes in AI-supported environments. This requires
a shift from assessment models focused on reproduction of information towards
approaches that prioritise critical thinking, application, reflection and the
responsible use of tools. This has direct implications for the achievement and
verification of learning outcomes, which underpin qualifications frameworks and
the broader coherence of higher education systems in line with Bologna
commitments. Misalignment between learning, teaching and assessment risks
undermining the reliability, comparability and trust in qualifications across the
EHEA.

At the same time, many academic staff are not sufficiently supported or prepared
to integrate AI into their teaching, further contributing to inconsistencies in how AI
is addressed across programmes and institutions. This highlights the need for
systematic institutional support, rather than reliance on individual staff initiative.

ESU stresses that responses to AI must prioritise pedagogical development rather
than restriction. The key challenge is not the existence of AI, but whether institutions
are willing to adapt learning outcomes, assessment design and teaching practices
accordingly. This adaptation must not rely on excessive standardisation of
teaching and assessment practices, but instead should promote diverse, student
centred approaches to learning and teaching.

Digital transformation has also blurred the boundaries between academic
responsibilities and personal time. The implicit expectation of constant connectivity
on digital platforms creates an unsustainable environment that harms students’
mental and physical well-being while also contributing to the negative trend of
desocialisation. The longterm effects of AI-supported learning remain insufficiently
understood and overreliance on AI systems in education should be avoided without
adequate evidence of their impact on students’ learning and development. Digital
tools should support student-centred learning rather than imposing a culture of
overwork and permanent availability. ESU highlights that safeguarding the well
being of students and staff must be part of institutional AI strategies. AI must
support student-centred learning and educational quality, rather than narrowing
education into standardised or control-driven processes.

ESU calls for:

● coordinated European-level dialogue policymakers and Higher Education
experts to guide the redesign of assessment methods to reflect digital
learning environments, with aligned implementation at both national and
institutional levels.
● clear institutional guidelines on AI use in teaching and assessment.
● pedagogical training and support for staff.
● measures addressing the impact of digitalisation on student well-being.
● institutional support and guidance ensuring responsible and critical use of
AI tools.
● measures addressing the impact of digitalisation on student well-being,
learning and the right to disconnect.

5. AI and digital literacy

AI and digital literacy are fundamental conditions for meaningful participation in
higher education. Students, academic staff and institutions are increasingly
expected to engage with AI systems that shape access to knowledge, learning
processes and assessment. Without adequate understanding of how these
systems function, as well as their limitations and risks, students cannot participate
on equal terms or make informed decisions about their own learning.

ESU stresses that AI literacy must go beyond technical use, including in study
programmes preparing students to consider ethical implications of AI system
developments. AI literacy must also include understanding the environmental,
ethical and societal implications of AI systems and their long-term impact. In
particular, students must be able to assess when and how AI can be used
appropriately, and where its use may undermine learning, academic integrity or
fairness. Critical AI literacy is also necessary in a broader societal context, as AI is
increasingly used to spread disinformation, heighten disengagement and
polarisation, and contribute to democratic erosion. In this context, AI and digital
literacy are closely linked to the development of critical thinking and citizenship
competences, enabling students to engage responsibly with information,
participate in democratic processes and navigate increasingly complex digital
environments.

At present, access to these competences remains uneven. Many students are
expected to develop AI-related skills independently, without structured support or
clear institutional guidance. This creates unequal learning conditions and
reinforces differences between institutions, disciplines and student groups. These
disparities are closely linked to broader issues of digital inequality and digital
poverty, where students may be expected to develop competences in AI systems
to which they have limited or no meaningful access, further exacerbating existing
inequalities in higher education.

AI literacy is also closely linked to responsibility. Without adequate understanding
of AI systems, expectations placed on students regarding appropriate use remain
unclear and inconsistently applied. This risks shifting responsibility onto students
without providing them with the tools needed to meet those expectations.

ESU therefore considers AI literacy not only as a competence, but as an institutional
responsibility and a prerequisite for fair and transparent learning environments.

ESU calls for:

● integration of AI and digital literacy across curricula in all fields of study.
● clear, accessible institutional guidance on appropriate and responsible AI
use.
● recognition of AI literacy as a core competence for all students. This includes
critical understanding of algorithmic bias, automated decision-making,
disinformation, the impact of deepfaking, environmental impact and data
governance.

6. Academic Integrity, Academic Freedom and Surveillance

cademic integrity is a collective and systemic responsibility, and should not be a
burden placed solely on students. Framing students as primary violators obscures
broader structural issues within Higher Education systems. Students, academic
staff, HE leadership and now technology providers all shape the integrity of HE. The
increasing use of AI and digital tools has introduced new forms of both compliance
and misconduct, in some cases bypassing pedagogical standards, reducing
transparency and undermining fair assessment practices. Academic integrity
frameworks must adapt to AI-integrated learning environments while ensuring
responsible, fair, transparent and inclusive use of AI.

ESU underlines that academic integrity must be co-created with students
recognised as equal partners, grounded in trust-based pedagogies, clear
expectations and inclusive assessment design that continuously adapt to
technological advancements in education.

Digitalisation and AI must protect the academic freedom of both students and
staff, ensuring the autonomy to decide when and how these tools are used based
on pedagogical needs. This freedom is undermined when rigid or arbitrary
restrictions are imposed, particularly where academic staff exert disproportionate
control over AI use. At the same time, AI-enabled monitoring and control over
teaching, learning and research can further limit the ability to engage freely in
knowledge creation and sharing. Together, these dynamics create a “chilling
effect”, where students may avoid using AI due to fear of reprisal, turning what
should support inquiry into an instrument of compliance. This is particularly evident
in the increasing use of surveillance-based approaches, including online
proctoring systems that raise serious concerns regarding fundamental rights as
such systems heavily rely on AI, biometric data and continuous monitoring.
Furthermore, the rise of “black box” detection tools has introduced a structural trust
problem, where some institutions start with the general assumption that students
are trying to cheat. The lack of transparency that comes with these tools limits
students’ ability to meaningfully contest or appeal decisions relating to academic
integrity procedures.

While these systems are often justified as tools to uphold academic integrity, they
undermine the trust between students and institutions, shifting the focus from
pedagogical solutions to technological control. ESU stresses that academic
integrity cannot rely on surveillance-based approaches that compromise
students’ rights to privacy, data protection and dignity. Any use of digital
assessment tools must be strictly proportionate and aligned with fundamental
rights and human oversight.

ESU calls for:

● co-created academic integrity frameworks involving students as equal
partners.
● assessment models that do not rely on detection-based approaches.
● proportionate, clear and transparent expectations on the use and disclosure
of AI in academic work.
● strict limitations on surveillance-based tools, including AI-enabled online
proctoring and biometric monitoring systems, which should only be used
where demonstrably necessary, proportionate and subject to meaningful
human oversight.
● alignment of technological developments with the fundamental values of
higher education.
● the abandonment of “black box” detection tools in favour of assessment
models that do not rely on a general assumption of student misconduct.

7. Inequality, exclusion, and uneven digital transformation

Digitalisation and AI can often exacerbate structural inequalities. 2024 Eurostat
figures show that despite 94.2% EU internet access, affordability and regional gaps
remain significant. For example, 7.6% of people at risk of poverty still cannot afford
an internet connection, directly affecting students’ ability to participate in digital
learning environments. ESU’s findings indicate that students with greater financial
resources benefit from access to premium AI tools and better infrastructure,
creating a measurable productivity advantage over peers using free or limited
versions. Moreover, this opportunity gap does not only manifest itself in access to
software and programmes, but also to hardware and physical infrastructure, such
as recent and high-performance laptops.

Students with disabilities, first generation students, students from lower socio
economic backgrounds and students from minority backgrounds face additional
risks of exclusion. At the same time, the potential of AI to support inclusion and
accessibility for underrepresented students is often overlooked, as implementation
primarily focuses on administrative efficiency. The TechSonar report published by
the European Data Protection Supervisor (EDPS) shows that AI systems used in
higher education contexts can misinterpret atypical behaviour or assistive
technologies, while language processing tools disadvantage non-native speakers
and minority language users.

The EHEA remains a “patchwork” of uneven learning conditions where institutional
wealth dictates the quality of a student’s digital rights. This reflects broader
systemic failures to ensure that digital transformation in higher education is
inclusive, equitable and publicly supported.

ESU stresses that without coordinated public investment, inclusive design and
rights-based governance, digitalisation risks deepening inequalities, fragmenting
the European Higher Education Area and undermining its fundamental values.

Equitable access to AI tools must also be balanced with environmental
sustainability and responsible resource use. ESU stresses that AI governance in
higher education should consider energy consumption, resource use and long
term environmental impact, ensuring that AI is implemented where it brings clear
educational value.

ESU calls for:

● targeted public investment in high-quality, privacy-compliant digital
infrastructure, including equitable access to AI tools and systems, accessible
to all students.
● inclusive, universal design of digital learning environments that fully
integrate assistive technologies.
● investment in modern, accessible and up-to-date academic infrastructure,
both physical and digital, ensuring that learning environments enable
effective use of digital and AI tools.
● investment for targeted supports that promote equitable physical access to
technologies that support learning (such as laptop loan schemes).

8. Data, commercialisation, and the public responsibility of Higher Education

The expansion of AI and digital platforms in higher education is not only a
technological shift, it is a structural one in how data, power and responsibility are
distributed.Both AI systems and educational technology providers increasingly rely
on large-scale data collection. This creates significant risks for student privacy,
institutional autonomy and the public nature of higher education.

Students must be recognised as active rights-holders in data governance, rather
than passive data subjects. This includes the right to clear and accessible
information on how their data is collected, processed and used, as well as the
ability to meaningfully consent to or refuse such practices. In many cases, students
are
required to engage with digital systems without sufficient transparency or
alternatives, limiting their autonomy and control over their own data. Students are
frequently required to use systems without full clarity on how their data is collected,
processed or reused.

Digital dependency on a limited number of private providers poses risks to
institutional resilience, data sovereignty, and the public mission of higher
education. ESU believes that higher education institutions should actively work to
prioritise digital solutions that support open standards and comply with relevant
regulations, for instance GDPR where applicable, provided by European actors.
Geopolitical unrest also poses a growing risk to digital infrastructure in European
higher education and civic society. Incidents of external interference with
institutional digital services demonstrate how digital dependency has
consequences for academic freedom, students’ right to privacy, and institutional
autonomy, thus reinforcing the need for resilient and sovereign digital systems for
institutions across the EHEA.

ESU stresses that higher education must retain control over its digital infrastructure
and data. The use of AI must not compromise transparency, accountability or
education as a public good.

ESU calls for:

● full transparency on data collection and the use of student data in the
development and training of AI systems, including meaningful opt-out
possibilities where applicable.
● strong legal framework that create binding protections of student data and
rights.
● investment in public and open digital solutions.
● clear limits on dependency on private providers, particularly those that do
not fall under European regulatory framework.

9. Interoperability and mobility

Interoperability must be recognised as a key principle in the ongoing digitalisation
of higher education, especially in the context of supporting mobility. As digital tools,
platforms, and credentials become more embedded in learning and
administration, systems must be able to communicate and function across
institutional and national borders to avoid fragmentation and ensure equal access
to opportunities. The European Higher Education Interoperability Framework (2025)
identifies critical gaps in the digital journey, noting that without cross-border
standards, the “seamless learner journey” remains fragmented, creating technical
hurdles that block the automatic recognition of credits and credentials. At the
same time, the proliferation of different digital systems and standards risks further
fragmenting the EHEA if not coordinated at European level.

This directly affects the recognition of digital credentials and the portability of
learning records, which should enable students to move flexibly between
institutions and countries without facing technical or administrative barriers.
Without interoperable systems, digitalisation risks introducing new barriers to
mobility rather than removing them. ESU stresses that interoperability is a
necessary condition for meaningful mobility and calls for coordinated European
approaches that prioritise open standards, transparency, and student control over
their data. This includes ensuring that students have meaningful control over how
their data is shared across systems, and are not subject to automatic data
transfers without clear information and consent.

ESU calls for:

● coordinated European approaches to interoperability based on common
and open standards.
● systems enabling automatic and transparent recognition of digital
credentials across borders.
● full portability of learning records between institutions and countries
● ensuring that students retain control over their data within interoperable
systems.

10. Regulation, responsibility, and red Lines

Artificial intelligence in higher education requires clear regulatory frameworks,
defined responsibilities and enforceable limits. As AI becomes embedded in
teaching, assessment and institutional processes, the absence of coherent
governance creates risks for students’ rights, fairness and trust in higher education
systems.

ESU stresses that responsibility for the use of AI cannot be shifted onto individual
students. The development, procurement and implementation of AI systems
involve institutions, public authorities and technology providers, all of whom must
be accountable for how these systems affect learning conditions, assessment and
access to education. At present, regulatory approaches remain fragmented, with
institutions often defining their own practices without sufficient guidance or
coordination. This results in inconsistent standards and unequal levels of protection
for students across the EHEA. Without stronger coordination and enforcement, this
risks entrenching a fragmented system where students’ rights and protections
depend on where they study.

The European Commission underlines that AI systems must be explainable,
accountable and subject to human oversight, particularly where they affect
individuals’ rights and opportunities. ESU emphasizes that these principles must be
meaningfully implemented in higher education and not remain purely declarative.
AI governance in higher education must also be grounded in clear ethical
principles, including fairness, transparency, non-discrimination, respect for
fundamental rights and sustainability. It must also remain aligned with the public
responsibility of higher education and its role in serving society rather than market
interests. HEIs must evaluate and take stances on the environmental and societal
impact of AI systems, both during their deployment and their training. This includes
energy consumption, resource use, broader ecological footprint as well as human
rights concerns at all steps of the process. HEIs must ensure that digital
transformation, including AI use and training aligns with sustainability goals and
the social responsibility of education. These principles are not optional and must
guide both policy and practice. At the same time, ESU emphasises that certain uses
of AI are incompatible with the values of higher education and must be clearly
rejected.

ESU calls for:

● coherent and coordinated regulatory frameworks across the EHEA.
● clear allocation of responsibility among institutions, public authorities and
providers.
● mandatory human oversight in all AI systems affecting students. This
oversight must include the right to meaningful explanation, contestation and
human review of automated decisions.
● transparency and explainability of AI systems used in education.
● the environmental & social impacts of AI, including issues such as energy
and water consumption, to be critically reflected on and addressed in
teaching.
● explicit prohibition of practices that:
○ undermine student rights, dignity or privacy
○ replace academic judgement in assessment or decision-making
○ rely on opaque or unverifiable automated decisions
○ create discriminatory or biased outcomes
○ force students to use proprietary digital platforms that harvest and
monetize personal data as a prerequisite for participating in core
academic activities.

11. Conclusion

Artificial intelligence and digitalisation are not neutral developments. They are
reshaping how higher education is accessed, governed and experienced, and must
therefore be shaped through democratic, student-centred and rights-based
approaches.

ESU stresses that the integration of AI in higher education must not be driven solely
by technological possibility or market interests, but by pedagogical objectives,
public responsibility and the fundamental values of the European Higher Education
Area. Students must be recognised as equal partners in this transformation, with a
meaningful role in shaping how these technologies are developed, implemented
and governed. Without coordinated action, AI and digitalisation risk reinforcing
inequalities, fragmenting the EHEA and undermining trust in higher education
systems. With the right frameworks, however, they can support more inclusive,
flexible and accessible education. ESU notes with concern that many of the
challenges identified in its previous statement on artificial intelligence, particularly
regarding coherent implementation, student participation and institutional
guidance, remain insufficiently addressed.

ESU therefore calls on higher education institutions, public authorities and
European level actors to ensure that AI and digital transformation strengthen,
rather than weaken education as a public good.