Category: News

The rapid development of Artificial Intelligence (AI) has created both excitement and apprehension across global academic landscapes. This web article explores the transition from technical experimentation to digital fluency, drawing insights from the blended courses and real-classroom implementations of AI scenarios that were recently developed as part of Work Package 4 of the INFINITE (https://infinite-erasmus.eu/) Erasmus+ project. By moving beyond the initial “hype”, institutions can develop deep, sustainable AI literacy among both faculty and students.

The Power of Scenario-Based Learning

One of the most effective ways to integrate AI into Higher Education (HE) is through scenario-based pedagogy. This approach is grounded in the pedagogical tradition that emphasises constructivism and experiential learning, as theorised by authors such as Jean Piaget and Lev Vygotsky (Della Volpe, 2024).

According to Piaget, learning is an active process occurring through the interaction between the individual and their environment. Similarly, Vygotsky highlights the importance of social context, arguing that knowledge is acquired through dialogue and collaboration. Scenario-based learning applies these principles by creating environments that require active participation, placing students in complex situations where they must solve problems and make decisions. (Della Volpe, 2024)

Hence, rather than teaching AI as a standalone technical subject, embedding it within these real-world academic or professional challenges allows learners to:

• Move beyond technical experimentation: In line with Piaget’s “active process”, participants transition from simply “playing” with tools to making reflective, informed decisions based on specific goals.
• Contextualise AI use: By applying AI to specific tasks—such as digital presentations creation, or research—the technology becomes a relevant partner in the learning process, facilitating the “interaction with the environment”.
• Reduce barriers to adoption: Using “discipline-neutral” scenarios that are easily adaptable helps faculty quickly customise AI activities for their specific subjects. This enhances the collaborative environment Vygotsky advocated, without requiring a deep computer science or IT background.

The Ethics-First Approach

A critical pillar of developing long-term AI readiness is the systematic embedding of ethical reflection. Rather than being treated as a separate, theoretical topic, ethics should be integrated in AI-related activities. This includes:

• Critical Evaluation: Developing the ability to assess AI-generated outputs for accuracy, reliability, and potential algorithmic bias.
• Academic Integrity: Establishing clear guidance to help students understand the boundaries between “AI-assisted” work and “AI-generated” work.
• Responsible Data Practices: Promoting an awareness of data privacy and intellectual property.

Building Long-Term Capacity

Evidence from the INFINITE project implementations suggests that a blended approach—combining self-paced online learning with face-to-face collaborative application—creates the strongest foundation for AI readiness. While asynchronous courses provide the necessary theoretical framework, in-class sessions offer the pedagogical depth needed for peer discussion and real-time troubleshooting.

To sustain this growth, HE institutions must view AI literacy not as a one-time training session, but as a continuous journey of development. As technology evolves, they must ensure that the academic community remains both technically skilled and critically aware.

References

Della Volpe, V. (2024). Scenario-Based Learning: An Inclusive Methodology. Journal of Research & Method in Education, 14(6), 1-5.

As Artificial Intelligence (AI) continues to reshape higher education, institutions across Europe are gradually moving beyond initial curiosity and experimentation. The question is no longer whether AI should be used, but how it can be integrated in ways that are meaningful, responsible, and pedagogically sound.

Within the INFINITE project, the work on WP4: AI Capacity Building and Courses responds directly to this challenge. By bringing together experiences from multiple European countries, it focuses on equipping educators and students with the knowledge, skills, and confidence needed to engage with AI in real educational contexts.

Across the project, dozens of educators and students from partner institutions participated in training and pilot activities, contributing to a rich, practice-based understanding of how AI can be applied in higher education.

Building Capacity Across European Contexts

The activities were implemented across diverse institutional and national environments, including the Netherlands, Greece, Ireland, Cyprus, and Belgium, involving participants with different levels of familiarity with AI. Some were already experimenting with AI tools in their teaching, while others were being introduced to the topic for the first time.

Despite these differences, a shared need quickly became evident. Educators and students alike are looking for structured support that goes beyond theory—support that helps them understand how AI can be used in practice, within the realities of higher education.

The transnational dimension played a key role here. By working across these different contexts, the project was able to identify common challenges and priorities, while also benefiting from a variety of perspectives and approaches.

From Learning About AI to Using It

At the core of this work are the blended courses developed for higher education academics and students. These courses were designed to move beyond abstract discussions of artificial intelligence and focus instead on its practical application in teaching and learning.

Participants consistently highlighted the value of this approach. The combination of conceptual understanding with hands-on exploration allowed them to better grasp both the potential and the limitations of AI tools. Whether in designing course activities, supporting student learning, or reflecting on assessment practices, the courses provided a space to experiment and reflect.

Importantly, the courses also addressed the ethical dimension of AI. Participants were encouraged not only to use AI tools, but to question them—considering issues such as transparency, bias, and responsible use.

Learning Through Piloting and Practice

A key strength of the activities lies in their implementation in real educational settings. The courses were piloted within higher education institutions, allowing participants to directly apply what they were learning.

This practical engagement proved essential. It enabled educators to test new approaches in their teaching and gave students the opportunity to interact with AI tools in authentic learning environments. As a result, feedback was grounded in real experience rather than hypothetical scenarios.

As one participant noted:

“I will incorporate them into my course.”

This simple statement reflects a crucial outcome of the work—not just awareness, but actual intention to apply the project results in practice.

Common Insights Across Countries

While the implementation contexts differed, several shared insights emerged from the transnational experience.

There is a strong and consistent demand for practical guidance. Participants are not looking for more information about AI in general, but for concrete examples, frameworks, and resources that can be applied immediately.

At the same time, the importance of ethics and critical thinking was emphasised across all contexts. AI is seen not just as a technological tool, but as a factor that influences decision-making, teaching practices, and learning processes.

Finally, the experience highlighted the role of institutions. Individual motivation and interest are high, but long-term impact depends on institutional support. Integrating AI into higher education requires not only training, but also strategic alignment and continuous professional development.

A Connected Ecosystem of Resources

The work on AI Capacity Building and Courses is closely connected with other key results of the INFINITE project. The AI Literacy Toolkit provides structured guidance and practical examples for educators, while the AI Digital Hub offers access to tools, resources, and emerging trends.

Together, these elements form a coherent ecosystem that supports both learning and implementation. Training is reinforced by access to resources, and exploration of tools is guided by pedagogical and ethical considerations.

Looking Ahead

The transnational experience confirms that higher education institutions are ready to engage with AI, but also underlines the need for continued support.

Moving forward, the focus will be on strengthening practical application, expanding training opportunities, and supporting institutions in embedding AI into their practices. The transition from experimentation to systematic integration is already underway.

In this context, the work on AI Capacity Building and Courses represents an important step—demonstrating how collaboration across countries can support meaningful and responsible innovation in higher education.

Ultimately, it reflects a broader shift: from understanding artificial intelligence as a concept, to actively shaping its role in education.

Artificial intelligence (AI) is rapidly becoming part of everyday life in higher education. It helps students write, supports teachers in preparing materials, and promises to make learning more efficient. But what if AI is more than just a helpful tool?

In her paper “AI is not a tool”, Nataliia Laba (2025) challenges this common assumption and invites us to think more critically about how AI shapes creativity, knowledge, and learning. For the INFINITE project, this perspective is especially important, not just how we use AI, but how we understand it.

The story behind AI

AI is often introduced with a familiar promise. It will make things faster, easier, and more creative. But this promise is built on a powerful narrative. First, human effort or creativity is framed as limited. Then, AI is presented as the solution. Finally, concerns are dismissed as resistance to inevitable progress. 

This way of thinking makes AI feel essential, but also leaves little room for critical reflection.

When creativity changes

Traditionally, creativity in education is about exploration, effort, and learning through doing. AI, however, can shortcut this progress. With just a few prompts, students can generate results instantly. 

While this can be useful, it raises an important question: if we remove the effort from learning, what happens to the learning itself?

The risk is not that AI replaces creativity, but that it reshapes how we value it.

AI is not neutral

AI systems are not simply tools. They reflect the data, assumptions, and priorities of those who build them. This means they shape how knowledge is produced and presented. 

In higher education, this matters. AI can influence what perspectives are visible, how ideas are formed, and how students engage with knowledge. Recognising this is essential for using AI responsibly.

Looking beyond the future

AI is often framed as the future of education, constantly improving and full of potential. But focusing only on the future can distract from present challenges, such as bias, environmental impact, and the changing nature of academic work. 

For universities, the key is to engage with AI critically now, not later.

The role of INFINITE

This is where INFINITE plays an important role. The project goes beyond simply promoting the use of AI. It encourages a deeper understanding of what AI means for education.

By supporting educators and students to think critically about AI, INFINITE helps shift the focus:

– From efficiency to meaningful learning

– From adoption to reflection

– From tools to systems that shape knowledge

A more thoughtful way forward

Seeing AI as “just a tool” is easy, but incomplete. AI is influencing how we learn, create, and think.

By recognising this, higher education can take a more thoughtful approach. This means balancing innovation with critical awareness.

Projects like INFINITE are key to this process, helping ensure that AI supports learning without redefining its core values.

Reference:

Laba, N. (2025). AI is not a tool. AI & Society. https://doi.org/10.1007/s00146-025-02784-y

23–24 April 2026 Groningen, Netherlands

The Erasmus+ funded INFINITE project officially concluded with a two day final event in Groningen, the Netherlands, bringing together academics, educators, researchers, and students from across Europe to reflect on the role of Artificial Intelligence in higher education and to celebrate the project’s achievements.

Hosted by the University of Groningen, the event combined a public Final Conference on 23 April with the project’s Final Transnational Partner Meeting on 24 April. Over 70 participants attended the event, fostering collaboration and exchange.

A Final Conference Focused on AI and Sustainable Education

The first day took place at the House of Connections and was co-organised with the Up-STEAM project under the theme STEM Education for Sustainable Futures, strengthening synergies.

The conference opened with an expert panel moderated by Mohammad Gharesifard and Francisco Castillo from the University of Groningen. Speakers included Justin Dillon, Digna Couso, Christine Fox, Luana Silveri, and Carol Garzón López. Discussions explored AI, informal education, and sustainable futures in STEM learning. 

The afternoon programme featured parallel breakout sessions where participants engaged with the main outputs developed through the INFINITE project, including:

• The AI Literacy Toolkit
• The AI Digital Hub
• AI capacity building courses for higher education

The event concluded with a poster session and networking reception, creating space for participants to exchange ideas, discuss future collaboration, and explore the project resources in an informal setting.

Feedback from participants highlighted strong interest in the project’s outputs and a high level of satisfaction with the conference programme.

Consortium Meeting Marks the Official Closing of the Project

On 24 April, consortium partners gathered at the Linnaesbourg building of the University of Groningen for the project’s Final TPM.

During the meeting, partners reviewed and formally concluded the project’s five work packages:

• WP1: Project Management, Quality Assurance, and Evaluation led by University of Groningen and CARDET
• WP2: AI Literacy Toolkit presented by UNIC
• WP3: AI Digital Hub presented by University College Dublin
• WP4: AI Capacity Building Courses presented by University of the Aegean
• WP5: Dissemination and Sustainability led by All Digital

The afternoon focused on sustainability planning, with partners discussing how the project’s tools, resources, and networks can continue supporting higher education institutions beyond the Erasmus+ funding period.

The meeting closed with a final round of reflections from all partners and a social dinner celebrating the conclusion of the project and the collaboration developed throughout its implementation.

Throughout its implementation, the INFINITE project worked to strengthen AI literacy and support higher education institutions in navigating the growing role of Artificial Intelligence in teaching and learning.Its main resources, including the AI Literacy Toolkit, AI Digital Hub, and AI capacity building courses, will remain openly accessible for educators, institutions, and learners across Europe, ensuring that the project’s impact continues well beyond its formal conclusion. Moreover, Action Plan and Sustainability Plan are currently being developed by partners, ensuring the widest uptake of the project results.

“AI won’t replace teachers — but teachers who use AI may replace those who don’t.“

For centuries, the teacher occupied an almost sacred role: the custodian and transmitter of knowledge. That archetype is dissolving — not because teachers have become less important, but because artificial intelligence is rapidly absorbing the parts of teaching that were, in truth, always mechanical. [1] What remains — and what AI cannot replicate — is the irreducibly human work of guiding curiosity, nurturing critical thought, and holding the ethical line. The classroom of 2025 does not need a teacher who knows everything. It needs one who knows how to build an environment where students can think.

 ‘’The more compelling narrative is not AI automating education, but teachers working with AI to craft transformative learning experiences.’’

World Economic Forum, 2025 [1]

The Automation Opportunity — and Its Shadow

The productivity gains are real and already in motion. [1] Roughly 60% of teachers now use AI tools in their classrooms to handle routine tasks — grading multiple-choice assessments, tracking progress, generating practice exercises — freeing time for deeper instructional work. [1] The global AI in education market is projected to leap from $5.18 billion in 2024 to $112.3 billion by 2034. [1] Yet automation carries a shadow risk: that teachers begin to cede the judgement, care, and accountability that make education meaningful. UNESCO’s 2024 AI Competency Framework for Teachers warns explicitly that over-reliance on AI could cause educators to lose key professional competencies if they delegate too heavily to algorithmic systems. [3]

Three Roles Teachers Must Now Own

As AI absorbs the transactional, teachers are stepping into three defining roles — each one distinctly human in character.

The Competencies That Cannot Wait

UNESCO’s AI Competency Framework for Teachers — launched in September 2024 — defines five dimensions every educator must develop: a human-centred mindset, AI ethics, foundational AI knowledge, AI pedagogy, and professional AI learning. [3] These are not optional electives. Research from the European Journal of Teacher Education (2025) confirms that nearly half of self-reported digitally literate teachers have still not integrated AI into their practice, underscoring a knowing-doing gap that professional development must close. [5]

Teachers also need what no framework can fully script: the moral imagination to ask whether an AI tool should be used before asking how. As the World Economic Forum has observed, teaching involves far more than imparting information — AI should augment, never replace, the teacher’s role. [6]

The 21st-century teacher is, above all, a learning architect — someone who designs conditions in which human minds grow, integrates AI as a powerful but subordinate instrument, and keeps ethics at the centre of every choice. The transition is demanding, but the invitation is extraordinary: to become not less essential in an age of AI, but more deliberately, more irreplaceably human.

Sources & References

1. World Economic Forum. (2025, January). How AI and human teachers can collaborate to transform education. weforum.org
2. Frontiers in Psychology. (2025, October). Promoting teaching innovation among university teachers through AI literacy from the perspective of planned behavior. frontiersin.org
3. UNESCO. (2024). AI Competency Framework for Teachers. unesdoc.unesco.org
4. EdTech Hub. (2025, May 21). AI Tutors and Teaching: How Might the Role of the Teacher Change in an Age of AI? edtechhub.org
5. Heine, S. & König, J. (2025). Applying artificial intelligence in teacher education: preservice teachers’ attitudes and reflections in using ChatGPT for teaching and learning. European Journal of Teacher Education, 48(5). tandfonline.com
6. World Economic Forum. (2024, April). The future of learning: AI is revolutionising education 4.0. weforum.org

The rapid development of Artificial Intelligence (AI) has created both excitement and apprehension across global academic landscapes. This web article explores the transition from technical experimentation to digital fluency, drawing insights from the blended courses and real-classroom implementations of AI scenarios that were recently developed as part of Work Package 4 of the INFINITE (https://infinite-erasmus.eu/) Erasmus+ project. By moving beyond the initial “hype”, institutions can develop deep, sustainable AI literacy among both faculty and students. 

The Power of Scenario-Based Learning 

One of the most effective ways to integrate AI into Higher Education (HE) is through scenario-based pedagogy. This approach is grounded in the pedagogical tradition that emphasises constructivism and experiential learning, as theorised by authors such as Jean Piaget and Lev Vygotsky (Della Volpe, 2024). 

According to Piaget, learning is an active process occurring through the interaction between the individual and their environment. Similarly, Vygotsky highlights the importance of social context, arguing that knowledge is acquired through dialogue and collaboration. Scenario-based learning applies these principles by creating environments that require active participation, placing students in complex situations where they must solve problems and make decisions. (Della Volpe, 2024) 

Hence, rather than teaching AI as a standalone technical subject, embedding it within these real-world academic or professional challenges allows learners to: 

• Move beyond technical experimentation: In line with Piaget’s “active process”, participants transition from simply “playing” with tools to making reflective, informed decisions based on specific goals. 

• Contextualise AI use: By applying AI to specific tasks—such as digital presentations creation, or research—the technology becomes a relevant partner in the learning process, facilitating the “interaction with the environment”. 

• Reduce barriers to adoption: Using “discipline-neutral” scenarios that are easily adaptable helps faculty quickly customise AI activities for their specific subjects. This enhances the collaborative environment Vygotsky advocated, without requiring a deep computer science or IT background. 

The Ethics-First Approach 

A critical pillar of developing long-term AI readiness is the systematic embedding of ethical reflection. Rather than being treated as a separate, theoretical topic, ethics should be integrated in AI-related activities. This includes: 

• Critical Evaluation: Developing the ability to assess AI-generated outputs for accuracy, reliability, and potential algorithmic bias. 

• Academic Integrity: Establishing clear guidance to help students understand the boundaries between “AI-assisted” work and “AI-generated” work. 

• Responsible Data Practices: Promoting an awareness of data privacy and intellectual property. 

Building Long-Term Capacity 

Evidence from the INFINITE project implementations suggests that a blended approach—combining self-paced online learning with face-to-face collaborative application—creates the strongest foundation for AI readiness. While asynchronous courses provide the necessary theoretical framework, in-class sessions offer the pedagogical depth needed for peer discussion and real-time troubleshooting. 

To sustain this growth, HE institutions must view AI literacy not as a one-time training session, but as a continuous journey of development. As technology evolves, they must ensure that the academic community remains both technically skilled and critically aware. 

References 

Della Volpe, V. (2024). Scenario-Based Learning: An Inclusive Methodology. Journal of Research & Method in Education, 14(6), 1-5. 

Key insights from the INFINITE WP4 implementation at the University of the Aegean

How do we move beyond abstract discussions about Artificial Intelligence in higher education and support responsible, meaningful, and ethical use of AI in everyday teaching and learning? This question was at the heart of the Greek implementation of the INFINITE project, carried out at the University of the Aegean.

Over the course of 2025, academics and students engaged with AI not as a shortcut or replacement for learning, but as a tool for reflection, critical thinking, and pedagogical innovation. What emerged were not only new skills, but also valuable lessons about what works—and what needs careful attention—when AI enters the higher education classroom.

AI training works best when pedagogy comes first

One of the strongest insights from the Greek implementation is that AI capacity building is most effective when it is pedagogically framed. Both academics and students participated in blended courses built around the INFINITE learning scenarios, which emphasise intentional use, ethical awareness, and “human-in-the-loop” approaches.

Rather than focusing on mastering specific tools, participants explored why, when, and how AI might support learning. This shift in focus proved crucial. Post-course feedback shows increased confidence not only in recognising AI applications, but also in evaluating their limitations and risks. Importantly, participants did not become uncritical adopters of AI; instead, they developed more reflective and selective attitudes toward its use.

Real classrooms reveal real challenges—and real learning

Two undergraduate classroom implementations provided a powerful reality check. When AI tools were integrated into courses on literature and history education, students initially tended to trust AI-generated content too easily. Hallucinations, oversimplified language, biased perspectives, and factual inaccuracies quickly surfaced.

Rather than treating these issues as failures, instructors used them as learning opportunities. By applying the INFINITE Visualised Framework and AI Readiness Checklist, students were guided to question outputs, verify information, and revise AI-generated material using their own disciplinary knowledge. This process transformed moments of uncertainty into deep learning experiences.

A key lesson here: AI literacy grows strongest where friction exists. Encountering the limits of AI helped students sharpen critical thinking, ethical judgement, and disciplinary awareness—skills that are central to higher education and future teaching professions.

Ethical awareness resonates with both students and academics

Across courses and classroom activities, ethical considerations consistently stood out as one of the most meaningful aspects of the experience. Participants reported the greatest learning gains in areas such as:

• recognising bias and inaccuracies

• understanding authorship and academic integrity

• evaluating when AI use is appropriate—and when it is not

Notably, even after the courses, both academics and students remained cautious about using AI in assessment and high-stakes academic work. This nuance is significant: the goal was never to promote unrestricted AI use, but to cultivate informed, responsible decision-making.

Using multiple AI tools builds digital resilience

Another important insight concerns the value of working with more than one AI system. By comparing outputs from tools such as ChatGPT, DeepSeek, and Gemini, participants quickly realised that AI tools are neither neutral nor interchangeable.

This comparative approach helped demystify AI and supported the development of what instructors described as digital resilience: the ability to assess outputs critically, adapt prompts, and avoid overreliance on any single system. For students—especially pre-service teachers—this understanding is vital for navigating an evolving digital landscape.

Lessons for the future

The Greek WP4 experience offers several clear lessons for future AI initiatives in higher education:

• Critical AI literacy must remain central. Technical skills alone are insufficient without ethical reflection and verification practices.

• Discipline-specific examples matter. Humanities and social sciences benefit greatly from tailored scenarios and prompts.

• Assessment needs rethinking. Hybrid human–AI work requires transparent criteria and redesigned evaluation methods.

• Flexibility supports participation. Blended and asynchronous formats help academics and students engage meaningfully despite workload pressures.

Above all, the experience shows that AI training can act as a catalyst for broader pedagogical reflection. Academics reconsidered teaching and assessment practices, while students reflected on their future professional responsibilities as educators.

Moving forward with intention

By meeting—and in many cases exceeding—its participation and engagement targets, the University of the Aegean’s implementation of WP4 demonstrates how the INFINITE project can translate European priorities into grounded, classroom-level change.

The key takeaway is clear: responsible AI integration is not about doing more with technology, but about thinking better with it. When supported by thoughtful pedagogy, ethical frameworks, and reflective practice, AI can become a powerful tool for learning—not by replacing human judgement, but by strengthening it.

If you were to walk around campus and interview students about their experience with artificial intelligence, it probably would not take long to find someone who has used AI tools for coursework before. As AI usage spreads rapidly through university classrooms, so do the controversies surrounding it. 

In June 2025, a group of academics from diverse fields, such as computing science, sociology, law, philosophy, cognitive science, and artificial intelligence, published an open letter urging all Universities and Universities of Applied Sciences of the Netherlands to “stop the uncritical adoption of AI technologies in academia” (Open Letter: Stop the Uncritical Adoption of AI Technologies in Academia, n.d.). By now, more than 1,600 professionals, lecturers, and students from around the world have signed the letter, and it has also been translated into other languages. This emphasises a broad agreement throughout different positions in academia and society that AI technologies, in their current use, cause harm to the quality and reputation of work done by staff and students in higher education institutions. Some of the authors elaborate their stance in an opinion paper that adds insight into why AI technologies should not be used and glorified without appropriate reflection in (higher) education (Guest et al., 2025). Essentially, using certain kinds of AI systems in higher education contradicts many of academia’s values, like diversity, openness, critical reflection, and sustainability. In their paper, the authors examine the challenges arising from the vague use of the term ‘artificial intelligence’. They argue that, as a buzzword, ‘AI’ often lacks a precise definition, which can generate hype and make it difficult for users to evaluate these tools objectively. To address this, the authors offer explanations of the most common AI terms and add links for the reader to further investigate on their own. It is worth taking a look at the abundance of references used, which include scientific papers, news articles, and blog posts, as the article serves as a good starting point for building a critical perspective on AI technology in education. 

The article criticises AI companies’ marketing strategies by suggesting that the AI tools have abilities that they actually do not have. One example being the claim that large-language models can read or write: There is no such thing as a robot crafting a text similar to how a human would do, it is all just a most likely prediction based on predominantly intransparent algorithms. Special caution is needed regarding profit-based AI technologies such as ChatGPT: The company behind it, OpenAI, does not publicly provide any source code, which strongly goes against fundamental academic principles such as transparency.

The chatbot ‘ChatGPT’, designed by the company OpenAI, is especially popular amongst students, though it brings massive risks of deskilling.

  From: https://www.pexels.com/photo/close-up-of-a-person-holding-a-smartphone-displaying-chatgpt-16461434/ 

The users do not know how the output of this tool, often perceived as some sort of universal remedy,  is actually computed. The commercial field of artificial intelligence repeatedly goes through hype cycles but keeps on “over-promising and under-delivering” (Guest et al., 2025, p. 10), as the authors put it. Similarly, the term ‘artificial intelligence’ somehow suggests that human intelligence is something that can easily be replicated artificially, while this vastly simplifies the cognitive processes that are commonly understood as contributing to ‘intelligence’. Generally, the article reminds us how the concept of intelligence is historically problematic because of its racist, sexist, classist, and ableist background. AI easily falls into the trap of reproducing those exact issues and therefore is especially “harmful to minoritised and vulnerable groups” (Guest et al., 2025, p. 4). Though sometimes believed, artificial intelligence will not bring more justice and equality as a solution. When people use AI tools regularly, it gives tech companies a lot of social power and might lead to data colonialism. The perception that a large-language model is in any way neutral or objective because it is a machine ultimately leads to dehumanisation. Additionally, AI, in many cases, is harmful to the environment. For example, data centres use a lot of land, water, and energy to make the tools available. 

Approaching the conflict of AI use in higher education and academic values, the paper urges a reflection on the relationship between society and specific technologies in a more general sense. To stimulate this train of thought, they provide a nice metaphor: One could compare AI use to driving responsibly in traffic. If a regular person is speeding, this poses a risk to everyone around them. However, if a paramedic is driving very fast to transport a patient to the hospital, this is not considered unethical or wrong, given that the paramedic is specifically trained for driving safely at higher speeds. Similarly, there is a moral difference between trained experts using AI in scientific practice and laypersons using AI in their daily lives. This analogy helps to understand why it is relevant for lecturers in higher education to carefully and responsibly train their students to use AI tools appropriately. 

Lecturers should remind their students of the original purpose of higher education, which is more than getting a degree

From: https://www.pexels.com/photo/a-class-having-a-recitation-8199166/ 

Rather than mindlessly reproducing the marketed talking points of AI companies, teachers in higher education should carefully consider which pedagogical goals they are pursuing with their classes and whether AI can meet these. Does the AI tool of choice really have a significant added value to the learning process? According to the paper, successful education is based on mutual trust between educators and students, and to prevent students from cheating, e.g., letting AI tools generate an essay but present it as their own work, teachers should remind students of the general purposes of education. Especially, the question of copyright is a central issue of AI use at universities: Not only does the regular use of LLMs normalise to appropriate work as one’s own, but it also devalues the actual work of students and staff who abstained from AI. Following this line of argumentation, it reduces academics, who formerly were workers, to customers. Writing a good prompt does not need the same level of skills as writing a good paper. It is possible as an educator to teach AI literacy without needing to encourage the use of the AI tools discussed. According to the authors, the increasing prevalence of AI use in education will likely lead to an increase in illiteracy and, in a broader sense, deskilling of students, who will also become dependent on the technology industry. The latter, however, could also be argued for any kind of technology, such as writing programs, digital learning environments, reference managers, etc., where most of them could still be considered harmless.  Handling AI usage well in the classroom remains a challenge, since merely raising awareness of its risks does not take them away. In the long-term, even talking about AI critically might reinforce a certain normalisation of the topic. 

Applying the five core principles of the Netherlands Code of Conduct for Research Integrity to AI usage, the paper clearly lays out tensions: Honesty would demand that researchers disclose when AI have been used, and refrain from making unsupported claims about their capabilities.  Scrupulousness requires only using AI tools whose functions are well-specified, validated and relevant for the field, and that the researchers can justify why the technologies are used. Transparency includes that the AI tools used should be open source and computationally reproducible, which disqualifies most of the large-language model chatbots of major tech companies. Independence of such AI firms could pose big challenges for the researchers to stay unbiased and without a conflict of interest. Finally, responsibility obliges academics to avoid any AI tools that are harmful to people, animals, the environment or legal guidelines. In sum, the paper states how most current AI technologies struggle to meet even the basic ethical and methodological standards of academic research. 

Students themselves often recognise the risks of AI or even prefer outright bans. The argument is straightforward: To truly acquire a skill, you must practice it. In the paper, this is highlighted through examples such as learning basic arithmetic: When this is learned in school, pupils are usually not allowed to use a calculator, so that they are forced to go through the effort of learning relevant skills. Similarly, it would be advisable to ban AI systems in higher education in order to allow students to properly learn what it means to work scientifically. Unfortunately, the current academic system already pressures individuals to take unethical shortcuts, but the spread of AI accelerates the problem. Yet the spread of AI is not inevitable, despite how it is often presented. While reversing course becomes more difficult over time, it is not impossible: Dutch schools, for example, have successfully reintroduced phone bans, with measurable benefits for student focus and learning outcomes (Kohnstamm Instituut, 2025).  Ultimately, AI systems cannot really replace the depth and quality of human craft and thinking. The challenge for academia is not just to resist the hype, but to actively reclaim the values and practices that make education meaningful. 

References 

• Guest, O., Suarez, M., Müller, B., Edwin, V. M., Arnoud, O. G. B., Ronald, D. H., Andrea, R. E., Blokpoel, M., Scharfenberg, N., Kleinherenbrink, A., Camerino, I., Woensdregt, M., Monett, D., Brown, J., Avraamidou, L., Alenda-Demoutiez, J., Hermans, F., & Iris, V. R. (2025). Against the uncritical adoption of “AI” technologies in academia. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.17065099  

• Kohnstamm Instituut (2025). Url: https://open.overheid.nl/documenten/54c01e11-5a20-4779-9243-f4ed5fda1c9f/file  

• Open Letter: Stop the uncritical adoption of AI technologies in Academia. (n.d.). https://openletter.earth/open-letter-stop-the-uncritical-adoption-of-ai-technologies-in-academia-b65bba1e 

The University of Leiden has organised the seminar on the same topic, the recording is available at: https://www.universiteitleiden.nl/en/events/2026/02/against-the-uncritical-adoption-of-ai-technologies-in-academia  

Introduction

As distance learning cements its place in higher education, Artificial Intelligence (AI) is emerging as a game-changer. From adaptive learning paths to real-time feedback and intelligent tutoring systems, AI technologies promise to transform how we teach and learn remotely. But are higher education institutions—and their students—ready to harness this potential responsibly?

This article explores the expanding role of AI in distance higher education, its benefits and risks, and how current student practices reflect a broader need for institutional support, as evidenced by recent research conducted at the Hellenic Open University.

The Promise of AI in Distance Learning

AI offers a powerful toolkit for distance education, especially in areas where traditional teaching methods struggle to meet the needs of remote learners. Some of the most promising applications include:

• Personalized learning environments that adapt to individual pace and learning styles;

• Automated feedback systems that provide immediate responses to assignments or quizzes;

• Chatbots and virtual assistants that offer 24/7 academic support;

• Predictive analytics that identify students at risk of disengagement or dropout.

These tools help bridge the gap created by physical distance, offering a more flexible and responsive learning experience. In asynchronous or self-paced programs—common in distance higher education—AI can act as a digital learning companion when human interaction is limited.

A Reality Check: What Students Are Really Doing

Despite the growing availability of AI tools, many students still struggle to integrate them meaningfully into their studies. A recent study by Kostas and Manousou (2025) at the Hellenic Open University surveyed 373 postgraduate distance learners across two academic years. The findings revealed that:

• Most students were aware of AI tools but lacked the confidence or knowledge to use them effectively;

• AI usage was sporadic and superficial, often limited to general writing or grammar tools;

• Students expressed concerns about reliability, academic integrity, and ethical ambiguity in AI-generated content;

• A significant number pointed to a lack of institutional training or guidance as a barrier to responsible use.

This highlights a key challenge: technological availability does not equal readiness. For AI to truly enhance distance learning, students need structured support, not just access to tools.

Barriers to Adoption: Not Just Technical

Why aren’t more students fully embracing AI in distance learning?

• Digital literacy gaps: Knowing how to use AI critically is different from knowing it exists.

• Ethical concerns: Issues like plagiarism, transparency, and data privacy cause hesitation.

• Limited institutional frameworks: Many universities have yet to provide clear guidelines or training on appropriate AI use.

• Risk of overreliance: Students worry about losing their critical thinking and autonomy when AI becomes a shortcut.

These concerns underline the need for holistic AI integration—one that goes beyond tools and focuses on skills, values, and policies.

What Institutions Can Do: A Call for Action

For distance education to benefit fully from AI, higher education institutions must step up by:

1. Providing AI literacy training for both students and faculty;

2. Developing ethical frameworks and usage policies that are transparent and inclusive;

3. Integrating AI into course design in pedagogically sound and human-centered ways;

4. Encouraging reflective use of AI—not just functional, but critical engagement.

Projects like INFINITE are already leading the way by offering open educational resources, practical guides, and institutional tools that promote ethical, informed, and inclusive use of AI in higher education.

Conclusion

AI has the potential to make distance higher education more engaging, accessible, and effective—but only if implemented thoughtfully and ethically. The student perspective from the HOU study is clear: curiosity is high, but support is lacking. To close this gap, institutions must move beyond just offering AI tools—they must foster the skills and culture needed to use them responsibly.

Further Reading

Kostas, A., & Manousou, E. (2025). Benefits and challenges of AI in higher distance education: Students’ perceptions and practices in Hellenic Open University (HOU).Advances in Mobile Learning Educational Research, 5(2). https://doi.org/10.25082/AMLER.2025.02.011