AI-HED

AI-HED Final Event: Lessons Learned and Best Practices

Tatiana Nunes — Wed, 11 Nov 2026 11:00:00 +0000

On 6 October 2026, the AI-HED consortium came together in Amsterdam for its Final Event, “From Improvisation to Effective Integration: AI in Teaching and Learning in Higher Education”. Discover what happened, explore the key takeaways and access the recordings and presentation slides.

Hosted by the Amsterdam University of Applied Sciences (AUAS), the hybrid event brought together educators, researchers, students and higher education leaders to share the outcomes of the Erasmus+ AI-HED project and reflect on one of the most pressing challenges facing higher education: how can we move beyond experimenting with artificial intelligence towards its meaningful, responsible and effective integration into teaching and learning?

Explore all AI-HED Final Event resources

From experimentation to evidence-based practices

The event opened with remarks by Bernard Smeenk (watch recording ) and a welcome address by Sharan Visser, Head of Operations of the Faculty of Business and Economics (watch recording ).

What Did We Learn from the AI-HED Pilots? Five Lessons for the Future of Higher Education

Erik Kostelijk (AUAS) then introduced the AI-HED project, presenting its objectives, activities and key findings.

His presentation, “What the AI-HED Pilots Teach Us”, highlighted the outcomes of 24 AI-enhanced pilot courses involving more than 1,000 students across different disciplines. Through pre- and post-semester surveys, teachers’ logbooks and focus groups, the project explored how the deliberate integration of AI influenced students’ learning, educators’ practices and approaches to curriculum design and assessment.

The findings revealed encouraging developments. Students reported improvements in their AI-related digital competencies, confidence and ability to use AI in their learning. Educators also strengthened their competencies and increasingly recognised AI as a valuable tool to support their role as facilitators of learning. Notably, 70% of participating teachers would highly recommend integrating AI into education to their colleagues, while none expressed opposition.

However, the results also highlighted important challenges. AI can improve the quality of students’ outputs without necessarily ensuring meaningful learning. This raises fundamental questions about how to assess students’ actual competencies, maintain their ownership of the learning process and ensure that AI supports, rather than replaces, critical thinking and cognitive effort.

Drawing on these findings, Erik presented five key recommendations for higher education institutions: rethink educational models rather than simply add AI tools; build coherent AI competence development across study programmes; redesign assessment to make student learning visible; ensure equitable access to AI; and establish institutions capable of continuously learning and adapting to technological change.

The presentation reinforced one of the project’s central conclusions: moving from improvisation to effective AI integration requires not only technological innovation, but also pedagogical transformation and institutional commitment.

Read the full article | Watch the recording | Download the slides (PDF)

Best Practices

At the heart of the event were the experiences and lessons learned from the project’s pilot courses, implemented across four European higher education institutions in the Netherlands, Austria, Portugal and Croatia.

Four best practices illustrated how generative AI can be integrated into different disciplinary contexts:

1. Using an AI Persona as a Research-Informed Thinking Partner

Alexandra Benitez Burgos — AUAS, Netherlands

Alexandra presented how AI personas can support research-informed thinking, helping students explore different perspectives and strengthen their analytical and decision-making skills.

Watch the recording | Download the slides (PDF)

2. Designing Smart Assignments for Productive AI Use in Learning

Friedrich Stallinger — UAS for Business & Society, Austria

Friedrich explored how to design assignments that encourage productive AI use while maintaining students’ active engagement, critical thinking and responsibility for their own learning.

Watch the recording | Download the slides (PDF)

3. AI-Assisted Multiplatform Storytelling: From Written News to Short-Form Video

Vera Moutinho — ESCS-UPL, Portugal

Vera demonstrated how generative AI can support multiplatform journalism education, helping students transform written news into short-form video while preserving human creativity, editorial judgement and critical oversight.

Watch the recording | Download the slides (PDF)

4. AI-Supported Digital Marketing: Teaching Critical Evaluation

Magdalena Kuštelega — FOI - University of Zagreb, Croatia

Magdalena shared how AI was integrated into digital marketing education, with particular emphasis on developing students’ ability to critically assess AI-generated content and make informed professional decisions.

Watch the recording | Download the slides (PDF)

Together, these examples highlighted a central message of AI-HED: the value of AI in higher education depends not simply on the technology itself, but on how it is integrated into pedagogical practices, learning objectives and assessment.

Want to explore the AI-HED Best Practices in more depth?

Discover how AI was integrated into real teaching and learning contexts, explore the pedagogical approaches behind the pilot courses, and find inspiration for your own teaching practice.

Explore the AI-HED Best Practices

Turning insights into practice: interactive workshops

The afternoon continued with five interactive workshops, offering participants opportunities to explore practical approaches to AI integration in higher education.

The workshops addressed different dimensions of AI-enhanced education:

GenAI: How do we assess? — Erik Kostelijk and Raúl Casado Linares (AUAS).
Staying AI-HED: Innovating Teaching & Curricula — Dietmar Paier (UAS for Business & Society).
Knowledge Management and Research with AI — Bruno Amaral (ESCS-UPL).
Generative AI for Educators: Adapting, Creating and Reflecting — Larisa Hrustek, Iva Gregurec, Magdalena Kuštelega and Matija Šajn (University of Zagreb – FOI).
Generative AI for Educators: Adapting, Creating and Reflecting (Online) — Bogdan Okreša Đurić (University of Zagreb – FOI).

These sessions connected the project’s findings with the everyday challenges faced by higher education educators, encouraging reflection, experimentation and the exchange of practical approaches.

What does it take to become an AI-ready university?

The event concluded with a panel discussion entitled “The AI-ready university: What does it take?”, moderated by Erik Kostelijk (AUAS), bringing together institutional leaders, researchers, educators and students to reflect on the future of higher education in an AI-driven world: Henk Geveke (Vice-Chair, AUAS Executive Board), Dietmar Paier (Head of Teaching & Learning Center, UAS for Business & Society), Hannah Berkers (Researcher and Coordinator, AUAS), Wilco Verdoold (Lecturer, AUAS) and Jarad Tromp (Student, AUAS).

The discussion highlighted that becoming an AI-ready university is not simply about adopting new technologies, but about fundamentally rethinking teaching, learning, assessment and the competencies students need for their future professional lives. Participants emphasised the importance of developing critical thinking, professional judgement and the ability to make informed decisions about when, why and how to use AI, while maintaining human agency and avoiding excessive dependence on technology.

Student perspectives reinforced the need for higher education to adapt more quickly to technological developments and to provide structured opportunities to develop AI competencies. The discussion also addressed the importance of equitable access to AI tools, continuous professional development for educators and more meaningful collaboration between teachers and students.

A central message emerged: AI integration should be guided by human needs, educational purposes and institutional values, rather than by technology itself. Building an AI-ready university requires a shared vision, collaborative experimentation and a commitment to ensuring that AI enhances the quality of education, meaningful work and human development.

Read the full article | Watch the panel discussion

Couldn’t attend? Explore the resources available

The conversation does not end with the event.

Whether you missed the presentations, attended only part of the programme or would like to revisit a particular topic, the recordings of the main presentations and their slides are now available online.

Explore the project overview, discover four teaching practices, access the available workshop materials and revisit the panel discussion.

Explore the AI-HED Final Event: recordings, slides and programme

The AI-HED Final Event marked an important milestone in the project’s journey, bringing together insights from experimentation, evaluation and collaboration across institutions.

The next challenge is to turn these lessons into sustainable educational practices, ensuring that AI enhances human learning, critical thinking and the role of educators in shaping the future of higher education.

What Did We Learn from the AI-HED Pilots? Five Lessons for the Future of Higher Education

Tatiana Nunes — Wed, 11 Nov 2026 10:30:00 +0000

24 pilot courses, more than 1,000 students and four European higher education institutions. What happens when artificial intelligence is deliberately integrated into teaching and learning? The AI-HED project explored this question, generating valuable insights into how AI is changing students’ learning, educators’ roles, curricula and assessment practices.

At the AI-HED Final Event, held on 6 October 2026 at the Amsterdam University of Applied Sciences (AUAS), Erik Kostelijk, Associate Professor at AUAS and AI-HED project coordinator, presented the project’s main findings in a session entitled “What the AI-HED Pilots Teach Us”.

His presentation addressed a fundamental challenge: how can higher education move from individual experimentation with AI towards its effective, responsible and sustainable integration into teaching and learning?

AI-Enhanced Courses

1,000+

Students

70%

Teachers Would Highly Recommend AI Integration

From experimentation to evidence: the AI-HED pilots

Over two years, the Erasmus+ AI-HED project brought together four European higher education institutions from the Netherlands, Austria, Portugal and Croatia to explore the implications of AI for higher education.

Following initial activities focused on developing educators’ AI literacy and preparing pilot courses, the consortium implemented 24 AI-enhanced courses across different disciplines, including marketing, communication, journalism, languages and business process management.

More than 1,000 students participated in courses where AI was deliberately integrated into teaching and learning activities.

To understand the impact of these experiences, the project adopted a structured evaluation approach, combining pre- and post-semester surveys with teachers’ weekly logbooks and focus groups involving students and educators.

The findings revealed encouraging developments, alongside important questions about the future of higher education.

Students developed greater AI competence and confidence

One of the clearest findings was the improvement in students’ self-reported AI-related digital competencies across five dimensions: information and data literacy, communication and collaboration, digital content creation, safety and responsible use, and problem-solving.

Following the pilot semester, students reported higher competency levels across all five areas, particularly in information searching, content creation, communication and responsible AI use.

Students’ confidence in using AI also increased: the proportion reporting high confidence rose from approximately 40% to more than 50%, while positive attitudes towards using AI in a professional context increased from 33% to 45%.

However, the findings also revealed an important distinction between using AI and using it critically.

Students gradually moved beyond treating AI as a simple question-answering tool towards more sophisticated interactions, including questioning, refining and critically evaluating AI-generated responses.

This development highlighted the continued importance of disciplinary knowledge. Students need sufficient understanding of a subject to interpret AI outputs, identify weaknesses and make informed decisions.

AI competence and disciplinary competence must develop together.

Although responsible and safe AI use improved, it remained one of the areas with the lowest self-reported competency levels, reinforcing the need for continued attention to critical and responsible AI literacy.

Educators also learned — and their role is evolving

The impact of the pilots was not limited to students.

Educators reported substantial improvements in their AI-related competencies, with increases across all five digital competence dimensions. Their confidence in using AI for teaching also increased, from below 40% to approximately 60%.

Perhaps one of the most encouraging findings was that 70% of participating educators would highly recommend integrating AI into teaching to their colleagues, with none expressing opposition.

The pilots also highlighted how AI can reinforce the educator’s role as a facilitator of learning.

By supporting students with initial questions and routine tasks, AI created opportunities for teachers to focus more on deeper discussions, individual guidance, critical reflection and meaningful feedback.

Rather than diminishing the educator’s role, effective AI integration can make pedagogical expertise and human interaction even more valuable.

These findings also demonstrate that educators’ AI competence develops through practical experience, not only through theoretical training.

AI is changing what we teach — and how we assess learning

Using Bloom’s taxonomy as a reference, the AI-HED evaluation examined how AI supports different levels of cognitive learning.

The findings suggested that AI can contribute not only to basic learning activities but also to higher-order cognitive processes, particularly analysing, evaluating and creating.

Students and teachers reported benefits in understanding complex concepts, applying knowledge and supporting learning activities. More than 90% of participating teachers agreed that AI integration improved the overall learning experience in their courses.

However, these positive findings also raised important questions about curriculum design.

If students increasingly use AI to complete complex tasks, how can higher education ensure that they still develop the foundational knowledge and competencies required to evaluate those outputs?

Erik highlighted the need to integrate AI progressively throughout study programmes, aligning its use with students’ growing disciplinary knowledge and professional competence.

Assessment presents another fundamental challenge.

As AI becomes increasingly capable of producing high-quality written outputs, the final product alone can no longer provide sufficient evidence of what a student actually knows or can do.

The focus of assessment must therefore shift towards demonstrating competence, explaining decisions and establishing student ownership of the learning process.

This may require greater use of oral assessment, process-based evidence and opportunities for students to explain and defend their work.

However, these approaches also demand more time and resources from educators, creating a challenge for institutions seeking to scale AI integration beyond individual pilot courses.

The risks: learning, equity and institutional adaptability

Despite the positive outcomes, the project identified several risks that higher education institutions must address.

One of the most significant concerns is the difference between improving task performance and improving learning.

AI can help students produce better results, but it can also perform cognitive work that students need to develop independently. A high-quality assignment does not necessarily demonstrate that meaningful learning has taken place.

Another challenge concerns equity.

Students do not necessarily have equal access to AI tools, particularly when more advanced capabilities require paid subscriptions. If access to certain tools becomes necessary for learning, differences in financial resources may translate into unequal educational opportunities.

Finally, universities face the challenge of adapting to a technology that evolves much faster than traditional institutional processes.

Individual pilot courses can demonstrate what works, but sustainable integration requires coordinated approaches across programmes and institutions.

Five recommendations: from improvisation to effective integration

Drawing on the results of the pilots, Erik presented five recommendations for higher education institutions.

1. Rethink education, not just AI use

AI is changing how students learn. Universities must reconsider what students should learn, how learning takes place and how competence is recognised, rather than simply adding AI tools to existing educational models.

2. Build progression across the student journey

Individual courses can innovate, but coherent development of AI and disciplinary competencies requires programme-level coordination. Institutions should establish clear frameworks, programmes should design progressive learning pathways, and educators should develop learning activities aligned with these objectives.

3. Make competence visible without making assessment unmanageable

Assessment must provide evidence of students’ understanding, decision-making and ownership of their work. Institutions need approaches that combine meaningful assessment with realistic workloads for educators.

4. Ensure equitable access to AI

If AI is an essential part of learning, access cannot depend on students’ financial resources. Universities must ensure that all students have access to tools with the capabilities required to achieve their learning objectives.

5. Build universities that keep learning

AI will continue to evolve, and higher education institutions must evolve with it. This requires continuous professional development, opportunities for experimentation, the sharing of good practices and institutional frameworks that can adapt to changing educational needs.

The future of AI in higher education is a pedagogical and institutional challenge

The AI-HED pilots demonstrated that meaningful AI integration can strengthen students’ competencies, support educators’ professional development and create valuable learning opportunities.

At the same time, the findings show that successful integration requires more than technological proficiency. It demands pedagogical innovation, curriculum alignment, new approaches to assessment, equitable access and institutional adaptability.

Above all, AI should enhance learning without replacing the cognitive effort, critical judgement and responsibility that education aims to develop.

The central message from AI-HED is clear: moving from improvisation to effective integration requires universities that are willing and able to keep learning.

Watch the presentation and explore the AI-HED results

Interested in discovering the evidence, findings and recommendations behind these conclusions?

Watch Erik Kostelijk's presentation Download the slides (PDF)

Discover how educators translated these insights into classroom practices:

Explore the AI-HED Best Practices — From the Classroom

The AI-ready university: What does it take? Insights from the AI-HED Final Event

Tatiana Nunes — Wed, 11 Nov 2026 10:00:00 +0000

Is higher education adapting fast enough to artificial intelligence? What should students still learn when AI can perform an increasing number of tasks? And how can universities ensure that technology enhances, rather than replaces, human judgement and learning?

These were some of the questions explored during the panel discussion “The AI-ready university: What does it take?”, held on 6 October 2026, as part of the AI-HED Final Event, hosted by the Amsterdam University of Applied Sciences (AUAS).

Moderated by Erik Kostelijk (AUAS), the discussion brought together institutional leaders, researchers, educators and students, including Henk Geveke (Vice-Chair, AUAS Executive Board), Dietmar Paier (Head of Teaching & Learning Center, UAS for Business & Society), Hannah Berkers (Researcher and Coordinator, AUAS), Wilco Verdoold (Lecturer, AUAS) and Jarad Tromp (Student, AUAS).

The conversation went beyond the opportunities and challenges of individual AI tools to address a more fundamental question: how should higher education evolve in a world where AI is becoming an integral part of learning, working and professional practice?

Beyond technology: rethinking education itself

One of the central themes of the discussion was that integrating AI into higher education requires much more than introducing new technological tools.

Panel members questioned whether traditional approaches to curriculum development, teaching and assessment can keep pace with the rapid evolution of AI. While educational programmes often take years to redesign, technological capabilities can change within months.

The challenge, therefore, is not simply to adapt existing practices to new tools, but to reconsider what universities teach, how students learn and which competencies education should develop.

Dietmar Paier described AI as an opportunity to rethink the long tradition of academic education, while other contributions highlighted the importance of reconsidering learning objectives and assessment methods rather than focusing exclusively on technological solutions.

The question is shifting from “How can we use AI in education?” to “What should education become in an AI-driven world?”

Human judgement: an increasingly valuable competence

Throughout the discussion, participants emphasised that technological proficiency alone is insufficient to prepare students for their future professional lives.

Hannah Berkers advocated a human-centred approach in which professionals develop the ability to decide when, why and how AI should be used. Rather than simply following automated recommendations, graduates need to critically assess outputs, understand their implications and retain responsibility for professional decisions.

Wilco Verdoold highlighted the importance of competencies that are difficult to measure or automate, including communication, professional judgement, trust and creativity.

The discussion also addressed the risk of overreliance on AI, including the tendency of AI systems to reinforce users’ assumptions rather than challenge them.

As AI becomes increasingly capable of performing routine and analytical tasks, higher education faces an important responsibility: ensuring that students develop the knowledge, judgement and confidence to work with AI without becoming dependent on it.

What do students expect from an AI-ready university?

Student contributions brought a particularly relevant perspective to the debate.

For students already accustomed to using AI as part of their academic work, the question is not necessarily whether AI should be used, but how universities can help them use it effectively, responsibly and critically.

Participants highlighted that students often develop their AI skills independently, outside formal education. This raises questions about the extent to which universities are providing sufficient guidance and structured learning opportunities.

At the same time, students stressed the continuing value of classroom interaction, group work and collaborative problem-solving.

These perspectives reinforced the idea that AI integration should not reduce opportunities for human interaction. Instead, it should create space for learning experiences that develop cooperation, reflection and critical thinking.

The discussion also raised concerns about unequal access to AI tools and competencies, highlighting universities’ responsibility to provide fair learning opportunities for all students.

From individual experimentation to institutional transformation

Another important theme was the need to move beyond isolated initiatives towards a shared institutional approach.

Panel members discussed the importance of developing AI literacy across academic communities, supporting educators’ professional development and creating opportunities for experimentation and knowledge exchange.

The AI-HED project itself was highlighted as an example of how collaboration across institutions, disciplines and national borders can help educators develop their competencies and explore innovative pedagogical approaches.

However, participants also recognised that experimentation alone is not enough. Universities need a shared vision of the future of education, informed by the perspectives of teachers, researchers, students and institutional leaders.

Such a vision should provide direction without eliminating the flexibility required to respond to continuous technological change.

Putting people at the centre of AI integration

Perhaps the strongest message to emerge from the discussion was the importance of adopting a human-centred — or even humanity-centred — approach to AI.

As Hannah Berkers emphasised, institutions should begin by identifying human needs and educational challenges, rather than allowing technological developments to determine their priorities.

This perspective places the quality of teaching, learning and professional work at the centre of institutional decisions. AI should support meaningful learning experiences, empower educators and students, and contribute to broader societal goals.

The discussion also underlined that universities must prepare students not only for employment, but for fulfilling lives in which creativity, autonomy, human relationships and a sense of purpose remain essential.

From discussion to action: the next steps for higher education

The panel concluded with a call for practical action.

Among the priorities identified were strengthening AI literacy among educators and students, developing collaborative approaches to curriculum innovation, ensuring equitable access to AI tools and involving different members of the academic community in shaping institutional strategies.

The discussion demonstrated that becoming an AI-ready university is not a destination, but an ongoing process of learning, experimentation, reflection and adaptation.

For the AI-HED consortium, this message resonates strongly with the project’s central ambition: moving from improvisation towards the effective, responsible and human-centred integration of AI in higher education.

Ultimately, the future of higher education will depend not only on how effectively universities adopt AI, but also on how successfully they preserve and strengthen the human value of education.

Watch the full panel discussion

Want to explore these questions further and hear the different perspectives shared during the event?

Watch the recording

Explore more presentations, best practices and resources from the AI-HED Final Event:

Access the AI-HED Final Event recordings and slides