Opportunities and challenges of AI

The integration of generative AI into the university opens many opportunities but also raises challenges that require careful reflection and responsible use.

Its impact can be observed in teaching, learning, research and academic work as a whole.

OPPORTUNITIES OF GENERATIVE AI

1. Personalised learning

Generative AI can adapt explanations, examples and activities to the level and needs of each student. It can reformulate concepts, create alternative materials or propose personalised study strategies.

Examples: content at different difficulty levels, personalised explanations, adaptive exercises.
Why it matters: it promotes more effective competence development and a more inclusive learning experience.

2. Strengthening key competences

When used appropriately, generative AI can support critical thinking, information management and self-regulated learning. Students can compare responses, contrast perspectives or explore different ways of reasoning.

Examples: analysis of multiple explanations, evaluation of arguments, guided exploration.
Why it matters: it encourages deeper, more reflective and more autonomous learning.

3. Support for teaching

Generative AI can help academic staff create materials, prepare activities and locate complementary resources. It also facilitates diversification of teaching strategies and reduces repetitive tasks.

Examples: exercise generation, rubric design, didactic proposals, resource synthesis.
Why it matters: it frees time for pedagogical planning and student interaction.

4. Boost to research

These tools make it easier to conduct literature reviews, synthesise large volumes of information and explore new research avenues. They can also assist in drafting and organising scientific documents.

Examples: automatic summaries, thematic analysis, initial drafts, methodological suggestions.
Why it matters: it increases research efficiency and strengthens scientific output.

5. Fostering creativity

Generative AI enables the creation of prototypes, designs, visualisations and innovative proposals without requiring advanced technical knowledge. It facilitates experimentation and the exploration of new ideas.

Examples: image generation, scripts, concept maps, musical compositions.
Why it matters: it stimulates innovation and expands creative possibilities for students and staff.

CHALLENGES OF GENERATIVE AI

1. Rethinking academic assessment

When a tool can generate texts or complete tasks, assessment must move from focusing solely on the final product to evaluating the learning process. Student participation, reasoning and progression become central.

Examples: debates, guided challenges, in-person activities, oral presentations.
Why it matters: it ensures assessment reflects genuine learning and supports academic integrity.

2. Risk of dependence and loss of autonomy

Excessive reliance on AI may weaken essential skills such as creativity, critical thinking or independent problem solving. A balanced use is necessary.

Examples: unsupervised AI-generated responses, over-automation of key cognitive processes.
Why it matters: it protects intellectual autonomy and personal development.

3. Impact on the human dimension of learning

AI cannot replace educational interaction, dialogue or collective knowledge building. Its role must remain complementary.

Examples: tutorials, classroom discussions, collaborative work.
Why it matters: it preserves the irreplaceable human dimension of university education.

4. Digital skills gap

Generative AI requires new competencies, such as formulating effective prompts, evaluating outputs or identifying potential biases. Strengthening digital literacy is essential.

Examples: training sessions, critical analysis workshops.
Why it matters: it ensures safe, equitable and effective use of AI tools across the university.

5. Ethics, transparency and responsible use

Generative AI raises issues related to privacy, bias, copyright and the reliability of generated content. A critical and transparent approach is necessary.

Examples: bias detection, source verification, proper citation practices.
Why it matters: it upholds academic values and ensures a trustworthy technological environment.

Generative AI invites universities to rethink processes, methodologies and strategies.

Used responsibly, it not only opens new possibilities but also reinforces the core elements of the academic experience: human interaction, critical thinking and shared knowledge creation.

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