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Item AI Governance Strategies: A University Perspective(GRIAL Research Group, 2026-05-13) García-Peñalvo, Francisco JoséKeynote at the T4E Transformational Leadership Programme, held 13-14 May 2026 in the University of Alicante, Spain. This keynote argues that universities must move beyond viewing artificial intelligence as a mere technological trend and recognise it as a core challenge for institutional governance, digital transformation, and academic responsibility. The presentation begins by framing digital transformation and AI as key terms in higher education government, but immediately questions a technology-centred view. Digital transformation is presented not only as the optimisation of processes through technology, but as a change in mindset, operating models, and institutional culture. Its central element is people, not tools. The talk then defines the real challenge for universities: rethinking digital transformation from digitising processes to governing AI-enabled sociotechnical ecosystems with meaningful human oversight. AI is shown as affecting the three main university functions: teaching, administration, and research. In teaching, it enables personalised learning and engagement; in administration, automation and efficiency; and in research, data analysis and discovery acceleration. However, the presentation stresses that AI also creates risks: bias, opacity, legal non-compliance, privacy breaches, academic integrity concerns, dependence on third-party providers, and uneven access. A major section focuses on responsible AI adoption through the Safe AI in Education Manifesto, whose principles include human oversight, confidentiality, alignment with educational strategies and didactic practices, accuracy, explainability, comprehensible interfaces, ethical model training, and transparency. These principles map to university governance strategies: human-oriented, infrastructure-oriented, and a governance/assurance layer. The presentation also highlights the need for ethical AI policies, critical AI literacy, communities of practice, and shared good practices. The keynote further explores the strategic dilemma between relying on third-party proprietary tools and developing one’s own infrastructure based on open LLMs. It argues that there is no single best option: universities must evaluate privacy, cost, internal capacity, transparency, auditability, deployment speed, and strategic autonomy. In-house intelligent applications, such as learning assistants, are presented as examples of governed institutional services. The closing message is that AI governance must be strategic, participatory, and ethical. Universities should not merely adopt AI systems but build an AI-augmented academic culture grounded in values, critical engagement, institutional responsibility, and human-centred innovation.Item AI-Assisted UML Learning: Toward Ethical Integration of Generative Artificial Intelligence in Software Engineering Education(Servicio de Publicaciones Universidad de Zaragoza, 2025-06-11) Vázquez-Ingelmo, Andrea; Castillo-Salguero, Cristian Alejandro; García-Peñalvo, Francisco José; Conde, Miguel Ángel; García-Holgado, Alicia; Therón, RobertoThis paper presents a web-based chatbot platform designed to support the teaching of UML domain modeling in software engineering education. Leveraging locally executed generative AI (DeepSeek-v2), the tool provides students with anonymized, interactive feedback and problem generation capabilities while preserving data privacy and promoting ethical AI use. The platform fosters autonomy, digital literacy, and critical reflection, offering a scalable and sustainable solution for integrating AI into higher education.Item AI-Assisted UML Learning: Toward Ethical Integration of Generative Artificial Intelligence in Software Engineering Education(Servicio de Publicaciones Universidad de Zaragoza, 2025-06-11) Vázquez-Ingelmo, Andrea; Castillo-Salguero, Cristian Alejandro; García-Peñalvo, Francisco José; Conde, Miguel Ángel; García-Holgado, Alicia; Therón, RobertoThis paper presents a web-based chatbot platform designed to support the teaching of UML domain modeling in software engineering education. Leveraging locally executed generative AI (DeepSeek-v2), the tool provides students with anonymized, interactive feedback and problem generation capabilities while preserving data privacy and promoting ethical AI use. The platform fosters autonomy, digital literacy, and critical reflection, offering a scalable and sustainable solution for integrating AI into higher education.Item From Vision to Reality: AI at the Heart of University Digital Transformation(Grupo GRIAL, 2025-05-17) García-Peñalvo, Francisco JoséKeynote at the 2ème Edition du Colloque International Communication et Transformation Numérique: Enjeux, Dynamiques Pratiques Innovantes, held 15-17 May 2025 in Oujda and Berkane, Maroc. The digital transformation of higher education has evolved from a technical aspiration into an institutional imperative. Catalysed by the COVID-19 pandemic, universities worldwide were forced into a rapid digital shift, revealing profound structural, pedagogical, and social vulnerabilities. While technology was essential to continuity, the most critical insight from this experience is that digital transformation is not just about tools or platforms—it is, fundamentally, about people, culture, and mindset. This keynote explores how artificial intelligence (AI), and more specifically, generative AI (GenAI), has become both a catalyst and a challenge in the evolving landscape of higher education. The arrival of tools like ChatGPT and other GenAI models has created an inflection point between vision and reality. No longer confined to specialized research domains, AI has entered the everyday fabric of teaching, learning, and governance. It generates new possibilities for personalization, creativity, and operational efficiency, but also introduces complex ethical, social, and strategic dilemmas. A central thesis of this keynote is that AI adoption must be governed by a values-driven, participatory, and strategic approach. Drawing on international frameworks, including the EU Artificial Intelligence Act, UNESCO recommendations, and the Safe AI in Education Manifesto, the presentation outlines how universities can move from fragmented experimentation to coherent AI governance. This involves aligning institutional strategies with legal and ethical standards, promoting human oversight, and ensuring transparency, inclusivity, and innovation. The presentation also examines the perceptions, concerns, and aspirations of key university stakeholders (teachers, students, researchers, and decision-makers) in relation to AI. For teachers, GenAI offers support in creating content, diversifying assessments, and facilitating personalized learning. Yet it also raises concerns about authorship, evaluation integrity, and overdependence on technology. Students benefit from AI-enhanced creativity, productivity, and language support, but face risks related to superficial learning, equity, and ethical boundaries. Researchers gain efficiency through automation and synthetic data, but must contend with challenges around source reliability, academic honesty, and privacy. Meanwhile, university leadership is tasked with balancing innovation and competitiveness with accountability and sustainability. To address these complexities, the keynote proposes a structured governance framework for AI in universities, built on four core principles: 1. Legality: AI must comply with existing regulations such as the GDPR and the EU AI Act. 2. Neutrality: Systems must be designed to mitigate algorithmic and data biases. 3. Transparency: All processes involving AI should be explainable and open to scrutiny. 4. Innovation: Responsible experimentation must be encouraged to foster institutional growth. These principles translate into practical governance structures, including the creation or reinforcement of: • An AI Commission for strategic direction and institutional coordination. • An Ethics Committee to oversee fairness and human dignity in AI use. • A Data Protection Officer with AI-specific responsibilities. • A Technical Services Unit to ensure operational alignment. • An Expert Advisory Group with interdisciplinary insight to assess evolving challenges. This ecosystemic approach enables universities to integrate AI into their digital transformation strategies while protecting their academic mission and institutional integrity. Finally, the keynote emphasizes that universities must not merely react to AI but lead its ethical integration and pedagogical reimagination. The goal is not to build AI-powered systems, but to cultivate an AI-augmented academic culture, a culture in which critical thinking, collaboration, and human-centred innovation remain at the core of educational practice. In conclusion, this keynote is a call to action for universities to move from vision to reality by embracing AI not only as a technological opportunity but as a profound responsibility. By investing in governance structures, training programs, and ethical foresight, universities can position themselves as stewards of the digital era, ensuring that the rise of AI strengthens, rather than disrupts, the foundational values of education.Item Safe AI in Education Manifesto. Version 0.4.0(2024-10-08) Alier-Forment, Marc; García-Peñalvo, Francisco José; Casañ, María José; Pereira, Juanan; Llorens-Largo, FaraónThe Safe AI in Education Manifesto outlines ethical principles for integrating AI into educational environments. It emphasizes the need for human oversight, ensuring AI complements rather than replaces educators. Decision-making must remain transparent and appealable, protecting the educational process's integrity. Confidentiality is paramount; institutions must safeguard student data and ensure AI systems comply with stringent privacy standards. AI tools should align with educational strategies, supporting learning objectives without enabling unethical practices or adding complexity. The manifesto calls for AI systems to respect didactic practices, adapting seamlessly to instructional designs without burdening educators or students. It stresses accuracy and explainability, requiring AI outputs to be reliable, transparent, and verifiable. Interfaces must be intuitive, communicating their limitations to foster trust and critical engagement. Ethical training and transparency in AI model development are essential, including minimizing biases and disclosing data sources. The manifesto commits to advancing AI’s potential in education while prioritizing privacy, fairness, and educational integrity, providing a living framework adaptable to technological evolution. It can be signed at: https://manifesto.safeaieducation.org/