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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/Item Generative Artificial Intelligence in Education: From Deceptive to Disruptive(Universidad Internacional de la Rioja, 2024-03-12) Alier, M.; García-Peñalvo, F. J.; Camba, J. D.Generative Artificial Intelligence (GenAI) has emerged as a promising technology that can create original content, such as text, images, and sound. The use of GenAI in educational settings is becoming increasingly popular and offers a range of opportunities and challenges. This special issue explores the management and integration of GenAI in educational settings, including the ethical considerations, best practices, and opportunities. The potential of GenAI in education is vast. By using algorithms and data, GenAI can create original content that can be used to augment traditional teaching methods, creating a more interactive and personalized learning experience. In addition, GenAI can be utilized as an assessment tool and for providing feedback to students using generated content. For instance, it can be used to create custom quizzes, generate essay prompts, or even grade essays. The use of GenAI as an assessment tool can reduce the workload of teachers and help students receive prompt feedback on their work. Incorporating GenAI in educational settings also poses challenges related to academic integrity. With availability of GenAI models, students can use them to study or complete their homework assignments, which can raise concerns about the authenticity and authorship of the delivered work. Therefore, it is important to ensure that academic standards are maintained, and the originality of the student's work is preserved. This issue highlights the need for implementing ethical practices in the use of GenAI models and ensuring that the technology is used to support and not replace the student's learning experience.Item Evaluating the Effectiveness of Human-Centered AI Systems in Education(Departamento de Informática y Automática. Universidad de Salamanca, 2024-03-01) Shoeibi, N.; Therón, R.; García-Peñalvo, F. J.This thesis examines how AI can improve human-computer interaction (HCI) and user experience in education. A systematic litera-ture review (SLR) and LATILL case study show how AI can be used in education. The SLR examines existing literature to determine how AI af-fects user experience and HCI in education, highlighting personalization and adaptability of learning experiences, improved task performance, and improved user experience for teachers and students. AI implementation in education faces obstacles. Using CEFR levels and linguistic traits, the LATILL project uses a user-centered design to give students personali-zed guidance and support. It transforms language instruction and fosters engaging and successful learning by encouraging educator collaboration and resource sharing. This study emphasizes the importance of user ex-perience and HCI principles in designing AI-driven educational systems. AI and user-centered design can improve learning, student engagement, and educational outcomes.Item KoopaML: Application for receiving and processing DICOM images(CEUR-WS.org, 2023-12-05) Fraile-Sanchón, R.; Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.AI algorithms application to medical data has gained relevance due to their powerful benefits among different research tasks. However, medical data is heterogeneous and diverse, and these algorithms need technological support to tackle these data management challenges. KoopaML enables users to unify medical data, especially DICOM images and apply AI algorithms to them in a straightforward way through an online web application. This work presents a new feature in the KoopaML platform: a Machine Learning platform to assist non-expert users in defining and applying ML pipelines. The feature comprises the reception, storage, and management of DICOM images. These images are received through a connection with a PACS (Picture Archiving Communication System) system already configured by users on the platform and, after storing the images, it is possible to apply AI algorithms to them and make modifications or annotations.Item Education and generative artificial intelligence. Open challenges, opportunities, and risks in higher education(Grupo GRIAL, 2023-09-28) García-Peñalvo, F. J.Keynote at the 14th International Conference on eLearning ELEARNING23, held in Belgrade Metropolitan University, Belgrade, Serbia, on September 28th, 2023.Item Generative Artificial Intelligence: New Scenarios in Teaching, Learning, and Communication(Grupo GRIAL, 2023-09-06) García-Peñalvo, F. J.Keynote at the VIII Congreso Internacional de Estudios sobre Medios de Comunicación, held in Universidad Complutense de Madrid on September 6th, 2023. In recent years, the landscape of Artificial Intelligence (AI) has witnessed a seismic shift with the emergence of Generative Artificial Intelligence (GenAI). This keynote explored the ground-breaking applications of GenAI in reshaping the arenas of teaching, learning, and communication. The historical trajectory of AI, from its inception to its current pinnacle, has been meteoric. Traditional AI models, mainly rule-based and deterministic, have evolved into sophisticated generative models capable of creating content that is often indistinguishable from that crafted by humans. Key exemplars in this category include the GPT series and DALL-E from OpenAI. Nevertheless, what exactly is GenAI? Unlike traditional AI models that are primarily reactive, GenAI models can produce new, previously unseen content. Their inherent characteristics enable them to simulate the process of human creation. Algorithms such as Generative Adversarial Networks (GAN), Long Short-Term Memory networks (LSTM), and Transformers stand as a testament to the diversity and capability of generative models. Applying these models transcends sectors, presenting immense opportunities and challenges in equal measure. The sphere of education stands on the cusp of a revolution thanks to GenAI. Personalised learning, a goal long sought by educators, is now a palpable reality. GenAI can tailor educational pathways to fit individual student needs, thus ensuring that no student is left behind. Beyond personalisation, virtual tutoring systems have started to bridge the gap in areas with teacher shortages. Equipped with GenAI, these systems can provide instantaneous feedback, ensuring continual student progress. Content creation, an integral facet of education, has also benefitted from GenAI. GenAI is pivotal in generating reading materials customised to each student’s reading level and formulating challenging questions based on current curricula. Moreover, GenAI fosters creativity among students. Tools equipped with generative models can assist students in crafting art, composing music, or even writing essays, all tailored to their unique style and preferences. Shifting the lens to communication, the potential of GenAI is equally profound. Automated content generation, once a lofty ideal, is now commonplace. News articles, financial reports, and even creative pieces can be produced by GenAI, often at speeds unmatched by humans. Personalised marketing campaigns harnessing the power of GenAI can target potential consumers with unparalleled precision, ensuring maximum outreach and engagement. Real-time translation, a boon in our increasingly globalised world, has seen leaps in accuracy thanks to generative models. Lastly, natural language processing, a subset of GenAI, has augmented human-computer interactions, making them more intuitive and organic. However, with immense power comes immense responsibility. The adoption of GenAI is full of challenges. Ensuring the accuracy and appropriateness of generated content is paramount. We need robust quality control mechanisms to mitigate the risk of misinformation or inappropriate content generation. Moreover, the sheer dependency on machines raises concerns. More reliance on AI could lead to cognitive stagnation in students, thwarting the very purpose of education. Additionally, the scalability of these models, given their intensive processing power and data requirements, is an area of concern. Ethically, the canvas of GenAI is mottled with grey. AI models, reflecting the data they are trained on, can inadvertently perpetuate societal biases. Ensuring these models are equitable and do not further deepen societal divides is crucial. The potential job displacement due to the widespread adoption of GenAI is a looming concern. GenAI takes over tasks once reserved for humans, so we must ensure a just transition for those affected. Lastly, the issue of authenticity remains salient. In a world where distinguishing between human and AI-generated content becomes increasingly challenging, ensuring trust and transparency is paramount. In conclusion, the future illuminated by Generative Artificial Intelligence is both promising and perplexing. As GenAI continues to reshape teaching, learning, and communication paradigms, our collective responsibility is to ensure that its journey is anchored in ethics, equity, and excellence.Item What Do We Mean by GenAI? A Systematic Mapping of The Evolution, Trends, and Techniques Involved in Generative AI(2023-08-01) García-Peñalvo, F. J.; Vázquez-Ingelmo, A.Artificial Intelligence has become a focal point of interest across various sectors due to its ability to generate creative and realistic outputs. A specific subset, generative artificial intelligence, has seen significant growth, particularly in late 2022. Tools like ChatGPT, Dall-E, or Midjourney have democratized access to Large Language Models, enabling the creation of human-like content. However, the concept 'Generative Artificial Intelligence' lacks a universally accepted definition, leading to potential misunderstandings. While a model that produces any output can be technically seen as generative, the Artificial Intelligent research community often reserves the term for complex models that generate high-quality, human-like material. This paper presents a literature mapping of AI-driven content generation, analyzing 631 solutions published over the last five years to better understand and characterize the Generative Artificial Intelligence landscape. Our findings suggest a dichotomy in the understanding and application of the term "Generative AI". While the broader public often interprets "Generative AI" as AI-driven creation of tangible content, the AI research community mainly discusses generative implementations with an emphasis on the models in use, without explicitly categorizing their work under the term "Generative AI".Item The perception of Artificial Intelligence in educational contexts after the launch of ChatGPT: Disruption or Panic?(Ediciones Universidad de Salamanca, 2023-02-06) García-Peñalvo, F. J.The year 2022 has ended with one of those technological innovations that have a hard-to-predict behaviour, a black swan, hogging the limelight in traditional media and digital media. Indeed, it is ChatGPT. Although artificial intelligence had already been in the news and often masked under various other meanings, the ChatGPT phenomenon has once again brought this discipline and its positive and negative effects on our society to the forefront. Reactions to its launch, influenced mainly by its ease of access and use, have been varied, ranging from the enthusiasm of innovators and early adopters to the almost apocalyptic terror of the Terminator movie. Of the multiple applications of this tool, the most significant debate focuses on its implications in Education and Academia due to its tremendous power to generate texts that could very well pass for human creations. We are at the dawn of a technology that has gone from being a toy tool to bidding to become a disruptive innovation. Whether it succeeds or not will depend on many factors, but if it does not, it will be another one like it. Denying it or banning it will do absolutely nothing to stop the tsunami effect that has already begun. For all these reasons, we must first understand these technologies based on large language models and know their benefits and weaknesses, as well as what they really mean for a specific sector of activity, such as Education. After getting to know the technology and the tool, one would be in a position to use (or not) its potential and to prevent or detect its possible pernicious effects, presumably by changing and adapting processes that are probably profoundly rooted and that, therefore, forced to leave the comfort zone, which is always the cause of resistance to change and extreme reactions. These responses usually will not stop technology from reaching its productivity plateau when it becomes part of the daily life of a sufficient majority of users. This is always the cause of resistance to change and extreme reactions that will not usually stop technology from reaching its productivity plateau when it becomes part of the daily lives of a sufficient majority of users, especially when it is also a question of transversal tools that will spread their usage patterns among the different application domains.Item Integrating Emotion Recognition Tools for Developing Emotionally Intelligent Agents(2022-09-20) Marcos-Pablos, S.; Lobato, F.; García-Peñalvo, F. J.Emotionally responsive agents that can simulate emotional intelligence increase the acceptance of users towards them, as the feeling of empathy reduces negative perceptual feedback. This has fostered research on emotional intelligence during last decades, and nowadays numerous cloud and local tools for automatic emotional recognition are available, even for inexperienced users. These tools however usually focus on the recognition of discrete emotions sensed from one communication channel, even though multimodal approaches have been shown to have advantages over unimodal approaches. Therefore, the objective of this paper is to show our approach for multimodal emotion recognition using Kalman filters for the fusion of available discrete emotion recognition tools. The proposed system has been modularly developed based on an evolutionary approach so to be integrated in our digital ecosystems, and new emotional recognition sources can be easily integrated. Obtained results show improvements over unimodal tools when recognizing naturally displayed emotions.