Thesis
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Item From Ethics to Agency: Participatory Design of a Teacher Training Course for AI in Education(Grupo GRIAL, 2025-09-18) Mouta, AnaArtificial Intelligence is increasingly being integrated into educational settings, yet its ethical implications and impact on pedagogical agency remain underexplored. This thesis investigates the ethical challenges and agency-related concerns in education through an educational design research process, with the aim of developing a teacher training course for K-12 educators, designed through their own voices. The study begins with a systematic literature review (2011–2022), conducted using PRISMA guidelines, which maps the current state of research on AI in education. This phase identifies substantial gaps in ethical frameworks, teacher-specific guidance, and the preservation of educational agency. Building on this foundation, the research adopts a participatory futures methodology, using the Delphi Method to co-construct eight future scenarios. These scenarios explore the socio-technical imaginaries shaping AI's pedagogical implications, including issues of equity, assessment, student voice, and professional autonomy. Subsequent research phases engage teacher educators through iterative focus groups, exploring how AI alters agency dynamics, subjective, intersubjective, and collective, within educational contexts. Findings reveal a pressing need to move beyond dominant techno-solutionist narratives and instead support teachers in reclaiming their roles as ethical and relational agents. These insights inform the co-design of a professional development course, which integrates dialogic, experiential, and reflective learning practices. The course is hosted on a custom-designed Canva platform and structured around a three-layered framework of educational agency, offering educators conceptual and practical tools to critically engage with AI. By foregrounding the symbolic, relational, and ethical dimensions of education, this thesis argues that responsible AI integration must not only be technically sound but also aligned with the core purposes of education: subjectification, qualification, and socialisation. It proposes that sustaining teacher agency requires special attention to the preservation and care of the educational lexicon, one that sustains complexity, openness, ethical discernment, as well as desire and memory in the face of algorithmic pressures. For it is through desire that alternative imaginaries of socio-technical systems and comprehensive educational ecosystems are made possible. This dissertation contributes four main outcomes: (1) a comprehensive ethical mapping of AI in education, (2) a participatory ethical dilemma toolkit, (3) a conceptual framework of agency in AI-mediated education, and (4) a context-responsive, agency-centred professional 8 Ana Mouta. 2025 development course for K–12 educators. Together, these outcomes constitute a theoretically grounded and empirically informed contribution to ongoing scholarly and professional efforts aimed at cultivating educational environments in which decisions regarding the use of AI, and the conditions under which it is integrated, are co-constructed through dialogic, participatory processes that uphold educational purpose, human agency, and the democratic ethos of schooling. It counters the depoliticising and deprofessionalising tendencies of technocratic models by supporting teachers in critically engaging with AI, resisting unreflective automation, and challenging algorithmic normativisation.Item On data-driven systems analyzing, supporting and enhancing users’ interaction and experience(Grupo GRIAL, 2018-09-03) Cruz-Benito, J.The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human-Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and data-driven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques.