This study proposes a theoretical analysis of algorithmic bias as an ethical challenge for the responsible use of Artificial Intelligence (AI) in education. By extensive analysis of conceptual review of recent literature, the paper examines how bias originates and propagates across the data, algorithmic and interaction layers of AI-based educational systems. The paper offers an integrative theoretical framework built on five interrelated principles — transparency, contestability, human oversight, fairness auditing and contextual sensitivity — intended to guide the ethical design and governance of educational AI. The discussion has no empirical component; its contribution is conceptual, oriented toward informing future research, institutional policy and teacher training on the responsible use of generative and adaptive AI in educational contexts.

This study examines how faculty professional identity is negotiated within postdigital learning ecologies, focusing on generative artificial intelligence (AI) at the University of Cartagena, Colombia. Informed by postdigital learning ecology theory, it adopts a sequential explanatory mixed-methods design. The quantitative phase will survey 300 faculty members, stratified by age and disciplinary area, using a 15-item questionnaire on professional identity and six dimensions of postdigital learning ecologies. This abstract reports an exploratory reading of an administration completed by 73 volunteer faculty members; these responses are preliminary. Three dimensions emerge: the reconfiguration of teaching authority and movement from knowledge transmission toward learning mediation; uncertainty about the sustainability of academic careers, skepticism about AI’s lasting effects, and reassessment of assessment practices; and hope that AI may enable more creative pedagogies. Findings suggest that AI is prompting negotiations of professional identity, knowledge, and academic work, contingent on pedagogical and institutional contexts.

Spanish school leadership teams must produce institutional documents such as the Annual General Plan (PGA) and the End-of-Year Report. These documents often recycle generic text and rarely connect with the school’s self-evaluation. This design-based contribution presents a conversational assistant built on a large language model with retrieval-augmented generation (RAG). Its approach reverses the usual role of generative AI: it questions leadership teams rather than writing for them. It asks short, evidence-focused questions across six dimensions: planning, evaluation, tutoring, innovation and training, coexistence and inclusion, and leadership. Vague answers prompt follow-up questions until they are backed by evidence. The answers are then turned into measurable PGA objectives and indicators. Human oversight is built into seven checkpoints, from configuring the system to collegial validation. Data protection follows the GDPR and Spain’s data protection law (LOPDGDD). A five-phase implementation process for schools is proposed. In this design, the machine asks and people decide.

Generative AI enables non-programmers to build functional apps through natural language, a practice termed «vibe coding» (Karpathy, 2025). Platforms such as Lovable.ai make it accessible, yet turning a pedagogical idea into an effective prompt requires structure most teachers lack. This communication reports the first field use of Wizziapp, a conversational seven-stage wizard that guides teachers through pedagogical and technical decisions and turns their answers into a well-formed prompt. It was piloted in an online workshop with in-service and pre-service teachers from Bolivia, following a Design-Based Research rationale. Of 78 participating teachers, just over half completed the wizard and generated a prompt. Post-use surveys indicate favorable reception, low to moderate difficulty, and a large majority who would not modify the prompt before submitting it. Within low-code/no-code literature, results offer exploratory evidence that structured wizards can lower technical barriers for teachers, while raising questions about prompt reinterpretation and transparency.