Vicente Vicente, María JoséValmorisco Pizarro, SegundoBaltazar, María SaudadeCrespo González, Jorge DomingoValmorisco Pizarro, Segundo2025-12-032025-12-032025-11-15979-1-3700-6682-6https://hdl.handle.net/20.500.14352/128393Este trabajo examina cómo impacta la inteligencia artificial (IA) en el rol del profesorado universitario desde la perspectiva del estudiantado, tomando como base un trabajo empírico desarrollado en la Facultad de Ciencias Políticas y Sociología de la Universidad Complutense de Madrid durante el curso 2023/2024. Se identifican expectativas (antes de la introducción de la IA en el aula) y percepciones (después de dicha introducción), y se extraen consecuencias para el aprendizaje. Los resultados indican algunas tensiones entre democratización y elitismo en el uso de IA, así como un desajuste entre el entusiasmo del alumnado una vez conocidas y usadas las herramientas de la IA y la reticencia del profesorado. Además, se presenta un decálogo práctico para que el profesorado aborde la inclusión de la IA en las aulas, enfatizando su importancia como instrumento complementario. La irrupción de aplicaciones de IA generativa, como ChatGPT, ha marcado un punto de inflexión en los entornos educativos, generando debates sobre su potencial disruptivo. Si bien existe una literatura creciente sobre su impacto en la educación, hay un déficit de estudios centrados en las percepciones de los actores críticos, y especialmente del estudiantado; cómo ve éste el rol profesoral en este contexto es un aspecto del máximo interés académico y práctico. Por ello, este trabajo tiene como objetivo explorar estas percepciones y evaluar el impacto de la IA en el aprendizaje universitario. Las preguntas de investigación son: ¿Cómo impacta la IA en el rol que desempeña el profesorado en el proceso de aprendizaje? ¿Existen diferencias entre las expectativas iniciales y las percepciones tras el uso efectivo de la IA en el aula? Finalmente, este análisis busca ofrecer recomendaciones prácticas para integrar la IA en el aprendizaje universitario, abordando tanto sus beneficios como sus riesgos.This chapter examines the impact of artificial intelligence (AI) on university teaching from the perspective of students, drawing on empirical research conducted at the Faculty of Political Science and Sociology of the Complutense University of Madrid during the 2023–2024 academic year. The study investigates how AI reshapes the role of university instructors, identifies differences between students’ expectations and their perceptions after engaging with AI-based learning activities, and proposes practical recommendations for integrating AI into higher education teaching . The theoretical framework builds on contemporary scholarship on educational innovation and educational technologies, particularly the pedagogical implications of generative AI systems such as ChatGPT. Three strands of literature guide the analysis: the democratization of knowledge enabled by AI, the challenges AI poses to traditional pedagogy, and the paradoxes of automated learning. While AI can expand access to high-quality educational resources, it may also reinforce inequalities due to differential digital competencies. Moreover, although AI can increase efficiency and support creativity, scholars warn of potential negative effects on critical thinking and other foundational cognitive skills . Methodologically, the study employs a mixed-methods design combining quantitative and qualitative techniques. Data were gathered through two self-administered questionnaires distributed to 332 undergraduate and master’s students: one at the beginning of the course, capturing expectations, and another at the end, evaluating perceptions after hands-on AI activities. Closed-ended items were complemented with open-ended questions to explore students’ conceptualizations of AI, perceived benefits and risks, and concerns around ethics and learning. Qualitative data were further enriched by discussions within the teaching innovation team. Statistical analysis and the use of a Guttman-type scale supported the development of a ten-step “AI teaching decalog” intended to guide instructors through progressive levels of AI integration . The findings show a marked contrast between students’ initial reservations and their later assessments. Before exposure to AI, students expressed skepticism—termed *AI-scepticism*—particularly regarding its implications for evaluation, ethical competencies, and critical thinking. Only a minority believed AI could support personalized or practical learning. After participating in AI-based activities, however, students demonstrated clear *AI-affinity*: most viewed AI as a useful complementary knowledge source, supported its incorporation into university teaching, and reported practical benefits such as improved writing, information search, and presentation design. Nonetheless, students perceived their instructors as hesitant or insufficiently trained to adopt AI tools effectively, highlighting a need for capacity-building initiatives. They also identified persistent paradoxes: although AI can democratize access to information, effective use requires cognitive and technological skills that not all students possess, making the technology simultaneously inclusive and exclusionary . The chapter synthesizes these results into a practical ten-point decalog for instructors wishing to integrate AI ethically, critically and effectively. The guidelines emphasize recognizing AI’s complementary value, designing responsible AI-enhanced activities, fostering digital literacy, ensuring equitable access, balancing human creativity with machine assistance, monitoring ethical risks, validating AI-generated outputs, promoting collaborative learning, and continuously evaluating pedagogical impact . In conclusion, the study argues that AI is transforming university teaching by shifting instructors’ roles from transmitters of knowledge to facilitators of learning. Although students’ attitudes become more positive after practical engagement with AI, concerns remain regarding inequalities and overreliance on automated tools. The authors call for structured faculty training, institutional support, and systematic evaluation to ensure that AI strengthens rather than undermines the quality of university learning .spaAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/book parthttps://dialnet.unirioja.es/servlet/libro?codigo=1016657metadata only access32:37IA, profesorado, estudiantado, nuevas tecnologías, educaciónCiencias Sociales59 Ciencia Política58 Pedagogía56 Ciencias Jurídicas y Derecho