This study explores the integration of artificial intelligence (AI) into educational data mining (EDM), human-assisted machine learning (HITL-ML), and machine-assisted teaching, with the aim of improving adaptive and personalized learning environments. A systematic review of the scientific literature was conducted, analyzing 370 articles published between 2006 and 2024. The research examines how AI can support the identification of learning patterns and individual student needs. Through EDM, student data are analyzed to predict student performance and enable timely interventions. HITL-ML ensures that educators remain in control, allowing them to adjust the system according to their pedagogical goals and minimizing potential biases. Machine-assisted teaching allows AI processes to be structured around specific learning criteria, ensuring relevance to educational outcomes. The findings suggest that these AI applications can significantly improve personalized learning, student tracking, and resource optimization in educational institutions. The study highlights ethical considerations, such as the need to protect privacy, ensure the transparency of algorithms, and promote equity, to ensure inclusive and fair learning environments. Responsible implementation of these methods could significantly improve educational quality.

The assessment of faculty or teaching staff performance is key in quality systems in the university context. This assessment is usually done through student satisfaction surveys that use Likert or BARS (Behavioral Anchored Rating Scales) instruments to measure student perceptions of teaching staff effectiveness. This paper examines the ambiguity, clarity, and precision of these two types of instruments. The authors, using an experimental methodology and with the participation of 2,223 students from four Spanish universities, during six academic years (between 2019 and 2024), analyze the three aspects mentioned (ambiguity, clarity, and precision) in both types of questionnaires. The results confirm the existence of significant differences between the instruments. The results also show that although doubts about the ambiguity, lack of clarity and precision of Likert-type questionnaires are justified, these aspects can be improved by BARS-type instruments. The conclusions drawn invite administrators and policymakers, quality agencies, and university managers to consider which of these two instruments is more appropriate for gathering the information they need to make better decisions about faculty promotion.

La obra que se ha editado con el título “Innovación en escenarios educo-sociales”” coordinada por docentes de distintas universidades, recoge en sus siete capítulos una reflexión profunda de la innovación en contextos socioeducativos; a través de marcos teóricos, análisis de prácticas pedagógicas y experiencias innovadoras.