Background: Digital health literacy is crucial for navigating social media as a primary health information source. However, its interactive and unregulated nature fosters misinformation, requiring critical evaluation skills. Existing tools assess general internet use, but no validated instrument measures competencies specific to social media. This study aimed to adapt and validate the eHealth Literacy Scale (eHEALS) for this context. Methods: A content validation process was conducted with 33 experts, who evaluated the clarity, coherence, and relevance of the adapted questionnaire using item-level (I-CVI) and scale-level (S-CVI) content validity indices. A pilot study was then carried out with 250 participants to assess the instrument’s reliability and feasibility, measured through Cronbach’s alpha and McDonald’s Omega. Results: The adapted eHEALS demonstrated excellent content validity (S-CVI > 0.90) and high internal consistency (Cronbach’s α = 0.92; McDonald’s Ω = 0.92). The tool effectively captures key competencies for evaluating and applying health information in social media contexts, with exploratory factor analysis confirming a unidimensional structure that explained over 60% of the variance, supporting its robustness for use in population-based studies. Conclusions: This validated instrument provides a reliable method for assessing digital health literacy in social media, supporting the development of educational interventions to enhance critical appraisal skills and mitigate the impact of misinformation.

Objective. This work sought to know the view of Nursing professors and students about the competencies the faculty staff must have to deploy their educational function with maximum quality and efficiency. Methods. Descriptive qualitative study through focus groups conducted with professors, students and recent Nursing career graduates from universities in Spain. Results. The importance of the proposed teaching competencies was delved into, highlighting the importance of professors knowing the context in which they teach, having the ability to self-evaluate their activity, and having adequate interpersonal communication skills, and deploy the teaching-learning process by performing proper planning, using new technologies, and knowing how to engage in teamwork. Moreover, a small discrepancy was detected in relation to disciplinary competence, which students felt was of importance, but which academics indicated is taken for granted in nursing professors; competencies directly related to the act of teaching must be enhanced. Conclusion. Practical unanimity was found between academics and students in affirming that the competencies investigated are important for adequate development of the teaching activity in nursing professors. In all cases, the urgent need was highlighted for nursing professors to have adequate teaching training to provide their students with formation of the highest quality.

Background: Artificial intelligence (AI) is increasingly incorporated into higher education. However, most empirical studies focus on technological adoption or learner satisfaction rather than on how pedagogical design, perceived learning impact, and student experience interact within AI-mediated learning environments. Understanding these relationships is essential to determine whether AI supports higher-order reasoning processes rather than merely increasing technological engagement. Objective: This preliminary study aimed to develop and evaluate a theory-driven AI-mediated pedagogical framework and examine relationships between pedagogical design, perceived learning impact, and student satisfaction in a university learning context. Methods: An observational educational evaluation was conducted during implementation of an AI-mediated instructional framework in an undergraduate physiotherapy course. The full academic cohort (n = 22) completed a 24-item questionnaire assessing seven pedagogical domains on a 5-point Likert scale. Descriptive statistics, Pearson correlations, exploratory regression modeling, and factor analysis were used to examine relationships among domains. Academic performance indicators were summarized descriptively. Results: Students reported high evaluations across all domains (means > 4.5/5). The strongest association with satisfaction was perceived learning impact (r = 0.79, p < 0.001). Moderate correlations were found for usability (r = 0.66), AI content quality (r = 0.61), pedagogical coherence (r = 0.58), critical thinking (r = 0.52), and ethical integration (r = 0.47). Academic pass rates exceeded 90%. Conclusions: Perceived learning impact emerged as the central mechanism linking AI-mediated instructional design to student satisfaction, suggesting that the educational value of AI depends on alignment with cognitively demanding learning processes.