Biography
Dr. Marta Salvucci is a research collaborator at the Department of Humanities at the University of Urbino Carlo Bo, Italy, where she obtained her Ph.D. in Humanities, specializing in Human Sciences. Her doctoral research focused on the design and development of an eco-sustainable educational curriculum, reflecting her academic interest in sustainability and educational innovation. She currently teaches Assessment in Early Childhood Education Services and Evaluation of Educational Interventions within the Education Sciences program. Her research interests primarily focus on educational assessment, docimology, the development and validation of assessment scales, and the evaluation of educational practices and interventions. Through her teaching and research activities, Dr. Salvucci contributes to advancing knowledge in educational measurement and evaluation, with particular attention to improving assessment methodologies and promoting evidence-based educational practices. Her academic work reflects a commitment to enhancing educational quality, supporting effective teaching and learning processes, and fostering innovation in educational assessment and curriculum development.
Conference contributions 1
GENERATIVE AI IN HIGHER EDUCATION:
Empirical evidence, competencies, training needs, and pedagogical challenges
The rapid and massive integration of Generative Artificial Intelligence (AIG), particularly via user-friendly chatbot applications, are shifting human experiences into an "onlife" hybrid environment. In higher education, this widespread yet spontaneous adoption introduces important pedagogical challenges, including the risk of cognitive outsourcing, epistemic deprivation, and an inadequate conception of knowledge and learning. To explore the current state of AI literacy, the contribution presents an empirical study conducted at the University of Urbino Carlo Bo. The study utilized an adapted Italian version of the standard SDSU (San Diego State University) AI Survey, specifically analyzing responses from 38 professors and 74 students regarding competencies and institutional training. The findings reveal a significant cultural and institutional gap. While 65% of students feel confident using AI, their security is tied to practical usage rather than algorithmic understanding. Conversely, only 30% of professors feel capable of explaining basic AI mechanics, and 72% report a profound lack of adequate institutional training. Furthermore, 82% of students declare to operate in a normative "grey area" without clear guidelines. Despite these shortcomings, strong interest exists for future integration: 88% of students demand practical AI workshops for their professional curricula, and 60% of faculty support mandatory training modules, while expressing concerns over increased workloads. Bridging this gap requires moving beyond mere instrumental mastery toward a comprehensive "AI literacy" framework. In particular, the paper advocates for robust Faculty Development programs and innovative instructional designs centered on human-machine collaboration and aimed to foster higher-order cognitive skills, ethical-intellectual virtues, and a flexible mindset.