The chapter explores the transformative potential of Generative AI (GenAI) in higher education and academic research, with a particular focus on its anticipated impact on the future of academic training. GenAI, driven by advanced deep learning models, can replicate human-like content generation in response to complex stimuli such as images and text. This capability has attracted increasing attention from higher education institutions, which are increasingly studying the integration of such technologies to enhance both teaching and learning experiences. A key aspect of this exploration lies in understanding how key stakeholders, primarily students, perceive the value of GenAI in their academic journey. The research aims to uncover the main drivers shaping students’ perspectives on the use of GenAI in higher education through perceived usefulness and ease of use of the technology, hedonic motivation, and concerns related to personal development, well-being, and ethical considerations. To support this analysis, a quantitative methodology was employed, involving a questionnaire to collect data from students. The study's findings indicate that a well-thought-out application of GenAI could lead to a significant paradigm shift in university education. This shift could foster more interactive, engaging, and inclusive learning environments that meet the diverse needs and preferences of students. In fact, the value of this study lies in its ability to harmonize AI with humanistic values, offering a fresh perspective on how academic training can be redefined. Rather than focusing solely on the technological capabilities of AI, the research delves into how key factors shape students’ acceptance of these tools. By doing so, it reveals the potential for AI to not only personalize but also enrich learning experiences, ultimately bridging the gap between innovation and the human-centered needs of education.
Redesigning education by unlocking the potential of Generative AI in academic learning
Mario Testa
;Maddalena Della Volpe;Antonio D’Amato;Adriana Apuzzo
2025
Abstract
The chapter explores the transformative potential of Generative AI (GenAI) in higher education and academic research, with a particular focus on its anticipated impact on the future of academic training. GenAI, driven by advanced deep learning models, can replicate human-like content generation in response to complex stimuli such as images and text. This capability has attracted increasing attention from higher education institutions, which are increasingly studying the integration of such technologies to enhance both teaching and learning experiences. A key aspect of this exploration lies in understanding how key stakeholders, primarily students, perceive the value of GenAI in their academic journey. The research aims to uncover the main drivers shaping students’ perspectives on the use of GenAI in higher education through perceived usefulness and ease of use of the technology, hedonic motivation, and concerns related to personal development, well-being, and ethical considerations. To support this analysis, a quantitative methodology was employed, involving a questionnaire to collect data from students. The study's findings indicate that a well-thought-out application of GenAI could lead to a significant paradigm shift in university education. This shift could foster more interactive, engaging, and inclusive learning environments that meet the diverse needs and preferences of students. In fact, the value of this study lies in its ability to harmonize AI with humanistic values, offering a fresh perspective on how academic training can be redefined. Rather than focusing solely on the technological capabilities of AI, the research delves into how key factors shape students’ acceptance of these tools. By doing so, it reveals the potential for AI to not only personalize but also enrich learning experiences, ultimately bridging the gap between innovation and the human-centered needs of education.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


