DME - Comunicações a Conferências / ConferenceItem
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- Key factors influencing university students’ intention to use generative AI and its impact on satisfactionPublication . Silva, Osvaldo; Sousa, ÁureaABSTRACT: This study aims to explore the key determinants influencing university students’ behavioural intention to use Generative Artificial Intelligence (BI_GAI) tools in educational settings, as well as the impact of this intention on student satisfaction (SS). Grounded in the Technology Acceptance Model (TAM), the research incorporates the traditional constructs of Perceived Ease of Use (PE) and Perceived Usefulness (PU) and extends the model by integrating Perceived Intelligence (PI), Perceived Trust (PT), Perceived Risk (PR), Expected Benefits (EB), and Technology Self-Efficacy (TSE). Data were collected from 775 students at a Portuguese higher education institution through a questionnaire comprising 40 items across nine constructs (PE, PI, PU, PT, PR, BI_GAI, EB, TSE, and SS), alongside sociodemographic variables. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results reveal that PE and PI have a significant positive effect on Behavioural Intention to Use GAI (BI_GAI), whereas PU does not have a statistically significant direct influence. Perceived Trust (PT) emerges as a key mediating variable in the relationship between PU and BI_GAI, while Perceived Risk (PR) does not act as a significant mediator between the TAM constructs and BI_GAI. Behavioural Intention to Use GAI has the strongest direct influence on Student Satisfaction (SS), highlighting its central role in understanding students’ engagement with GAI tools. Moreover, both EB and TSE significantly affect SS, both directly and indirectly through BI_GAI. These findings support the development of an expanded TAM-based model that provides a more holistic perspective on the technological, psychological, and educational factors shaping GAI adoption in higher education. The inclusion of constructs such as PI, PT, and TSE offers deeper insights into the mechanisms through which students evaluate and adopt GAI for learning purposes, ultimately contributing to enhanced academic satisfaction.
- Potential factors promoting university student motivation and satisfaction in a blended learning contextPublication . Silva, Osvaldo; Sousa, ÁureaABSTRACT: Blended learning has gained prominence in higher education in response to the growing integration of technology in society, aiming to enhance student engagement by combining face-to-face instruction with online components. This pedagogical approach supports autonomy, encourages active participation, and promotes critical thinking by enabling students to apply theoretical concepts to practical situations through digital tools and interactive platforms. This study investigates the key factors influencing student motivation and satisfaction in blended learning environments. It draws on two theoretical frameworks: Self-Determination Theory (SDT), which highlights the psychological needs of autonomy, relatedness, and competence as essential for intrinsic motivation; and the Technology Acceptance Model (TAM), which focuses on Perceived Usefulness (PU) and Perceived Ease of use (PEOU) as predictors of technology acceptance. The research was conducted at a Portuguese university with a sample of 444 students, who completed a questionnaire comprising 38 items distributed across seven constructs (namely, Autonomy, Relatedness, Competence, PU, PEOU, Learning Motivation (LM), and Learning Satisfaction (LS)), along with sociodemographic data. The analysis employed Partial Least Squares Structural Equation Modelling (PLS-SEM), evaluating both the measurement model and the structural model, using bootstrapping to assess the significance of the path coefficients. The results confirmed most of the formulated hypotheses. Perceived ease of use and perceived usefulness were the strongest predictors of learning motivation, which, in turn, significantly influenced learning satisfaction. Additionally, PU and PEOU mediated the relationship between the SDT factors and learning motivation. However, PEOU did not mediate the relationship between competence and PU. These findings offer valuable insights for university leaders, educators, and student organisations, highlighting the importance of aligning blended learning strategies with students’ psychological and technological needs to enhance their motivation and overall satisfaction.