In recent years, the rise of generative artificial intelligence (GAI) has significantly influenced various educational landscapes, particularly higher education. A recent study conducted in Ghana by researchers Salifu, Arthur, Acquah, and their colleagues seeks to unravel the utilization patterns of GAI among graduate students. This pioneering research leverages an extended UTAUT2 model, applying sophisticated analytical methods such as Partial Least Squares Structural Equation Modeling (PLS-SEM), Importance-Performance Map Analysis (IPMA), and fuzzy-set qualitative comparative analysis (fsQCA) to underline the convergence of technology acceptance and learning outcomes.
Generative artificial intelligence has emerged as a transformative force, altering how knowledge is consumed and created within educational contexts. The study conducted by Salifu and his team positions itself at the intersection of technology, education, and behavioral studies. It emphasizes the dual role of GAI, serving not only as a tool for content generation but also as a catalyst for enhancing the educational experience for graduate students in Ghana. The findings illustrate how students are adapting to and integrating these advanced technologies into their academic routines, marking a significant shift in the traditional educational paradigms.
As GAI continues to evolve, understanding the persisting challenges and opportunities it poses is essential. The research applies the extended UTAUT2 model, which includes several key constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit. Each of these constructs plays a critical role in determining how graduate students perceive and utilize GAI tools in their studies. The study highlights that performance expectancy—believing that GAI will enhance student performance—significantly drives the adoption of these technologies.
Furthermore, the examination of effort expectancy sheds light on how easy students find it to use GAI systems. With rapidly changing technology, students may face a steep learning curve. The implications of this research are profound; by identifying which factors ease the adoption of GAI, universities can better support their graduate students through training and resources. The role of educational institutions becomes increasingly pivotal as they can provide a structured environment that fosters GAI proficiency.
The concept of social influence also adds another layer of complexity. Graduate students often rely on their peers and educators for guidance on adopting new technologies. The research indicates that when influential figures, such as professors or fellow students, endorse the use of GAI, individuals are more likely to integrate these tools into their learning processes. This insight is crucial for developing educational programs that encourage GAI’s use, highlighting the importance of creating advocacy within educational spheres.
In addressing facilitating conditions, the study underscores that access to technology and support systems play a vital role in the successful implementation of GAI in graduate studies. Many students may possess limited access to advanced GAI tools due to socio-economic factors or institutional constraints. Therefore, the findings suggest that institutions must strive to provide equitable access to both technology and requisite training, allowing all students to harness GAI’s potential fully. The disparities in technology access remain a critical issue in higher education, calling for targeted interventions.
Hedonic motivation, another key factor identified in the research, refers to the intrinsic enjoyment that students derive from using GAI tools. The enjoyment associated with leveraging intelligent technology can significantly enhance the learning experience and motivate students toward innovative inquiries. The intersection of enjoyment and educational effectiveness could lead to more robust engagement and academic success, underlining the necessity of incorporating GAI in curricula creatively.
The study by Salifu et al. employs advanced quantitative methods – PLS-SEM – to analyze the data obtained from graduate students. This methodology allows for a comprehensive understanding of the relationships between various constructs of the extended UTAUT2 model. By utilizing this statistical approach, the researchers can derive insights about the significance and directionality of each factor contributing to GAI adoption among students.
To deepen the analysis, the research incorporates Importance-Performance Map Analysis (IPMA), which highlights the critical attributes that require more focus to improve overall GAI usage among graduate students. The findings indicate that while factors such as performance expectancy and social influence are crucial, there are areas where institutions can enhance conditions for GAI utilization. For example, providing better training programs could significantly increase students’ confidence in using these technologies.
Fuzzy-set qualitative comparative analysis (fsQCA) offers a nuanced perspective by allowing the research to highlight combinations of factors that lead to successful adoption of GAI. This method enables researchers to move beyond strict statistical assumptions and explore how multiple conditions interact synergistically. The insights gleaned from fsQCA provide a more holistic view of the technology adoption landscape, essential for educators and policymakers aiming to foster GAI integration in graduate education.
Salifu and his team’s research captures a zeitgeist in higher education that recognizes the pivotal role of generative artificial intelligence. The dual focus on technological acceptance and educational outcomes reflects the urgent need to align pedagogical methods with technological advancements. As educational institutions grapple with the implications of these findings, they must cultivate environments that promote seamless integration of GAI, ensuring that all students can benefit from this technological revolution.
In summary, the research presents compelling evidence of the importance of understanding the factors that influence the adoption of GAI among graduate students in Ghana. The findings reveal a complex interplay between individual perceptions, institutional support, and socio-cultural influences. As graduate education continues to evolve amid technological advancements, the insights gleaned from this research will be instrumental in shaping future educational policies and practices. By embracing generative artificial intelligence, institutions can not only enhance student learning outcomes but also prepare graduates for an increasingly digital world.
The insights from the study fundamentally challenge educators and leaders to rethink their strategies in incorporating technology into the curriculum. The necessity for targeted resources and training tailored to the unique needs of graduate students cannot be overstated. As institutions evolve, the adoption of generative artificial intelligence will likely serve as a benchmark for future educational innovations, fostering a more engaging and effective learning environment.
In conclusion, Salifu et al.’s research offers a crucial glimpse into the promising yet complex realm of generative artificial intelligence within graduate studies. As education adopts these emerging technologies, understanding and addressing the unique challenges and opportunities associated with such innovations will be essential for enhancing educational outcomes in a continuously evolving landscape.
Subject of Research: Generative Artificial Intelligence in Graduate Education
Article Title: Exploring graduate students’ use of generative artificial intelligence in Ghana: insights from an extended UTAUT2 model, PLS-SEM, IPMA, and fsQCA.
Article References:
Salifu, I., Arthur, F., Acquah, B.Y.S. et al. Exploring graduate students’ use of generative artificial intelligence in Ghana: insights from an extended UTAUT2 model, PLS-SEM, IPMA and fsQCA.
Discov Educ 4, 305 (2025). https://doi.org/10.1007/s44217-025-00603-6
Image Credits: AI Generated
DOI: https://doi.org/10.1007/s44217-025-00603-6
Keywords: Generative artificial intelligence, graduate education, UTAUT2 model, technology adoption, educational outcomes.
