In recent years, the field of education has undergone a remarkable transformation, particularly with the rise of technology and artificial intelligence (AI). Amidst this evolution, a pressing question emerges: how do we foster an environment conducive for students to embrace AI technology, particularly in the context of Ghana? The research conducted by Abreh, Arthur, Akwetey, and their colleagues aims to unravel this very question, delving deep into STEM students’ intentions to learn about AI through a comprehensive modeling approach utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy set Qualitative Comparative Analysis (fsQCA).
The study focuses primarily on Ghana’s educational landscape, where the integration of AI into the curriculum presents new opportunities as well as challenges. The authors argue that understanding the factors influencing students’ intention to learn AI is crucial for policymakers and educators aiming to enhance the educational experience and job readiness of future generations. In an era defined by digital progression, an examination of student motivations and aspirations is not only relevant but essential in shaping the future of education in Ghana and beyond.
By employing the PLS-SEM approach, the researchers parsed through various dimensions, including individual characteristics, social influences, and perceived educational effectiveness, to determine how these factors impact students’ willingness to engage with AI. The data generated by this method offers a robust mechanism to visualize complex interrelations that traditional research methods might overlook. Importantly, PLS-SEM serves as a powerful tool to facilitate an understanding of both direct and indirect influences on students’ learning intentions.
In conjunction with PLS-SEM, the application of fsQCA provided an innovative lens through which to evaluate the heterogeneous nature of student populations. This method recognizes that varying combinations of factors can lead to the same outcome—in this case, the intention to learn AI. The researchers found that while certain commonalities existed among students, unique pathways also emerged depending on individual backgrounds, learning environments, and available resources. This nuanced understanding allows educators to craft tailored interventions that meet diverse learner needs.
Ghana’s demographic landscape presents both advantages and hurdles in increasing students’ interest in AI. The nation is youthful, with a significant percentage of the population being students. Capitalizing on this demographic dividend requires systematic educational reforms that align with the global demand for AI competency. By showcasing the vast potentials of AI, classrooms can become incubators for innovation where students are not only passive recipients of knowledge but active creators of technology.
The research highlights that students often struggle with understanding what AI entails and its relevance to their future careers. There is a gap between theoretical knowledge and practical application. To address this divide, educational institutions must incorporate hands-on learning experiences that engage students with real-world AI applications. Workshops, internships, and collaborative projects could serve as catalysts for interest and excitement in AI studies.
Moreover, the role of peer influence cannot be understated. The study underscores the importance of social interactions in shaping attitudes toward learning AI. Mentorship programs and peer-led initiatives can provide a supportive atmosphere wherein students encourage one another to delve deeper into AI topics. Creating a collaborative rather than competitive learning environment enhances motivation and retention of knowledge.
Further, the researchers found that exposure to technology and AI-related content significantly boosts students’ intentions to learn. Integrating AI concepts across various disciplines—be it economics, healthcare, or environmental science—can broaden students’ perspectives and demonstrate the interdisciplinary applications of AI. Students should be able to see AI not just as a tool but as a transformative force that can solve complex problems in diverse fields.
The findings of this study also resonate beyond Ghana, highlighting the global need to assess students’ readiness to embrace emerging technologies. Countries grappling with similar educational challenges can adopt and adapt the models presented in this research. As we move into a future increasingly dominated by AI, educational methodologies must evolve to prepare students not only to consume technology but to innovate and lead in this field.
To ensure these educational reforms are sustainable, government support and investment are imperative. Stakeholders must collaborate to provide the necessary funding, infrastructure, and resources for educational institutions to thrive in the AI domain. Encouraging partnerships between academia, industry, and government can lead to synergies that enhance learning outcomes and pave the way for a skilled workforce equipped for the challenges of the 21st century.
Importantly, the study’s implications extend to teacher training programs as well. Educators themselves must be well-versed in AI technologies and methodologies to effectively teach their students. Professional development opportunities focused on AI can empower teachers, enabling them to inspire and guide students as they explore new territories in technology.
In essence, this research encapsulates a vital exploration of factors influencing students’ intentions to engage with AI in Ghana’s educational space. By employing advanced modeling techniques and reflecting on the complexities of various student experiences, the authors provide valuable insights that can inform effective teaching practices and policies. As AI continues to reshape the world, the educational approaches guided by this research may well serve as stepping stones toward a future where students are not only consumers of technology but innovative contributors to an AI-driven world.
Subject of Research: Intention of STEM Students to Learn Artificial Intelligence in Ghana
Article Title: Modelling STEM students’ intention to learn artificial intelligence (AI) in Ghana: a PLS-SEM and fsQCA approach
Article References:
Abreh, M.K., Arthur, F., Akwetey, F.A. et al. Modelling STEM students’ intention to learn artificial intelligence (AI) in Ghana: a PLS-SEM and fsQCA approach.
Discov Artif Intell 5, 223 (2025). https://doi.org/10.1007/s44163-025-00466-8
Image Credits: AI Generated
DOI: 10.1007/s44163-025-00466-8
Keywords: Artificial Intelligence, Education, STEM, Learning Intentions, Ghana, PLS-SEM, fsQCA, Student Engagement, Educational Reform, Technology Integration, Teacher Training, Peer Influence, Interdisciplinary Learning.
Tags: AI integration in curriculumAI learning intentionsdigital progression in educationeducational transformation in Ghanaenhancing job readinessfactors influencing AI learningfuture of education in Ghanafuzzy set qualitative analysisGhanaian STEM educationPLS-SEM methodologystudent motivation for AItechnology in education
