Challenges and prospects of integrating generative AI in constructivist learning environments in higher education: perspectives of faculty and students
Alex Kumi Yeboah, Yvonne Appiah Dadson, Zacharia Mohammed, Mubarak HusseinPurpose
Constructivist learning focuses on active, student-centered engagement with content, promoting critical thinking and problem-solving skills. GenAI technologies help to transform teaching methods, learning experiences and the overall educational landscape in higher education. Yet, limited research remains about the challenges and prospects of integrating GenAI in constructivist learning in higher education. Further, few studies have explored the experiences of faculty and students using GenAI in teaching and learning environments. Therefore, this qualitative study explored the challenges and prospects of integrating Generative AI (GenAI) in constructivist learning environments within higher education. Data were collected following a qualitative research design via in-depth semi-structured interviews with 30 faculty (16 female and 14 male) and 20 students (12 female and 8 male) in four programs. Data analyses were done via a constant comparative approach. Findings indicated that faculty and students faced challenges in integrating GenAI, including concerns about technological readiness, ethical considerations, a lack of quality training and information and data security and privacy issues. Faculty and students stated the potential benefits of GenAI to include enhanced personalized learning, improved access to resources, support for special needs students, adaptive learning platforms and content creation and generation. The findings suggest that while there are significant challenges, the effective integration of GenAI can align with constructivist principles, offering new opportunities for collaborative, interactive and learner-driven educational experiences.
Design/methodology/approach
A qualitative research approach was chosen to thoroughly explore the challenges and opportunities of incorporating Generative AI (GenAI) into constructivist learning environments in higher education. Semi-structured interviews were used because their flexible structure enables a deep exploration of students’ personal experiences with AI and reveals the nuanced, individual aspects of their learning processes (Christodoulou et al., 2024). This study recruited 30 faculty (16 female and 14 male) and 20 students (12 female and 8 male, aged between 24 and 40 for students and 36 and 60 years above for faculty) in four programs.
Findings
An analysis of 12 semi-structured interviews identified several key themes: concerns about technological readiness, insufficient attention to ethical issues, a lack of quality training and information and concerns regarding data security and privacy. Both faculty and students highlighted potential advantages of generative AI, such as enhanced personalized learning, support for special needs students and adaptive learning platforms.
Research limitations/implications
This study is not without limitations, as the results could be more meaningful if a larger sample size were involved, with larger samples of faculty and students conducted through quantitative methods. Therefore, as the use of AI continues to increase in higher education, institutions must enact policies and guidelines regarding the application of AI tools for academic research work. Furthermore, the study primarily focused on the voices and experiences of faculty and students at the graduate level regarding the application and use of Gen AI tools in higher education. Thus, a larger representation of faculty, leadership, staff and students at all levels could have helped to make a generalized data decision.
Practical implications
The findings reveal that successful GenAI integration requires careful attention to maintaining constructivist principles while leveraging technological capabilities. This means ensuring that AI tools enhance rather than replace the human interaction, collaborative inquiry and reflective practice that define effective constructivist learning environments. The study suggests that GenAI is most effective when it supports the active knowledge construction that constructivism demands rather than providing ready-made answers that circumvent the learning process. This requires faculty to develop a sophisticated understanding of how to use AI as a mediating tool that facilitates student engagement with content rather than replacing that engagement.
Social implications
The interconnected nature of the challenges identified (technological readiness, ethical considerations, training needs and privacy concerns) suggests that successful implementation requires comprehensive, systematic approaches rather than piecemeal solutions. Institutions must address these challenges holistically while maintaining focus on the pedagogical principles that define effective constructivist education.
Originality/value
The findings illuminate how GenAI technologies intersect with constructivist pedagogical principles, offering insights into the nuanced realities of AI adoption in higher education settings that prioritize active, collaborative and student-centered learning. The analysis surfaced seven interconnected themes that capture the current state of GenAI integration: technological readiness concerns, insufficient attention to ethical issues, lack of quality training and information, data security and privacy concerns, enhanced personalized learning opportunities, support for special needs students and the emergence of adaptive learning platforms.