Project Management16 March 2025

Generative AI in Education and Social Sustainability

Introduction

The rapid advancement and increasing integration of Generative Artificial Intelligence (AI) across various sectors have garnered significant attention. Characterized by the ability of machines to perform tasks traditionally associated with human intelligence, and employing machine learning methods to enhance current procedures, Generative AI has demonstrated its transformative potential in diverse domains. Within the specific context of education, this groundbreaking field offers promising avenues for personalized learning by adapting educational content to meet individual student needs and the potential to streamline administrative tasks, allowing educators to focus more on teaching. Despite these anticipated benefits, the role of Generative AI in promoting social sustainability, particularly within the educational domain, remains an underexplored area. Recognizing the critical importance of social sustainability in ensuring equitable and inclusive educational outcomes, this literature review aims to summarize, synthesize, and evaluate the research presented in the provided source regarding the factors influencing the use of Generative AI in education and its subsequent impact on social sustainability.

Main Themes Across the Sources

The Rise and Potential of Generative AI in Education: Generative AI is defined as the ability of machines to carry out tasks traditionally associated with human intelligence, utilizing deep learning models to produce content resembling human creation, such as images and text, based on diverse and intricate prompts. The debut of modern applications like ChatGPT, Bard (Gemini), Mid Journey, and Dalle-2, enhanced by extensive language models and generative intelligence technologies, has ignited fresh interest in human and machine-driven creativity. Generative AI equips management educators with tools to foster accessibility, improve learning outcomes, and promote innovation. It holds the potential to significantly enhance personalized learning, improve engagement, and automate administrative tasks. Furthermore, chatbots powered by Generative AI offer substantial opportunities for students to deepen their understanding and acquire relevant skills. The anticipated annual growth rate (CAGR…
Social Sustainability in Education and the Role of Technology: Understanding the role of Generative AI in promoting social sustainability, particularly within the educational domain, remains unexplored, yet it is crucial for ensuring equitable and inclusive educational outcomes. The integration of Generative AI tools into educational frameworks aligns with the UN Sustainable Development Goal 4 (SDG 4), aimed at ensuring inclusive and equitable quality education. AI, more broadly, has the potential to promote inclusivity, ensure equitable access to resources, and foster social unity. The Technology-Environmental, Economic, and Social Sustainability Theory (T-EESST) provides a holistic framework for analyzing how technological advancements, including Generative AI, influence societal outcomes by emphasizing the interconnectedness of technology usage with environmental, economic, and social dimensions.
Barriers to Adoption: Perceived Threats and Technology Threat Avoidance Theory (TTAT): The Technology Threat Avoidance Theory (TTAT) specifically focuses on how individuals perceive and respond to technological threats. It provides a crucial lens through which to examine users’ negative perceptions and concerns about Generative AI. TTAT centers on the idea that perceptions of threats related to technology significantly influence behavioral intentions, such as avoidance or acceptance. Perceived threats encompass concerns, uncertainties, and potential negative consequences associated with AI adoption, including privacy breaches and algorithmic bias. Key constructs contributing to perceived threats include:
Perceived Severity: The extent to which an individual perceives that negative consequences caused by malicious IT are severe. In this study, it pertains to how serious students believe the potential negative consequences of Generative AI are, such as privacy or ethical issues. It is hypothesized to have a significant positive correlation with perceived threats.
Perceived Susceptibility: An individual’s subjective probability that malicious IT will negatively affect them. In this context, it relates to how vulnerable students feel to the potential negative impacts of Generative AI. It is also hypothesized to have a significant positive correlation with perceived threats. The study posits a significant negative correlation between perceived threats and Generative AI use.
Enablers of Adoption: Knowledge Management (KM) Factors: Knowledge management (KM) factors, particularly knowledge acquisition and knowledge application, hold significant importance in shaping the outcomes of Generative AI adoption.
Knowledge Acquisition: The mechanisms and processes by which university students engage with Generative AI tools to access and retrieve information pertinent to their educational objectives. It involves using AI models for explanations, examples, and content summaries. The study hypothesizes a significant positive correlation between knowledge acquisition and Generative AI use.
Knowledge Application: Effectively utilizing acquired knowledge from Generative AI in practical and academic settings, such as completing assignments and solving problems. The study also hypothesizes a significant positive correlation between knowledge application and Generative AI use.
The Integrated Model: To bridge the gap in understanding both the advantages and concerns of IT adoption, this study synthesizes the principles of TTAT, T-EESST, and key KM constructs. This integrated model allows for a more comprehensive analysis that addresses the barriers to technology adoption due to perceived threats and explores the potential benefits of technology use in achieving social sustainability goals.

Points of Agreement, Debate, and Gaps in the Research.

The research presented in the source agrees with existing literature that AI has the potential to revolutionize learning by offering personalized content and predictive insights. It also aligns with prior research indicating that perceived severity and susceptibility significantly impact perceived threats in the context of technology adoption. Furthermore, the finding that perceived threats exert a negative influence on technology use is consistent with previous studies on AI adoption in various settings. The study's findings also support prior investigations highlighting the positive role of knowledge acquisition and application in affecting the use of different information systems and AI-based tools.

In terms of debate and nuance, while acknowledging the potential of AI to advance SDG 4, the source points out an existing research gap regarding the extent to which AI's innovative solutions tangibly enhance the quality of education. The study itself aims to contribute a more nuanced understanding by developing a model that considers both the positive potential and negative threats associated with AI deployment in educational settings, addressing a limitation in prior models that often focus on one aspect over the other.

The source explicitly identifies significant gaps in the research. Notably, it states that no prior study has yet scrutinized the enablers and barriers influencing the adoption of Generative AI technologies and its consequent effects on social sustainability in educational environments. Additionally, there is a lack of empirical studies exploring the consequences of Generative AI in higher education, particularly concerning its impact on social sustainability. The specific mechanisms through which AI models facilitate knowledge enhancement in educational contexts are also highlighted as a critical yet under-examined area.

Relevant Methodologies That the Studies Used (Focus on the Current Source).

The study employed a cross-sectional, survey-based quantitative research design, chosen for its efficiency in collecting data from a large, geographically dispersed sample within a specific timeframe to examine perceptions and behaviors related to technology adoption. Data were collected using an online survey that targeted Malaysian university students who were actively engaged with Generative AI technologies. The selection of Malaysia was based on its socio-cultural diversity and proactive commitment to technology-driven initiatives in education. A non-probability sampling method, specifically purposive sampling, was utilized to ensure that only participants with direct experience in using Generative AI technologies were included in the study. The survey instrument, administered in English, adapted measurement items from validated scales used in previous studies to ensure construct validity and reliability. A five-point Likert scale, ranging from "strongly disagree" to "strongly agree," was used for assessing these items.

The data analysis involved using Partial Least Squares-Structural Equation Modeling (PLS-SEM), executed with SmartPLS 4 software. PLS-SEM was preferred due to its suitability for predictive objectives and theory development, particularly when dealing with complex models and when the research leans towards exploratory purposes. The evaluation process involved a two-phase approach: assessing the outer measurement model (reliability and validity of constructs) and then examining the inner structural model (relationships between constructs). Reliability was assessed using composite reliability (CR) and Cronbach’s Alpha (Cα), while convergent validity was confirmed through item loadings and average variance extracted (AVE). Discriminant validity was assessed using the Heterotrait-Monotrait Ratio (HTMT) criterion. The structural model was evaluated by examining the coefficient of determination (R²), the significance and relevance of path coefficients, and by checking for multicollinearity using the variance inflation factor (VIF). The statistical significance of the PLS-SEM outcomes was determined using the PLS bootstrapping method with 5000 resampling iterations.

The Development in the Field Over Time (As Reflected in the Source).

The emergence of modern applications of Generative AI, such as ChatGPT, is a recent development that has ignited renewed interest in exploring human-machine creativity. The academic discourse on Generative AI in higher education is described as being in its infancy, with a scarcity of empirical studies investigating its consequences, especially concerning social sustainability. However, the rapid expansion in the functionalities and applications of Generative AI across numerous sectors, coupled with its robust anticipated annual growth rate, indicates a rapidly evolving technological landscape. The study itself, with its publication in 2025, contributes to this developing field by providing much-needed empirical insights into the factors influencing the adoption of these cutting-edge technologies within education and their potential impact on social sustainability.

Critical Insights on How This Body of Work Aligns or Diverges in Context with Each Other.

The research presented in the source offers a cohesive and logically structured framework by integrating TTAT to explain perceived threats as barriers, knowledge management factors as enablers of Generative AI use, and T-EESST to examine the subsequent positive influence on social sustainability. The empirical findings of the study strongly support the proposed hypotheses, providing quantitative evidence for the theoretical relationships posited by the integrated model and bolstering its internal validity. This research directly addresses the identified gaps in the literature by specifically investigating the antecedents of Generative AI adoption in education and its connection to social sustainability through a rigorous quantitative methodology.

The study's focus on university students in Malaysia offers a specific contextual understanding of AI adoption in education within a particular demographic and geographical setting. While this specific context may have implications for the generalizability of the findings to other cultural or educational environments, it provides valuable and nuanced insights into the perceptions and behaviors of a significant group of technology users in a country actively promoting digital education. Furthermore, the research builds upon and validates established theories (TTAT and T-EESST) by applying them to the novel context of Generative AI in education, thereby extending their theoretical applicability and reach. The incorporation of knowledge management factors further enriches the theoretical understanding of technology adoption dynamics within educational contexts by highlighting the crucial roles of knowledge acquisition and application.

Conclusion

In conclusion, the research presented in the source makes significant contributions to the understanding of the determinants of Generative AI use in education and its positive impact on social sustainability. The study effectively demonstrates how perceived threats act as significant deterrents to the adoption of Generative AI, while knowledge acquisition and application serve as crucial enablers. Moreover, the findings highlight the positive correlation between Generative AI use and social sustainability, underscoring the potential of these technologies to contribute to inclusive and equitable quality education. The study's integrated theoretical framework and robust empirical analysis provide valuable insights for policymakers, management educators, and AI developers in fostering the ethical and effective integration of Generative AI in educational settings to promote social good. While acknowledging limitations such as the focus on the social dimension of sustainability and the single-country context, this research lays a crucial foundation for future investigations into the broader sustainability implications and cross-cultural dynamics of Generative AI adoption in education.

FAQs on generative AI in education

What is the primary focus of this research regarding generative AI in education?

This research primarily focuses on understanding the role of generative AI in promoting social sustainability within the educational domain, specifically among university students. It aims to explore this underexamined area by investigating the factors that influence the adoption and use of generative AI and how this usage subsequently impacts equitable and inclusive educational outcomes, which are crucial for social sustainability.

What theoretical framework does this study utilize to understand generative AI adoption and its impact?

This study develops an integrated model that combines insights from three main theoretical perspectives: the Technology Threat Avoidance Theory (TTAT), the Technology-Environmental, Economic, and Social Sustainability Theory (T-EESST), and key knowledge management (KM) factors (knowledge acquisition and knowledge application). TTAT helps to understand students' perceptions and responses to potential threats associated with generative AI, while T-EESST provides a framework for analyzing the broader impact of technology use on social sustainability. KM factors are included to explore how students' ability to acquire and apply knowledge influences their use of generative AI.

How do perceived threats, as defined by TTAT, influence the use of generative AI in education according to this study?

According to this study, perceived threats, which encompass students' concerns about potential negative consequences such as privacy breaches, algorithmic bias, and erosion of social cohesion, have a significant negative correlation with the use of generative AI. This suggests that the more students perceive generative AI as dangerous or risky, the less likely they are to adopt and utilize these technologies in their educational activities.

What role do knowledge acquisition and knowledge application play in the adoption of generative AI by university students?

The study found that both knowledge acquisition and knowledge application are critical drivers for the effective use of generative AI among university students. Knowledge acquisition, the process of accessing and retrieving information using AI tools, positively influences the likelihood of students using these tools. Similarly, knowledge application, the ability to effectively utilize the acquired knowledge in practical academic settings, also positively impacts the use of generative AI. This indicates that as students become more proficient in acquiring and applying knowledge through generative AI, they are more inclined to integrate these technologies into their learning processes.

How does the use of generative AI in education relate to social sustainability, as indicated by this research?

The research demonstrates that the use of generative AI has a significant and positive impact on social sustainability within the educational context. This suggests that by enhancing learning processes, personalizing educational content, and potentially democratizing access to information, generative AI can contribute to more inclusive and equitable educational outcomes, which are key components of social sustainability.

What were the key findings regarding the factors that contribute to perceived threats related to generative AI?

The study identified perceived severity (the extent to which negative consequences are believed to be severe) and perceived susceptibility (an individual's subjective probability of being negatively affected) as significant positive predictors of perceived threats associated with generative AI. The more students believe that the potential negative outcomes of using generative AI are severe and the more susceptible they feel to these negative impacts, the higher their overall perception of threats related to these technologies.

What are some practical implications of this research for educators and policymakers?

The findings suggest several practical implications. For educators, it highlights the need to build a trustworthy learning environment by addressing students' perceived threats through education on ethical AI practices, data privacy, and algorithmic biases. Encouraging hands-on experiences that facilitate knowledge acquisition and application with generative AI tools is also crucial. For policymakers, the positive impact of generative AI on social sustainability calls for the integration of AI ethics frameworks in higher education policies and the fostering of partnerships between educational institutions and AI developers to create effective and ethically sound AI tools for learning.

What are the limitations of this study and what future research directions are suggested?

The limitations of this study include its focus solely on the social dimension of sustainability, the collection of data exclusively from Malaysia, which may limit the generalizability of the findings, and its cross-sectional nature, which provides a snapshot in time. Future research is suggested to explore the environmental and economic dimensions of generative AI's impact on sustainability, conduct cross-cultural studies to compare findings across different contexts, and employ longitudinal studies to understand the long-term evolution of perceptions and impacts of generative AI in education.

(Al-Emran et al., 2025)

Reference:

Al-Emran, M., Al-Qaysi, N., Al-Sharafi, M.A., Khoshkam, M., Foroughi, B. and Ghobakhloo, M., 2025. Role of perceived threats and knowledge management in shaping generative AI use in education and its impact on social sustainability. International Journal of Management Education, 23(1). https://doi.org/10.1016/j.ijme.2024.101105.