Why university faculty is not ready for AI integration?

While generative artificial intelligence (genAI) promises to transform classrooms through personalized learning, automated feedback, and real-time content generation, new evidence suggests that institutional readiness remains uneven, particularly in public education systems.

A new study titled "Generative AI Readiness in Public Higher Education: Assessing Digital Teaching Competence in Paraguay Through Machine Learning Models," published in Applied Sciences, reveals on how faculty preparedness, institutional support, and digital competence shape AI adoption in higher education.

Based on a nationwide survey of 800 university faculty members across Paraguay, the research combines statistical analysis with machine learning models to identify the strongest predictors of readiness to adopt generative AI tools in teaching. The findings reveal a complex interplay between individual skills, institutional ecosystems, and broader structural inequalities that continue to shape digital transformation in education.

Digital teaching competence emerges as the strongest driver of AI adoption

The study identifies digital teaching competence as the most influential factor determining whether educators are prepared to integrate generative AI into their teaching practices. This competence is not limited to basic technical skills but encompasses a multidimensional set of abilities, including digital pedagogy, technological literacy, content creation, and awareness of emerging technologies.

Faculty members who demonstrated higher levels of digital competence were significantly more likely to report readiness to adopt AI tools. The analysis shows that competence in using digital platforms, designing technology-enhanced learning activities, and evaluating digital resources plays a critical role in shaping educators' confidence and willingness to experiment with AI-based tools.

Machine learning results reinforce this finding. Among the predictive variables tested, digital teaching competence accounted for the largest share of influence in determining readiness, surpassing institutional factors and attitudinal variables.

This suggests that the success of AI integration in education depends fundamentally on human capacity rather than technological availability alone. Even as generative AI tools become more accessible, their effective use requires educators who can critically engage with these technologies and adapt them to pedagogical contexts.

The study also sheds light on important gaps. While many faculty members reported moderate competence in basic digital communication and platform use, fewer demonstrated advanced skills in areas such as data-driven teaching, digital content creation, and integration of emerging technologies. This uneven distribution of skills indicates that many educators remain underprepared for the demands of AI-driven teaching environments.

Institutional support systems shape readiness across universities

Institutional support also plays a critical role in enabling AI adoption. Faculty readiness varies significantly depending on access to technological infrastructure, professional development opportunities, and clear policy frameworks guiding the use of AI in teaching.

Universities with stronger support systems, including training programs and access to digital tools, showed consistently higher levels of readiness among faculty. In contrast, institutions with limited infrastructure or weak institutional guidance reported lower levels of preparedness, highlighting the importance of organizational ecosystems in shaping technological adoption.

Professional development emerges as a particularly influential factor. Faculty members who had access to training opportunities related to digital technologies and AI were more confident in their ability to integrate these tools into teaching. This finding points to the importance of continuous learning in keeping pace with rapidly evolving technologies.

The study also identifies the role of institutional policies in reducing uncertainty around AI adoption. Clear guidelines regarding ethical use, academic integrity, and pedagogical integration help create a structured environment where educators feel more secure experimenting with new technologies.

However, the research reveals significant disparities across institutions. While some universities have begun investing in digital transformation strategies, others lag behind, reflecting broader structural inequalities within the education system. These disparities are particularly evident in differences between urban and rural institutions, as well as variations in resource allocation and technological access.

The findings suggest that without coordinated investment in infrastructure, training, and governance, the introduction of generative AI could reinforce existing inequalities rather than reduce them.

Machine learning models reveal complex, non-linear adoption patterns

The study utilized machine learning models to analyze faculty readiness for AI adoption. By applying Logistic Regression, Random Forest, and Gradient Boosting algorithms, the researchers were able to capture complex relationships between variables that traditional statistical methods may overlook.

The results show that ensemble models such as Random Forest and Gradient Boosting outperform simpler linear models, indicating that readiness for AI adoption is shaped by non-linear interactions between multiple factors.

Gradient Boosting, in particular, achieved the highest predictive performance across key metrics, suggesting that it is especially well-suited for analyzing multidimensional educational data. This finding highlights the value of advanced analytical techniques in understanding complex social and technological processes.

The models reveal that readiness is not determined by a single factor but emerges from the interaction of digital competence, institutional support, attitudes toward AI, and other contextual variables. This multidimensional nature of adoption underscores the need for holistic strategies that address both individual and organizational dimensions.

Additionally, the study cautions against overinterpreting predictive results. Because some variables, such as attitudes toward AI and readiness, are conceptually related, the models may capture structured relationships rather than independent causal effects. This highlights the importance of combining machine learning insights with theoretical and contextual analysis.

Attitudes toward AI are positive but tempered by ethical concerns

While the study finds generally positive attitudes toward generative AI among faculty, it also reveals a cautious approach to adoption. Many educators recognize the potential of AI to enhance teaching and learning processes, particularly through personalized instruction, automated feedback, and improved access to educational resources.

However, concerns about ethics, academic integrity, and the reliability of AI-generated content remain significant. These concerns reflect broader debates in the field of AI in education, where questions about transparency, bias, and responsible use continue to shape policy discussions.

The research suggests that these concerns do not necessarily prevent adoption but influence how educators approach AI integration. Faculty members appear willing to experiment with AI tools but emphasize the need for clear guidelines and ethical frameworks to ensure responsible use.

This dual perspective highlights the need to balance innovation with caution. As AI technologies become more embedded in educational systems, institutions must address not only technical challenges but also ethical and social implications.

Digital inequality remains a critical barrier to transformation

The study asserts that access to technology alone is not sufficient to ensure meaningful participation in digital transformation. Differences in digital skills, resource availability, and institutional capacity continue to shape how educators engage with AI.

In Paraguay, these inequalities are particularly evident in disparities between institutions and regions. While national initiatives have improved connectivity and infrastructure, significant gaps remain in the quality of access and the level of digital competence among educators.

Digital transformation in higher education must address multiple levels of inequality, including access to devices, quality of infrastructure, and availability of training opportunities. Without such an integrated approach, the adoption of generative AI may exacerbate existing disparities rather than promote inclusive development.

Toward human-centered AI integration in education

Universities need to adopt a human-centered approach to AI integration in education. In this model, educators remain central to the teaching process, using AI to support instructional design, provide feedback, and personalize learning experiences. This perspective aligns with emerging frameworks that emphasize human–AI collaboration as the foundation of effective educational innovation.

The study also highlights the importance of ethical governance in this context. As AI systems become more prevalent, institutions must develop policies that address issues such as data privacy, algorithmic transparency, and academic integrity. These frameworks are essential for building trust and ensuring that AI technologies are used responsibly.

Implications for policy and future research

The study highlights the need for:

• Comprehensive strategies that combine technological investment with professional development and institutional support.
• Strengthening digital teaching competence: Training programs that integrate technical, pedagogical, and ethical dimensions will be critical in preparing educators for the challenges of AI-driven education.
• More refined measurement tools and longitudinal research to better understand the dynamics of AI adoption. Future studies should incorporate behavioral data and real-world usage patterns to complement self-reported measures.
• Greater attention to the social and ethical dimensions of digital transformation. Ensuring that AI contributes to inclusive and sustainable education requires a holistic approach that addresses both technological and human factors.