Enterprise AI Analysis
Generative Artificial Intelligence for Sustainable Digital Transformation in Agro-Environmental Higher Education in Ecuador
This study analyses the integration of Generative Artificial Intelligence (GenAI) in agro-environmental higher education in Ecuador, focusing on its contribution to sustainable digital transformation aligned with Sustainable Development Goals (SDGs) 4 and 9. The research was conducted at the Faculty of Agricultural and Environmental Engineering (FICAYA) of Universidad Técnica del Norte (UTN) using a quantitative, cross-sectional, and analytical design. A validated digital survey grounded in established technology-acceptance frameworks—the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) was administered to 94% of the student population, showing satisfactory internal consistency (Cronbach's a = 0.87). Data was analysed using descriptive statistics and multivariate techniques, including Principal Component Analysis (PCA) and k-means clustering. The results obtained in Microsoft Forms® indicate that ChatGPT-5 is the most widely used GenAI tool (54.2%), followed by Gemini (11.9%). Students reported perceived improvements in academic performance (62.5%), conceptual understanding (74.6%), and task efficiency (69.1%). PCA explained 67% of the total variance, identifying three latent dimensions: effectiveness and satisfaction, institutional access and support, and ethical concerns versus operational benefits. Furthermore, k-means clustering (k = 2) segmented users into two distinct profiles Integrators, characterised by frequent use and positive perceptions, and Cautious Users, exhibiting lower usage and greater ethical or technical concerns. Overall, the findings highlight GenAI as a catalyst for sustainable education and underline the need for institutional and ethical frameworks to support its responsible integration in Latin American universities.
Executive Impact Summary
The digital era has transformed access to information and learning methodologies in Ecuadorian higher education. In this context, artificial intelligence (AI) has become a key tool for training in various branches of engineering, streamlining processes that once required days and can now be completed in hours or even minutes. This development underscores the need to strengthen both digital transformation and AI literacy [1]. Furthermore, AI optimises academic dynamics and redefines problem-solving for students and lecturers by enabling personalised learning, large-scale data analysis, and predictive modelling, opening new opportunities to enhance the quality, sustainability, and effectiveness of education [2]. In this regard, university digitalisation is also aligned with the Sustainable Development Goals (SDGs), particularly Goals 4 and 9, by promoting inclusive and innovative education capable of responding to contemporary environmental and technological challenges [3].
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Deep Analysis & Enterprise Applications
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Methodology
GenAI Adoption
Key Dimensions
User Profiles
Implications
The research employs a quantitative, cross-sectional, and descriptive-analytical approach to rigorously explore the patterns of GenAI use, perception, and academic effects among undergraduate students at FICAYA, UTN. A digital survey, grounded in TAM and UTAUT, was administered to 94% of the student population (1104 valid responses), exhibiting strong internal consistency (Cronbach's α = 0.87). Data analysis involved descriptive statistics, Principal Component Analysis (PCA) to identify latent dimensions, and k-means clustering to segment user profiles.
ChatGPT is the most widely used GenAI tool (54.2%), followed by Gemini (11.9%), for academic tasks such as report writing and concept understanding. Students reported significant improvements in academic performance (62.5%), conceptual understanding (74.6%), and task efficiency (69.1%), indicating a positive appraisal of GenAI as a learning support tool.
PCA identified three principal components explaining 67% of the total variance: PC1 (Effectiveness and Satisfaction with AI, 50.1%), PC2 (Institutional Access and Support, 9.4%), and PC3 (Ethical Concerns versus Operational Benefits, 7.5%). This structure highlights that GenAI adoption is driven by perceived utility, influenced by institutional support, and balanced against ethical considerations.
K-means clustering segmented students into two profiles: Integrators (52.8%), characterized by high frequency of use and positive perceptions, and Cautious Users (44.7%), exhibiting lower usage and greater ethical or technical concerns. This segmentation underscores varying levels of digital literacy and technological adaptation within the university community.
The study recommends promoting digital and ethical AI literacy, ensuring equitable access to advanced tools, and implementing adaptive academic assessments focused on creativity and critical thinking. Longitudinal monitoring of AI use is essential to integrate GenAI ethically and sustainably, contributing to SDGs 4, 9, and 13.
Enterprise Process Flow
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Outputs & Interpretation
74.6%
Improvement in Conceptual Understanding Reported by Students
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GenAI's Role in Ecuador's Agro-Environmental Sector
The Faculty of Agricultural and Environmental Engineering (FICAYA) at UTN is a strategic setting for GenAI integration, given Ecuador's agricultural sector accounts for 42% of national exports and 8% of GDP. GenAI tools support interpreting climate data, processing soil information, and designing sustainability strategies, directly linking digital innovation with environmental responsibility and national development.
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Your AI Implementation Roadmap
A phased approach to integrate GenAI effectively and sustainably into your enterprise operations.
Phase 1: Discovery & Strategy Alignment
Conduct a comprehensive audit of current workflows, identify key GenAI opportunities, and align AI strategy with core business objectives and ethical guidelines. Establish an internal AI task force.
Phase 2: Pilot Program & Ethical Framework Development
Implement a targeted GenAI pilot in a high-impact, low-risk area. Develop and integrate ethical AI use policies, data privacy protocols, and responsible AI governance structures based on pilot findings.
Phase 3: Scaled Integration & Workforce Empowerment
Expand GenAI deployment across relevant departments, focusing on custom solutions and platform integration. Develop comprehensive training programs for employees, fostering digital literacy and critical evaluation skills.
Phase 4: Performance Monitoring & Iterative Optimization
Establish continuous monitoring of AI performance, ROI, and user feedback. Implement an iterative optimization loop to refine models, update strategies, and ensure long-term sustainable impact and competitive advantage.
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