### Background Literature  
The integration of artificial intelligence (AI) into higher education today explores its transformative potential, encompassing its applications in teaching, learning, and administrative processes, while addressing the ethical implications [9]. Contemporary definitions positions AI as the simulation of human cognitive functions by machines [10], with generative AI (GenAI) tools capable of producing original content from user prompts, becoming increasingly prevalent across disciplines [9; 11]. These technologies promise personalization, efficiency, and innovation but simultaneously raise urgent concerns regarding ethics, equity, and the redefinition of pedagogical roles [12; 13; 14].  
Studies by [PERSON] [15] and [PERSON] [3] have explored the dual nature of AI's potential in education, highlighting both its benefits and inherent limitation. Several studies report positive attitudes among students and faculty toward AI's capacity to enhance educational outcomes, promote accessibility, and support engagement and streamlining institutional processes [16; 17; 18; 19]. Yet optimism is tempered by caution, issues such as data privacy, algorithmic opacity, threats to academic integrity, and the erosion of human-centered teaching practices continue as dominant themes in the literature [13; 20]. These ethical and philosophical concerns have led to calls for greater transparency, clear regulatory frameworks, and professional development initiatives focused on algorithmic literacy [21].  
Despite the growing body of research, several gaps remain. First, many studies are limited to either the student or faculty perspective, neglecting administrative staff and the institutional dimensions of AI integration. Second, demographic factors, such as age, gender, and academic discipline, are often considered in isolation rather than in intersectional terms. Furthermore, studies examined how a user's prior experience with AI influences not only their acceptance of the technology but also their ethical instance and trust in its application. Existing research also tends to reflect global North or Anglophone contexts, underrepresenting higher education institutions in Latin America, where infrastructural, cultural, and pedagogical variables may shape AI perceptions differently [22; 23; 24; 25; 26].  
Transparency and explainability are crucial for responsible AI implementation [27]. This is, to have clear documentation of data collection, labels, and algorithms to ensure traceability, identify errors, and prevent future mistakes.  
AI decisions impacting human lives must be communicated effectively, ensuring that all stakeholders fully understand them [28]. Autonomy in academic decision-making is key, emphasizing the need for AI-influenced decisions to be transparent, understandable, and fair. This transparency is essential for fostering trust in the educational system [29].  
This study addresses these limitations by providing a multidimensional analysis of AI perceptions across three stakeholder groups, students, educators, and administrative staff, within a Latin American university context. By integrating demographic variables (age, gender and discipline) occupational roles, and AI experience levels, the research offers a holistic view of how attitudes, expectations, and ethical concerns converge in shaping AI's role in higher education. In doing so, it contributes to a more significant understanding of user diversity, intentionally in AI adoption, and the ethical frameworks necessary to support inclusive and sensitive technology integration.