Abstract:

The rapid advancement of artificial intelligence (AI) is transforming educational systems worldwide and redefining traditional teaching and learning processes in higher education. AI-powered technologies offer new opportunities to develop adaptive learning environments by enabling the analysis of learning data, automation of educational processes, and the delivery of personalised learning experiences tailored to students’ individual needs and learning pace.

In recent years, personalised learning has gained significant attention within educational research and innovation. Unlike traditional instructional models that rely on standardised teaching approaches, personalised learning aims to adapt educational content, learning pathways, and feedback according to students’ abilities, interests, and prior knowledge. Artificial intelligence technologies have become key enablers of this transformation by facilitating real-time feedback, intelligent tutoring, and adaptive learning support.

Despite the increasing use of AI-based tools in higher education, empirical evidence regarding their impact on students’ academic outcomes and learning experiences remains limited, particularly in developing and Latin American educational contexts.

This study examines the integration of artificial intelligence tools within a Computational Thinking course at a public Ecuadorian university, where students received guided instruction on how to use AI tools (e.g., ChatGPT and similar applications) as part of their learning activities. The research aims to analyse the relationship between AI tool usage, perceived learning personalisation, perceived academic performance, and student satisfaction following this instructional intervention.

A quantitative research design was employed using a structured survey administered to 214 undergraduate students after the implementation of AI-supported learning activities during the 2025 academic period. The questionnaire included 24 Likert-scale items measuring four dimensions: AI usage, perceived learning personalisation, perceived academic performance, and student satisfaction.

The results indicate that students actively used AI-based tools to support academic activities such as information search, concept explanation, problem solving, and writing assistance. Statistical analyses reveal a positive relationship between AI usage and perceived learning personalisation. Furthermore, higher levels of perceived learning personalisation are associated with higher levels of perceived academic performance and student satisfaction.

These findings suggest that the guided integration of artificial intelligence tools within formal coursework can support personalised learning experiences and enhance students’ perceptions of their academic performance and satisfaction. The study contributes empirical evidence from a Latin American context and provides insights for universities seeking to integrate AI into teaching practices through structured and instructor-led approaches.

Keywords:

Artificial Intelligence, Personalised Learning, Higher Education, Educational Technology, Student Satisfaction, Academic Performance.