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Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 24th Aug 2026, 05:32:54am America, Santiago

Daily Overview

Session

72B: E-Learning & EdTech
Location: Room 03: Manquehue

Presentation 4

11:36am - 11:48am

Learning, Motivation, and Ethical Awareness in Computer Science Students: AI as a Learning Support

Luis Humberto González-Guerra, María Yolanda Burgos-Lopez, Arturo E. Cerón-López

Instituto Tecnológico de Monterrey - Campus Monterrey, México

Contemporary Computer Science students increasingly exhibit high levels of digital fluency and familiarity
with artificial intelligence (AI) technologies. This paperdescribes an instructional approach implemented in an undergraduate course on Analysis and Design of Algorithms that integrates AI-supported learning to enhance student understanding, motivation, and ethical awareness. Students engaged with AI tools outside the classroom to explore explanations of specific algorithms. At the same time, inclass sessions focused on critically examining, discussing, and adapting these explanations through problem-oriented tasks.

This design positioned AI as a supportive learning resource rather than a substitute for student reasoning, promoting active engagement with algorithmic concepts. Analysis of student learning experiences indicates that combining AI-supported exploration with challenging in-class activities was associated with increased interest and effort. Students demonstrated heightened motivation when working with contemporary technologies, particularly when required to analyze, adapt, and apply algorithmic solutions. Ethical considerations also emerged as a key dimension of the learning experience, with students expressing awareness of the risks of overreliance on AI-generated content and the importance of personal responsibility and academic integrity.

Overall, this work illustrates the educational potential of AIsupported learning when embedded within a pedagogically guided framework. Learning outcomes were assessed through pre-test and post-test instruments to measure learning gains, while student motivation and ethical awareness were evaluated using a structured questionnaire. These instruments provided evidence of the approach’s influence on student motivation, learning development, and ethical awareness in computer science education.