Conference Agenda

Fully online programs have expanded access to higher education for working adult students, yet sustaining their engagement over time remains a critical institutional challenge. This study aimed to identify the factors that explain student satisfaction in 100% online undergraduate engineering programs at a Chilean university. Using an adapted UTAUT2-OLE instrument, data were collected from 230 working adult students and analyzed through blockwise hierarchical linear regression. Perceived Quality, Performance Expectancy, Attitude toward Online Learning, Behavioral Intention, and Online Learning Value jointly explained 89.5% of satisfaction variance, while Effort Expectancy, Facilitating Conditions, Social Influence, and Habit were non-significant. Notably, prior online experience emerged as a negative predictor, suggesting that experienced learners apply higher evaluative standards. No sociodemographic differences in satisfaction were observed. These findings underscore that instructional quality and perceived utility are the primary levers for enhancing satisfaction and retention in online engineering education, with implications for institutional design in Latin American contexts.

AI-based educational chatbots have established themselves as innovative tools in higher education, demonstrating positive impacts on key dimensions such as intrinsic motivation and student engagement. This study analyzes the application of an experiential framework for the strategic implementation of conversational systems in Mining Engineering courses. The proposal is built upon a discipline-specific database for the course on mining extraction methods, whose curricular contents have been pedagogically validated with the aim of addressing critical metacognitive dimensions in educational innovation processes. Its design seeks to strengthen autonomous learning and to strategically complement traditional instruction.

The experience is framed within a quasi-experimental design that contemplates the sequential application of pretest and posttest instruments to evaluate cognitive (conceptual understanding), motivational (self-efficacy) and perceptual (satisfaction with the tool) variables. The intervention takes place over eight weeks and progressively integrates theoretical-practical activities aligned with the syllabus, together with an assessment mediated by the chatbot, with the purpose of analyzing its impact in real learning contexts.

Preliminary results show high technological acceptance and statistically significant improvements in motivational dimensions, particularly in students’ confidence to tackle more complex engineering problems and in the perceived pedagogical value of the chatbot as a support tool. Together, these findings indicate that conversational systems, when designed with a student-centered approach and articulated with active teaching strategies, constitute a viable, transferable, and scalable pedagogical resource to strengthen personalized and interactive educational models aimed at developing specific competencies in Engineering education.

Student engagement remains a relevant challenge in engineering education, particularly in courses that require strong conceptual understanding and sustained cognitive effort. This study analyzes engineering students’ perceptions of engagement after the implementation of a structured instructional intervention integrating Video Learning and Virtual Learning Objects (VLOs) in a mechanics course. The intervention followed a three-phase learning sequence that combined short instructional videos with interactive digital activities.

A quantitative, non-experimental descriptive approach was adopted using the Student Engagement Questionnaire (SEQ) to assess five engagement dimensions: Intellectual Abilities, Teamwork, Teaching Methods, Teacher–Student Relationship, and Student–Student Relationship. Data were collected through a self-report survey administered to undergraduate engineering students upon completion of the intervention.

Results show positive student perceptions across all engagement dimensions, with particularly strong responses related to cognitive engagement and instructional clarity. The use of Video Learning and VLOs supported self-directed learning, conceptual understanding, and students’ perceptions of effective teaching methods. In contrast, the Student–Student Relationship dimension obtained comparatively lower ratings, indicating limited peer interaction within an instructional design primarily focused on individual learning processes.

These findings highlight the role of intentional pedagogical design when integrating digital resources into engineering education. While Video Learning and VLOs can effectively enhance cognitive and instructional engagement, the development of social engagement requires the explicit incorporation of collaborative learning strategies. This study provides baseline evidence to inform the design of technology-enhanced learning environments and offers practical insights for educators seeking to balance autonomy, structure, and social interaction in engineering courses.

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.

Quality assurance in engineering faculties constitutes a core component of universities’ commitment to enhancing educational quality and learning outcomes. Addressing this challenge requires a systematic and evidence-based approach in order to respond effectively to evolving regulatory frameworks, accreditation standards, and institutional quality requirements. This paper presents the results derived from the implementation of a structured mechanism for the continuous improvement of undergraduate courses at the Faculty of Engineering and Sciences of the Universidad de La Frontera. The proposed mechanism enables the systematic identification of courses whose performance indicators fall below established thresholds, thereby triggering the implementation of targeted improvement actions. Within this framework, the paper reports results obtained between the first semester of 2022 and the second semester of 2025, highlighting a sustained reduction in the number of courses with pass rates below 50%, as well as a 69% decrease in courses exhibiting insufficient and/or poor teaching evaluations. These outcomes demonstrate the contribution of the mechanism to strengthening the quality of educational processes within the Faculty.