Educational chatbots in the classroom: student perceptions and their role in supporting learning in Mining Engineering

Abstract

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.