The convergence of artificial intelligence and educational innovation is redefining how knowledge is constructed and assessed in higher education. Within this transformation, ANECAIQuim emerges as an intelligent chatbot designed to enhance academic performance, student engagement, and conceptual understanding in chemistry. This study presents the theoretical framework and empirical findings derived from the implementation of ANECAI, a narrative and collaborative learning methodology supported by artificial intelligence. ANECAI is structured into four instructional phases, contextualized problem framing, AI-assisted storytelling, knowledge synthesis, and collaborative AI-mediated assessment, designed to promote active and reflective learning. The intervention combined structured pedagogical strategies with technological tools such as ANECAI and ANECAIQuim to promote active, technology-enhanced learning. The experimental design involved 71 engineering students, who participated in a four-week intervention consisting of instructional videos followed by pair-based analytical tasks evaluated through immediate feedback provided by the ANECAIQuim chatbot. Statistical analysis using a paired-sample t-test revealed a significant improvement in academic performance, with the mean grade increasing from 85.26 to 93.52(p< 0.05) and a reduction in variance from 64.01 to 25.73, indicating more homogeneous learning outcomes. These findings demonstrate the pedagogical potential of AI-driven systems to foster equitable, reflective, and data-informed learning experiences aligned with the vision of Education 6.0.
