### Phase 5: Evaluation, Testing, and Refinement
The evaluation was conducted over a limited sample of 100 test cases designed by the development team, without direct input from end users during validation. Metrics such as coverage and relevance rely on subjective interpretation of retrieval adequacy and may be influenced by manual annotation of metadata and corpus selection. The absence of formal usability testing or inter-rater reliability analysis further limits the generalizability of the findings. Finally, as the corpus was specific to one institution and course, potential overfitting to its content cannot be ruled out. Nonetheless, these instances were relatively infrequent and did not substantially detract from the overall perceived quality or pedagogical value of the responses, as reflected in the high excellence rating. ## 6 Conclusions

This work presented the design, implementation, and evaluation of NOVA, an educational virtual assistant tailored for the Parallel Computing course at _Universidad Politecnica Salesiana_. Built on a Retrieval Augmented Generation (RAG) framework and operating entirely in Spanish, NOVA demonstrated the potential of integrating curated academic corpora, semantic search, and Large Language Models to support technical education in Latin American contexts. The assistant successfully delivered contextualized responses for question-and-answer sessions, quiz generation, and the creation of practical guides, effectively aligning outputs with instructional materials and demonstrating strong pedagogical relevance. These outcomes support the hypothesis that RAG architectures, when combined with pedagogically aligned datasets and specialized prompt engineering, can produce coherent and instructional responses in complex subjects like parallel computing. The use of Spanish as the system's operating language addressed the accessibility gap in AI-enhanced educational tools, ensuring native language interaction and culturally appropriate explanations. This localization effort expands the relevance of RAG-based assistants to underrepresented academic regions and opens the door for broader integration across other domains and languages. Future research will focus on extending NOVA's capabilities, exploring its long-term impact on learning, and broadening its application to other academic fields. Conceptualization, G.A.L.-P., L.A.-N., and K.D.P.-G.; methodology, G.A.L.-P., L.A.-N., and K.D.P.-G.; software, L.A.A.-N. and K.D.P.-G.; validation, G.A.L.-P., L.A.A.-N., and K.D.P.-G.; formal analysis, G.A.L.-P.; investigation, G.A.L.-P., L.A.A.-N., and K.D.P.-G.; resources,G.A.L.-P.; data curation, G.A.L.-P.; writing--original draft preparation, L.A.A.-N. and K.D.P.-G.; writing--review and editing, G.A.L.-P.; visualization, L.A.A.-N. and K.D.P.-G.; supervision, G.A.L.-P.; project administration, G.A.L.-P.; funding acquisition, G.A.L.-P. All authors have read and agreed to the published version of the manuscript. This research has been supported by the Universidad Politecnica Salesiana (UPS). Not applicable. The data presented in this study are available on request from the corresponding author. Due to academic privacy policies and ongoing research extensions, the datasets are not publicly available but can be shared upon reasonable request for scientific validation purposes. During the preparation of this manuscript, the authors used Draw.io for the creation of the architecture diagram, Graphviz (via Python) for the methodology diagram, and native tools from the n8n software suite to generate the workflow diagrams. Additionally, ChatGPT (OpenAI, GPT-4) was used to assist in the structuring, drafting, and refinement of the manuscript. All outputs generated by these tools were thoroughly reviewed and edited by the authors, who take full responsibility for the final content of this publication. The authors declare no conflicts of interest. ## References

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