Integration of Intelligent Assistants and Adaptive Learning in Engineering Pedagogy: A Case Study in Nanotechnology and Life Sciences

Jorge Cruz-Ángeles; Mariana E. Elizondo-García; Genaro Zavala; Jorge Membrillo-Hernández

doi:10.3991/ijep.v16i4.61461

Authors

Jorge Cruz-Ángeles School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Mexico https://orcid.org/0000-0001-6693-535X
Mariana E. Elizondo-García School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Mexico https://orcid.org/0009-0005-1479-7991
Genaro Zavala Institute for the Future of Education, Tecnológico de Monterrey, Monterrey, Mexico
Jorge Membrillo-Hernández School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Mexico; Institute for the Future of Education, Tecnológico de Monterrey, Monterrey, Mexico https://orcid.org/0000-0001-7650-8116

DOI:

https://doi.org/10.3991/ijep.v16i4.61461

Keywords:

Educational innovation, Large Language Models, Generative AI, STEM education, Adaptive Learning

Abstract

The proliferation of large language models (LLMs) and generative artificial intelligence has catalyzed an unprecedented pedagogical paradigm shift within higher education. This study investigates the adoption, efficacy, and cognitive impact of these technological tools among undergraduate cohorts in engineering and life sciences. Recognizing that baseline familiarity with AI does not inherently translate into advanced operational competency or prompt engineering literacy, this study evaluates the deployment of both standard LLMs and retrieval-augmented generation (RAG)-based customized assistants. The investigation employs a rigorous dual-phase methodology: an exploratory assessment of technology acceptance using standard ChatGPT, followed by a tightly controlled quasi-experiment evaluating the impact of domain-specific “GPT Custom” mentors on academic performance on complex engineering tasks. The empirical results demonstrate that customized AI assistants significantly improve final academic outcomes, yielding average grade increases of more than 15% on data-intensive analytical assignments. Furthermore, the deployment of customized assistants notably reduced grade variability among students, indicating a homogenization of academic performance that effectively levels the learning environment without compromising rigor. This improvement was statistically amplified when students utilized premium, high-capacity versions of the models for extensive synthesis tasks. Ultimately, the data indicate that while AI offers robust adaptive scaffolding, its efficacy depends on users’ critical-thinking capacities, underscoring the urgent need for educational frameworks that cultivate prompt-engineering literacy and responsible human-AI collaboration.

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