Application of Learning Analytics and Adaptive AI Tools in Higher Engineering Education: Scoping Review (#231)

Authors

Ciña, Joseph

Vasquez, Xiomara

Cornejo, Jose

Rodriguez Alvarez, Silvia Rita

Abstract

In recent years, Artificial Intelligence (AI) has experienced remarkable growth, influencing a wide range of industries, particularly the educational sector. This became more noticeable during the pandemic, forcing distance learning. The incorporation of AI-driven educational platforms has enabled continuous assistance to students and educators, largely fueled by advancements in Deep Learning. Within engineering education, AI-based learning platforms have emerged as tools for customizing educational experiences, boosting academic outcomes, and enhancing administrative processes. Research indicates that these platforms can elevate student engagement by up to 23% and significantly improve knowledge retention. Nonetheless, their widespread adoption also introduces challenges, such as fostering technological dependency and diminishing critical thinking, particularly in self-directed learning environments. As these systems become increasingly embedded in academic settings, it is essential to assess their real impact on learners and faculty. Moreover, the flexibility of Natural Language Processing (NLP) technologies may lead to misuse due to limited oversight and technical literacy, compromising education. Although many publications have explored AI’s role in education, few have provided an analytical approach to its consequences. This research aims to fill that gap by evaluating how AI-powered platforms can be better leveraged to support balanced, responsible, and effective learning practices in engineering higher education.