Exploring Personalized Teaching Paths inMechanical Drawing Education with the Aid of ArtificialIntelligence

DOI: https://doi.org/10.62517/jhet.202515430

Author(s)

Zhu Xiaomei

Affiliation(s)

Geely University of China, Chengdu, Sichuan, China

Abstract

The integration of Artificial Intelligence (AI) into educational frameworks presents a paradigm shift from traditional one-size-fits-all teaching methodologies to dynamic, student-centric learning experiences. This paper explores the development and implementation of personalized teaching paths in mechanical drawing education, a discipline that fundamentally relies on a sophisticated synthesis of spatial reasoning, precision, and adherence to complex international standards. Traditional instruction in this field often struggles to cater to the diverse learning paces, cognitive styles, and pre-existing knowledge bases of students, leading to significant gaps in understanding and practical skill acquisition, which can hinder future professional success. This research posits that an AI-driven educational model can effectively address these challenges by offering adaptive learning content, real-time intelligent assessment with granular feedback, and customized pedagogical interventions. We investigate the architecture of an AI-assisted system designed to meticulously analyze student performance data, identify specific areas of difficulty—such as orthographic projection, the interpretation of section views, or the correct application of geometric dimensioning and tolerancing (GD&T)—and dynamically adjust the curriculum to reinforce weak points while accelerating progress in areas of established strength. The proposed personalized path is not merely a linear sequence of topics but an interactive ecosystem. Within this ecosystem, students engage with AI-powered tutors, receive immediate, actionable feedback on their drawing submissions—identifying errors down to the level of a single incorrect line or dimension—and are guided through a curriculum that is continuously tailored to their unique learning trajectory and evolving competency profile. This study examines the theoretical underpinnings, practical implementation, and potential impact of such a system, using detailed comparative data and qualitative student feedback to rigorously evaluate its effectiveness against conventional teaching methods. The findings strongly suggest that AI-assisted personalization significantly enhances learning efficiency, deepens conceptual understanding, improves long-term knowledge retention, and boosts student engagement and confidence in the demanding field of mechanical drawing.

Keywords

Artificial Intelligence; Personalized Learning; Mechanical Drawing Education; Intelligent Tutoring System; Adaptive Learning; Educational Technology; Computer-Aided Drawing (CAD)

References

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