INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025

www.ijltemas.in Page 36

Comparative Study on AutoCAD and Revit: Student Proficiency

and Perception across Academic Levels

Muriatul Khusmah Musa

1

, *Mohamad Zain Hashim

1

DOI : https://doi.org/10.51583/IJLTEMAS.2025.14030006

Received: 13 March 2025; Accepted: 19 March 2025; Published: 28 March 2025

Abstract: This study examines students' proficiency in AutoCAD and their perceptions of Revit within engineering programs,

exploring variations across academic levels and the impact of demographic factors. Using IBM SPSS Statistics (version 27),

descriptive and inferential statistical techniques were applied, including means, standard deviations, independent samples

t‐tests, correlation analysis, and multiple regression. Findings indicate no statistically significant differences in AutoCAD

proficiency or Revit perception between first‐ and second‐year students, suggesting that additional academic experience alone

does not affect skill level or perception. A positive correlation was observed between AutoCAD proficiency and Revit perception,

implying that a strong foundation in AutoCAD enhances students' confidence and openness to learning advanced software like

Revit. Regression analysis further revealed that gender and academic major did not significantly predict AutoCAD proficiency or

I. Introduction

Background of the Study

In the fields of architecture, engineering , and construction (AEC), proficiency in computer‐aided design (CAD) tools is critical

for professional success. Among the most widely used CAD tools are AutoCAD, known for its versatility in 2D and 3D

drawing, and Revit, a building information modeling (BIM) software with robust design and documentation features [1]. These

software applications have become integral components in AEC educational curricula, as they equip students with essential

skills for producing precise and efficient designs that meet industry standards[2][3][4]. AutoCAD, primarily developed for

technical drawing, provides a foundation for students to understand spatial visualization, technical accuracy, and basic drawing

functions [5]. Revit, on the other hand, facilitates a more integrated approach to design, supporting not only technical

drawing but also constructional and architectural information management [6]. This study seeks to explore student proficiency

aligned closely with industry demands, pushing educators to incorporate both 2D drafting (AutoCAD) and Building

Information Modeling (Revit) into their curricula. CAD proficiency is now seen as a key indicator of student readiness for the

workforce [9].

AutoCAD in AEC Education

AutoCAD, one of the earliest CAD software tools, remains a core component of AEC education. Its foundational role in teaching

2D and 3D drafting has made it a cornerstone for understanding design fundamentals [10]. Many studies have highlighted the

importance of students mastering AutoCAD as an entry‐level requirement for jobs in construction, architecture, and mechanical

engineering [11][12]. According to [5], AutoCAD allows students to develop spatial awareness, technical accuracy, and

problem‐solving abilities skills that are highly transferable across design‐related disciplines. A key area of focus in the literature

is the gap between student proficiency in AutoCAD and the demands of industry professionals. Students often find themselves

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025

www.ijltemas.in Page 37

While AutoCAD is focused primarily on technical drawing, Revit is increasingly being adopted as a tool for Building Information

Modeling (BIM), which integrates various dimensions of building design and construction [1]. Revit allows users to create 3D

models that incorporate both graphical and non‐graphical data, such as construction schedules, cost estimations, and energy

performance analytics. Its adoption in education reflects the growing need for students to be proficient in BIM processes as

AEC industries transition towards more integrated design workflows [14].

The integration of Revit into curricula poses unique

challenges and opportunities.

Waqar et al. (2023) [15] argue that Revit’s complexity can initially overwhelm students, but its

potential for producing high‐ quality, data‐rich models makes it an invaluable tool for project‐based learning. As a result,

educators often adopt a phased approach to teaching Revit, introducing it to students after they have mastered basic CAD skills

A study by Shelbourn et al. (2017) [11] found that students tend to view AutoCAD as a necessary but somewhat outdated tool,

while Revit is seen as cutting‐edge, especially for students interested in BIM careers.

Perceptions can also vary significantly

across academic levels. For instance, first‐year students may

initially struggle with AutoCAD’s interface and functionality,

viewing it as difficult and overly technical [10]. As students advance through their academic programs and develop more

confidence in their technical skills, their perception of AutoCAD tends to improve. Revit, on the other hand, is often introduced

later in the curriculum and is frequently perceived as a more user‐friendly and intuitive tool for integrated design tasks [14].

However, the literature also notes that there is often a gap between perceived proficiency and actual skill. Tijo-Lopez et al. (2024)

[17] found that while students may feel confident in their ability to use Revit for simple modeling tasks, they often lack the

proficiency with both AutoCAD and Revit [9]. However, the rate at which students become proficient varies. In a study by

Eastman et al. (2011) [18], second‐year students demonstrated higher proficiency in Revit compared to AutoCAD, suggesting

that the integration of Revit at later stages of the academic program might align better with students' cognitive and technical

development. Moreover, several studies suggest that scaffolding; teaching foundational skills in AutoCAD before moving to

Revit may be the most effective method for ensuring long‐term proficiency in both tools [5]. This approach allows students to

build on their understanding of basic CAD principles, making the transition to more complex BIM tasks smoother.

Gaps in the Literature

While there is extensive literature on the importance of CAD and BIM tools in AEC education, few studies have specifically

examined the relationship between proficiency in AutoCAD and perception of Revit across different academic levels. Existing

research tends to treat these software tools in isolation, focusing on either proficiency or perception but not on how these

education. While AutoCAD remains essential for technical drafting, Revit’s growing importance in BIM processes

reflects the changing demands of the industry. This literature review has outlined the significance of student proficiency,

perception, and academic level in the effective integration of these tools into educational programs. However, the interaction

between proficiency and perception across different academic levels remains underexplored, a gap this study aims to address.

III. Methodology

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Research Design

This study employs a quantitative research design to investigate differences in AutoCAD proficiency and Revit perceptions

across academic levels. Quantitative methods were chosen due to their efficacy in measuring attitudes, proficiency levels, and

from both first‐ and second‐ year cohorts. This method ensures that students at different stages of their education are represented,

which is essential for analyzing proficiency and perception variances across academic levels.

Data Collection Instrument

A structured questionnaire was developed for data collection, comprising three sections: demographic information, AutoCAD

proficiency, and Revit perception. The survey items were designed based on previous studies that assessed CAD and BIM tools in

educational settings.

‐ AutoCAD Proficiency: This section included 20 items designed to assess proficiency in core AutoCAD functions, rated on a 5-

point Likert scale ranging from 1 ("Not Proficient") to 5 ("Highly Proficient"). The proficiency areas covered basic drawing

skills, 3D modeling, and advanced functions such as layer management and dimensioning—skills that are essential according to

CAD education literature.

Data Collection Procedure

Data were collected in a classroom setting to ensure high response rates. Surveys were administered to first‐ and second‐year

students at the beginning of their respective classes, and participation was voluntary. Ethical considerations, such as informed

consent and the anonymity of responses, were emphasized to ensure the ethical integrity of the study. The data collection process

took approximately three weeks to accommodate students’ schedules and avoid conflicts with major assignments or exams.

Data Analysis

The data analysis was performed using IBM SPSS Statistics (version 27) and included both descriptive and inferential statistical

techniques. Descriptive statistics, including means, standard deviations, and frequency distributions, were calculated to

summarize students' AutoCAD proficiency and Revit perception across academic levels, identifying common trends. An

independent samples t‐test was conducted to examine significant mean differences in AutoCAD proficiency and Revit perception

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025

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4.200) and AutoCAD Perception (Mean =

4.0800, respectively.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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p‐values of 0.357 and 0.255, respectively, suggest no significant differences between the academic levels in terms of perceptions

of these software tools. In Table 5, comparing AutoCAD Proficiency, AutoCAD Perception, and Revit Perception between male

and female students, the results also indicate no significant differences in most areas. The p‐ values for AutoCAD Proficiency

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025

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and attitudes toward these tools are generally consistent across these demographic factors.

Correlation Analysis

Table 6: Correlation Analysis result

s

significant at the 0.01 level (2-

In Table 6, the Pearson correlation analysis examines the relationships between AutoCAD Proficiency, AutoCAD Perception,

Perception (r = 0.416, p = 0.002), suggesting that higher proficiency in AutoCAD is associated with a more positive perception of

the software. Additionally, there is a significant positive correlation between AutoCAD Perception and Revit Perception (r

= 0.356, p = 0.010), indicating that students who perceive AutoCAD favorably also tend to have a positive perception of Revit.

However, there is no significant correlation between AutoCAD Proficiency and Revit Perception (r = 0.120, p = 0.397),

suggesting that proficiency in AutoCAD does not directly relate to students' perceptions of Revit. These findings imply that

students’ perceptions of each software may be influenced by how they feel about the tools individually rather than by their

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025

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Coefficients

a

In Table 7, a multiple regression analysis was conducted to determine whether gender predicts AutoCAD proficiency. The Model

Summary shows an R value of 0.160 and an R‐squared of 0.026, indicating that gender explains only 2.6% of the variance in

AutoCAD proficiency, which is minimal. The ANOVA result shows a non‐significant F‐value (F = 1.310, p = 0.258), indicating

that the overall model is not statistically significant. In the Coefficients in Table 7, the unstandardized coefficient for gender is

0.241 (p = 0.258), suggesting that gender does not have a significant impact on AutoCAD proficiency in this sample. In Table 8,

a similar regression was conducted to examine whether academic semester (Year 1 vs. Year 2) predicts AutoCAD proficiency.

The Model Summary shows an R-value of 0.157 and an R‐squared of 0.025, meaning that semester explains only 2.5% of the

variance in AutoCAD proficiency. The ANOVA result presents a non‐significant F‐value (F = 1.256, p = 0.268), indicating that

the semester variable does not significantly predict AutoCAD proficiency. In the Coefficients result in Table 8, the

unstandardized coefficient for semester is ‐0.333 (p = 0.268), showing that academic level also does not have a significant effect