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Rethinking 3D Modeling Software Tools in Industrial Design Education: A Student-Informed Framework Based on Experiences and Expectations

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Title: Rethinking 3D Modeling Software Tools in Industrial Design Education: A Student-Informed Framework Based on Experiences and Expectations
Language: English
Authors: Onur Yilmaz (ORCID 0000-0002-2489-1480), Koray Gelmez (ORCID 0000-0003-1306-2852)
Source: International Journal of Technology and Design Education. 2026 36(2):707-726.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 20
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Descriptors: Computer Simulation, Computer Assisted Design, Computer Software, Foreign Countries, Higher Education, College Students, Usability, Student Attitudes, Student Experience, Computer Uses in Education
Geographic Terms: Turkey
DOI: 10.1007/s10798-025-10016-z
ISSN: 0957-7572
1573-1804
Abstract: Industrial designers' expectations of modeling software differ significantly from those of other professions, primarily due to the unique nature of design and the ways of creative thinking. Understanding the expectations and experiences of students within industrial design education is also of critical importance--both for the design education literature and for software developers aiming to create tools that better support the needs of emerging designers. This study investigates the factors influencing industrial design students' choice of 3D modeling software, their experiences and expectations from these tools. A survey was conducted among 201 students from 19 universities in four different cities of Türkiye, gathering both quantitative and qualitative data. Findings indicate that students prioritize affordability, ease of use, and compatibility when selecting modeling software. Peer learning plays a crucial role in software adoption, with 60% of students engaging in peer-supported learning activities. Additionally, the study proposes a framework for evaluating 3D modeling software based on eleven key dimensions, including understandability, flexibility, and collaboration features. The findings suggest that industrial design curricula should incorporate a more student-centered approach, allowing flexibility in software selection and supporting peer learning environments. Furthermore, software developers can utilize these insights to create tools that better align with students' needs and expectations.
Abstractor: As Provided
Entry Date: 20260101
Accession Number: EJ1510491
Database: ERIC
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  Value: <anid>AN0192418127;ogv01apr.26;2026Mar23.07:41;v2.2.500</anid> <title id="AN0192418127-1">Rethinking 3D modeling software tools in industrial design education: A student-informed framework based on experiences and expectations </title> <p>Industrial designers' expectations of modeling software differ significantly from those of other professions, primarily due to the unique nature of design and the ways of creative thinking. Understanding the expectations and experiences of students within industrial design education is also of critical importance—both for the design education literature and for software developers aiming to create tools that better support the needs of emerging designers. This study investigates the factors influencing industrial design students' choice of 3D modeling software, their experiences and expectations from these tools. A survey was conducted among 201 students from 19 universities in four different cities of Türkiye, gathering both quantitative and qualitative data. Findings indicate that students prioritize affordability, ease of use, and compatibility when selecting modeling software. Peer learning plays a crucial role in software adoption, with 60% of students engaging in peer-supported learning activities. Additionally, the study proposes a framework for evaluating 3D modeling software based on eleven key dimensions, including understandability, flexibility, and collaboration features. The findings suggest that industrial design curricula should incorporate a more student-centered approach, allowing flexibility in software selection and supporting peer learning environments. Furthermore, software developers can utilize these insights to create tools that better align with students' needs and expectations.</p> <p>Keywords: Industrial design education; 3D modeling software; Peer learning; Digital tools; Software evaluation; Computer aided design; Education Specialist Studies In Education Information and Computing Sciences Computer Software</p> <p>Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</p> <hd id="AN0192418127-2">Introduction</hd> <p>Design education embodies various gaps, uncertainties and confusions inherently. These qualities do not only make students' and tutors' life harder, but also makes it challenging to understand the dynamics of the design education itself. Still, looking at the design studio courses closer helps to see things more explicitly due to the fact that these courses lie in the heart of design education.</p> <p>Firstly, design studio courses follow a master-apprentice model, where tutor–student interactions aim to develop the habits and skills essential for professional life (McDonald & Michela, [<reflink idref="bib28" id="ref1">28</reflink>]). Tutors create a learning environment that fosters creativity through various pedagogical tools and activities (Ibrahim & Utaberta, [<reflink idref="bib21" id="ref2">21</reflink>]). They guide students in their design processes, encouraging observation, research, and learning by doing (Schön, [<reflink idref="bib42" id="ref3">42</reflink>]), while also closely tracking their progress, providing feedback, and collaborating when necessary to support their development (McLain, [<reflink idref="bib29" id="ref4">29</reflink>]; Mewburn, [<reflink idref="bib30" id="ref5">30</reflink>]). This approach simulates real-world practice through creative dialogues, making design studios inherently experience-based (Ferreira, [<reflink idref="bib14" id="ref6">14</reflink>]; Ferreira et al., [<reflink idref="bib15" id="ref7">15</reflink>]).</p> <p>Learning takes place through projects addressing hypothetical or real-world problems, where tutors establish the framework, and students generate solutions. Through activities such as researching, brainstorming, sketching, and modeling, students refine their ideas while continuously integrating feedback. This iterative process enhances their ability to navigate complex design challenges within a structured yet exploratory environment.</p> <hd id="AN0192418127-3">Design tools</hd> <p>The "vagueness" of design knowledge also makes it challenging to understand design studio courses comprehensively. First, design studios are "designed" to reveal "intrinsic creativity of students through the learning environment" (Grover et al., [<reflink idref="bib17" id="ref8">17</reflink>], p.2). They accommodate a web of "unstated values, attitudes, and norms which stem tacitly from the social relations" (Dutton, [<reflink idref="bib12" id="ref9">12</reflink>], p. 16). In other words, learning and creativity depends on implicit factors too. Still, for the needs of this unique way of higher education courses, design studio courses have their own apparent characters. They can be seen as collaborative and highly material spaces (Vyas et al., [<reflink idref="bib53" id="ref10">53</reflink>]). Communication, engagement and interaction act as main pedagogic tools in design studio courses and mostly knowledge of design emerges during interactions of the tutors and students. Also, their materiality can be visible for someone even walking through a design school: Walls, corridors and desks are mostly full of posters, drawings, post-its or mockups that function as learning materials. This reality relies on the fact that design collaboration needs more than linguistic skills: The use of material artifacts are vital (Clark, [<reflink idref="bib7" id="ref11">7</reflink>]). These artifacts vary greatly with their own utilities and they can be in forms of model making tools, marker pens, posters, tablets, cameras, printers, laptops, mockups, collection of materials and so on (Slack, [<reflink idref="bib46" id="ref12">46</reflink>]) Some of them can be classified under communication or representation tools by their role, or digital and analog, and sometimes they are intertwined. But as the widest aspect, by including all, we call them "design tools". In this viewpoint, design tools can be defined as all the objects that can facilitate students to research, design, present in/for design studio courses.</p> <p>Industrial designers utilize a diverse range of design tools to "externalise, develop, propose and specify design solutions" (Self, [<reflink idref="bib44" id="ref13">44</reflink>], p.3). According to Slack ([<reflink idref="bib46" id="ref14">46</reflink>]), they are instruments that designers use to define an idea and communicate their findings. They are not merely facilitators but indispensable and inseparable parts of design education (Ozen Yavuz and Yildirim, [<reflink idref="bib33" id="ref15">33</reflink>]). The literature shows that they support creativity and facilitate communication, teaching or learning in design studio courses (Hettithanthri & Hansen, [<reflink idref="bib19" id="ref16">19</reflink>]). Design tools such as sketches (Vyas, [<reflink idref="bib52" id="ref17">52</reflink>]), timetables (Bardram & Bossen, [<reflink idref="bib4" id="ref18">4</reflink>]) and models (Schmidt & Wagner, [<reflink idref="bib41" id="ref19">41</reflink>]) function as translators of intangible work practices to visible ones (Vyas et al., [<reflink idref="bib53" id="ref20">53</reflink>]) which fosters both learning and communication. Also, the selection of the appropriate tool is highly related with the success of the design process (Stolterman, [<reflink idref="bib47" id="ref21">47</reflink>]).</p> <p>Pei et al. ([<reflink idref="bib35" id="ref22">35</reflink>]) categorize design tools into sketches, drawings, models, and prototypes based on their contributions to the design action, detailing subcategories and their contributions to the design process. Stolterman et al. ([<reflink idref="bib48" id="ref23">48</reflink>]) defines four categories of tools: physical tools, software tools, theoretical tools, and others, which are selected based on their suitability for the designer's activities. Topal ([<reflink idref="bib50" id="ref24">50</reflink>]) classifies tools in product design as forms of representation, dividing them into three groups: two-dimensional prototypes, three-dimensional prototypes, and digital prototypes. Similarly, Karadoğaner ([<reflink idref="bib23" id="ref25">23</reflink>]) categorizes representational tools into pictorial, physical, and digital and virtual tools. Pipes ([<reflink idref="bib37" id="ref26">37</reflink>]) defines tools as means of embodiment and facilitators for understanding complex problems, categorizing them as sketches, drawings, models, and prototypes. Olofsson and Sjölen ([<reflink idref="bib32" id="ref27">32</reflink>]) describe various types of sketches, such as investigative and explorative sketches used for problem definition, explanatory sketches for conveying function and form, and persuasive sketches aimed at conveying emotional aspects in addition to form and function.</p> <hd id="AN0192418127-4">CAD and digital tools</hd> <p>The advent of digital tools has significantly transformed how design is taught and practiced. Digital tools enhance the effectiveness of design communication, shorten the design process, and support creative thinking by allowing for more flexible revisions (Hettithanthri & Hansen, [<reflink idref="bib19" id="ref28">19</reflink>]; Robertson & Radcliffe, [<reflink idref="bib38" id="ref29">38</reflink>]). They provide recording ideas to communicate and share within different groups of people (Dennis & Williams, [<reflink idref="bib8" id="ref30">8</reflink>]) and consequently foster collaboration (Dreamson, [<reflink idref="bib11" id="ref31">11</reflink>]).</p> <p>Computer-Aided Design (CAD), refers to the processes of creation, modification, analysis, and optimization in design being carried out with the assistance of computers (Zeid & Sivasubramanian, [<reflink idref="bib57" id="ref32">57</reflink>]). Computer-aided design (CAD) software plays a crucial role in modern industrial design by significantly enhancing the efficiency of product development, lowering production costs, and supporting more rational and feasible design solutions (Zhong, [<reflink idref="bib58" id="ref33">58</reflink>]). Studies show that proficiency in CAD software is among the top expectations employers have for industrial designers, nearly as important as their design knowledge (Süel, [<reflink idref="bib49" id="ref34">49</reflink>]; Kındı, [<reflink idref="bib24" id="ref35">24</reflink>]; Erkarslan et al. [<reflink idref="bib13" id="ref36">13</reflink>]). Also, the emergence of emerging technologies such as artificial intelligence and mixed reality has opened new horizons for the field of CAD (Hunde & Woldeyohannes, [<reflink idref="bib20" id="ref37">20</reflink>]).</p> <p>Over the past decades, computer-aided design (CAD) tools have become an essential part of industrial design practice. However, the development of these tools has often failed to keep pace with the evolving logic and demands of the profession. Arrighi et al. ([<reflink idref="bib2" id="ref38">2</reflink>]) argue that industrial design operates through a unique design logic—characterized by an iterative process between the known and the unknown—which distinguishes the modeling practices of industrial designers from those of other professional domains. Similarly, Fuge et al. ([<reflink idref="bib16" id="ref39">16</reflink>]) emphasize the need of flexible, constraint-free tools that support open-ended exploration, enabling designers to fully engage in creative discovery. In line with this, Robertson and Radcliffe ([<reflink idref="bib38" id="ref40">38</reflink>]) point out that although offering various advantages, many current tools restrict potential creative thinking. These perspectives collectively point to the need for tools specifically tailored to the workflows and cognitive processes of industrial designers—rather than adapting tools from adjacent domains such as engineering or animation.</p> <p>In spite of their prevalence in profession and education, digital tools should not be seen as the only instrument for designers. Henry ([<reflink idref="bib18" id="ref41">18</reflink>]) advocates that instead of one replacing another one, digital and analog tools should be seen as complementary. In parallel, Shih et al. ([<reflink idref="bib45" id="ref42">45</reflink>]) offers switching between analog and digital tools iteratively. Also, Ibrahim and Rahimian's ([<reflink idref="bib22" id="ref43">22</reflink>]) study also shows that the usage of CAD tools and hand sketching in architectural education together provides higher quality in design solutions. Despite the anticipated rapid digitalization of industrial design education, digital tools have not entirely replaced physical and analog tools, with hand sketching and material interaction continuing to play a significant role (Aldoy & Evans, [<reflink idref="bib1" id="ref44">1</reflink>]).</p> <p>If we look at the taxonomy, Arrighi et al. ([<reflink idref="bib2" id="ref45">2</reflink>]) classify CAD tools into three categories: those for engineers (e.g., Solidworks, AutoCAD), those for 3D artists (e.g., 3ds Max, Maya, ZBrush), and those for industrial designers (e.g., CATIA Imagine and Shapr3D). They also explain that how these software tools are designed inherently defines how users are expected to interact with them. In this context, they claim that industrial designers require hybrid software that combines the production-oriented and geometrically constrained tools developed for engineers with the flexible modeling capabilities intended for 3D artists, who typically work with single digital artifacts (Arrighi et al., [<reflink idref="bib2" id="ref46">2</reflink>]). Zhong ([<reflink idref="bib58" id="ref47">58</reflink>]) identifies four types of CAD software. Two-dimensional software, like Photoshop, is convenient for 2D renderings but lacks strong 3D capabilities. Three-dimensional software, such as Rhino, allows dynamic 3D viewing but its models cannot be directly used for production. Rendering software, like Vray, creates realistic images but does not support modeling, while engineering software, such as Solidworks, enables mechanical analysis and direct production use but involves complex modeling and requires a background in science and engineering. However, the distinctions between these categories have become increasingly blurred, with many recent software incorporating multiple functions. This study focuses on software those center modeling as the primary function.</p> <p>Dönmez's ([<reflink idref="bib10" id="ref48">10</reflink>]) study shows that the primary criteria of a modeling software choice is sectoral demand for students, while tutors prioritize the modeling method. Zhong ([<reflink idref="bib58" id="ref49">58</reflink>]) indicates that students from schools with different curricula tend to specialize in different types of software. According to them, two-dimensional software, favored by art students for its intuitive and fast design, while three-dimensional software, preferred by engineering students for its spatial and multi-angle analysis and detailed design refinement (Zhong, [<reflink idref="bib58" id="ref50">58</reflink>]). As a relatively early work, Aydoğan's ([<reflink idref="bib3" id="ref51">3</reflink>]) study shows that architecture students learn the most common CAD software, AutoCAD from different sources such as self-learning (63,3%), private courses (13,3%), peers (16,7%) and school classes (6,7%). According to Yang and Lynch's ([<reflink idref="bib55" id="ref52">55</reflink>]) study conducted in the Taiwanese context, preferred learning methods and styles in CAD software largely depend on individual preferences.</p> <hd id="AN0192418127-5">The study</hd> <p>Focusing on modeling software tools in the context of industrial design education, where modeling tools play a critical role in shaping students' learning and designing experiences is also vital for many reasons. First, expectations and needs from these tools often differ significantly between students and design professionals (Self, [<reflink idref="bib44" id="ref53">44</reflink>]). Also, as mentioned in the previous section, the needs and expectations of industrial designers from 3 d modeling software are highly different from engineers or 3 d artists.</p> <p>Moreover, students frequently depend on informal learning processes to gain proficiency in modeling tools, primarily due to limited instructional hours and the demands of other coursework. Investigating these informal learning pathways can offer a deeper understanding of students' learning experiences, revealing valuable insights into their motivations and expectations. Such inquiry also provides a fertile ground for contributing to the growing body of research on informal learning within the context of design education.</p> <p>This study aims to provide a broad understanding of the modeling tools used by industrial design students. In this context, the research questions of the study are as follows:</p> <p></p> <ulist> <item> What factors shape the choice of modeling software among industrial design students? Which factors students prioritize while choosing a modeling software?</item> <p></p> <item> What factors are effective in learning modeling software among industrial design students? What is the role of peer learning in these processes?</item> <p></p> <item> What expectations do industrial design students have from modeling software? Do emerging technologies cover their expectations?</item> <p></p> <item> How can a framework for evaluating 3 d modeling software for industrial design education be developed?</item> </ulist> <p>Finally, Türkiye was strategically selected as the site of the study, given the researchers' access to a diverse pool of instructors and students across institutions, which significantly enhances the feasibility and impact of participant engagement.</p> <hd id="AN0192418127-6">Survey design</hd> <p>The survey includes multiple-choice, open-ended, and Likert-scale questions to gather a range of data. The first four questions focus on collecting basic demographic information, identifying the most commonly used modeling software, and determining whether participants were taught this software in their formal education.</p> <p>Diaz Lantada et al. ([<reflink idref="bib9" id="ref54">9</reflink>]) outline six key factors for evaluating computer-aided engineering software: "ease of learning, ease of use, versatility, quality of results, ease/difficulty ratio, and user-program relationship (interfaces)" (p. 646), along with its future usefulness in the industry (p. 647). We adapted these factors for the context of design education, adding the availability of learning resources and the software's prevalence among peers of the students. After consulting with a professional industrial designer, we decided to remove the"ease/difficulty ratio"due to its ambiguity and redundancy. Following a second review by an undergraduate student, we included"accessibility to the software license,"as they highlighted it as a major factor influencing software selection among their peers. Additionally, we added an "other" option to account for unforeseen factors that might influence students'choices.</p> <p>The subsequent question aims to prioritize these factors by employing a Likert scale to assess whether they significantly impact participants'preferences. The following questions explore the key factors that support learning modeling software, as well as the potential role of peer learning in this process (see Appendix A). The survey was designed and conducted using the Google Forms platform due to its compatibility with both mobile and desktop devices. The results were analyzed collaboratively using Google Sheets, which facilitated real-time teamwork and data management.</p> <p>The open-ended survey responses were analyzed through content analysis, a methodological approach designed to systematically describe, code, and categorize qualitative data, thereby facilitating its interpretability (Krippendorff, [<reflink idref="bib25" id="ref55">25</reflink>]; Patton, [<reflink idref="bib34" id="ref56">34</reflink>]). This analytical method is recognized for its systematic and replicable nature, as well as its capacity to construct meaningful categories and derive deeper insights from the data (see Weber, [<reflink idref="bib54" id="ref57">54</reflink>]).</p> <hd id="AN0192418127-7">Results</hd> <p>Multiple approaches were employed to encourage participation in the survey. According to the Council of Higher Education (YÖK), there are 39 institutions in Türkiye offering undergraduate industrial design programs. The researchers contacted the coordinators of 37 undergraduate programs, whose websites they were able to access, via email to invite participation. Additionally, the Industrial Designers Association of Türkiye (ENTA) was approached to disseminate the invitation to its student members. The researchers also used LinkedIn and Instagram platforms to invite students meeting the criteria to participate in the survey and to encourage the invitation to be shared with eligible students. Lastly, the researchers made use of their existing professional networks in Türkiye by contacting instructors at various institutions to support survey distribution. In some cases where official communication channels did not yield responses, these personal connections helped ensure broader outreach, contributing to a larger sample and more generalizable findings.</p> <p>Between October 21, 2024, and January 6, 2025 a total of 201 students from 19 different universities in Türkiye participated in the survey. Among these participants, 139 (69.2%) were studying in public universities, while 62 (30.8%) were at private (foundation) universities. The universities with the highest participation rates were Istanbul Technical University (66 participants, 32.8%), Eskişehir Technical University (27 participants, 13.4%), and Bahçeşehir University (23 participants, 11.4%). The distribution of participants by year of study was as follows: 8 (4.0%) were in their 1 st year, 53 (26.4%) in their 2nd year, 50 (24.9%) in their 3rd year, and 91(45.3%) in their 4th year. It can be said that the number of participants has reached a sufficiently comprehensive number in terms of institution, software preference, and the class they study in (see Fig. 1).</p> <p>Graph: Fig. 1 Distribution of participants (n and %) by city, university, and software preference</p> <hd id="AN0192418127-8">Motivations, learning processes and experiences</hd> <p>The participants reported their primary modeling tools as follows: Rhinoceros (139 participants), Fusion 360 (29 participants), Blender (18 participants), Shapr3D (8 participants), 3ds Max (3 participants), Solidworks (4 participants), and Catia (1 participant). A majority of the participants (71.6%) stated that they learned these software programs as part of their university coursework, while the remaining 28.4% learned them through extracurricular activities, which is parallel with Rhinoceros to be the most dominant (See Fig. 2).</p> <p>Graph: Fig. 2 Participants' software preferences by learning method, showing the total number (n) and the percentage of those who learnt each software in school</p> <p>In terms of learning resources, 51.2% of the participants identified online resources as their primary sources for learning the software. Tutors were the main resource for 22.9% of participants, while 12.4% relied primarily on their peers for support during the learning process (See Fig. 3). Other sources such as private courses, internship co-workers, AI tools were also stated.</p> <p>Graph: Fig. 3 Overall distribution of participant responses</p> <p>When focusing on participant levels and learning methods, 88.7% of second-year students reported using software taught within the curriculum for their modeling processes. This percentage drops to 72.0% among third-year students and further to 64.4% in the fourth year. (First-year students were excluded from this comparison due to their limited engagement with modeling tasks at that stage). These findings clearly suggest that as students gain more experience, they become more proactive in finding their own ways of working and more open to exploring alternative tools.</p> <p>When participants were asked why they preferred these software tools, no single option emerged as dominant, with none surpassing 44.8% of the responses (See Fig. 4). However, participants'reasons for their preferences were relatively evenly distributed. For instance, ease of learning, ease of use, and prevalence in the industry were each cited by 44.8% of the participants as key factors influencing their choice. Similarly, availability of learning resources was marked by nearly the same number of participants.</p> <p>Graph: Fig. 4 Factors influenced software choices of participants</p> <p>Additionally, factors such as variety the software offers, access to licensing, prevalence among peers, and output quality were also significant reasons for preference among many participants. Among those who selected the"others"option, 17 participants specifically mentioned that the software was taught to them as part of their curriculum as the primary factor. Other reasons included the using the particular software being mandatory at their school, its manufacturing-focused capabilities, and the sense of creative freedom it provides.</p> <p>As part of the survey, participants were asked to describe how they learned to use the software they primarily use. The responses were analyzed using a content analysis method (see Krippendorff, [<reflink idref="bib25" id="ref58">25</reflink>]; Patton, [<reflink idref="bib34" id="ref59">34</reflink>]; Weber, [<reflink idref="bib54" id="ref60">54</reflink>]). The data were read multiple times to develop category suggestions, and coding was finalized accordingly. From this analysis, three main categories emerged: self-learning, peer learning, and formal learning.</p> <p>A closer look at participants' learning processes revealed that no single source was solely responsible; rather, multiple ways contributed to their learning. The analysis identified 21 (10.2%) statements that received three codes, 87 (42.4%) statements that received two codes, 85 (41.5%) statements with one code, and 8 (3.9%) uncoded statements. The statements coded under all three categories referenced the combined impact of self-learning, peer learning, and formal learning on the software learning process. For example, one participant stated,"I started learning alongside the curriculum and tried to resolve areas I struggled with or didn't understand by asking my friends or watching videos."Another participant explained their learning process as,"After learning the basics as part of the course content at school, I continued by experimenting on my own and learning shortcuts from my friends."The uncoded statements were either incomprehensible or left blank.</p> <p>A total of 123 statements (60.0%) were categorized under peer learning (see Fig. 5). These statements highlighted the learning processes that took place among students both inside and outside of school. Participants mentioned not only collaborating with classmates but also receiving guidance from senior students. Group assignments were also noted as an effective means of learning. For example, one participant stated,"We preferred to work on assignments together with our friends, and whenever we got stuck, we would ask each other how we did it."Another participant described their experience as,"We held live sessions to help each other."</p> <p>Graph: Fig. 5 Distribution of codes of participants' statements on their modeling software learning processes</p> <p>Meanwhile, 119 statements (58.0%) were coded as self-learning. These primarily emphasized learning through online resources, tutorials, and trial-and-error methods. For instance, one participant explained,"I learned by trying things out myself. I often didn't realize what I was doing was wrong or unnecessarily complicated until I found a simpler way, so most of my learning happened through trial and error."Another participant shared,"It was taught in class at school. Although I didn't fully grasp it at first, I understood it better as I continued to work on it."</p> <p>Out of 205 participants, 80 (39.0%) statements were categorized under formal learning. These participants mostly indicated that they learned the software through courses offered at their schools or that the courses provided a foundational knowledge. However, 6 of these 80 statements expressed that formal learning did not significantly contribute to their learning process. For example, one participant stated,"Since it coincided with the COVID period, it was very inefficient at school."Another noted,"Although it appears that we learned it through the curriculum, the classes were too short and online, so most people, including myself, did not benefit from those lessons."</p> <p>Another question in the survey aimed to understand participants'experiences with the software more deeply:"When you encounter a problem while modeling, how do you handle it?"The responses were analyzed using content analysis, followed by a coding process (see Krippendorff, [<reflink idref="bib25" id="ref61">25</reflink>]; Patton, [<reflink idref="bib34" id="ref62">34</reflink>]; Weber, [<reflink idref="bib54" id="ref63">54</reflink>]). In the initial coding, the factors participants sought help from when encountering problems were identified as peers & tutors, tutorials, search engines, community forums & blogs, artificial intelligence (AI) tools, software interface, and self-solution. However, during the second round of coding, it was noticed that there was a high degree of overlap between tutorials, search engines, and community forums & blogs, and it was decided that these three elements should be grouped under a single code, online sources.</p> <p>The analysis revealed that participants often used multiple methods sequentially, in combination with individual preferences. For example, one student described their process as follows:"I first refer to online sources, if I can't resolve it, I consult my friends, and if that doesn't work, I ask my professors for help."Another participant expressed,"I use Google first, then AI tools, and if needed, I ask a friend."</p> <p>As a result of the analysis, 162 participants (80.6%) indicated that they use online sources when encountering problems with modeling software (see Fig. 6). These sources include online videos, community blogs, forums, and social platforms. In addition, participants mentioned that they can also seek help from various websites by describing their issues in writing or visually through search engines. For example, one participant said,"I search for the error online to see if anyone else has encountered and solved it."Another participant shared,"I take a screenshot of the error screen, search it on Google Lens, and check the results. If it's something I can't take a screenshot of, I first look for a tutorial on YouTube; if I don't find it, I check forums (such as Reddit). If I still can't find a solution, I try to get help from ChatGPT."</p> <p>Graph: Fig. 6 Distribution of codes of participants' source of support when encountered a problem during modeling</p> <p>The second most common source of support was peers and tutors. 85 participants (42.3%) reported receiving help from individual peers, professors, or peer WhatsApp groups for modeling software-related issues. One participant said,"I ask someone who has experience in this area and try to find the error."Additionally, participants who received support from peers and tutors often mentioned a secondary source of help. For example, one participant said,"First, I consult my friends in my department; if no one is available, I search for a tutorial on YouTube that might contain the solution. My friends usually suggest more practical solutions, so if I'm not in a rush, I can wait for a friend I trust to be available."</p> <ulist> <item>22 participants (10.9%) reported using self-solution as a method for addressing problems encountered during modeling. One student described their process as,"I try different commands or use the problematic command in different ways through trial and error."Another participant explained,"I try to solve the problem based on my own experience, and if I can't, I do some research for resources."</item> <item>21 participants (10.4%) reported using AI tools to help solve problems in modeling software. One student explained,"I try to solve the issue using available tutorials. If there's a tool I don't know, I ask ChatGPT or try to find a YouTube tutorial."Another participant stated,"I ask ChatGPT (I get very helpful answers)."Finally, 4 participants reported using the software's built-in support interface, and 1 participant reached out to the software company via email for assistance.</item> </ulist> <p>In the final section of the survey, participants were asked whether they used emerging technologies such as virtual reality (VR), augmented reality (AR), or mixed reality (MR) in their design processes. A total of 37 participants (18.4%) indicated that they had experience with these technologies. Among them, some mentioned utilizing AR technology within the Shapr3D interface, which allowed them to visualize their models in real-world settings. One participant emphasized the importance of being able to perceive their product in relation to other objects. Additionally, some participants shared their experiences with Vizcom, an AI-powered visualization tool, for product design visualization. One participant recounted presenting via VR during their internship and described the experience as"odd."</p> <hd id="AN0192418127-9">Expectations</hd> <p>As part of the survey, participants were asked which factors influenced their choice of modeling software in a Likert-scale question. The results indicate that participants assigned similar importance to a variety of criteria, aligning with the factors they considered when selecting their own software. Approximately 75% of participants stated that nearly all factors are important in their decision-making process in preferring a modeling software (see Fig. 7). The only notable exception was <emph>prevalence among peers</emph>, which was identified as a determining factor by only 35% of participants. Also, it is noteworthy that over 54% of participants strongly agreed that <emph>availability of learning resources</emph> was a key factor. This suggests that one of the participants'highest expectations from modeling software is access to comprehensive learning resources.</p> <p>Graph: Fig. 7 Students'responses to the Likert scale question about which factors are reasons of preference when choosing a modeling software</p> <p>In another question, participants were asked to describe the characteristics of an ideal modeling software. Their responses were analyzed using content analysis, as previously described. It was observed that participants referred to both abstract qualities, such as being intuitive, easy to use, or understandable, and practical features, such as having a user-centered interface or producing high-quality renders. Consequently, the content analysis aimed to uncover the fundamental attributes underlying these practical needs and software variations, since qualitative content analyses require interpretation of the content within its social and personal context (Schreier, [<reflink idref="bib43" id="ref64">43</reflink>]). For example, while initial coding included categories like"interface"and"high-quality rendering,"further detailed analysis refined these categories into more conceptual categories such as understandability (of the interface) or outcome orientation. This refinement was based on the understanding that participants' demands for an easy-to-use and comprehensible interface could be addressed by a more fundamental quality of the software being understandable. Similarly, the desire for high-quality rendering was framed not as a specific technical requirement but as a broader expectation for superior output quality.</p> <p>According to the analysis, an ideal modeling software should be;</p> <p></p> <ulist> <item> <bold> Understandable: </bold> A significant majority of participants (152; 75.6%) emphasized the importance of understandability in ideal modeling software. This expectation was articulated in various ways: one participant described it as requiring a"simple interface and clear explanations,"another stated it should be"easy to use,"while others referred to it as having a"user-friendly and flexible interface"or"clarity in the interface and logic of command shortcuts."One participant highlighted that"the most frequently used features should be the most accessible (in line with universal design principles), and basic operations should not require extensive use of complex functions."</item> <p></p> <item> <bold> Directive: </bold> Forty-three participants (21.4%) stressed the importance of directiveness, meaning the software should provide interactive feedback to guide users. Examples included"instinctive guidance,"as described by one participant, and another stated,"If a command cannot be executed, the software should clearly explain the reason and offer solutions."Others mentioned the need for the software to"identify issues in the model and explicitly point them out"or proposed enhancing guidance and prediction processes with AI. One participant noted,"It should integrate with AI to anticipate my next steps and reduce my workload."</item> <p></p> <item> <bold> Outcome-orientated: </bold> Forty-one participants (20.4%) highlighted the need for software to be outcome-oriented, emphasizing the production of high-quality visual outputs and high-resolution renders. One participant expressed this as"producing quality results and enabling forms suitable for rendering or 3D printing,"while another noted the importance of"providing high-quality renders and animation outputs."It is worth emphasizing that certain software applications are developed exclusively for rendering purposes and the practice among students to employ separate tools for modeling and rendering tasks are quite common. Although the primary focus of this study is on modeling software, participants' expectations concerning rendering capabilities should be interpreted as an implicit demand for modeling tools to also support high-quality rendering functions.</item> <p></p> <item> <bold> Versatile: </bold> Thirty-two participants (15.9%) valued versatility, emphasizing the ability to model using various methods and incorporating a richness of features. Comments included"the ideal software should encompass most industrial methods,""it should have tools for almost everything I might want to do,"and"it should offer multiple ways to achieve a shape."</item> <p></p> <item> <bold> Compatible: </bold> Twenty-six participants (12.9%) stressed the importance of compatibility with file formats and other software. Suggestions included that the software should"allow seamless transitions between different applications"and"provide a consistent interface across iOS and Windows platforms."</item> <p></p> <item> <bold> Affordable: </bold> Twenty-five participants (12.4%) emphasized the importance of financial accessibility in modeling software. One participant remarked, "There should be affordable options for students. I despise pirated software, but while Adobe is affordable, other software are not as easy to afford."</item> <p></p> <item> <bold> Resourceful: </bold> Twenty participants (10.0%) highlighted the need for resourcefulness, emphasizing the importance of access to learning materials. For example, one participant noted,"There should be resources to help us fully utilize its features (Rhino excels in this regard)."</item> <p></p> <item> <bold> Flexible: </bold> Twelve participants (6.0%) underlined the importance of flexibility in modeling processes. One participant stated,"I should be able to model like playing with clay and relate it to realistic elements,"while another mentioned,"A flexible modeling approach has always made my work easier."</item> <p></p> <item> <bold> Stable: </bold> Nine participants (4.5%) pointed to stability as a crucial feature, particularly in handling complex scenes. One participant commented,"Performance and stability in heavy scenes are important. For example, while Blender is not as stable as 3D Max or Maya in handling heavy scenes, its speed and ease of learning make it a preferred choice."</item> <p></p> <item> <bold> Precise: </bold> Seven participants (3.5%) emphasized precision in measurements, with one participant suggesting,"The precision of Rhino in measurements combined with the flexibility of Blender's modeling features would make for an ideal software."</item> <p></p> <item> <bold> Collaborative: </bold> Finally, four participants (2.0%) highlighted the need for collaboration features, such as"cloud storage and the ability for teams to work on a single design,"and"multi-screen functionality."</item> </ulist> <hd id="AN0192418127-10">Conclusion and discussion</hd> <p>This study aimed to explore the learning processes, experiences, and expectations of industrial design students regarding modeling software, which plays a significant role in their education and professional practice. A survey designed for this purpose was completed by 201 students from 19 different universities in Türkiye. The data were analyzed using various methods tailored to the nature of the questions, allowing the findings to be presented in a meaningful context.</p> <p>The results suggest that students prioritize factors such as ease of learning, ease of use, prevalence in industry, availability of learning resources, and interface usability when choosing modeling software. These factors were also reflected in their expectations for such tools. The results indicate that 71.6% of students prefer to use 3D modeling software introduced as part of their school curriculum. Additionally, the primary resource utilized during the learning process for these tools is online sources, cited by 51.2% of students. Furthermore, 22.9% of students identify their tutors, while 12.4% consider their peers as their primary source of learning. Notably, 13.5% of students report relying on alternative resources such as private courses, internship coworkers, or AI tools as their main method for acquiring proficiency in these software programs.</p> <p>Furthermore, this study aims to highlight two key points. The first is the introduction of an evaluation framework specifically developed for 3D modeling software. The second is to discuss how the findings reveal a significant research potential in the context of peer learning, emphasizing its implications and future avenues for exploration.</p> <hd id="AN0192418127-11">Discussions in developing a framework to evaluate modeling software</hd> <p>Students'expectations for modeling software were further explored through a question about the ideal features of such tools. A content analysis of the responses identified 11 categories, as outlined in the previous section. We offer to refine these categories as dimensions of modeling software, which could serve as a framework for understanding the expectations of industrial design students and as a potential tool for evaluating modeling software (Table 1). The framework's potential contributions are discussed in relation to four key audiences sequentially: curriculum developers, software developers, students, and design scholars.</p> <p>Table 1 The student-informed framework for evaluating 3D modeling software in industrial design education</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left"><p>Dimension</p></th><th align="left"><p>Description</p></th></tr></thead><tbody><tr><td align="left"><p>Understandability</p></td><td align="left"><p>Highlights the software being easy to learn, easy to use, and equipped with an intuitive interface</p></td></tr><tr><td align="left"><p>Directiveness</p></td><td align="left"><p>Implies that the software guides users effectively, simplifying the execution of desired actions and tasks</p></td></tr><tr><td align="left"><p>Outcome Orientation</p></td><td align="left"><p>Implies producing high-resolution renders and visual outputs</p></td></tr><tr><td align="left"><p>Versatility</p></td><td align="left"><p>Indicates the offered variety, ensuring the software caters to various modeling processes and strategies</p></td></tr><tr><td align="left"><p>Compatibility</p></td><td align="left"><p>Highlights the seamless integration with other software, tools, and hardware for efficient workflows</p></td></tr><tr><td align="left"><p>Affordability</p></td><td align="left"><p>Underlines the need for software to be affordable and accessible to users</p></td></tr><tr><td align="left"><p>Resourcefulness</p></td><td align="left"><p>Highlights the availability of a broad network of resources to support learning and problem-solving</p></td></tr><tr><td align="left"><p>Flexibility</p></td><td align="left"><p>Emphasizes enabling users to explore creative possibilities freely and achieve their desired forms with ease</p></td></tr><tr><td align="left"><p>Stability</p></td><td align="left"><p>Indicates the necessity of software that operates smoothly, minimizing crashes and bugs to enhance user confidence</p></td></tr><tr><td align="left"><p>Precision</p></td><td align="left"><p>Highlights the precision and reliability in modeling processes to ensure accurate outcomes</p></td></tr><tr><td align="left"><p>Collaboration</p></td><td align="left"><p>Implies the importance of features that enable effective teamwork, such as collaborative tools and cloud-based functionalities</p></td></tr></tbody></table> </ephtml> </p> <p>As previously discussed, the development of modeling tools aligned with the logic, methodology, and demands of design practice is essential. However, prioritizing only professional demands risks neglecting the equally important role of industrial design education within the discipline. Modeling software must also be evaluated in terms of how students interact with it and how it supports their learning within educational settings. In this regard, curriculum developers can benefit by adopting this student-centered framework that considers the relationship between students' expectations and the functionalities offered by the software. This would enable the integration of software into the curriculum that are better aligned with students'learning styles and pedagogical needs.</p> <p>In contrast to existing studies, our work takes a holistic, student-centered perspective on which qualities in modeling software are most valued by industrial design students. This approach yields a framework that goes beyond traditional usability dimensions—such as understandability and stability—to include qualities that may be particularly relevant in educational contexts, such as resourcefulness and affordability. Software designed with this framework in mind can better reflect the nature of design education and more effectively support the learning needs of industrial design students.</p> <p>The literature indicates that once students acquire proficiency in a particular CAD tool, their ability to adapt to other modeling software improves considerably (Lang et al., [<reflink idref="bib26" id="ref65">26</reflink>]). Within this context, the proposed framework may serve as a pedagogical guide to support students in selecting new tools that align with their individual design approaches, thereby enhancing both their creative capacity and overall productivity. Accordingly, curriculum structures can be designed to promote student agency in software selection, encouraging learners to navigate their own developmental trajectories rather than adhering to a singular software path.</p> <p>Finally, this framework opens up new avenues for empirical research by design scholars. For instance, it could be used to investigate whether students perform better when using software that aligns with their expectations or to identify which software attributes most significantly influence learning outcomes. In this way, more student-centered approaches to software selection and pedagogy in design education can be fostered.</p> <hd id="AN0192418127-12">Discussions on peer learning activities</hd> <p>Peer learning encompasses all activities where students learn from one another, either formally or informally (Boud et al., [<reflink idref="bib5" id="ref66">5</reflink>]). Key characteristics of peer learning include fluidity in roles (Boud et al., [<reflink idref="bib5" id="ref67">5</reflink>]) and the absence of power asymmetry (Dutton, [<reflink idref="bib12" id="ref68">12</reflink>]). Pelsmakers et al. ([<reflink idref="bib36" id="ref69">36</reflink>]) emphasize the importance of learning not only from educators but also from peers, highlighting the value of collaborative knowledge-building. Similarly, Saunders ([<reflink idref="bib40" id="ref70">40</reflink>]) underscores the key role of mutual benefit as a driving force in effective peer learning processes. In parallel, according to Robinson ([<reflink idref="bib39" id="ref71">39</reflink>]), peer learning activities turn learning into a collaborative experience, where the main objective is to enhance skills and expand knowledge together.</p> <p>In the context of design education, peer learning can be considered more vital compared to many other disciplines. In this regard, Dutton ([<reflink idref="bib12" id="ref72">12</reflink>]) argues that peer learning is not just a supporting element in design education but rather plays a complementary role. Similarly, McClean and Hourigan ([<reflink idref="bib27" id="ref73">27</reflink>]) support Dutton by highlighting the distinction between student–student interaction and student–teacher communication. In parallel, it is argued that peer learning may involve a new type of knowledge that does not emerge in traditional teacher-student relationships (Yilmaz & Gelmez, [<reflink idref="bib56" id="ref74">56</reflink>]). Also, Chiu ([<reflink idref="bib6" id="ref75">6</reflink>]) claims that classmates are the primary sources of information for design students.</p> <p>In design education, peer learning is commonly observed in the form of peer critiques. This activity originates from the tradition of design studio education and is defined as students providing feedback on each other's projects. While it is common for students to develop their peer critiques by observing their teachers (Turner, [<reflink idref="bib51" id="ref76">51</reflink>]) and taking inspiration from formal critiques in terms of both content and style (Yilmaz & Gelmez, [<reflink idref="bib56" id="ref77">56</reflink>]), peer critiques significantly contribute to students becoming independent learners (Nicol et al., [<reflink idref="bib31" id="ref78">31</reflink>]).</p> <p>The analysis of the collected data reveals that 60.0% of participants engaged in peer learning activities during their experiences with learning 3D modeling software. Additionally, 42.3% of participants reported seeking support from their tutors or peers when encountering issues with these software. The significant proportion underscores the necessity of understanding how peer learning structures are sustained through both in-class and extracurricular activities, as well as the dynamics at play. When the results of this study are examined in light of this information, it is evident that there is significant potential for future research. The study provides valuable insights into the natural processes of peer learning among students, indicating the formation of various collaboration and organizational patterns. For instance, one participant describes their process as follows:"My closest friend explained all the basic commands to me like a private lesson. After learning the interface and basic commands, I watched instructional videos online to learn the rest of the application when I couldn't figure it out. I didn't learn much from my professor at school."Another participant states,"Everyone shares what they remember. If someone gets stuck, they ask the person next to them, and it becomes productive for both the teacher and the learner."Yet another participant notes,"We usually exchange ideas, and if the modeling breaks, we discuss how to fix it. Based on previous projects, each of us has strengths in specific areas, and when we encounter problems, we listen to the advice of those who are skilled in those areas."</p> <p>Considering all of this, we can claim that gaining a deeper understanding of the dynamics and roles that emerge in students'peer learning activities would significantly contribute to a more systematic and comprehensive understanding of a complementary part in design education. These outcomes would help uncover important elements of the learning experience that might be overlooked in formal curricula, shedding light on an essential aspect of student development. In doing so, we can provide a more holistic perspective on the learning process, which is crucial for refining educational practices and fostering an environment where peer learning can thrive. Consequently, future studies will focus on exploring peer learning dynamics and roles in design education to provide further insights into how such interactions can be better integrated and supported in educational settings.</p> <hd id="AN0192418127-13">Acknowledgements</hd> <p>This study (Project ID: MDK-2025-46949) was supported by the Scientific Research Projects Department of Istanbul Technical University. This paper is part of an ongoing Ph.D. thesis conducted by the first author at Istanbul Technical University. We would like to thank Assoc. Prof. Dr. Abdüsselam Selami Çifter and Assoc. Prof. Dr. Orkan Zeynel Güzelci for their valuable contribution. Also, we would like to express our sincere gratitude to all the students and academics who supported the announcement of the survey and contributed through their participation.</p> <hd id="AN0192418127-14">Declarations</hd> <p></p> <hd id="AN0192418127-15">Generative AI and AI-assisted technologies in the writing process</hd> <p>During the preparation of this work the author(s) used Google Translate and ChatGPT for translation and grammar checking purposes. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.</p> <hd id="AN0192418127-16">Appendix A</hd> <p>Online survey questions</p> <p></p> <p> <ephtml> <table rules="groups"><tbody><tr><td align="left"><p>I understand the procedures explained above. My questions have been answered satisfactorily, and I consent to participate in this study, provided I retain the right to withdraw at any time</p></td></tr><tr><td align="left"><p>(Checkbox)</p><p>Q1: Which university are you studying at?</p><p>Q2: What year are you in?</p><p>1</p><p>2</p><p>3</p><p>4</p><p>Q3: What is the most frequently used modeling software by you?</p><p>Hinoceros</p><p>Blender</p><p>Autodesk Maya</p><p>Zbrush</p><p>3ds Max</p><p>Shapr 3D</p><p>Fusion 360</p><p>SketchUp Other:</p><p>Q4: Was this software taught as part of your curriculum at your university?</p><p>Yes</p><p>No</p><p>Q5: What is the reason you prefer this software? Please select one or more options. Ease of learning Ease of use Prevalence in the industry Output quality</p><p>User interface usability</p><p>Variety of features</p><p>Availability of learning resources (Tutorials, forums, blogs, etc.)</p><p>Prevalence among my friends</p><p>Access to software license</p><p>Other:</p><p>Q6: During your learning process with this software, where did you get the most support from?</p><p>My professors at school</p><p>My friends</p><p>Online (web-based) resources (Tutorials, forums, blogs, etc.)</p><p>My colleagues during internships</p><p>Courses and training outside of school</p><p>Other:</p><p>Q7: Please indicate the option that best fits your opinion for the following statements. (Strongly disagree, Disagree, Neutral, Agree, Strongly agree) Ease of learning is a preference for me when choosing modeling software</p><p>Ease of use is a preference for me when choosing modeling software. Prevalence in the industry is a preference for me when choosing modeling software</p><p>Output quality is a preference for me when choosing modeling software. Usability of the user interface is a preference for me when choosing modeling software</p><p>Variety of features is a preference for me when choosing modeling software. Availability of learning resources (Tutorials, forums, blogs, etc.) is a preference for me when choosing modeling software</p><p>Prevalence among my friends is a preference for me when choosing modeling software. Access to software license is a preference for me when choosing modeling software</p><p>Q8: When you encounter a problem while modeling, how do you handle it?</p><p>Q9: How did your learning process for this modeling software take place? If you received or provided support from friends while learning modeling software, how did that happen?</p><p>Q10: What features should an ideal modeling software have, in your opinion?</p><p>Q11: Have you used emerging technologies (such as virtual reality (VR), augmented reality (AR), mixed reality (MR), etc.) in your design processes before? If yes, for what purpose and how did you use them? What results did you achieve?</p><p>If you would like to participate in the interview session for the next step of this research, please provide your email address</p><p>Email address:</p></td></tr></tbody></table> </ephtml> </p> <hd id="AN0192418127-17">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0192418127-18"> <title> References </title> <blist> <bibl id="bib1" idref="ref44" type="bt">1</bibl> <bibtext> Aldoy N, Evans MA. 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  Data: Rethinking 3D Modeling Software Tools in Industrial Design Education: A Student-Informed Framework Based on Experiences and Expectations
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  Data: <searchLink fieldCode="AR" term="%22Onur+Yilmaz%22">Onur Yilmaz</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-2489-1480">0000-0002-2489-1480</externalLink>)<br /><searchLink fieldCode="AR" term="%22Koray+Gelmez%22">Koray Gelmez</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-1306-2852">0000-0003-1306-2852</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Technology+and+Design+Education%22"><i>International Journal of Technology and Design Education</i></searchLink>. 2026 36(2):707-726.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: Industrial designers' expectations of modeling software differ significantly from those of other professions, primarily due to the unique nature of design and the ways of creative thinking. Understanding the expectations and experiences of students within industrial design education is also of critical importance--both for the design education literature and for software developers aiming to create tools that better support the needs of emerging designers. This study investigates the factors influencing industrial design students' choice of 3D modeling software, their experiences and expectations from these tools. A survey was conducted among 201 students from 19 universities in four different cities of Türkiye, gathering both quantitative and qualitative data. Findings indicate that students prioritize affordability, ease of use, and compatibility when selecting modeling software. Peer learning plays a crucial role in software adoption, with 60% of students engaging in peer-supported learning activities. Additionally, the study proposes a framework for evaluating 3D modeling software based on eleven key dimensions, including understandability, flexibility, and collaboration features. The findings suggest that industrial design curricula should incorporate a more student-centered approach, allowing flexibility in software selection and supporting peer learning environments. Furthermore, software developers can utilize these insights to create tools that better align with students' needs and expectations.
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