Research on Symmetry Optimization of Designer Requirements and Prototyping Platform Functionality in the Context of Agile Development

Abstract

Aligning designer requirements with prototyping platform functionalities remains a challenge in agile development (AD) environments, as existing tools often fail to accommodate evolving needs. This study proposes a systematic approach to optimizing prototyping platforms by bridging the gap between user needs and functional design. First, a designer requirement architecture was constructed using grounded theory, identifying three core elements: interaction needs, collaboration needs, and visualization and testing needs. The F-KANO model was used to categorize requirements, while DEMATEL was used to prioritize them based on interdependencies. Finally, quality function deployment (QFD) was used to map designer needs to functional specifications, deriving an optimization strategy. Empirical evaluation through user testing indicated notable improvements in workflow efficiency, usability, and collaboration effectiveness. This research offers a systematic framework for refining prototyping platforms in AD, improving design efficiency and UX.

1. Introduction

In the era of digital transformation, agile development (AD) has been widely adopted in design and development processes. The core principles of AD encompass iterative development, user-driven design, and cross-functional collaboration [1], focusing on the frequent release of functional software in short development cycles to swiftly adapt to evolving market demands [2]. Compared with traditional waterfall development, AD prioritizes cross-functional collaboration and swift user feedback integration, requiring rapid requirement validation and iterative functionality updates within tight timeframes [3]. However, while AD enhances software iteration efficiency, it also introduces multifaceted challenges for designers. In an AD environment, designers must swiftly and effectively access essential tools for interface design, prototyping, and user testing. They are required not only to adapt to frequent design changes but to complete interface design, optimize user experience (UX), and facilitate cross-team communication within constrained time limits to ensure that the final product aligns with user expectations [4]. Consequently, enhancing design efficiency has become a critical factor influencing the overall effectiveness and quality of product development within an AD framework.

With the increasing challenges posed by AD, an increasing number of scholars have conducted research from the perspectives of design tools, design strategies, and team structures to address these issues. Design tools (e.g., Figma, Sketch, Adobe XD) integrate key functionalities such as visual prototyping, assisting designers in improving design efficiency. Although these tools play a crucial role in design and prototyping, they still fail to fully meet designers’ core needs in AD environments, such as efficient version iteration management, enhanced collaboration and requirement synchronization, and improved consistency in visual standard [5]. For instance, while some prototyping platforms offer real-time collaboration features, studies have shown that these tools often lack efficient mechanisms for managing design requirements dynamically, which can lead to workflow inefficiencies and inconsistencies in team-based design environments [6].

Some studies suggest that leveraging componentization and reusable assets can effectively enhance design efficiency. Some studies contend that creating a unified design language or component library not only minimizes redundant effort but enhances development teams’ comprehension of design principles [7]. However, these studies mainly focus on visual consistency and technical implementation, without delving into how requirement management can be efficiently integrated at the design tool level. Other research has explored design process optimization, such as Magistretti’s study, which recommended incorporating UX sprints or design sprints into team structures to rapidly focus on UX goals at the beginning of each iteration [8]. However, these studies predominantly emphasize internal workflow optimization within teams, without proposing actionable design strategies for prototyping platforms. Despite the growing number of studies on prototyping tools and agile workflows, existing research still lacks a structured methodology for translating designer needs into concrete platform functionalities. Research has highlighted shortcomings in design platforms’ handling of version control, interaction complexity, and multiuser collaboration [9], but there is no unified framework that systematically prioritizes and implements optimizations to bridge these gaps.

While existing research has explored various aspects of prototyping tools in AD, it lacks a structured methodology to systematically align designer needs with platform functionalities. Current studies focus mainly on isolated tool optimizations [10] or workflow refinements [11] without prioritizing user needs in a data-driven manner. Moreover, existing approaches lack an integrated prioritization mechanism, often relying on ad hoc feature additions rather than a strategic alignment with designer workflows. This leads to inefficiencies in iteration, usability, and collaboration. By taking designers as the primary demand subjects within AD environments, this research refines their requirement hierarchies and priorities, ultimately exploring how structured optimization methods can improve platform usability, enhance collaboration, and support high-frequency iteration cycles. The proposed framework offers a systematic approach to bridging the gap between user needs and design functionalities, ensuring that prototyping platforms in AD environments effectively facilitate iterative design and cross-team collaboration.

The core objective of this study is to optimize prototyping platforms in AD environments, ensuring a more precise and efficient alignment between designer needs and platform functionalities, thereby improving design efficiency and UX. This research aims to explore the core demands of designers in AD environments, identify the major challenges they face when using design platforms, and apply scientific methods to classify, prioritize, and map designer needs, ultimately enhancing workflow efficiency, information management, and UX.

First, interviews were conducted with 12 designers, including UI designers, UX designers, and product managers, all of whom had at least three years of experience in agile development environments. Grounded theory was employed to construct a conceptual framework for designers’ needs in AD environments. Subsequently, the fuzzy Kano (F-KANO) model was employed to categorize and filter user requirements, followed by the decision-making trial and evaluation laboratory (DEMATEL) method to prioritize the filtered user needs and determine key design requirements. Quality function deployment (QFD) analysis was then used to map designer needs to design specifications. Finally, based on the above requirement analysis, an optimized prototyping platform tailored for AD environments was developed, and designers were invited to test the optimized platform, with user feedback collected for evaluation.

Section 2 provides a comprehensive literature review, examining the latest research developments in AD, user interface (UI) design, and prototyping platforms, establishing the theoretical foundation for this study. Section 3 presents the methodology, detailing the research framework based on grounded theory, the F-KANO model, the DEMATEL method, and QFD analysis. Section 4 shows the systematic extraction of core designer needs in AD environments through interviews and survey data analysis. By classifying requirements, assigning weights and priority rankings, this chapter establishes a mechanism for aligning user needs with technical requirements. Section 5 exhibits an empirical analysis to validate the effectiveness of the optimized requirement framework, proposing a demand management optimization strategy for product design platforms in AD environments. Finally, Section 6 summarizes the findings and contributions of this study, discussing its practical implications for improving design efficiency in AD workflows and outlining future research directions.

2. Literature Review

2.1. Interface Design in the Context of AD

Currently, UI research in the context of AD focuses primarily on adaptability and usability, often centering on diverse user scenarios across different user groups to optimize UX and enhance interaction efficiency. Choi conducted usability testing on interface interaction patterns for mobile e-commerce applications and proposed a design strategy that simplifies information hierarchy to reduce the user’s cognitive load [12]. Lei, in a study on online education platforms, integrated survey data and eye-tracking technology to examine the effects of text–image combinations and information architecture logic on learning efficiency [13]. Similarly, Cappiello’s research focused on multidevice consistency, emphasizing that cross-platform interfaces should maintain visual and interaction coherence to ensure a seamless transition between devices [14]. These studies, employing experimental methods and field studies, assessed specific user groups (e.g., students, consumers, and mobile users) by measuring task completion time, error rates, or subjective satisfaction, thereby providing insights into interface design strategies for external users across various task scenarios. However, existing research concentrates primarily on product usability for external users, with limited attention given to the unique requirements of UI design for professional tools. As a result, while these findings offer valuable design perspectives for AD-driven design tasks, there remains a research gap in the interface design of professional tools used internally, particularly in prototyping platforms.

Several studies have highlighted that professional design tools (e.g., Axure, Sketch, and Figma) serve as critical hubs for prototype creation, interactive demonstration, and requirement communication within agile teams. However, existing research is largely confined to tool functionalities and team collaboration efficiency, with limited exploration of workflow usability and interaction design in these tools. Hussain, Slany, and Holzinger observed that while prototype tools are frequently utilized during agile sprints, research focuses predominantly on the comprehensiveness of tool functionalities and the accuracy of deliverables, rather than the usability and workflow of the tool interfaces [15]. Tohidi, from the perspective of rapid prototype validation, emphasized that implementing low- or mid-fidelity prototypes in early development stages enhances creative quality [16]. However, this study did not further investigate the specific interface requirements of design tools in this process. These findings indicate that current research on tool interfaces is primarily descriptive or fragmented, lacking a systematic investigation into the information architecture, interaction modalities, and visual layout of prototyping platforms in professional design workflows.

While agile design teams continuously iterate and refine external product interfaces, the prototyping platforms used internally often fail to undergo similar adaptations and optimizations to align with agile workflows [17]. A review of existing literature suggests that there may be a significant misalignment between designers’ needs and platform interface functionalities. Currently, there is a lack of systematic research on how prototyping platforms address key professional requirements, such as high-frequency collaboration, rapid task switching, visual annotations, and multiversion management at the interface level. Hinderks and Winter emphasized the crucial integration of UX principles within agile methodologies for product development. However, their research focused primarily on optimizing the UX of end-user products [18], with limited exploration of the operational bottlenecks and information design deficiencies encountered by designers when using prototyping platforms. Building in this gap, the present study aims to systematically examine the structure and characteristics of designers’ needs within AD contexts and propose a structured approach to analyzing and optimizing prototyping platform interfaces and functionalities. The objective is to mitigate the misalignment between prototyping platform design and the specialized requirements of designers, thereby enhancing efficiency and usability in agile workflows.

2.2. Interface Design from User Requirements

User-centered design methodologies have been widely adopted in interaction design and UX research. Whaiduzzaman systematically reviewed usability studies and HCI methodologies, highlighting that techniques such as interviews and usability testing effectively capture user needs and provide objective foundations for product interface iteration [19]. Through a literature analysis of multiple design studies, Palopak and Huang observed that many teams, when developing product prototypes, tend to collect requirements directly from target consumers or business stakeholders, while rarely considering professional tool users as part of the user base [20]. Additionally, Pichler, in his exploration of agile product management, emphasized the relationship between requirement prioritization and user stories, revealing that requirement management focuses primarily on market and customer value [21], with limited frameworks addressing the specific needs of internal professional users such as designers and engineers when using software tools.

Some studies have explored the unique requirements of technology and creative professionals. In a study on design team collaboration, He conducted preliminary interviews with designers and found that version control and team discussion features are essential in prototyping tools [22]. However, their study did not conduct an in-depth analysis of specific interface components or information presentation methods. Another study developed a rapid prototyping platform for outdoor game design, enabling designers to iterate and test game prototypes more efficiently [23]. Similarly, Kahlon and Fujii, from a design cognition perspective, investigated how designers employ metaphorical descriptions to facilitate understanding and task execution when interacting with design representations [24]. However, their findings remained at the conceptual level, without offering practical interface optimization strategies. Current research on designers as a user group focuses primarily on creative processes and cognitive models, while efficiency-related issues in operational interfaces remain largely overlooked. The perspectives on designer requirements are fragmented, lacking a systematic classification of different need types, such as interaction needs, collaboration needs, and prototyping needs. As a result, existing studies fall short in proposing targeted solutions from an architectural perspective that bridges designer needs with technical implementations.

Existing UI design research focuses predominantly on general users, with objectives centered on enhancing UX and service satisfaction for the broader audience. However, there is a lack of in-depth investigation into the multifaceted needs of professional users, particularly designers using design tools. While some studies have acknowledged the differences between designer needs and those of general users, they have focused primarily on team collaboration workflows or creative thinking methodologies rather than the specific operational challenges designers encounter in prototyping tools. In an AD environment, teams must rapidly construct and validate many prototypes within extremely short iteration cycles, yet research on whether prototyping platform interfaces can effectively support high-density creative output and rapid adjustments remains limited. A misalignment often exists between designer needs and platform functionalities, where tools offer a plethora of plugins and complex interactive components but lack intuitive, efficiently navigable interface mechanisms. Therefore, this study focuses on optimizing the design of prototyping platforms in AD environments to better accommodate designer requirements. By efficiently collecting, filtering, analyzing, prioritizing, and aligning designer needs, this research establishes a mapping mechanism between designer requirements and technical functionalities. The goal is to enhance platform interfaces to support high-frequency tasks in AD workflows while proposing scientifically grounded design optimization strategies.

3. Methodology

3.1. Research Framework

This study aims to address the misalignment between product design platforms and designers’ needs in AD environments. To achieve this, the F-KANO model, DEMATEL method, and QFD analysis were employed to establish a mapping mechanism between user requirements and platform technical specifications, ultimately enhancing designers’ UX and work efficiency. To fulfill this objective, a systematic research framework is proposed, encompassing the entire process from collecting designer requirements to platform optimization and evaluation. The framework is illustrated in Figure 1 and consists of the following key components:

• User Research: A qualitative interview analysis approach is employed to construct designer personas in AD environments, ensuring representative sampling. Additionally, the interview protocol is refined to enhance the specificity and accuracy of requirement collection.
• User Requirement Collection and Analysis: Grounded theory is applied to conduct in-depth interviews and analyses of designers’ needs in AD contexts. Open coding, axial coding, and selective coding are utilized to extract key designer requirements, leading to the construction of a conceptual framework for these needs.
• Requirement Classification and Filtering: The F-KANO model is used to classify and filter requirements, retaining M (must-be), O (one-dimensional), and A (attractive) categories, while eliminating I (indifferent) requirements, ensuring that optimization efforts focus on the most valuable designer needs.
• Requirement Prioritization: The DEMATEL method is applied to prioritize the retained designer requirements, establishing a hierarchy of needs to ensure that technical implementations effectively support designers’ core requirements.
• Identification and Prioritization of Design Requirements: Based on the ranked user needs, corresponding design requirements are identified. QFD analysis is then employed to map designer needs to specific design requirements, ensuring alignment between technological development and user expectations.
• Design Implementation and Feedback Analysis: Interface optimizations for the product design platform are executed according to prioritized technical requirements. User testing is conducted to evaluate and refine the optimized design. Ultimately, an optimized interface design solution is formulated, leading to the development of strategic guidelines for prototyping platform optimization in AD environments.

Figure 1. Proposed Framework.

Figure 1. Proposed Framework.

To ensure the rigor of this study, semistructured interviews were conducted to collect qualitative data, and structured surveys were used to support quantitative analysis. Participants were selected based on their professional experience in agile development environments to ensure representativeness. All participants provided informed consent, and personal data were anonymized in accordance with research ethics guidelines. The study was reviewed and approved by the institutional ethics committee. This integrated methodological approach ensures a systematic and data-driven optimization process rather than an arbitrary selection of techniques. By linking user-driven insights (grounded theory), requirement prioritization (F-KANO and DEMATEL), and functional alignment (QFD), this study constructs a coherent and rigorous pathway for improving prototyping platform efficiency in AD environments.

3.2. Grounded Theory

Grounded theory is an inductive qualitative research methodology designed to derive concepts and theories from data, enabling the interpretation of phenomena within specific contexts [25]. In this study, grounded theory was employed to collect and analyze the core needs of designers in AD environments. A semistructured interview approach is utilized to gather insights into designers’ actual requirements and pain points when using prototyping platforms. The collected data were systematically processed through open coding, axial coding, and selective coding, ultimately leading to the construction of a conceptual framework that encapsulated designer needs within AD contexts. To ensure the authenticity and reliability of the research data, follow-up validation with interviewees was conducted when necessary. This ensured that the extracted designer requirements were firmly grounded in real-world AD scenarios, thereby providing a robust data foundation for subsequent requirement classification and design optimization.

3.3. F-KANO Model

The F-KANO model is an advancement of the traditional Kano model, integrating fuzzy mathematics to enhance the accuracy of user requirement classification and mitigate the issue of discrete categorization [26]. Under the framework of this study, the F-KANO model was utilized to classify and refine designers’ requirements, ensuring that optimization efforts were concentrated on the most impactful needs. Based on the classification principles of the Kano model (Figure 2), requirements were divided into the must-be (M), one-dimensional (O), attractive (A), and indifferent (I) categories. To quantify user preferences, an F-KANO questionnaire was administered, where participants assigned fuzzy values ranging from 0 to 1 to indicate satisfaction and dissatisfaction levels, ensuring that the total sum equaled 1. This methodology allowed for a more precise interpretation of how different design elements influence user experience, effectively capturing the nonlinear relationship between satisfaction levels and product attributes. By transforming subjective perceptions into quantifiable factors, the F-KANO model serves as a crucial tool for evaluating product performance, identifying priority features, and guiding user-centric design enhancements. Consequently, this approach ensured that prototyping platform optimizations were data-driven and strategically aligned with designer expectations. The satisfaction coefficient S_i and dissatisfaction coefficient D_i for a given requirement i-th were derived using Equations (1) and (2), respectively.

$$S_i={\displaystyle \frac{A_i+O_i}{A_i+O_i+M_i+I_i}}$$

(1)

$$D_i=-1\cdot {\displaystyle \frac{M_i+O_i}{A_i+O_i+M_i+I_i}}$$

(2)

3.4. DEMATEL Method

The DEMATEL method is a multicriteria decision-making approach based on expert evaluation, designed to identify causal relationships among influencing factors and quantify their relative importance [28]. In this study, the DEMATEL method was employed to analyze and rank the weight of core designer requirements, ensuring that optimization strategies prioritize the most critical needs.

(1)

Direct Influence Matrix: A direct influence matrix was constructed based on the M, O, and A requirements filtered through the F-KANO model. Experts and senior designers were invited to evaluate the degree of influence between each pair of requirements, generating an n × n direct influence matrix A (Equation (3)). A five-point Likert scale was used to quantify the influence level, ranging from no influence (1) to very strong influence (5).

$$A=\left[\begin{array}{cccc}0& a_{12}& \cdots & a_{1n}\\ a_{21}& 0& \cdots & a_{2n}\\ ⋮& ⋮& 0& ⋮\\ a_{n1}& a_{n2}& \cdots & 0\end{array}\right]={\left(A_{ij}\right)}_{n\times n}$$

(3)

In the equations, the direct influence of factor i on factor j is denoted as A_ij, where i and j are integers satisfying 1 ≤ I ≤ n, 1 ≤ j ≤ n, and n represents the total number of influencing factors. Since self-influence is not considered, A_ij = 0 when i = j.

(2)

Normalization Matrix: To mitigate potential biases resulting from multiple expert evaluations or dimensional inconsistencies, the direct influence matrix A was normalized (Equation (4)) to obtain the normalized matrix X, ensuring that 0 ≤ x_ij ≤ 1.

$$X=A/\underset{1\le i\le n}{max}\sum _{j=1}^n A_{ij}={\left(x_{ij}\right)}_{n\times n}$$

(4)

(3)

Total Influence Matrix Calculation: The comprehensive relationship between direct and indirect influences among requirements was computed using Equation (5).

$$Z=X_1+X_2+\cdots +X_n=X{\left(I-X\right)}^{-1}={\left(Z_{ij}\right)}_{n\times n}$$

(5)

(4)

Causal Relationship Measurement: The influence degree (f), affected degree (e), centrality degree (m = f_i + e_i), and causal degree (n = f_i − e_i) were determined using Equations (6) and (7).

$$f_i=\sum _{j=1}^n Z_{ij}\left(1\le i\le n,1\le j\le n\right)$$

(6)

$$e_i=\sum _{j=1}^n Z_{ij}\left(1\le i\le n,1\le j\le n\right)$$

(7)

(5)

Influence Network Relationship Map Construction: The graph (Figure 3) was generated by categorizing factors into four quadrants based on the average values of horizontal vector centrality (f_i + e) and vertical vector causality (f_i − e_i). This visualization illustrates the hierarchical position of each requirement within the system and its overall impact. Key requirements with the highest system influence are identified, establishing their optimization priority.

(6)

User Requirement Weight Calculation: The importance weight (W_Ui) of each user requirement was determined based on Equation (8).

$$W_{U_i}={\displaystyle \frac{\left(f+e_i\right)}{\sum _{i=1}^{N^{\prime }} \left(f_i+e_i\right)}}$$

(8)

3.5. QFD Analysis

QFD is a systematic product development methodology, with its core tool being the House of Quality (Figure 4). QFD enables the quantitative transformation of user requirements into design requirements based on weighted priorities, ensuring that prototyping platform optimization aligns with target user needs. In this study, QFD analysis was employed to convert the prioritized core requirements, as determined through the DEMATEL method, into corresponding design requirements. Additionally, the prioritization of design requirements was established, providing a structured framework to guide the optimization of the prototyping platform.

(1)

Construction of a Matrix of User Requirements and Design Requirements:

The collected user requirements were first analyzed and translated into specific design requirements. A panel of interaction design experts was invited to discuss and establish the mapping relationships between user requirements and design requirements. The expert group categorized user needs based on their functional attributes and interaction characteristics, ensuring that each design requirement accurately reflected core user requirements. The core designer requirement weights (W_Ui) obtained from the DEMATEL method served as input for constructing the correlation mapping matrix. In this matrix, rows represented user requirements, while columns corresponded to design requirements. A panel of experts composed of senior designers, product managers, and software engineers assessed the strength of the association between each user requirement and design requirement. They assigned relevance scores to establish a decision-making framework for the relationship between user requirements and design requirements. Let the set of user requirements be $U=\{U_1,U_2,\dots ,U_n\}$, and the set of design requirements be $D=\{D_1,D_2,\dots ,D_m\}$. The user requirement–design requirement mapping matrix W_UD is formulated as follows:

$$W_{UD}=\left[\begin{array}{cccc}{w}_{11}& w_{12}& \dots & w_{1m}\\ w_{21}& w_{22}& \dots & w_{2m}\\ ⋮& ⋮& \ddots & ⋮\\ w_{n1}& w_{n2}& \dots & w_{nm}\end{array}\right]$$

(9)

where W_nm represents the relevance score between user requirement U_n and design requirement D_m.

(2)

Calculation of Design Requirement Weights:

$$S_T=\sum _{i=1}^n (W_{U_i}\times W_{UD})$$

(10)

where S_Tj represents the weighted score of design requirement T_j, W_Ui denotes the user requirement weights derived from the DEMATEL method, and W_UD represents the correlation between user requirement U_n and design requirement D_m.

After computing the weighted scores for all design requirements, they were ranked in descending order to prioritize the most critical design elements for implementation. This study successfully applied the QFD methodology to translate user requirements into design requirements, and the user requirement–design requirement mapping matrix was utilized to quantify the importance of each design requirement.

4. Case Study

4.1. User Research

This study identified the key pain points and requirements of designers in AD environments and constructed three user personas. Additionally, a semistructured interview framework was designed to support subsequent user requirement collection and analysis, requirement classification, and prototyping platform optimization. UI designers focus primarily on the usability of design tools and the automatic updating of design specifications, aiming to reduce the cost associated with repeated searches and modifications while ensuring quick access to relevant information when requirements change. UX designers emphasize tracking requirement changes and adjusting interaction logic, requiring a high degree of linkage between requirement documentation and design files to reduce cross-team communication costs and enhance design consistency. Product managers are concerned with the standardization of design workflows and project progress management, aiming to achieve traceability of design modifications through systematic tools and to quantify design team efficiency and output. Figure 5 presents a visual representation of the user information collected in this study.

4.2. User Requirement Analysis

In-depth interviews were conducted with 12 product designers experienced in AD to thoroughly explore their requirements and challenges in using product design systems (see

Table 1. Summary of interviewees’ information.

Category	Details
Age Distribution	26–35 years old, average 30.1 years
Gender Ratio	Male: 7 (58.3%), Female: 5 (41.7%)
Education Level	Master’s: 9 (75%), Bachelor’s: 3 (25%)
Field of Study	Design, Interaction Design, Computer Science, Software Engineering, etc.
Job Roles	UI Designer (4), UX Designer (4), Product Manager (3), Product Designer (1)
Work Experience	3–12 years, average 7.5 years

). Following the interview protocol, all conversations were recorded and transcribed immediately after the interviews. The transcripts were then reviewed and verified to ensure accuracy and completeness. To maintain data fidelity and credibility, follow-up validation with interviewees was conducted when necessary. A systematic grounded theory approach was applied, following the three-stage process of open coding, axial coding, and selective coding [31]. Next, open coding was performed on the raw interview transcripts. The interview data were examined line by line to identify key information related to the design platform, which was then conceptualized and assigned labels. Through this process, 41 initial concepts were extracted and categorized into 19 scopes. Analysis of the open coding results revealed logical relationships among the identified scopes. These 19 scopes were further clustered and abstracted, ultimately forming three core categories. The axial coding process and category definitions are detailed in

Table 2. Designer requirement architecture.

Main Scope	Scope	Scope Connotation	Initial Concept
Layout and Basic Visuals (C1)	Drag-and-Drop Layout (B1)	Arrange components quickly on the canvas through drag-and-drop, reducing the need for manual coordinate adjustments	Dragging Components (A1); Smart Snapping (A2); Auto Snap Lines (A3)
	Grid and Alignment (B2)	Provide grid and alignment rules to ensure consistent interface layout	Grid Display (A4); Alignment Guides (A5)
	Multicanvas and Responsive Layout (B3)	Support multiple canvas pages and allow quick switching between different screen sizes	Customizable Canvas Size (A6); Multidevice Canvas (A7)
	Quick Theme Configuration (B4)	Define global settings for colors, fonts, and brand identity	Theme Color Switching (A8); Brand Identity Set (A9)
	Reusable Component Library (B5)	Offer commonly used UI elements such as buttons, input fields, and navigation bars, enabling reuse and batch updates	Component Reusability (A10); Customizable Properties (A11)
	Automated Visual Standard Compliance (B6)	Perform basic validation for colors, contrast, and other visual parameters	Accessibility Detection (A12); Color Contrast Alerts (A13); Font Size Warnings (A14)
	Basic Graphic and Image Editing (B7)	Enable basic image editing within the platform, including cropping, transformation, and annotation	Crop/Scale (A15); Opacity Adjustment (A16)
Interaction and Prototype Iteration (C2)	Basic Interaction Triggers (B8)	Add interactive events such as click, hover, and drag actions to interface elements	Click-to-Jump (A17); Drag-and-Sort (A18)
	Animation and Transition Effects (B9)	Provide page transitions and animation effects, such as fade-ins and slide transitions, to enhance prototype immersion	Page Transitions (A19); Timing and Delay Control (A20)
	Quick Prototype Preview (B10)	Generate interactive preview links with a single click	Interactive Preview Links (A21); QR Code Mobile Viewing (A22)
	Version History and Rollback (B11)	Save snapshots of each design iteration	Snapshot Timeline (A23); Change Annotations (A24); One-Click Restore (A25)
	Integrated Usability Testing (B12)	Integrate or connect with basic usability testing features, such as click heatmaps and feedback collection	Click Tracking (A26); Embedded Survey Pop-ups (A27)
Collaboration and Management (C3)	Real-Time Multiuser Collaboration (B13)	Support real-time collaboration among multiple designers or team members, allowing visibility of each other’s cursor positions	Autosave in Real Time (A28); Online User Status Display (A29)
	Canvas Annotations and Comments (B14)	Enable teams to leave comments directly on interface elements	Pinpointed Annotations (A30); Threaded Discussions (A31)
	Role-Based Access Control (B15)	Differentiate user roles such as read-only, editable, and admin access, with options to restrict external access	Read-Only Links (A32); Operation Log Tracking (A33)
	Requirement Integration with External Tools (B16)	Integrate with external tools for requirement management and synchronization	Status Synchronization (A34); Change Notifications (A35)
	Task Management Kanban (B17)	Provide a simple task management board to help designers track iteration progress	To-Do/In-Progress/Completed Tasks (A36); Priority Tags (A37)
	Code and Annotation Export (B18)	Convert designs into frontend annotations (size, spacing, color values) or generate basic HTML/CSS code	Automated Asset Export (A38); Basic Code Generation (A39)
	Design Documentation and Handoff Files (B19)	Automatically generate design documentation based on styles and components used in the canvas	Operation Manual (A40); Online Sharing Link (A41)

.

Based on grounded theory, designer requirements were extracted from real-world AD environments, and a comprehensive conceptual framework of user needs was constructed. This framework provided systematic theoretical support for aligning designer requirements with the optimization of product design platforms. The results of the requirement analysis were then further refined to determine requirement priorities and drive platform optimization, as discussed in the next section.

4.3. Requirement Classification and Filtering

This section discusses the categorization and filtering of designer requirements in AD environments based on the user requirement conceptual framework, with the F-KANO questionnaire utilized to capture designers’ judgments on different requirement types. A total of 109 valid responses were obtained from UI designers, UX designers, and product managers. Following data collection, fuzzy mathematics was applied to transform subjective user evaluations into measurable indicators, ensuring an objective classification process.

To analyze the collected data, satisfaction and dissatisfaction matrices were formulated for each requirement. Specifically, a fulfilled requirement matrix P and an unfulfilled requirement matrix N were constructed based on user responses. Subsequently, an interaction matrix S was derived using S = P^T × N, which enabled the identification of requirement classification attributes. By correlating the dataset in

Table 3. F-KANO Evaluation.

Demands Satisfied or Not		Dissatisfied
		Like	Must-Be	Indifferent	Tolerable	Dislike
Satisfied	Like	Q	A	A	A	O
	Must-be	R	I	I	I	M
	Indifferent	R	I	I	I	M
	Tolerable	R	I	I	I	M
	Dislike	R	R	R	R	Q

Must-be requirements (M), one-dimensional requirements (O), attractive requirements (A), indifferent requirements (I), reverse requirements (R). (Q: contradictory user responses indicating inconsistent or conflicting perception of the requirement).

with matrix S, the membership vector T was computed for each requirement category, effectively determining their classification within the F-KANO framework.

Taking B₁ from

Table 4. F-KANO questionnaire (part).

Type	No.	Demand		Like	Must-Be	Indifferent	Tolerable	Dislike
Layout and Basic Visuals (C1)	B1	Drag-and-Drop Layout	Satisfied	0.3	0.6	0.1
			Dissatisfied				0.2	0.8
Interaction and Prototype Iteration (C2)	B8	Basic Interaction Triggers	Satisfied	0.8	0.2
			Dissatisfied			0.1	0.2	0.7
Collaboration and Management (C3)	B12	Integrated Usability Testing	Satisfied	0.1	0.3	0.6
			Dissatisfied			0.7	0.2	0.1

as an example, the unfulfilled requirement matrix N is represented as:

$$S=\left[\begin{array}{ccccc}0& 0& 0& 0.06& 0.24\\ 0& 0& 0& 0.12& 0.48\\ 0& 0& 0& 0.02& 0.08\\ 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0\end{array}\right]$$

By mapping the data from Table 3 to the interaction matrix S, the membership vector T₁ for each requirement can be obtained.

$$T_1=\left({\displaystyle \frac{0.56}{M^{\prime }}},{\displaystyle \frac{0.24}{O^{\prime }}},{\displaystyle \frac{0.06}{A^{\prime }}},{\displaystyle \frac{0.14}{I^{\prime }}},{\displaystyle \frac{0}{R^{\prime }}}\right)$$

Based on a confidence level of α = 0.4, the membership vector T₁ was used to classify requirements. If the α value in the membership vector T was greater than or equal to 0.4, the requirement was assigned t = 1 [27]. Otherwise, it was assigned t = 0. If the sequence contained at least two occurrences of 1, the requirement priority order followed (M, O, A, I, R). For example, given T₁ = (1, 0, 0, 0, 0), the corresponding requirement was classified as M in the F-KANO questionnaire.

By systematically applying this process to all collected questionnaires and conducting individual calculations, the occurrence frequency of each requirement category was determined. The category with the highest frequency was assigned as the final classification of the design requirement. Furthermore, the better–worse satisfaction coefficient was computed using Equations (1) and (2) to quantify the impact of each requirement on user satisfaction. A coefficient with an absolute value approaching 0 suggests minimal influence on user satisfaction, whereas values nearing 1 indicate a significant impact. The comprehensive classification outcomes are detailed in

Table 5. Statistical results on design requirements for the prototyping platform.

No.	Attribute Number					Attribute Classification	Better	Worse
	M	O	A	I	R
B1	72	16	13	5	3	M	0.28	−0.75
B2	78	11	9	7	4	M	0.31	−0.82
B3	27	17	47	16	2	A	0.64	−0.47
B4	22	15	51	17	4	A	0.59	−0.54
B5	29	45	20	14	1	O	0.52	−0.60
B6	20	17	58	13	3	A	0.66	−0.44
B7	20	16	17	48	8	I	0.45	−0.38
B8	78	14	11	6	0	M	0.24	−0.76
B9	24	11	63	8	3	A	0.64	−0.49
B10	17	13	54	20	5	A	0.57	−0.51
B11	67	16	12	10	4	M	0.29	−0.71
B12	19	21	51	16	2	A	0.61	−0.56
B13	20	54	19	15	1	O	0.55	−0.62
B14	24	44	20	15	5	O	0.59	−0.68
B15	19	12	16	57	5	I	0.42	−0.39
B16	19	55	18	13	4	O	0.54	−0.61
B17	19	18	14	56	2	I	0.41	−0.36
B18	17	12	16	60	4	I	0.45	−0.40
B19	22	21	13	52	1	I	0.43	−0.44

.

As presented in

Table 6. Classification of design requirements for the prototyping platform.

Designer Requirement Classification	Requirements
M (reserved)	Drag-and-Drop Layout (B1); Grid and Alignment (B2); Basic Interaction Triggers (B8); Version History and Rollback (B11)
O (reserved)	Reusable Component Library (B5); Real-Time Multiuser Collaboration (B13); Canvas Annotations and Comments (B14); Requirement Integration with External Tools (B16)
A (reserved)	Multicanvas and Responsive Layout (B3); Quick Theme Configuration (B4); Automated Visual Standard Compliance (B6); Animation and Transition Effects (B9); Quick Prototype Preview (B10); Integrated Usability Testing (B12)
I (excluded)	Basic Graphic and Image Editing (B7); Role-Based Access Control (B15); Task Management Kanban (B17); Code and Annotation Export (B18); Design Documentation and Handoff Files (B19)

, the analysis of prototyping platform requirements identified four must-be (M) requirements, four one-dimensional (O) requirements, six attractive (A) requirements, and five indifferent (I) requirements. According to the F-KANO model, I-type requirements have a negligible impact on user experience (UX) and are not considered essential for platform optimization. Consequently, this study focused on M, O, and A-type attributes, retaining only those that significantly influenced UX, while excluding I-type attributes. The retained attributes served as the primary evaluation criteria for optimizing the prototyping platform.

4.4. Weighting Calculations

To ensure the accuracy and objectivity of the evaluation results, ten experts were invited to participate in the assessment, including seven professors specializing in UX design and three UX designers. A 0 to 4 rating scale was applied to quantify the judgment matrix, where 0 signifies no influence, 1 indicates a weak influence, 2 represents a moderate influence, 3 corresponds to a significant influence, and 4 denotes a very significant influence. After multiple rounds of discussion and analysis, the expert panel reached a consensus, as presented in

Table 7. Direct impact matrix.

No.	B1	B2	B8	B11	B5	B13	B14	B16	B3	B4	B6	B9	B10	B12
B1	0	1.2	2.7	3	3.4	2.8	2.2	2.4	3.5	2.6	2.2	3.4	3.4	3.2
B2	1.2	0	2.4	2.8	3.2	2.7	3.1	2.2	3.2	2.7	2.2	2.8	3.2	3
B8	0.6	1.2	0	1.5	2.1	2	1.2	1.7	2.6	2.1	1.2	1.8	2.7	2.4
B11	0.4	1	1.3	0	3.2	2.2	2.7	3	3.2	2.3	2.8	2.6	3	2.7
B5	1.3	1	0.7	1.3	0	2.5	1.2	1.3	1.2	1	3.2	2.1	1	1.2
B13	2.3	1.3	1.1	0.7	2.7	0	2.3	2.2	3.2	2.7	3.2	2.7	2.4	1.3
B14	2.2	2	2.2	2.6	3	2.4	0	2	2.9	2.5	2	2.5	2.8	2.7
B16	2.5	2.4	2.2	3.2	1.2	2.3	1	0	3	2.6	2	2.4	2.5	2.4
B3	1	1.8	2	2.5	1	2	0.5	1.2	0	2.2	1.2	2	2.4	2
B4	1.2	2	2.2	2.3	1.6	2.3	1	1.2	2	0	3.3	2.3	2.3	2.2
B6	2.5	3.2	2.5	3.2	2.3	2.8	1.8	2	2.6	2.8	0	3	3.2	3.2
B9	2	2.2	1.8	2.4	1.7	2.2	1.3	1.7	2.3	2.4	3	0	2.8	2.6
B10	2.4	2.5	2.6	2	2.2	2.5	2	2.3	1	2.2	2.6	2.8	0	3.2
B12	2	1.8	2.2	1.8	1.2	2	1.5	2	1.2	2.5	2.4	2.5	1.5	0

.

Using the DEMATEL calculation formulas, the total influence matrix Z was derived, followed by the computation of the influence degree (f_i) and affected degree (e_i) for each factor. Subsequently, the centrality degree (m) and causality degree (n) for each requirement indicator were determined. Finally, the weight values (W_Ui) for each requirement in the system were calculated. The detailed results are presented in

Table 8. DEMATEL calculated indicator values.

No.	Influence Degree (fi)	Influenced Degree (ei)	Central Degree (mi = fi + ei)	Cause Degree (ni = fi − ei)	Weights
B1	4.531	2.863	7.394	1.668	0.072
B2	4.393	3.132	7.525	1.260	0.073
B8	2.947	3.364	6.311	−0.417	0.061
B11	3.862	3.762	7.624	0.100	0.074
B5	2.563	3.667	6.230	−1.104	0.060
B13	3.649	3.957	7.606	−0.308	0.074
B14	4.059	2.841	6.900	1.218	0.067
B16	3.859	3.279	7.138	0.579	0.069
B3	2.850	4.036	6.886	−1.187	0.067
B4	3.390	3.953	7.342	−0.563	0.071
B6	4.512	4.088	8.600	0.424	0.083
B9	3.713	4.227	7.940	−0.515	0.077
B10	3.948	4.237	8.185	−0.288	0.079
B12	3.253	4.121	7.374	−0.868	0.072

.

To visually represent the causal and influence relationships among the requirement indicators, an influence network relationship map was generated for the prototyping platform requirements, with centrality plotted on the horizontal axis and causality on the vertical axis, with a defined threshold value (Figure 6). The importance ranking of design factors for the prototyping platform was as follows: B₁ > B₂ > B₁₄ > B₁₆ > B₆ > B₁₁ > B₁₀ > B₁₃ > B₈ > B₉ > B₄ > B₁₂ > B₅ > B₃.

4.5. Analysis of the Importance of Design Requirements

Based on the user requirement data in Table 7, the corresponding design requirements were derived (see

Table 9. Mapping of user requirements to design requirements.

Criterion Layer	User Requirements	Design Requirements
Interaction	Drag-and-Drop Layout (B1)	Free Dragging and Alignment (DR1)
	Grid and Alignment (B2)	Mesh Adsorption (DR2)
	Basic Interaction Triggers (B8)	Interactive Trigger Configuration (DR3)
Design Management	Version History and Rollback (B11)	Versioning (DR4)
	Reusable Component Library (B5)	Component Library (DR5)
	Real-Time Multiuser Collaboration (B13); Canvas Annotations and Comments (B14)	Multiplayer Collaboration (DR6)
	Requirement Integration with External Tools (B16)	Requirement Synchronization (DR7)
	Multicanvas and Responsive Layout (B3)	Multicanvas (DR8)
Visualization and Feedback	Quick Theme Configuration (B4); Automated Visual Standard Compliance (B6)	Visual Specification (DR9)
	Animation and Transition Effects (B9)	Animation (DR10)
	Quick Prototype Preview (B10); Integrated Usability Testing (B12)	Preview and Testing (DR11)

). Subsequently, the design requirements, user requirements, and requirement weights were input into the House of Quality matrix, where their associations were analyzed. Following the evaluation criteria, the determined relationships were mapped onto the House of Quality, ensuring alignment between user needs and design specifications.

In the matrix shown in Figure 7, the degree of association between user requirements and design requirements is represented using a (9–3–1–0) scale, where 9 indicates a strong correlation; 3, a moderate correlation; 1, a weak correlation; and 0, no correlation. The final weights of the design requirements were computed by integrating user requirement weights into the matrix. The results indicated that DR₉ (Visual Specification, 2.254) had the highest weight, underscoring the critical role of automated validation and theme management in enhancing designers’ workflow efficiency. DR₃ (Interactive Trigger Configuration, 2.232) also exhibited a high weight, suggesting that designers strongly emphasize configurability in interactive behaviors and animation transitions. Additionally, DR₁₁ (Preview and Testing, 1.805) and DR₁₀ (Animation, 1.791) fell within the high-weight range, highlighting the importance of prototype visualization and interaction experience optimization for designers. Furthermore, DR₆ (Multiplayer Collaboration, 1.758) held a significant weight, indicating that real-time collaboration is indispensable for designers in AD environments. The platform must therefore facilitate robust real-time collaboration mechanisms, supporting multiuser editing, cursor visualization, and commenting/annotation functionalities, to enhance cross-functional team communication efficiency. Moreover, DR₄ (Versioning, 1.643) and DR₅ (Component Library, 1.537) warranted particular attention to ensure effective version management, component reuse, and synchronized updates, thereby maintaining design consistency and traceability.

Overall, the research findings reveal the complex mapping relationships between user requirements and design requirements, providing a clear representation of core designer needs in AD environments. In particular, the study highlights the critical importance of visual experience optimization, interactive animations, prototype preview, and multiuser collaboration as essential functionalities for enhancing the efficiency and effectiveness of design workflows.

4.6. Prototyping Platform Optimization

In this study, the core objective is to optimize the prototyping platform’s UX, ensuring that it meets AD requirements while precisely aligning user needs with design requirements. First, grounded theory was applied to analyze user needs, and the F-KANO model was used to classify these needs, identifying key design directions for optimizing the prototyping platform. Next, QFD analysis was used to construct a mapping model between user requirements and design requirements. By evaluating the strength of associations between user needs and design requirements and incorporating requirement weight results, the weight of each design requirement was calculated to determine optimization priorities. The results indicated that Visual Specification (DR₉) and Interactive Trigger Configuration (DR₃) were the most critical design requirements, exerting the greatest impact on UX optimization, as shown in Figure 8.

The optimized prototyping platform enhances user experience by systematically aligning designer needs with platform functionalities in AD environments. By refining interaction workflows, improving component and version management, and integrating data-driven testing mechanisms, the platform significantly enhances design efficiency and collaboration (Figure 9).

(a)

Canvas Page: Enhancing Interaction Efficiency

The canvas page introduced improvements in Free Dragging and Intelligent Alignment and Mesh Adsorption (DR₁ and DR₂), ensuring that elements snap accurately into place with minimal manual adjustment. The Interactive Trigger Configuration (DR₃) was optimized to provide a more intuitive method for linking components with animations and dynamic behaviors, reducing the complexity of interaction design. Additionally, Animation (DR₁₀) was integrated, allowing designers to quickly implement smooth transitions and state changes without repetitive configuration, streamlining the prototyping process.

(b)

System Center and Version Page: Strengthening Collaboration and Asset Management

The system center and version management module introduced key enhancements in Versioning (DR₄), Component Library (DR₅), and Requirement Synchronization (DR₇). The version control system supported snapshot archiving, version comparison, and rollback features, allowing designers to track design iterations with greater flexibility. The component library was upgraded to support cross-project reuse and automated updates, ensuring that assets remain consistent across different design tasks. Multiplayer Collaboration (DR₆) and Multicanvas (DR₈) further enhanced real-time teamwork, enabling designers to share cursors, track changes, and leave annotations in collaborative projects.

(c)

Testing Page: Data-Driven Evaluation and Optimization

The testing page focused on Preview and Testing (DR₁₁), equipping designers with tools for remote usability testing, click heatmaps, and task completion tracking. These features allow designers to analyze user interactions based on empirical data, making informed decisions about usability improvements.

In summary, based on user requirement weighting and design requirement mapping analysis, the proposed optimization strategy for the prototyping platform improved the alignment between user needs and design requirements while simultaneously enhancing efficiency in AD environments. Compared with existing commercial solutions, the proposed platform enables designers to rapidly respond to high-frequency iterations and dynamic requirement changes, addressing limitations in requirement tracking and information alignment present in current tools. Additionally, the proposed solution reinforces visual standardization management, reducing the cost of repetitive modifications caused by visual inconsistencies through standardized application processes. Furthermore, in terms of efficient version control and component reuse mechanisms, the optimized platform enhances change management, design traceability, and cross-project component synchronization, offering greater traceability and reusability compared with existing products, thereby improving design asset management efficiency. The optimized platform demonstrates significant improvements in interaction fluidity, collaboration efficiency, and visual consistency. Moreover, by integrating a data-driven testing feedback mechanism, designers can now execute prototyping tasks with greater precision and efficiency. Overall, this study introduces an optimized prototyping platform that is more efficient and better aligned with AD requirements, fostering innovation and advancement in modern design workflows.

5. Discussion

5.1. Design Feedback and Evaluation

To assess the feasibility and user satisfaction of the optimized prototyping platform in real-world applications, 20 designers were invited to evaluate its performance. The evaluation criteria covered six key task dimensions: dragging and alignment, interaction creation, preview and testing, team collaboration, version management, and component management. A five-point rating scale was used, where 1 indicated “very dissatisfied” and 5 indicated “very satisfied”. By conducting statistical analysis on the designers’ feedback data, the average score for each evaluation metric was calculated. The results were visualized using a radar chart to provide an intuitive representation of user satisfaction (Figure 10).

The evaluation results indicate that designers exhibited a high level of overall satisfaction with the optimized design, as all evaluation metrics received average scores above 4.0. This suggests that the optimized design requirements effectively addressed core user needs. Among the evaluation criteria, dragging and alignment (4.6) and previewing and testing (4.5) received the highest scores, highlighting that the precise component alignment mechanism and efficient prototype preview functionality were highly valued by designers. Team collaboration (4.4) and creating interaction (4.3) also received strong ratings, emphasizing the importance of multiuser collaboration mechanisms and enhanced interaction configuration tools in improving workflow efficiency. Additionally, component management (4.2) and version control (4.1) were widely recognized by designers, reflecting that the optimized component synchronization, archiving mechanisms, and version control system significantly enhanced the stability and consistency of the design process.

The user feedback validates the overall feasibility of the optimization strategy, providing data-driven support for future design iterations and platform deployment. The positive designer responses not only confirm the effectiveness of the proposed optimization approach but offer practical insights for further refinement of prototyping tools and UX improvements.

5.2. Comparative Analysis of Interface Design Methods

To ensure the reliability of our findings and address methodological differences, we conducted an additional round of expert evaluations and employed two alternative weighting methods (entropy weight method and CRITIC) to compare with our original DEMATEL-based analysis (see

Table 10. Comparison of weight methods.

Number	Method	Measuring Unit	Rank Results
1	DEMATEL	DEMATEL Centrality	B1 > B2 > B14 > B16 > B6 > B11 > B10 > B13 > B8 > B9 > B4 > B12 > B5 > B3
2	Entropy Weight Method	Entropy Value in EWM	B1 > B14 > B2 > B6 > B11> B10 > B16 > B13 > B8 > B9 > B5 > B4 > B3 > B12
3	CRITIC	Information Content	B1 > B2 > B3 > B14 > B6 > B16 > B11 > B13 > B5 > B9 > B10 > B8 > B4 > B4

and Figure 11). The entropy weight method assesses the diversity of user demands [32], while the CRITIC method identifies the most discriminative factors based on variance and correlation [33]. After integrating these newly obtained expert scores into the QFD model, we observed that the highest-priority design requirements remained largely consistent across all three weighting approaches (see

Table 11. Comparison of design requirement results.

Control Method	DR1	DR2	DR3	DR4	DR5	DR6	DR7	DR8	DR9	DR10	DR11
Entropy Weight Method	0.087	0.074	0.223	0.049	0.025	0.024	0.022	0.063	0.211	0.176	0.122
CRITIC	0.083	0.079	0.208	0.060	0.038	0.035	0.031	0.065	0.221	0.178	0.106

). The convergence of results across different computational methods underscores the robustness of our optimization framework. Even when weighting schemes were adjusted or new data were introduced, the key design priorities exhibited minimal variation. This stability reaffirms that the integration of grounded theory, F-KANO classification, and QFD provides a solid foundation for capturing and prioritizing user needs, regardless of the decision-making technique applied. By demonstrating that multiple methodologies converge on similar conclusions, our comparative analysis further strengthens confidence in the proposed prototype platform optimization, confirming that it is both data-driven and resilient to methodological variations.

5.3. Optimization Strategies for Prototyping Platforms

In AD environments, prototyping platforms play a crucial role in enhancing design efficiency and cross-team collaboration. However, existing research has focused primarily on isolated improvements, such as AI-assisted interaction design [34,35] and collaborative design [36,37], without systematically addressing the alignment between user needs and platform functionalities. While these approaches contribute to specific aspects of UX, they often lack an integrated framework for prioritizing and implementing optimizations. To address these challenges and more accurately meet designers’ core needs in AD environments, this study, based on grounded theory, the F-KANO model, the DEMATEL method, and QFD analysis, proposes the following four optimization strategies:

(I)

Integration of Interaction Experience and Design Efficiency

Current prototyping tools still present a high learning curve in interaction configuration and animation adjustments, making it difficult for designers to quickly implement complex interaction logic within short iteration cycles. Wang’s study highlighted that the complexity of interaction configuration in existing tools may create communication barriers, limiting designers’ ability to achieve intricate interactions efficiently in fast-paced environments [38]. To optimize interaction triggers and animation configuration, the platform should offer an intuitive interaction editing interface, simplifying interaction setup workflows while supporting visual logic connections and providing a preset library of interactive animations to reduce redundant operations. Additionally, enhancing real-time preview and rapid testing features would enable designers to immediately refine interaction effects, reducing cognitive load caused by frequent tool-switching or manual debugging, thereby improving workflow fluidity and overall design efficiency.

(II)

Optimization of Multiuser Collaboration and Requirement Management

AD environments require real-time design progress sharing and rapid adaptation to requirement changes. However, existing prototyping platforms still face challenges such as editing conflicts and unclear requirement change tracking during multiuser collaboration. In the industrial design and manufacturing domain, Wang proposed a multiuser collaborative AR system that effectively optimizes these issues by improving collaborative editing, real-time synchronization, and annotation management [39]. To address the asymmetry between multiuser collaboration and requirement synchronization, the platform should incorporate features such as real-time cursor display and traceable change logs, ensuring that team members can view and annotate modifications in real time, thereby reducing design discrepancies caused by miscommunication. Additionally, the platform should integrate a requirement management system, enabling direct mapping of product requirement changes to prototype designs, with automatic change notifications and version comparison features, ensuring that all design adjustments are aligned with the latest requirement updates.

(III)

Standardization and Automated Detection of Visual Guidelines

During cross-team collaboration and high-frequency iterations, the absence of visual standardization management often leads to inconsistent interface styles and unstable design quality [40]. To address insufficient visual standardization, the platform should provide automated visual guideline detection, allowing designers to identify deviations in color, spacing, and typography hierarchy in real time, with automated optimization recommendations. Additionally, the theme management functionality should be enhanced to support one-click application of brand colors, typography, and component styles, ensuring that all interfaces adhere to predefined visual rules. Furthermore, the platform should allow teams to establish global visual templates, enabling different projects to inherit a unified design language, thereby improving style consistency across projects.

(IV)

Optimization of Version Management and Component Library

Existing prototyping tools still have significant room for improvement in version management and component library maintenance, particularly in version rollback and component synchronization. The lack of functionality in these areas makes it challenging for designers to efficiently manage design assets during iterative development cycles. To address the limitations of design asset management, the platform should optimize version history archiving and snapshot rollback functionalities, supporting version comparison, change annotations, and one-click reversion. This ensures that designers can flexibly adjust design plans without the risk of losing critical versions due to accidental modifications. Additionally, component library management should be enhanced to support global component updates, cross-project reuse, and automatic synchronization, ensuring that teams can maintain design consistency across multiple projects. This would significantly increase design asset reusability, reduce redundant design work, and improve overall maintenance efficiency.

This study addresses the misalignment between user needs and design requirements by proposing four key optimization strategies: reducing interaction design complexity, enhancing multiuser collaboration and requirement synchronization, unifying visual guideline management, and improving version control and component reuse. These optimization strategies not only enhance the functional adaptability of prototyping platforms but significantly improve the UX for designers, enabling them to complete prototyping tasks more efficiently in AD environments. In the future, as design tools become more intelligent, further exploration is needed into AI-assisted design, automated interaction generation, and other emerging technologies, providing new directions for enhancing design efficiency and innovation in prototyping platforms.

6. Conclusions

This study systematically explores the optimization of prototyping platforms in AD environments, addressing the misalignment between user needs and design requirements. By integrating user requirement weight computation and structured prioritization, this study enhances the adaptability of prototyping platforms in four key areas: interaction experience optimization, multiuser collaboration, visual standardization, and version management and component reuse. These improvements enhance both designers’ operational efficiency and collaboration experience in AD workflows.

The primary contribution of this study is the user requirement weight-based optimization framework, ensuring a systematic alignment between designer needs and platform functionalities. Using grounded theory, F-KANO, DEMATEL, and QFD analysis, we developed a structured methodology to classify, prioritize, and map designer needs into platform optimizations. User evaluation results confirmed the effectiveness of this framework, demonstrating improvements in interaction fluidity, collaboration efficiency, and visual consistency.

Despite its contributions, this study has several limitations. First, the participant sample (N = 12) consistedprimarily of experienced designers working in agile environments, which may not fully represent the diversity of prototyping tool users across industries. Future research should incorporate a broader sample, including novice designers and interdisciplinary teams. Second, while the study optimized platform functionalities, it did not fully address the evolving nature of designer needs in highly dynamic AD environments. Future research could explore adaptive requirement management mechanisms that integrate real-time user feedback into platform iterations. Third, this study evaluated immediate usability and efficiency improvements but did not assess the long-term impact on real-world design workflows. Longitudinal studies and field deployments are needed to measure how well these optimizations sustain efficiency gains over time.

Future research could explore several directions beyond the direct extensions of this study. One promising avenue is AI-driven prototyping enhancements, where artificial intelligence could be leveraged to automate component suggestions and recognize intelligent design patterns. Such advancements would not only streamline the design process but improve the adaptability of prototyping platforms to evolving user needs. Another important direction is real-world validation through industry partnerships, where optimized platforms could be deployed in live projects to assess their practical impact. Conducting field studies in professional settings would provide valuable insights into the usability and long-term effectiveness of the proposed framework, ensuring its applicability beyond controlled experimental conditions.