Digital Museum Interaction Design Based on Artificial Intelligence and User Role Modeling

With the rapid development of digital technology, digital museums have emerged as an emerging platform for cultural heritage and education. Digital museum refers to a new type of museum form that uses modern information technology, especially virtual reality, 3D modeling, multimedia and network technology, etc., to present the functions and functions of the physical museum in the form of digitization on the Internet [1–4]. And it successfully breaks through the time and space limitations of traditional museums, enabling viewers to access and experience the museum’s exhibition content anytime and anywhere through online platforms [5–7]. Digital museums not only provide rich digital resources of cultural relics, but also enhance the audience’s viewing interest and learning effect through immersive and interactive displays, becoming an important platform for cultural inheritance, education popularization and scientific research [8–11].

The interaction design of traditional museums is often oriented to physical objects, and information is conveyed through the physical display and graphic introduction of exhibits [12]. However, there are certain limitations in this display method, in which the audience can only passively accept the information and cannot deeply understand the connotation and the story behind the exhibits [13–14]. In order to break through this limitation, character models can be utilized to effectively focus the interaction design. Designers focus their energy on the most valuable user behavioral patterns and treat the preset personas as real people, so that they can better think and experience from the user’s perspective, and let them express the user’s goals and needs in the way of specific characters [15–18]. Through artificial intelligence technology, the historical background, cultural connotation, production process and other related information of the exhibits are integrated and displayed, so that the audience can learn more background knowledge while viewing the exhibits, and enhance the interesting and educational nature of viewing [19–21].

This paper analyzes the development of digital museum interaction based on artificial intelligence, and researches the interaction between visitors and collections during the visit. Taking the young and middle-aged group as the target research object, it is divided into three categories of learning, scientific research, and leisure, and the user role model is constructed. Interviews were conducted with representative users from different categories, and the functional elements of digital museum interaction design were selected from six aspects: exhibits, exhibition information, exhibit communication, guided tours, user-related and others. Subsequently, a fuzzy Kano questionnaire was designed, and the questionnaire data were collected and statistically analyzed to classify and confirm the attributes of the functional elements, and to classify the selected functional elements into different tiers. Based on this, relying on the tangible cultural heritage resources of Sanxingdui, its digital museum interaction design is explored. Finally, usability testing experiments are conducted by means of offline practical experience, analyzing users’ task completion time and satisfaction scale results to explore the design effect of the constructed digital museum.

2

Artificial intelligence-based interaction in digital museums

Artificial intelligence era under the construction of museums and traditional museums have essential differences in the construction process will be integrated into more intelligent technology, the physical museum will be transferred to the virtual cyberspace, the formation of digital museums, but also the integration of technology and the Internet, the formation of the “Internet + Museum” development model, and the integration of big data technology to enhance the information analysis capabilities of the museum. The integration of big data technology allows the museum’s information analysis ability to be enhanced, but also through the implantation of VR technology to allow visitors to enter the virtual space and cultural relics close contact. In the process of the development of digital museum, the main thing is to start with intelligent interaction.

Intelligent interaction is mainly divided into cultural relics management process administrators and collections interaction, interaction between physical components of the museum, the process of visitors and collections interaction, the public and the museum interaction several aspects. Intelligent interaction includes QR code technology, VR technology, big data technology and many other technologies.

First, the administrator and the collection interaction. After the integration of QR code technology, the administrator of the collection can know the various requirements for the preservation of the collection by scanning the QR code, and then it can be transferred to the corresponding storage area with great precision. Moreover, in the process of collection management, the QR code can also be used to confirm the identity of the administrator, and if it is found that it is not the administrator, an alarm will be issued.

Second, the interaction between the physical components of the museum. The entire museum has intelligence between the components, some components can be disassembled and assembled according to the need to achieve the interaction between the components. In addition, the weak electricity system in the building has good intelligence, which can automatically adjust the functions of monitoring, fire protection, dust control, listening, scanning, etc. according to the demand, so as to realize the comprehensive security management.

Third, the interaction between visitors and collections in the process of visiting. In the traditional collection management, it is difficult for visitors to observe the collection comprehensively, the perspective is incomplete, untouchable, etc. All of them make the visit become defective, and it is difficult to make visitors’ curiosity and learning psychology to be satisfied. Through the integration of the corresponding technology, visitors can interact with the collection, visitors can scan the QR code attached to the collection to understand all the information, have a systematic understanding of the history of cultural relics, and realize the social education function of cultural relics.

Fourth, the public and the museum interaction. Museums need to promote themselves to the society, and with the help of 5G technology, the social public outside the museums can get the information pushed by the museums to understand the characteristics of the cultural relics as well as the arrangement of the museums’ recent exhibitions, so that more people can interact with the museums and expand the influence of the museums.

This study combines artificial intelligence technology to focus on the interaction between visitors and collections during the visit, and conducts research on the interaction design of digital museums.

3

Selection of needs of digital museum user groups

3.1

User orientation

This study of digital museum exhibitions mainly targets young and middle-aged people because they are the main consumers of smart devices and electronic products, as well as the main venue for museum visits. In addition, this group is more receptive to new things, and digital products such as online exhibitions are easily recognized and highly regarded. By targeting this group, the exhibition design can better achieve the museum’s exhibition goals and enhance the user experience.

3.2

User Role Model

The concept of “user role model” aims to construct virtual characters based on the data obtained from the preliminary user research to deeply analyze and understand user needs. The purpose of this study is to systematically analyze and differentiate user groups with different characteristics based on the relevant data information of museum exhibition users, and to construct a typical user role model, so as to provide a basis for contextualized cognition. The adoption of user role models can help researchers to deeply understand user needs, better serve users, and enhance the user experience of digital museum exhibits.

This paper constructs a user role model for digital museums, divides online exhibition users into two types of users: leisure and learning, and carries out user clustering from five aspects, including basic user information, exhibition viewing motivation, behavioral habits, demand characteristics and emotional experience, and divides users into the following three types.

3.2.1

Recreational users

Leisurely museum users are interested in the display content of the museum, pursuing a more interesting and interactive display experience, initiating the vivid presentation of exhibits to stimulate the desire to explore, and also expecting the displays in the museum to be more interactive, allowing them to be more actively engaged in the exhibition and to gain a more in-depth visiting and learning experience.

3.2.2

Learning users

Learning-oriented museum visitors pay more attention to the details of the exhibits and the comprehensiveness of the display content, and expect to be able to obtain more intuitive exhibit information and story background through multi-angle observation and immersive experience. These expectations can effectively enhance the efficiency and experience of visiting exhibitions.

3.2.3

Research users

Scientific research users are mostly experts in a certain field or researchers with high cultural level, and the number of such users is relatively small. These users have a strong sense of purpose, clearly define the scientific research information they want to find, obtain instant cutting-edge information, look for knowledge exchange platforms, and pay attention to the comprehensiveness of the museum’s materials.

The user role model in this study can provide a scientific and accurate user profile for the design and optimization of digital museum experience, and thus improve user satisfaction and experience.

3.3

Digital Museum Functional Elements

3.3.1

User interviews

After constructing the user role model, the selection of functional elements for digital museum interaction design is carried out through interviews of three types of user roles, laying the foundation for the subsequent final calculation using the fuzzy Kano model.

The first type of interview subjects are learning users, mainly using the museum to obtain extracurricular knowledge, and two students specialized in design are selected. The second type of interview objects choose scientific research-oriented users, mainly through the museum to obtain cutting-edge knowledge and exchange of lecture activities, thus selecting the museum’s scientific researchers 2 people. The third category of interview subjects are leisure users, mainly using holidays to visit the museum, thus 4 public users are selected, and a total of 8 people are selected for this interview.

3.3.2

Functional elements

The functional elements of digital museum interaction design are selected by combining the functional elements of digital museums studied by previous researchers and the interview results of different types of user roles. The functional elements of digital museum interaction design are shown in Table 1, covering six dimensions: exhibits, exhibition messages, exhibit communication, guided tours, user-related and other dimensions.

Table 1.

Functional elements of digital museum interaction design

Function	Functional elements	Symbol
Exhibits	Exhibits basic information	F1
	Spread information of the exhibits	F2
	Image zooming of the exhibits	F3
	3D model exhibition	F4
	Relevant audio video	F5
Exhibition message	Exhibition and upcoming exhibition	F6
	End of the exhibition and exhibition playback	F7
	The time and place of the exhibition	F8
	Special information	F9
Exhibit communication	Community, activities, BBS communication	F10
	Share and mail sharing	F11
	Praise, comment, collect	F12
	Photo download, picture printing	F13
Guide	3d scanning and AR browsing	F14
	Exhibition hall map guide	F15
	Panoramic view of the exhibition hall	F16
User correlation	Login and registration	F17
	National versions	F18
	Search	F19
	Booking consulting and booking channels	F20
Other	Venter product	F21
Other	Museum development information and so on	F22

4

Analysis of functional requirements for digital museums

4.1

Methods of analysis

4.1.1

Fuzzy KANO modeling

Fuzzy Kano model is derived from the introduction of fuzzy theory on the basis of Kano model, which follows the basic theory of Kano model, and still divides the factors affecting satisfaction into five types: mandatory needs (M), expected needs (O), charismatic needs (A), undifferentiated needs (I), and reverse needs (R).

The fuzzy Kano questionnaire differs from the 0 and 1 in the Kano model evaluation, but is split into multiple experience data based on the actual user experience in terms of satisfaction and dissatisfaction. Fuzzy Kano model analysis method is a more practical method of analyzing survey results invented on the results of traditional Kano structured questionnaires and analysis methods. The purpose is to categorize and clarify the relationship between the priorities of the user’s fuzzy needs through quantitative methods.

4.1.2

Process of analyzing questionnaire data

The acquired fuzzy Kano questionnaire is summarized, analyzed and counted to obtain a classification table of quality attributes for each demand element. The specific steps are as follows: 1)

Establish a fuzzy matrix to generate the interaction evaluation matrix of a respondent’s design element W. Take the following table as an example, respondent m for f_i, assuming that has this design element matrix X = [0.8 0.2 0 0 0] and does not have this design element matrix Y = [0 ~ 0 ~ 0.6 ~ 0.2 ~ 0.2], generates the interaction evaluation matrix as: 1 $W = X^{T} Y = [\begin{matrix} 0 & 0 & 0.48 & 0.16 & 0.16 \\ 0 & 0 & 0.12 & 0.04 & 0.04 \\ 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 \end{matrix}]$

2)

Combine the matrix results with the fuzzy Kano assessment form to calculate the affiliation vector for each quality element T. The calculation process is as follows:

The required demand element (M) in matrix W is: 2 $S_{M} = W_{25} + W_{35} + W_{45} = 0.04 + 0 + 0 = 0.04$

The desired demand element (O) in Matrix W is: 3 $S_{o} = W_{15} = 0.16$

The charisma demand element (A) in Matrix W is: 4 $S_{A} = W_{12} + W_{13} + W_{14} = 0 + 0.48 + 0.16 = 0.64$

The undifferentiated demand element (I) in Matrix W is: 5 $\begin{array}{l} S_{1} & = W_{22} + W_{23} + W_{24} + W_{32} + W_{33} + W_{34} + W_{42} + W_{43} + W_{44} \\ = 0 + 0.12 + 0.04 + 0 + 0 + 0 + 0 + 0 = 0.16 \end{array}$

The reverse demand element (R) in Matrix W is: 6 $\begin{array}{l} S_{R} & = W_{21} + W_{31} + W_{41} + W_{51} + W_{52} + W_{53 +} W_{54} \\ = 0 + 0 + 0 + 0 + 0 + 0 + 0 = 0 \end{array}$

The affiliation vector of f_i for respondent m is calculated as: 7 $Membership degree vector T_{f 1} = (\frac{S_{M}}{0.04}, \frac{S_{O}}{0.16}, \frac{S_{A}}{0.64}, \frac{S_{I}}{0.16}, \frac{S_{A}}{0})$

3)

Introduce a confidence level of α. If an element of T has a value of ≥α, that element is represented by a 1, otherwise it is represented by a 0 (if more than one 1 occurs, the values are taken in order). Thus the vector of demand attributes of respondent m for f1 is Tf1 = (0,0,1,0,0).

4)

This process is performed on all questionnaire data and all results are accumulated and sorted. The design element with a high number of occurrences is the demand category of that element, and if the cumulative number of occurrences is equal, then the demand categories are taken in order of importance, and the order is: M>O>A>I>R.

5)

Create a table of quality attribute classification results.

4.1.3

Design element satisfaction values

When multiple requirements belong to the same category, there will be requirement attribute prioritization, i.e., prioritizing or focusing on the design of the requirements, at this time, the Better-worse coefficient is introduced to conduct the analysis.The Better-worse coefficient indicates the degree to which a certain feature can increase the satisfaction or eliminate the impact of disliking. The formula is as follows: 8 $S I = \frac{A_{i} + O_{i}}{A_{i} + O_{i} + M_{i} + I_{i}}$ 9 $D S I = - \frac{O_{i} + M_{i}}{A_{i} + O_{i} + M_{i} + I_{i}}$

The SI value represents that if the product provides this feature or service, the satisfaction level increases and the value is positive. The closer the value is to 1, the faster the user satisfaction rises when this feature or service is provided, i.e., the higher the priority of this feature or service.

A DSI value means that if the product does not provide this feature or service, satisfaction decreases and the value is negative. The closer the value is to -1, the greater the impact on user dissatisfaction, i.e., the higher the priority of this feature or service.

The horizontal coordinate of the Better-Worse coefficient plot is the absolute value of Worse, and the vertical coordinate is the absolute value of Better, thus both horizontally and vertically, the larger the better, the higher the priority.

4.2

Questionnaire

According to the fuzzy Kano model, the functional items in the functional elements of the digital museum were scored, and a two-way questionnaire on the user needs of the digital museum of the continuous fuzzy Kano model was made, and the questionnaire asked each functional item in both directions, that is, when there is such a need, the user’s feelings and when there is no such demand, the user’s feelings, and the questionnaire answers are in the interval of [1,5], and each integer 1, 2, 3, 4, and 5 represents “dislike”, “acceptable”, “neutral”, “should be”, and “like” respectively”.

A total of 157 questionnaires were recovered through field and online surveys, among which 18 questionnaires were suspicious and 139 questionnaires were valid, and the recovery rate of valid questionnaires was 88.54%.

4.3

Results of data analysis

4.3.1

Classification of functional elements

According to the evaluation guidelines of the fuzzy Kano model, the results of processing the valid data are as follows to determine the Kano model categorization of the functional demand items of the digital museum. According to the steps of the fuzzy comprehensive evaluation method, the evaluation indexes are quantified to get the Kano category affiliation degree of the functional requirements of the digital museum, and it is found that when it is 0.4, it can ensure that the information is not distorted and avoid the information cross.

The results of analyzing the functional elements of digital museums are shown in Table 2.Among the 22 functional elements of digital museum interaction design, there are 10 elements of undifferentiated attributes, 6 elements of charismatic attributes, 3 elements of expected attributes, and 3 elements of reversed attributes. Factors that do not have any effect on the functional requirements of digital museum interaction design can be excluded.R is the reverse requirement, i.e., the requirement that will not have any enhancement on user satisfaction, and even cause dissatisfaction.360-degree panoramic browsing F16, the versions of each country in the exhibition hall F18, and the cultural and creative products F21 belong to the reverse attribute requirements. No-difference factor I is the demand that does not enhance user satisfaction, but it may be a necessary demand, just that the user does not pay attention to it, and it needs to be analyzed in specific scenarios.

Table 2.

Analysis results of functional elements of digital museum

Symbol	Membership vector					Categories
Symbol	A	O	M	I	R	Categories
F1	19	35	29	49	7	I
F2	55	48	16	14	6	A
F3	16	22	39	57	5	I
F4	34	51	28	21	5	O
F5	57	33	31	14	4	A
F6	36	52	29	15	7	O
F7	29	32	20	53	5	I
F8	14	30	27	48	20	I
F9	10	26	41	51	11	I
F10	54	32	25	20	8	A
F11	18	29	36	49	7	I
F12	17	59	43	11	9	O
F13	16	36	34	45	8	I
F14	50	39	28	16	6	A
F15	13	23	46	50	7	I
F16	3	29	28	32	47	R
F17	45	32	27	24	11	A
F18	27	18	22	20	52	R
F19	25	32	20	51	11	I
F20	55	34	28	13	9	A
F21	11	30	31	18	49	R
F22	20	23	38	51	7	I

4.3.2

Determination of functional attributes

As A and O are charm attribute and expectation attribute, charm attribute is the attribute that exceeds the user’s will and makes the user feel satisfied while using the product and expectation attribute is the satisfaction that the user will feel when his/her expectation is fulfilled. And Better-Worse coefficient can calculate the degree to which a certain function can increase user satisfaction, calculate the percentage of each attribute, and filter the functional requirements.

The satisfaction coefficient of the functional requirements of the digital museum is shown in Figure 1. The Better coefficient of Exhibit Extended Information F2 is the largest at 0.774, followed by Reservation Consultation, Ticket Purchase Channel F20 and 3D Scanning Browsing, AR Browsing F14, which are 0.685 and 0.669, respectively.In the result of Worse coefficient, the absolute value of Liking, Commenting, Collecting F12 is the largest at 0.785, followed by Being Exhibit, Upcoming Exhibit F6 and 3D Model Exhibit F4, with Worse coefficient absolute values of 0.614 and 0.590.

Through the results of the calculation can be obtained to meet the user’s functional requirements of the elements can be as early as possible on the line, and eliminate the impact on user satisfaction is not high can be delayed on the line, in practice, but also through the relevant interests of the staff to conduct research on demand, to meet most of the needs of the priority on the line. According to the preliminary research, it can be obtained that the functions that can be launched on the line should best include the charismatic demand and the expected demand, and then according to the actual situation to make trade-offs.

The prioritization of digital museum functional elements is shown in Table 3, which identifies four levels of digital museum interaction design functional elements, with the first to the fourth levels including 8, 3, 8 and 3 elements respectively. Each functional requirement item of digital museum interaction design uses fuzzy Kano model to calculate the on-line priority, but specific analysis of specific problems, each case has its own different characteristics, when a function is on-line, it is necessary to make a comprehensive judgment, and the basic principle is that important and urgent requirements are prioritized on-line, and in the on-line process, it can be interspersed with unimportant and urgent requirements. For example, although “Exhibit Basic Information F1” and “Exhibit Time and Location F8” in the exhibit function and exhibition information are non-differentiated needs in the questionnaire survey, these two functional factors are the core functions of the product, and if they are delayed, they will affect the other displays of the exhibits. If they are delayed, they will affect the other displays of the exhibits and seriously neglect the core content of the museum exhibition, so even if they are non-differential factors, they should be in the first level.

Table 3.

Recommendations on the priority ranking of the functional elements of the digital

Functional requirements hierarchy	Symbol	Number
First level	F1, F2, F4, F6, F8, F12, F14, F20	8
Second level	F5, F10, F17	3
Third stage	F3, F7, F9, F11, F13, F15, F19, F22	8
Fourth stage	F16, F18, F21	3

5

Digital museum interaction design and evaluation

Based on the user role model and the functional elements of digital museum interaction design produced in the previous section, this chapter explores the interactive display form of augmented reality in the digital museum based on the tangible cultural heritage resources of Sanxingdui, and conducts usability testing to evaluate the design effect.

5.1

Information architecture and interaction prototypes

5.1.1

Information architecture design

This section focuses on the design of digital museum information architecture for Samsung Mound augmented reality application, and the design of digital museum information architecture is shown in Figure 2. From the user’s interaction stage, the experience of mobile augmented reality can be divided into three processes: before visit, during visit and after visit. Before the visit, it provides users with online ticketing, Sanxingdui Grand Exhibition, digital cultural relics library and other functions, and users can learn about the Sanxingdui in Guanghan in advance. During the visit, it provides users with AR guide of venues, AR guide of scenic spots, AR cultural relics model, treasure hunting card, voice explanation and other forms of augmented reality display and experience, providing video, voice, pictures and other forms of information supplement for the actual visit process. At the end of the visit users can share what they see and feel to the community and establish community connections, as well as view the collected exhibits and acquired treasure cards in the personal center.

5.1.2

Interactive page design

The interactive interface is designed on the basis of low-fidelity prototype drawings, adding visual specification levels, including textual content, color schemes, graphic symbols, labels, tips and other interface information elements. Users will perceive and prejudge the next system behavior through these interface elements, and good feedback can make users feel happy. In the design principles of information products, all interface design is based on user value, and the interface function design refers to the functional requirements elements of digital museum interaction design analyzed in the previous section.

5.2

Augmented Reality Module Design

5.2.1

Interactive Artifact Model

The three-dimensional digital model is chosen to be representative of the bronze Daliens, gold masks and bronze human heads. The first step is the acquisition of 3D digital model, first of all in the 3D max 3D modeling software for digital model construction, construction is completed after the material and texture overlay and rendering of the final 3D model, the model will be exported to the FBX type file, export 3D max need to be in the FBX export panel to check “Embed Media When exporting to 3D max, you need to check the “Embed Media” option in the FBX export panel, so as to avoid the loss of textures and maps. Make the model recognition image in Vuforia, the recognition image requires high accuracy, and graphics with low accuracy cannot be recognized successfully. Import the recognized image and the model file into Unity Hub together. When importing, you need to modify the parameters of the imported model, adjust the size and parameters of the model, overlay the voice and video resources, and build the augmented reality scene. During the process, you need to debug several times until it is successful.

5.2.2

AR Guided Tour

1)

AR Geotagging

Geotagging superimposes virtual tags onto the real world through AR technology and reflects target location and name information through the size and folding of the tag. It includes both internal and external environments. In the external environment, users can see all the geographic information around them. Even if there is occlusion between buildings, the occluded targets can be presented on the screen for the user to view. In the indoor environment, users can see the internal structure of the building, such as the parking lot, ticket office, restrooms, and escape routes. After the user clicks on the label, the target information will be displayed and the best exhibition route for the user will be shown.

2)

AR Walking Navigation

AR navigation is developed on the basis of AR geotagging. When the user uses walking navigation, the image and current position in the phone will change as the user’s position moves. When the user reaches the target location, the system will also obtain the user’s real-time location information through the server and present it, so that the user can understand where he/she is currently. The system can quickly find the destination in some complex environments and quickly find the best walking route to the destination. Walking navigation obtains the current user’s longitude and latitude information through a localization function and then determines the walking route through a map. The model and data are transferred to the server. After the route is built this path will be placed on a smart AR device through Unity Hub for virtual reality interaction.

5.3

Evaluation of design effectiveness

Usability testing is a testing method in which the designer evaluates the usability of a product by letting typical users complete the experience of using the design prototype, and by observing, recording and analyzing the user behavior and related data. Usability testing mainly includes content testing of the product’s usefulness, interaction flow and user satisfaction. During the testing process, the smoothness of the user’s operation flow and problems in operation are observed, as well as the user’s subjective satisfaction level. In the following section, usability testing is conducted on the designed digital museum form.

5.3.1

Testing process

The usability test was conducted in the form of offline experience, where the pre-designed high-fidelity interface was made into an interactive prototype, 10 users, including 5 men and 5 women, were selected to ensure the reasonableness of the data, and the users were allowed to simulate the real online use of the product on their cell phones for the test.

Users were given five main functional tasks (Task 1 ~ Task 5), and at the end of the tasks, with the help of a standardized user satisfaction scale designed by USE Interactive Interfaces, the testers were asked to find out their satisfaction with the product, and to find out whether the information architecture was reasonable, whether the interface information was clear and easy to use, and whether the product functions were reasonable and useful. The standardized user satisfaction scale includes four dimensions: usefulness, ease of use, ease of learning, and satisfaction, with four, three, two, and four questions designed respectively, for a total of 13 questions.

In the testing process, firstly, the design scheme of the digital museum is introduced to the users, and then the contents to be filled in the questionnaire of USE User Satisfaction Standardized Scale are introduced, and after the questionnaire is filled in the data of the questionnaire is roughly browsed, and questions are asked to the users, and the problems in the process of the interface experience are asked and recorded.

5.3.2

Test results

The results of users’ task completion are shown in Figure 3, which shows that users are basically able to complete the tasks of the main function. Among them, Task 3 and Task 4 spend a long time, with an average time of 59.6s and 62.4s. Task 1 and Task 2 spend a relatively short time as a whole, with an average time of 50s or less.

The standardized results of user satisfaction are shown in Figure 4. The average scores of the questions generally ranged from 6 to 8, and the test users considered that the digital museum generally satisfied the needs of the interactive experience. From the analysis of the results, the average scores of usefulness, ease of use, ease of learning and satisfaction of the interaction design of the digital museum were 7.55, 7.33, 7.30 and 7.00 respectively.

The following conclusions were summarized through the main task tests and the USE User Satisfaction Criteria Quantitative Scale: 1)

In the design of the digital museum information architecture, the functional design is basically recognized, but there is still some room for improvement for the operation steps and the learning cost of the users, and the layers of the map need to be raised.

2)

After communicating with users about the experience of using each function, most users are generally satisfied with the design of the main functions of the digital museum. In terms of the design of the interface, users think that its visual style is in line with the aesthetics, and the icons and guiding gestures better help users to operate. In terms of the use of the functions, it improves users’ motivation to participate and their knowledge of cultural relics. Therefore, for the current design experience, the overall functional design is relatively reasonable, and the operation steps and jump logic of some functions need to be further optimized.

6

Conclusion

In the era of artificial intelligence, museum construction needs to break the limitations of traditional museum construction. In this paper, based on the establishment of user role model, the functional elements of digital museum interaction design are selected, and the functional elements are analyzed in combination with the fuzzy KANO model. It further carries out the digital museum interaction design and evaluates its design effect. Using the fuzzy KANO model, the study divides the functional elements into 10 elements with or without difference attributes, 6 elements with charm attributes, 3 elements with expectation attributes and 3 elements with reverse attributes, and summarizes the four hierarchical elements of digital museum interaction design. Combining the user role model and the hierarchical categorization of functional elements, the interaction design experiment of the digital museum is carried out with the Samsungdui culture as an example. Through testing and evaluation, the time spent by users on setting tasks is mostly within 60s, and they are able to complete the main interactive tasks, and the scores of usefulness, ease of use, ease of learning, and satisfaction with the interaction design of the digital museum range from 7 to 7.55, which indicates that users are more satisfied with the interaction effect of the designed digital museum as a whole, and it is able to satisfy most of the leisure-type, learning-type, and scientific research-type users’ needs.

Through quantitative research, this paper summarizes the experience needs of users when visiting the display of exhibits and constructs a user role model, which provides a basis and guidance for the subsequent design. Based on artificial intelligence technology, the interaction design architecture of Sanxingdui Digital Museum is proposed, and the design effect evaluation is realized, which provides a reference case for the digital transformation of the museum.

Sprache:: Englisch

Zeitrahmen der Veröffentlichung:: 1 Hefte pro Jahr
Fachgebiete der Zeitschrift:: Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere

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Digital Museum Interaction Design Based on Artificial Intelligence and User Role Modeling

Lulu Qu

Zhigang Cheng

Online veröffentlicht: 19. März 2025

Eingereicht: 04. Okt. 2024

Akzeptiert: 30. Jan. 2025

DOI: https://doi.org/10.2478/amns-2025-0515

SchlüsselwörterArtificial intelligence, User role model, Fuzzy KANO model, Design effect evaluation, Digital museums

© 2025 Lulu Qu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Artificial intelligence, User role model, Fuzzy KANO model, Design effect evaluation, Digital museums