1. Introduction
Currently, AI is integrated into various fields, such as finance, national security, healthcare, transportation, and smart environmental technologies. The design industry is no exception, and AI is increasingly being used in the development of design products and services [1]. Its application has also caused widespread industry change, with a study on the likelihood of AI replacing jobs concluding that 47% of jobs are at risk of being replaced by AI in the foreseeable future [2]. Among other things, there is a growing discussion about whether artificial intelligence (AI) and robots will replace designers and architects. One view is that architects and designers will be replaced by AI because machine learning allows applications to synthesize a large number of projects in a short period of time, and users will not only be able to customize their intentions, requirements, and budgets within the application and conceive a customized design that is perfectly adapted to their own requirements in a short period of time but can also approximate time and money. There are also studies that point to the role of architects and designers evolving from being designers of the work to being catalysts for the development of AI as it is implemented [3,4].
Another perspective is that AI cannot replace architecture and designers. This is because the tasks of designers are very diverse and involve a wide range of intelligences, such as creativity and social activities, which computers currently do not possess, and, therefore, they do not have the potential to replace the work of designers. Computer technology is not being developed to replace designers but to support, facilitate, and speed up their work. Therefore, it is difficult to expect that technology will emerge in the near future, replacing the work of designers [5,6]. In addition, architects and designers need to communicate the design intent to their clients and assess their emotions and reactions to the design. This is an interpersonal interaction process that may be expressed by different people in diverse ways, making it difficult for AI systems to accurately recognize socialized emotional expressions [7].
While there is some cognitive bias in academia regarding the impact of AI on architects and designers, it is clear that AI has profoundly impacted the practice of design, that designers using AI are able to design more quickly, that the development of AI provides an experience for everyone to design and improve, and that the artificial does not disrupt the principles of design thinking, realizing the ultimate human-centered form [8,9,10]. Based on the nature of AI technology, it can be said that AI will become an important design tool and AI literacy will become an integral part of digital literacy for designers [11].
Currently, education is one of the most important areas for using and adapting AI technologies. The use of AI devices and programs makes it possible to access, collect, and analyze student performance data in real time, create learning profiles, and automatically provide customized content, feedback, and learning parameters. It is also possible to provide more targeted and relevant learning experiences that support students’ progress through learning materials [12]. On the other hand, communicative AI interacts with students not only to promote their cognitive development but also to act as empathic peers or mentors, thereby increasing interest in learning, self-regulation, and the ability to empathize and collaborate [13]. In summary, AI is increasingly penetrating the educational ecosystem through interactions and collaboration with students, establishing and maintaining social relationships, and providing personalized instruction. This suggests that the field of education has integrated non-human subjects such as collaborative objects, teachers, and assistants to provide learning services [14,15].
Although the interest in and demand for students to use AI in their studies is increasing, researchers have suggested that AI courses should be incorporated into higher education [16,17]. However, integrating AI into classroom activities and establishing AI-related curricula remains complex and challenging. This problem is also prominent in design. Currently, some students have started to spontaneously learn and use AI software and apply it to their design work, which makes people wonder about the current perception of AI and the basic status of using AI among college students in Chinese design colleges. To clarify this situation, the purpose of this study is to conduct a research study with a sample of design-college students to identify the basic situation of Chinese design-college students’ use of AI software; the basic situation and status of their use of AI soft-ware to participate in design work; the differences in their use in terms of gender, majors, and grades; their current relationship with AI and the main factors that constrain them from using it better; and whether the development of AI has made them anxious about their majors.
2. Literature Review
2.1. Artificial Intelligence and Higher Education
The concept of artificial intelligence (AI) began in the 1950s with the main goal of using machines to mimic human intelligence [18]. Currently, AI is defined as machines that have intelligence similar to that of humans or that, when coded electronically, can perform actions that require some kind of intelligence [19]. It is also understood as the innovation and development of technology that leads to computers, machines, and other artifacts mimicking human-like intelligence [20].
The current development of AI and industrial and service robots is an important global trend, and the use of AI in the field of education, which is heavily influenced by the development of information and communication technologies, has increased rapidly in the last few years [21,22]. The use of AI tools in education has become a catalyst for reshaping the learning experience, fostering innovation, and preparing individuals for the digital age [23]. AI can be used to adapt educational instruction to the needs of different types of learners, provide customized instant feedback, develop assessments, and predict academic success [24,25,26,27]. It can be said that AI has brought tremendous power of change to the education sector.
In a study on AI in Education (AIEd), it was also found that the higher-education sector published 28% of AI-related studies between 2016 and 2022, much higher than the 20% share of computer science. When examining AIEd’s target population, 72% of the studies focused on college students [28]. It can be said that current higher-education schools and university students are the main field and targets of research on AI in education. Nonetheless, there are still difficulties in developing AI in higher-education programs [29], such as interaction with AI, which may raise issues related to emotional privacy, emotional induction, and altering the moral development of staff [30]. The European Commission (2022) also reflects on some of the challenges posed by the use of AI-based systems in education, such as the fact that their impact on cognition is still unknown and that these technologies seem to limit not only student agencies but also teacher agencies [31]. Moreover, the use of AI to understand students through learning analytics raises issues of privacy concerns, security, trust, and fairness, as the data collected are not only relevant to the learners but also to their colleagues and even family members. Another aspect to consider is that private organizations, developers, government agencies, research centers, universities, and schools may have different ethical standards for AI [32,33].
2.2. The Cognitive Status of College Students Regarding Artificial Intelligence
Although there are still many difficulties in fully integrating AI into higher education, the enthusiasm of university students and researchers for the use of AI and research is outstanding. According to Kairu Caroline’s (2020) study of 385 university students, 39.06% believed that AI would have a positive impact on education, and 49.48% agreed that AI would affect learning [34]. Almaraz-López et al. (2023) made students aware of the impact of AI by having a portion of their classmates undergo a learning experience on AI, and students were willing to continue their education on AI even in the face of knowledge and other constraints [35]. Wang, Chi-Jane, et al. verified that taking a visual AI course lowers the learning threshold for students, making it possible for them to take more difficult AI courses. In turn, visual AI can be effective in helping students gain knowledge of AI, which is crucial for developing talent in the field of AI, by implementing a 3-hour AI course [36]. In addition, studies have shown that students’ exposure to AI and its application in their subject areas can enhance their creativity and problem-solving skills and make them more applicable to the labor market [37,38]. At the application level, AI showed more positive effects in the conclusions of related studies.
However, college students showed more complex attitudes toward their perceptions of AI. According to Jeffrey and Thomas (2020), college students generally have positive perceptions of AI but still have concerns about the rapid advancement of AI and how it will affect humanity. Moreover, this paradox exacerbates the social tensions associated with the inevitable advancement of technology and uncertainty regarding the effects of AI [39]. Mikhail V. Vinichenko et al. (2021), through a survey study of 1857 Generation Z college students, found that while digitization and the adoption of AI have increased, college students generally believe that gradual adaptation to convenient AI services risks decreases their motivation to develop and simplify their thinking and that there is a risk of lowering human intelligence. They also saw digitization and AI as posing a danger to human dependence on the digital environment [40]. Svitlana Hanaba et al.’s (2020) study based on the results of teaching through the use of AI during the global pandemic argues that, despite the robots’ computational power, simplistic interface, and ability to store information, they are unable to inspire. They lack empathy and cannot fully facilitate the realization of social skills [41]. Other studies have argued that, while enjoying the convenience and customized services offered by AI, college students also face various information-security threats, as well as the impact of AI. College students face the risk of weakening their subject position and breaking their consensus on value in the process of growth [42,43]. Some studies have pointed out that most students believe that AI will bring the risk of employment to themselves [44]. The study also found that college students’ perceptions of AI can differ significantly depending on their major [45,46].
2.3. The Impact of Artificial Intelligence on the Design Profession
Artificial intelligence is impacting design in several ways. For example, AI can reduce and accelerate the time required for design research and analysis by processing data in a short period through integrated data sources, allowing designers to devote more energy to design-conceptualization activities [47]. Karaata Ezgi (2018) expressed the same view in his study that AI can reduce the time-consuming workload and leave more time for designers to create. In this case, designers attempt to be more creative. He believes that AI graphic-design programs running in the current era lack creativity. As a result, the graphic-design profession does not seem to be under threat; rather, designers have more time than ever to be creative [48]. By testing the potential of AI tools to support creativity and motivation in the product-design process among design students, Elal İrem et al. (2024) clarified that AI can support the creative-thinking process in terms of form and aesthetics by creating contours of design concepts and supporting the creative-thinking process in terms of form and aesthetics, and it can also contribute positively to the time management of students. However, it is understood that there is a need to explain to students how they should use these tools, and the study argues that concerns about the development of AI should be addressed by raising students’ awareness of the future of AI. Notably, AI can negatively impact unconscious students or people who are used in off-the-shelf design processes and their creativity [49].
Additionally, AI-assisted human design improves team coordination and communication, leading to better teamwork and increased team adaptability, which in turn puts more of the team’s energy into information processing and the broader exploration of solution activities. In short, working with AI enables human team members to achieve more thinking and reduces labor intensity [50]. It has also been found that AI can organize tools and strategies for cyclic design in product design, thus enhancing the cyclicity of the product. In addition, the rapid prototyping and testing provided by AI can reduce waste during the design process. AI transmits accurate data and information regarding the availability, condition, and accessibility of materials and products, making product monitoring easier and enabling remote maintenance, reuse, remanufacturing, and repair [51].
3. Methods
First, this study’s investigation of the relevant literature on AI clarifies that there is some variability in the perceptions of AI among college students of various majors and clarifies the positive effects and the potential risks of AI-assisted design.
Second, the purpose of the study, division of the research stages, and content of the questionnaire were discussed specifically through an expert panel comprising four university professors and four current doctoral students. According to the purpose of the study, the content of the questionnaire was divided into the following three parts:
To investigate the basic status of design-college students’ use of AI and their basic knowledge of AI technology, a more comprehensive and consistent framework is needed to examine users’ acceptance and usage behavior of the technology, and the model should help to better understand the differences in AI use among samples with different backgrounds and characteristics. Therefore, the UTAUT model was selected. This study combines the UTAUT model to investigate Q1—gender; Q2—major; Q3—grade level; and Q4—whether they have ever used an AI program. The abovementioned four questions were investigated in the form of a single choice, and the frequency analysis was mainly used to clarify the results of the questionnaire. Students who chose “have not used” in Q4 skipped ahead to Q5 and Q6, while those who chose “have used” in Q4 skipped ahead to Q7 and Q8. The options in Q6 and Q7 are based on key components of the UTAUT model, including Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FCs), as described below.
Q5: Do you think it is necessary to learn about AI programs?
Q6: Which of the following factors are more likely to influence you to use an AI program?
Q6-1: Consider personal skills and productivity gains. (PE)
Q6-2: AI operates more simply and is easier to understand. (EE)
Q6-3: Influence of friends and classmates around you. (SI)
Q6-4: Schools provide appropriate curricula and equipment conditions. (FC)
Q7: Which of the following factors influenced you to go for an AI program?
Q7-1: Consider personal skills and productivity gains. (PE)
Q7-2: AI operates more simply and is easier to understand. (EE)
Q7-3: Influence of friends and classmates around you. (SI)
Q7-4: Schools provide appropriate curricula and equipment conditions. (FC)
Q8: In what situations do you typically use AI programs?
Q8-1: In the learning scenario.
Q8-2: In life scenarios.
All question items in Q5, Q6, Q7, and Q8 were used to collect data on a 5-point Likert scale, and the results of the questionnaire were described using a categorical summary method. Data from Q6 and Q7 were also integrated, and an independent-sample t-test was conducted to clarify the differences in the perceptions of students who had used the AI program and those who did not consider the elements of the UTAUT model. Among them, the independent-sample t-test is an analytical method used to compare whether there is a significant difference between the means of two independent groups.
The second part of the study focuses on students who have used AI programs in using AI to participate in coursework and design work, with the aim of identifying the types of AI programs used by Chinese design students to complete their coursework and design work, the use of AI programs at various stages of the design process, and exactly how they are used and whether they are reliant on them. In this study, the process of design is divided into the design-research stage, the initial design-thinking stage, the design-deepening stage, the presentation and expression of the design-solution stage, and the design-reflection stage, and the issues involved are shown below.
Q9: What types of AI programs do you typically use to complete coursework and design work?
Q9-1: Text-based AI (ChatGPT and Ernie Bot).
Q9-2: Image-based AI (Midjourney and Sora).
Q9-3: Plug-in class AI in design tool software (ArkoAI and PS v25 + StableDiffusion v1)
Q10: How do you typically use AI programs to complete coursework and design work?
Q10-1: Use AI to research relevant materials, knowledge, and background (design-research stage).
Q10-2: Use AI to provide ideas, for example, brainstorming and design proposals (initial design-thinking stage).
Q10-3: Use AI to optimize design solutions and enhance details (design-deepening stage).
Q10-4: AI is used to present and express design solutions (presentation and expression of the design-solution stage).
Q10-5: Use AI to validate and reflect on the designed solution (design-reflection stage).
Q11: Which of the following are more in line with your situation of using AI programs when dealing with coursework and design?
Q11-1: The desired result can be obtained through simple Q&A and debugging.
Q11-2: The desired result can be obtained through continuous Q&A and debugging.
Q12: Which processing method do you prefer for the results provided by an AI program? When dealing with coursework and design.
Q12-1: Usually, they are directly adopted.
Q12-2: Usually adapt and think about the results before adopting them.
Q12-3: Usually, these are not adopted.
Q13: Have you ever questioned or reflected on the results provided by an AI program?
Q14: Do you think you have become dependent on the AI program?
Q9, Q10, Q11, Q12, Q13, and Q14 were used to collect data on a 5-point Likert scale, and descriptive statistical analyses were used to account for the results of the questionnaire. To further clarify whether there are significant differences in the use of AI programs at the gender, major, and grade levels, this study analyzed the above questions using post hoc comparative analyses and to illustrate the results. Among them, post hoc comparisons are often used to further explore the specific sources of differences between different groups after performing ANOVA. When ANOVA shows significant differences between at least two groups, a post hoc comparative analysis can help researchers determine the groups that are significantly different from each other.
The main purpose of the third part is to investigate the current relationship with AI for students who have used AI programs, the main factors that currently constrain them to use AI better, and whether the development of AI makes them anxious about their majors, covering topics such as Q15, Q17, and Q19. For students who have not used AI, research the main factors that currently constrain them from not using AI, and whether the development of AI intelligence will make them anxious about their majors, covering topics such as Q16 and Q18. Details are as follows.
Q15: For you, which of the following roles does current AI prefer?
Q15-1: Role in delivering knowledge to me.
Q15-2: A tool to help improve me.
Q15-3: A collaborator who needs to be trained and nurtured by me.
Q16: Currently, what is the main factor that constrains you from using AI programs?
Q17: Currently, what is the main factor that constrains you from making better use of AI programs?
Q16-1/Q17-1: AI is immaturely developed and does not respond better to my needs.
Q16-2/Q17-2: Not having enough computer knowledge.
Q16-3/Q17-3: No better setup to support learning.
Q18/Q19: Does the development of AI make you anxious about your major?
All question items in Q15, Q16, Q17, Q18, and Q19 were used to collect data on a 5-point Likert scale, and the results were explained using a categorical summary method. Differences between students who had and had not used AI were then illustrated by integrating the data from Q16, Q17, Q18, and Q19 and performing an independent-sample t-test.
It is worth noting that the scale questions in this questionnaire focused on the investigation of importance and conformity, and the relationship between the options and the values is as follows: very unimportant/very non-conformity = 1, unimportant/non-conformity = 2, average = 3, important/conformity = 4, and very important/very conformity = 5. Since the final analysis results are usually presented as decimals, in order to clarify the attitudes of the sample subjects, the results of the analyses in this study are described in the following form: 1 ≤ very unimportant/very non-compliant < 2; 2 ≤ unimportant/non-compliant < 3; general = 3, 3 < important/compliant ≤ 4; and 4 < very important/very compliant ≤ 5. The detailed questionnaire is shown in Appendix A.
Third, an online questionnaire was distributed to design students studying at four universities in China. To ensure that the findings are representative, one university in the top 10% of Chinese universities, two universities in the top 20%, and one university in the top 50% were selected as targets for the study. The collected questionnaire data were organized and analyzed using SPSS (Statistical Package for the Social Sciences) software v27, and the corresponding results were derived from a step-by-step analysis according to the structural content of the questionnaire and related methods mentioned above, and the results were explained. In the Discussion section, the main findings of this study are elaborated in conjunction with related studies. Finally, this study is summarized in the Conclusion section, which explains its limitations.
It is worth noting that these studies were conducted with ethical approval.
4. Results
The questionnaire contained 19 topics and 48 items, and the questionnaire-answering time was no more than five minutes, ensuring that the information was collected within a reasonable time frame. A total of 487 questionnaires were collected in this research, and to ensure the authenticity and validity of the questionnaire results, this study first analyzed the results of the questionnaire for reliability and validity, and the results of the analysis are shown in Table 1 and Table 2. Reliability refers to the consistency and stability of the measurement results. Validity refers to whether a measurement tool or research method measures the variable it claims to measure.
Table 1 shows that the Cronbach’s alpha reliability coefficient is 0.891, which is greater than 0.8, indicating that the data reliability of the study was of high quality. For the “alpha coefficient for items that have been deleted,” there is no significant increase in the reliability coefficient when any question item is deleted, thus indicating that the question item should not be deleted.
Validity was verified using KMO and Bartlett’s test; as can be seen from the table above, the KMO value is 0.985, the KMO value is greater than 0.8, and the research data are suitable for extracting information (a good side reaction to the validity).
4.1. Basic Status of the Use of AI and Basic Knowledge of the Technology
After clarifying that the questionnaire had good reliability and validity, this study analyzed the basic information of the participants using the frequency analysis method, and the results are shown in Table 3.
As can be seen from Table 3, the sample subjects who participated in this research were 36.55% male and 63.45% female; in terms of majors studied, environmental design accounted for 45.17%, visual (graphic) design accounted for 35.32%, and industrial (product) design accounted for 19.51%; in terms of grade levels, freshman accounted for 25.46%, sophomore accounted for 27.93%, junior accounted for 25.67%, and senior 20.94%; and in terms of the use of AI programs, 36.34% of the students had not used AI programs and 63.66% had used AI programs.
In order to clarify the difference in the technical perception of AI programs between the “not used” and “used” groups, this study analyzed Q5, Q6, Q7, and Q8 via the method of categorical aggregation, and the specific results are shown in Table 4.
As can be seen from the above table, for the sample subjects who have “not used” AI, the mean values of the related questions are 4.232 for Q5, 4.226 for Q6-1, 4.113 for Q6-2, 3.418 for Q6-3, and 3.989 for Q6-4, meaning that the sample subjects who have not used an AI program believe that it is very necessary to learn AI. “Consider personal skills and productivity gains” and “AI operates more simply and is easier to understand” are very important factors that influence them to use AI; meanwhile, “schools provide appropriate curricula and equipment conditions” and “influence of friends and classmates around you” are important factors that influence them to use AI.
For the sample subjects who have “used” AI, the mean values of the relevant questions are 4.116 for Q7-1, 3.884 for Q7-2, 3.171 for Q7-3, 2.923 for Q7-4, 3.719 for Q8-1, and 3.094 for Q8-2; thus, for the sample subjects who have used AI programs, “consider personal skills and productivity gains” is a very important factor affecting their use of AI, “AI operates more simply and is easier to understand” and “Influence of friends and classmates around you” are important factors affecting their use of AI, and “schools provide appropriate curricula and equipment conditions” is not an important factor affecting their use of AI. Their use of AI in both learning and living scenarios is in a frequent state, with more use in learning scenarios.
In order to further clarify the differences in the perceptions of AI between the “not used” and “used” groups, this study summarized the Q6 and Q7 data and conducted an independent-samples t-test on the summarized data; the results of the analysis are shown in Table 5.
According to the results of the independent-samples t-test, it can be seen that Q4 will not show significance (p > 0.05) for a total of one item of Q6/Q7-1, implying that Q4 shows consistency for all of Q6/Q7-1, and there is no difference. In addition, Q4 showed significance (p < 0.05) for Q6/Q7-2, Q6/Q7-3, and Q6/Q7-4 for the three items, implying that Q4 has a differential for Q6/Q7-2, Q6/Q7-3, and Q6/Q7-4. A specific analysis showed the following:
Q4 presents a 0.01 level of significance for Q6/Q7-2 (t = 2.840, p = 0.005), and a specific comparison of the differences shows that the not-used mean (4.11) will be significantly higher than the used mean (3.88).
Q4 presents a significance level of 0.05 for Q6/Q7-3 (t = 2.061, p = 0.040), and the specific comparison of the differences shows that the not-used mean (3.42) will be significantly higher than the used mean (3.17).
Q4 presents a 0.01 level of significance for Q6/Q7-4 (t = 10.324, p = 0.000), and a specific comparison of the differences shows that the mean of the unused (3.99) will be significantly higher than the mean of the used (2.92).
That is, factors “AI operates more simply and is easier to understand,” “influence of friends and classmates around you,” and “schools provide appropriate curricula and equipment conditions” had a much higher impact for students who had not used AI than for those who had. Moreover, through the independent-sample t-test performed for the further effect-size analysis, it can be seen that, for the “schools provide appropriate curricula and equipment conditions” factor, there are two groups of cognitive differences in the magnitude of the largest and a Cohen’s d-value of 0.919, which is more than the critical point of 0.8; the specific results are shown in Table 6.
4.2. Involvement of Artificial Intelligence Programs in Coursework and Design Work
This section focuses on research conducted with college students majoring in design who have used AI. The results of the descriptive statistical analysis of questions Q9, Q10, Q11, Q12, Q13, and Q14 are shown in Table 7.
The results of the correlation within the mean, standard deviation, and median of the sample subjects for the relevant questions can be seen in the table above, where the mean Q9-1 of the correlation was 3.816, Q9-2 was 2.919, and Q9-3 was 2.723. In the process of completing coursework and involving work, the use of text-based AI is usually relevant to the actual situation of the sample object, whereas the use of image-based AI (Midjourney and Sora) and plug-in AI in the design tool software is non-compliant with the actual situation of the sample object. As can be seen from the average values, plug-in AI in the design tool software was the least frequently used.
The correlation mean values were 3.794 for Q10-1, 3.677 for Q10-2, 3.110 for Q10-3, 2.961 Q10-4, and 2.787 for Q10-5. That is, in the process of completing coursework and design work, sample subjects usually use AI to research relevant materials, knowledge, and background; use AI to provide ideas; use AI to optimize design solutions that are consistent with the sample subjects’ actual situation; use AI to present and express design solutions; and use AI to verify and reflect on whether the design is non-compliant with the actual situation of the sample object. The average value also shows a decreasing trend with the gradual deepening of the design process.
The mean correlations were 3.000 for Q11-1 and 3.613 for Q11-2. In other words, when dealing with coursework and design, it is only through constant questioning and debugging that the desired results comply with the actual situation of the sample subjects. Usually, simple Q&A and debugging can yield the desired results: 2.690 for Q12-1, 3.794 for Q12-2, and 2.439 for Q12-3. In other words, when dealing with coursework and design, it is usually appropriate to adapt and reflect on the results before adopting them to comply with the actual situation of the sample subjects. Directly adopting or not adopting the results is non-compliant with the actual situation, and there are more cases of directly adopting the results. Q13 is 3.648, indicating that, when dealing with coursework and design, it is consistent with the actual use of AI that the sample subjects will question and reflect on the results provided by the AI. Q14 was 2.819, indicating that the sample objects were practically independent of the AI program when dealing with coursework and design.
4.2.1. Differences in the Use of AI Programs at the Gender Level
To further investigate whether there is a difference in design students’ use of AI at the gender level, this study conducted a post hoc comparative analysis of questions Q9, Q10, Q11, Q12, Q13, and Q14. The results of the analysis are presented in Table 8 and Table 9.
From the results of the analysis of variance (ANOVA) in the above table, it can be seen that different Q1 samples will not show significance (p > 0.05) for a total of eight items of Q9-3, Q10-3, Q10-4, Q10-5, Q11-1, Q12-1, Q12-3, and Q14, implying that there is no consistency between different Q1 samples for Q9-3, Q10-3, Q10-4, Q10-5, Q11-1, Q12-1, Q12-3, and Q14, which all showed consistency and were not different. No post hoc test analysis was required. In addition, the Q1 samples showed significance (p < 0.05) for Q9-1, Q9-2, Q10-1, Q10-2, Q11-2, Q12-2, and Q13, implying that there is a difference between the different Q1 samples for Q9-1, Q9-2, Q10-1, Q10-2, Q11-2, Q12-2, and Q13, which can be analyzed using specific post hoc tests.
The abovementioned ANOVA revealed that different Q1 samples showed variability for Q9-1, Q9-2, Q10-1, Q10-2, Q11-2, Q12-2 pass, and Q13, specifically for the LSD method.
Q1 presents a 0.01 level of significance for Q9-1 (F = 23.182, p = 0.000), and the mean value for males (4.17), will be significantly higher than the mean value for females (3.61). Q1 presents a 0.01 level of significance for Q9-2 (F = 9.980, p = 0.002), and the mean value for males (3.18), will be significantly higher than the mean value for females (2.77). Q1 presents a 0.01 level of significance for Q10-1 (F = 9.486, p = 0.002), and the mean value for males (4.02), will be significantly higher than the mean value for females (3.66). Q1 presents a 0.01 level of significance for Q10-2 (F = 12.998, p = 0.000), and the mean value for males (3.94), will be significantly higher than the mean value for females (3.53). Q1 presents a 0.01 level of significance for Q11-2 (F = 7.009, p = 0.009), and the mean value for males (3.82), will be significantly higher than the mean value for females (3.49). Q1 presents a 0.01 level of significance for Q12-2 (F = 6.867, p = 0.009), and the mean value for males (3.96), will be significantly higher than the mean value for females (3.69). Q1 presents a 0.05 level of significance for Q13 (F = 5.705, p = 0.018), and the mean value for males (3.80), will be significantly higher than the mean value for females (3.56).
The above results indicate that men in the sample gave significantly higher conformity ratings for the use of text-based AI and image-based AI; the use of AI to research relevant materials, knowledge, and backgrounds; the use of AI to provide ideas; the use of AI that requires constant questioning and debugging to obtain desired results; the use of AI that is usually adapted and thought about before adopting results proposed by AI; and the level of questioning or reflecting on results provided by AI, which was rated significantly higher than the level of conformity for women.
4.2.2. Differences in the Use of AI Programs at the Major Level
To further investigate whether there is a difference in design students’ use of AI at the professional field level, this study conducted a post hoc comparative analysis of questions Q9, Q10, Q11, Q12, Q13, and Q14. The results of the analysis are presented in Table 10 and Table 11.
From the results of the analysis of variance (ANOVA) in the above table, it can be seen that different Q2 samples do not show significance (p > 0.05) for a total of four items, namely Q10-1, Q10-2, Q12-2, and Q13, implying that different Q2 samples show consistency for Q10-1, Q10-2, Q12-2, and Q13, and there is no difference. No post hoc test analysis was required. In addition, the Q2 sample showed significance (p < 0.05) for Q9-1, Q9-2, Q9-3, Q10-3, Q10-4, Q10-5, Q11-1, Q11-2, Q12-1, Q12-3, and Q14 for 11 items, implying that the Q2 sample was significant (p < 0.05) for Q9-1, Q9-2, Q9-3, Q10-3, Q10-4, Q10-5, Q11-1, Q11-2, Q12-1, Q12-3, and Q14. Q11-1, Q11-2, Q12-1, Q12-3, and Q14 have differences that can be analyzed specifically using post hoc tests.
The abovementioned ANOVA revealed that different Q2 samples presented variability for Q9-1, Q9-2, Q9-3, Q10-3, Q10-4, Q10-5, Q11-1, Q12-1, Q12-3, and Q14, which were specifically subjected to the LSD method:
Q2 for Q9-1 showed a 0.05 level of significance (F = 4.323, p = 0.014), with more significant differences in group mean scores comparing the results of “industrial design > environmental design, industrial design > graphic design.”
Q2 for Q9-2 showed 0.01 level of significance (F = 7.928, p = 0.000), with more significant differences in group mean scores comparing the results of “environmental design > industrial design, graphic design > industrial design.”
Q2 presents a 0.01 level of significance (F = 13.125, p = 0.000) for Q9-3, and the comparison of the mean scores of the groups with more significant differences is “environmental design > graphic design, environmental design > industrial design, graphic design > industrial design.”
Q2 for Q10-3 showed a 0.01 level of significance (F = 22.373, p = 0.000), with more significant differences in group mean scores comparing the results of “environmental design > graphic design; environmental design > industrial design; graphic design > industrial design.”
Q2 for Q10-4 showed a 0.01 level of significance (F = 19.968, p = 0.000), with a more significant difference in group mean scores comparing the results of “environmental design > graphic design; environmental design > industrial design; graphic design > industrial design.”
Q2 for Q10-5 showed a 0.01 level of significance (F = 22.932, p = 0.000), with a more significant difference in the group mean scores compared to the results of “environmental design > graphic design; environmental design > industrial design.”
Q2 for Q11-1 showed a 0.01 level of significance (F = 12.204, p = 0.000), with a more significant difference in the group mean scores compared to the results of “environmental design > graphic design; environmental design > industrial design; graphic design > industrial design.”
Q2 for Q11-2 showed a 0.01 level of significance (F = 6.818, p = 0.001), and there was a more significant difference between the group mean score comparison results for “industrial design > environmental design; industrial design > graphic design.”
Q2 for Q12-1 showed a 0.01 level of significance (F = 12.444, p = 0.000), with a more significant difference in group mean scores comparing the results of “environmental design > graphic design; environmental design > industrial design; graphic design > industrial design.”
Q2 for Q12-3 showed a 0.01 level of significance (F = 15.959, p = 0.000), with a more significant difference in the group mean score comparison results for “environmental design > graphic design; environmental design > industrial design.”
Q2 is significant at the 0.01 level for Q14 (F = 9.108, p = 0.000), and the mean scores of the groups with more significant differences are compared as follows: “environmental design > graphic design; environmental design > industrial design.”
These results illustrate that environmental-design students in the sample population gave significantly higher conformity ratings to the use of plug-in type AIs in design tool software; use of AI to optimize design solutions and enhance details; use of AI to present and express design solutions; use of AI to validate and reflect on the designed solution; the desired result can be obtained through simple Q&A and debugging; usually, they are directly adopted; usually, these are not adopted; and the reliance on AIs than did graphic-design and industrial-design students.
The use of text-based AI by industrial-design students in the sample object, as well as “the desired result can be obtained through continuous Q&A and debugging”, received significantly higher conformity ratings than it received from environmental-design and graphic-design students; meanwhile, for “the desired result can be obtained through simple Q&A and debugging,” “Usually, they are directly adopted” received significantly lower conformity ratings than it received from environmental-design and graphic-design students.
4.2.3. Differences in the Use of AI Programs at the Grade Level
To further investigate whether there was any variability in design students’ use of AI at the grade level, this study conducted a post hoc comparative analysis of Q9, Q10, Q11, Q12, Q13, and Q14. The results of the analysis are presented in Table 12 and Table 13.
From the results of the analysis of variance (ANOVA) in the above table, it can be seen that different Q3 samples will not show significance (p > 0.05) for a total of three items, namely Q9-2, Q12-1, and Q14, implying that different Q3 samples show consistency for Q9-2, Q12-1, and Q14; there is no variability; and there is no need to analyze the results of the post hoc test. In addition, Q3 showed significance (p < 0.05) for Q9-1, Q9-3, Q10-1, Q10-2, Q10-3, Q10-4, Q10-5, Q11-1, Q11-2, Q12-2, Q12-3, and Q13 for a total of 12 items, implying that different Q3 samples have different significance for Q9-1, Q9-3, Q10-1, Q10-2, Q10- 3, Q10-4, Q10-5, Q11-1, Q11-2, Q12-2, Q12-3, and Q13, which can be analyzed specifically by post hoc tests.
From the abovementioned ANOVA, it can be seen that different Q3 samples present variability for Q9-1, Q9-3, Q10-1, Q10-2, Q10-3, Q10-4, Q10-5, Q11-1, Q11-2, Q12-2, Q12-3, and Q13, which are specifically subjected to the LSD method:
Q3 showed a 0.01 level of significance for Q9-1 (F = 13.118, p = 0.000), and the comparison of group mean scores with more significant differences was “senior > freshman; senior > sophomore; senior > junior.”
Q3 showed a 0.01 level of significance for Q9-3 (F = 8.305, p = 0.000), and the comparison of group mean scores with more significant differences was “freshman > senior; sophomore > senior; junior > senior.”
Q3 showed a 0.01 level of significance for Q10-1 (F = 22.228, p = 0.000), and the comparison of group mean scores with more significant differences was “senior > freshman; senior > sophomore; senior > junior.”
Q3 showed a 0.01 level of significance for Q10-2 (F = 5.937, p = 0.001), and the comparison of group mean scores with more pronounced differences resulted in “senior > freshman; senior > sophomore; and senior > junior.”
Q3 showed a 0.01 level of significance for Q10-3 (F = 5.781, p = 0.001), and the comparison of group mean scores with more significant differences was “freshman > senior; sophomore > senior; junior > senior.”
Q3 showed a 0.01 level of significance for Q10-4 (F = 9.034, p = 0.000), with more significant differences in group mean scores comparing “freshman > senior; sophomore > Senior; and junior > senior.”
Q3 showed a 0.01 level of significance for Q10-5 (F = 24.872, p = 0.000), and the comparison of group mean scores with more significant differences was “freshman > senior; sophomore > senior; and junior > senior.”
Q3 showed a 0.05 level of significance for Q11-1 (F = 3.728, p = 0.012), and the comparison of group mean scores with more significant differences was “freshman > senior; sophomore > senior; junior > senior.”
Q3 showed a 0.01 level of significance for Q11-2 (F = 11.314, p = 0.000), and the comparison of group mean scores with more significant differences was “senior > freshman; senior > sophomore; senior > junior.”
Q3 showed a 0.01 level of significance (F = 12.853, p = 0.000) for Q12-2, and the comparison of group mean scores with more significant differences resulted in “senior > freshman; junior > sophomore; senior > sophomore; senior > junior.”
Q3 showed a 0.01 level of significance (F = 9.009, p = 0.000) for Q12-3, and the comparison of group mean scores with more significant differences was “freshman > senior; sophomore > senior; junior > senior.”
Q3 showed a 0.05 level of significance for Q13 (F = 3.554, p = 0.015), and the comparison of group mean scores with more significant differences was “junior > freshman; junior > Sophomore.”
The above results indicate that the senior students in the sample population gave significantly higher conformity ratings for “using text-based AI,” “use AI to research relevant materials, knowledge, and background,” “use of AI to provide ideas,” “the desired result can be obtained through continuous Q&A and debugging,” and “usually adapt and think about the results before adopting them” than freshmen, sophomores, and juniors did.
However, the senior students in the sample population gave significantly lower conformity ratings for “the use of plug-in class AI in design tool software,” “used AI to optimize design solutions and enhance details,” “used AI to do the presentation and expression of design solutions,” “used AI to validate and reflect on the designed solution,” “The desired result can be obtained through simple Q&A and debugging,” and “Usually, these are not adopted” than freshmen, sophomores, and juniors did.
In addition, junior students in the sample gave significantly higher ratings of “questioning and reflecting on the results provided by AI programs” than freshmen and sophomores in terms of conformity.
4.3. Research on Artificial Intelligence Programs and Their Own Development
On the one hand, it is important to identify the current relationship with AI for students who have “used” AI, the main constraints on their development, and whether the development of AI has made them anxious about their majors. On the other hand, in order to identify the main factors that constrain the “non-users” of AI programs to use AI and whether the development of AI makes them anxious about their majors, this study conducted a categorization and summary analysis of the questions in Q15, Q16, Q17, Q18, and Q19; the results are shown in Table 14.
From the above table, we can see that the mean values of the questions related to students who “used” AI were as follows: Q15-1 3.390, Q15-2 3.813, Q15-3 3.048, Q17-1 3.552, Q17-2 3.248, Q17-3 2.790, and Q19 3.310. It shows that students who have “used” AI believe that the current AI “fits” the role of helping to improve themselves, transferring knowledge, and collaborating with others, and that the role of AI in helping to improve themselves is more in line with the artificiality of Chinese design-college students. And for students who have “used” AI, the current constraints on the better use of AI are that it is immature, it is not responsive to their needs, and that they do not have enough computer knowledge, and it is not realistic for them to be without better equipment to support their learning; for students who have “used” AI, the development of AI can make them anxious about their majors.
The mean values of the questions related to students who did “not use” AI were as follows: Q16-1 = 3.164, Q16-2 = 3.836, Q16-3 = 3.492, and Q18 = 3.616. It is realistic to suggest that, for students who have “not used” AI, the current constraints on their use of AI are that they do not have enough computer knowledge, that they do not have better equipment to support their learning, and that the development of AI is not mature enough to better respond to my needs. One of the main factors is not having enough knowledge of AI itself; moreover, for students who have “not used” AI, the development of AI will make them anxious about their majors, and the degree of this anxiety is more prominent than that of students who have “used” AI.
In order to further clarify whether there is a significant difference between the perceptions of “used” and “did not use” AI students on the above questions, this study organized the questionnaire data and analyzed the related questions by the method of independent-samples t-test, and the results of the analysis are shown in Table 15.
From the above table, it can be seen that different Q4 samples for Q16/Q17-1, Q16/Q17-2, Q16/Q17-3, and Q18/Q19 all show significance (p < 0.05), which means that there is a difference between different Q4 samples for Q16/Q17-1, Q16/Q17-2, Q16/Q17-3, and Q18/Q19. The specific analysis is as follows:
Q4 for Q16/Q17-1 presents a 0.01 level of significance (t = −3.474, p = 0.001), and a specific comparison of the differences shows that the unused mean (3.16) will be significantly lower than the used mean (3.55).
Q4 presents a 0.01 level of significance for Q16/Q17-2 (t = 5.533, p = 0.000), and a specific comparison of the differences shows that the mean value of the unused (3.84) will be significantly higher than the mean value of the used (3.25).
Q4 presents a 0.01 level of significance for Q16/Q17-3 (t = 6.181, p = 0.000), and a specific comparison of the differences shows that the mean value of the unused (3.49) will be significantly higher than the mean value of the used (2.79).
Q4 presents a 0.01 level of significance for Q18/Q19 (t = 3.193, p = 0.002), and a specific comparison of the differences shows that the mean of the unused (3.62) will be significantly higher than the mean of the used (3.31).
It can be seen that Q4 shows a significant difference for all of Q16/Q17-1, Q16/Q17-2, Q17-3, and Q18/Q19. It shows that, among the current factors that constrain from using or better using AI, students who have not used AI show significantly higher levels of compliance with the factors “Not having enough computer knowledge” and “no better setup to support learning” than those who have used AI; for the factor “AI is immaturely developed and does not respond better to my needs,” students who had used AI showed significantly more compliance than those who had not; and in response to the question of whether or not the development of AI would make them anxious about their field of study, students who had not used AI showed significantly more anxiety than those who had used it.
5. Discussion
First, research on 487 Chinese design-college students shows that 63.66% of the students have used AI programs, and those who have not generally believe that it is necessary to learn AI; therefore, it can be said that the proportion of Chinese design students who have used AI will be greatly improved. This shows that Chinese design students are highly receptive to artificial intelligence and that artificial intelligence will become an important auxiliary for design in the foreseeable future. The determination of how the field of design education will adapt to this trend has become urgent.
Moreover, all design students cited “considering the improvement of personal skills and work efficiency,” that is, Performance Expectancy (PE), as a very important factor for using AI; Chinese design students believed that using AI could effectively improve personal skills and work efficiency, a result much higher than that of Kairu Caroline’s (2020) research on college students’ attitudes toward AI [34]. For students who have not used AI, AI that is simpler to operate and easier to understand, that is, Effort Expectancy (EE), is also an important factor that encourages them to pursue AI.
It is worth noting that the Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FCs) factors were much more influential for students who had not used AI than for those who had, especially the Facilitating Conditions factor, suggesting that they rely more on the school to be able to provide the appropriate program and equipment conditions. Thus far, students who have used AI have been more likely to use it in student scenarios.
Second, Chinese design students who use AI in the process of completing coursework and design work mainly use text-based AI (ChatGPT, ERNIE Bot, etc.) and usually use AI to research the knowledge and background of the relevant content for the relevant assignments or design work, to provide themselves with some ideas, and to partially optimize their solutions. It is worth noting that the frequency of the use of AI by design students decreases as the design process continues, a finding which seems to validate Karaata Ezgi’s (2018) view [48] that AI can leave more time and energy for designers to focus on their creations. On the other hand, the pre-design stage is the stage of problem mining, sorting out, and thought construction. Unlike the design stage, the pre-design stage needs a broader knowledge area, and such knowledge construction takes a lot of time, but now AI can help designers to solve this problem quickly, so that the pre-design stage not only accelerates, but the broader knowledge area provided by AI also helps them to think about the problem from different perspectives. It is worth noting that, although artificial intelligence can provide information and inspiration, true innovation still requires designers’ creativity and intuition; therefore, future designer competition will rely more on the results of the design.
Third, when Chinese design students use AI in their coursework or design process, they usually obtain the desired results through constant questioning and debugging. The results are often adapted and reflected upon before adoption, and the design staff questions and reflects on the results provided by AI in such a conscious use process. The present study argues that such usage can partially circumvent Elal İrem and Hüseyin Özkal Özsoy’s suggestion that artificial intelligence can have a negative impact on unconscious students or people accustomed to off-the-shelf design processes and their creativity [49]. Another noteworthy point is that most current design undergraduates report no reliance on AI when completing coursework or design.
Fourth, the results of the study also showed that there were significant differences in the use of AI among design students not only at the professional level [45] but also at the gender and grade level. The obvious differences at the gender level are men’s use of text-based AI and image-based AI; the use of AI to research relevant material, knowledge, and background; the use of AI to provide ideas; and the use of AI that requires constant questioning, answering, and debugging to obtain the desired results. The results presented by AI are usually adapted and reflected upon before being adopted, and the level of questioning or reflection on the results provided by AI is rated significantly higher than the level of conformity given to women. This could also be a side note to show that male students in design programs have a deeper and more proficient understanding of AI.
At the professional level, it is worth noting that industrial-design students are more inclined to obtain results through constant questioning, answering, and debugging and are less likely than environmental-design and graphic-design students to adopt AI results directly. It can be argued that this is a better aspect of what industrial-design students show for when AI is used; environmental-design students tend to obtain results through simple quizzes and debugging and adopt them directly, operating in a way that runs the risk of weakening students’ independent thinking.
What is noteworthy at the grade level is that design seniors are more inclined than freshmen, sophomores, and juniors to obtain results through constant Q&A and debugging, and to adapt and reflect on the results before adopting them; and juniors are more inclined to question and reflect on the results provided by AI than freshmen and sophomores; such results can be a side note to a more in-depth use of AI as expertise accumulates.
Fifth, a study by Kim Jinhee et al. suggested that students develop cooperation with AI in three stages: learning about AI, learning from AI, and learning with AI [16]. This study found that Chinese design-college students who have used AI generally view AI as a tool to help them improve, rather than simply delivering knowledge, and that Chinese design students have currently passed the stage of learning from AI. It is worth noting that design students who currently use AI believe that the main factor constraining their better use of AI is that it is currently underdeveloped and does not better respond to their needs; students who have not used AI are currently fully aware of the fact that the main factor constraining their better use of AI is the problem of their own knowledge structure.
Sixth, overall, Chinese design students, whether they have used AI or not, are anxious about the development of AI in their profession, which is consistent with Ghotbi Nader et al.’s research on Japanese university students [44]. Students who had not used AI were significantly more anxious than those who had. To cope with widespread anxiety, design education may place more emphasis on technology and computer science knowledge and focus on cultivating students’ understanding and application abilities of artificial intelligence tools.
6. Conclusions
The current research findings reveal a high level of acceptance of artificial intelligence among Chinese design-college students, with over 60% of students already using AI programs, and the vast majority of students believing that mastering AI skills is the key to future career development. This trend indicates that artificial intelligence will play an increasingly important role in the field of design, which may profoundly affect the way design practices and education are conducted. In this study, it was found that students tend to use text-based artificial intelligence tools to assist in data collection and idea generation in the early stages of design, indicating that artificial intelligence has a positive role in improving work efficiency and innovation ability. However, as the design process progresses, students’ dependence on artificial intelligence decreases, which may mean that designers tend to rely more on their own professional knowledge and creativity in the creative-core stage [49].
These findings have significant implications for future design education and practice. Educators need to reconsider how to integrate artificial intelligence technology into their curriculum to cultivate students’ digital literacy and technological-application abilities. Meanwhile, design students need to enhance their innovative thinking to ensure they maintain their core competitiveness in the AI-assisted design process. We will further explore the specific applications of artificial intelligence in different design stages in future research, as well as how to integrate AI more deeply with design education to improve teaching effectiveness and students’ practical abilities.
In summary, the integration of AI is changing the learning and design practices of design students. Future research and educational practice need to focus on how to balance the integration of technological applications and human creativity, as well as how to provide necessary support for students to ensure their competitiveness and innovation ability in a rapidly developing technological environment. It is worth noting that the increase in the sample size of this study may lead to some degree of fluctuation in the data results, and this fluctuation is also a limitation of this study.
Y.S., investigation, methodology, resources, validation, formal analysis, funding acquisition, writing—original draft preparation and writing—review and editing; S.W., data curation, software, formal analysis, writing—original draft preparation and writing—review and editing; All authors have read and agreed to the published version of the manuscript.
The data that support the findings of this study are available upon re-quest from the corresponding author, Shaochen Wang.
The authors declare no conflicts of interest.
Footnotes
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Reliability statistics (Cronbach’s alpha).
| Items | Corrected Item–Total Correlation (CITC) | Cronbach’s Alpha If Item Deleted | Cronbach’s α |
|---|---|---|---|
| Q1 | −0.047 | 0.892 | 0.891 |
| Q2 | −0.066 | 0.892 | |
| Q3 | 0.022 | 0.892 | |
| Q4 | 0.907 | 0.888 | |
| Q5 | −0.868 | 0.910 | |
| Q6-1 | −0.869 | 0.910 | |
| Q6-2 | −0.868 | 0.910 | |
| Q6-3 | −0.810 | 0.906 | |
| Q6-4 | −0.856 | 0.909 | |
| Q7-1 | 0.912 | 0.878 | |
| Q7-2 | 0.900 | 0.879 | |
| Q7-3 | 0.857 | 0.881 | |
| Q7-4 | 0.852 | 0.881 | |
| Q8-1 | 0.916 | 0.879 | |
| Q8-2 | 0.889 | 0.881 | |
| Q9-1 | 0.898 | 0.879 | |
| Q9-2 | 0.890 | 0.881 | |
| Q9-3 | 0.852 | 0.882 | |
| Q10-1 | 0.907 | 0.879 | |
| Q10-2 | 0.914 | 0.879 | |
| Q10-3 | 0.923 | 0.880 | |
| Q10-4 | 0.905 | 0.880 | |
| Q10-5 | 0.862 | 0.881 | |
| Q11-1 | 0.912 | 0.881 | |
| Q11-2 | 0.888 | 0.880 | |
| Q12-1 | 0.905 | 0.881 | |
| Q12-2 | 0.906 | 0.879 | |
| Q12-3 | 0.838 | 0.883 | |
| Q13 | 0.900 | 0.880 | |
| Q14 | 0.917 | 0.881 | |
| Q15-1 | 0.908 | 0.880 | |
| Q15-2 | 0.910 | 0.879 | |
| Q15-3 | 0.876 | 0.881 | |
| Q16-1 | −0.801 | 0.905 | |
| Q16-2 | −0.835 | 0.908 | |
| Q16-3 | −0.814 | 0.907 | |
| Q17-1 | 0.877 | 0.880 | |
| Q17-2 | 0.892 | 0.880 | |
| Q17-3 | 0.855 | 0.882 | |
| Q18 | −0.840 | 0.907 | |
| Q19 | 0.878 | 0.881 |
Remarks: Cronbach’s α (standardized) = 0.906.
Validity analysis.
| Items | Factor Loadings | Communalities | ||
|---|---|---|---|---|
| Factor 1 | Factor 2 | Factor 3 | ||
| Q1 | 0.006 | −0.111 | −0.758 | 0.588 |
| Q2 | 0.174 | −0.676 | −0.023 | 0.488 |
| Q3 | 0.097 | −0.198 | 0.740 | 0.596 |
| Q4 | 0.967 | 0.182 | −0.023 | 0.969 |
| Q5 | −0.956 | −0.143 | 0.056 | 0.938 |
| Q6-1 | −0.957 | −0.142 | 0.051 | 0.939 |
| Q6-2 | −0.960 | −0.136 | 0.051 | 0.942 |
| Q6-3 | −0.913 | −0.087 | −0.010 | 0.841 |
| Q6-4 | −0.951 | −0.138 | 0.046 | 0.925 |
| Q7-1 | 0.954 | 0.155 | 0.086 | 0.941 |
| Q7-2 | 0.940 | 0.166 | 0.046 | 0.914 |
| Q7-3 | 0.788 | 0.453 | −0.135 | 0.845 |
| Q7-4 | 0.758 | 0.480 | −0.147 | 0.826 |
| Q8-1 | 0.935 | 0.182 | 0.103 | 0.918 |
| Q8-2 | 0.809 | 0.453 | −0.049 | 0.863 |
| Q9-1 | 0.934 | 0.133 | 0.141 | 0.909 |
| Q9-2 | 0.797 | 0.441 | 0.085 | 0.837 |
| Q9-3 | 0.737 | 0.517 | −0.076 | 0.816 |
| Q10-1 | 0.932 | 0.153 | 0.140 | 0.912 |
| Q10-2 | 0.922 | 0.192 | 0.118 | 0.901 |
| Q10-3 | 0.816 | 0.484 | −0.023 | 0.901 |
| Q10-4 | 0.784 | 0.522 | −0.052 | 0.889 |
| Q10-5 | 0.729 | 0.564 | −0.149 | 0.872 |
| Q11-1 | 0.831 | 0.437 | −0.039 | 0.883 |
| Q11-2 | 0.925 | 0.120 | 0.126 | 0.885 |
| Q12-1 | 0.815 | 0.444 | −0.005 | 0.862 |
| Q12-2 | 0.944 | 0.140 | 0.101 | 0.921 |
| Q12-3 | 0.747 | 0.480 | −0.115 | 0.801 |
| Q13 | 0.932 | 0.175 | 0.053 | 0.902 |
| Q14 | 0.869 | 0.366 | −0.009 | 0.889 |
| Q15-1 | 0.871 | 0.349 | 0.060 | 0.883 |
| Q15-2 | 0.946 | 0.142 | 0.129 | 0.931 |
| Q15-3 | 0.799 | 0.440 | −0.132 | 0.850 |
| Q16-1 | −0.904 | −0.091 | 0.018 | 0.825 |
| Q16-2 | −0.934 | −0.127 | 0.085 | 0.896 |
| Q16-3 | −0.922 | −0.107 | 0.050 | 0.865 |
| Q17-1 | 0.922 | 0.125 | 0.107 | 0.877 |
| Q17-2 | 0.850 | 0.356 | 0.005 | 0.849 |
| Q17-3 | 0.772 | 0.460 | −0.088 | 0.816 |
| Q18 | −0.925 | −0.143 | 0.087 | 0.884 |
| Q19 | 0.902 | 0.232 | −0.045 | 0.869 |
| Eigenvalues (initial) | 32.183 | 1.923 | 1.152 | - |
| % of variance (initial) | 78.496% | 4.691% | 2.810% | - |
| % of cum. variance (initial) | 78.496% | 83.188% | 85.997% | - |
| Eigenvalues (rotated) | 29.515 | 4.335 | 1.409 | - |
| % of variance (rotated) | 71.987% | 10.574% | 3.436% | - |
| % of cum. variance (rotated) | 71.987% | 82.561% | 85.997% | - |
| KMO | 0.985 | - | ||
| Bartlett’s test of sphericity (chi-square) | 41353.007 | - | ||
| df | 820 | - | ||
| p value | 0.000 | - | ||
Note: Blue indicates that the absolute value of loading is greater than 0.4, and red indicates that the communality is less than 0.4.
Frequency analysis of basic information of sample subjects.
| Items | Categories | N | Percent (%) | Cumulative Percent (%) |
|---|---|---|---|---|
| Q1: What is your gender? | Male | 178 | 36.55 | 36.55 |
| Female | 309 | 63.45 | 100.00 | |
| Q2: What is your major? | Environment design | 220 | 45.17 | 45.17 |
| Visual (graphic) design | 172 | 35.32 | 80.49 | |
| Industrial (product) design | 95 | 19.51 | 100.00 | |
| Q3: What is your current grade level? | Fresh (enrollment in 2023) | 124 | 25.46 | 25.46 |
| Sophomore (enrollment in 2022) | 136 | 27.93 | 53.39 | |
| Junior (enrollment in 2021) | 125 | 25.67 | 79.06 | |
| Senior (enrollment in 2021) | 102 | 20.94 | 100.00 | |
| Q4: Have you ever used an AI program? | Not used | 177 | 36.34 | 36.34 |
| Used | 310 | 63.66 | 100.00 | |
| Total | 487 | 100.0 | 100.0 | |
Subtotal.
| Items | Q4: Have You Ever Used an AI Program? | Total | |
|---|---|---|---|
| Not Used | Used | ||
| Q5: Do you think it is necessary to learn about AI programs? | 4.232 | 0.000 | 1.538 |
| Q6-1: Consider personal skills and productivity gains. | 4.226 | 0.000 | 1.536 |
| Q6-2: AI operates more simply and is easier to understand. | 4.113 | 0.000 | 1.495 |
| Q6-3: Influence of friends and classmates around you. | 3.418 | 0.000 | 1.242 |
| Q6-4: Schools provide appropriate curricula and equipment conditions. | 3.989 | 0.000 | 1.450 |
| Q7-1: Consider personal skills and productivity gains. | 0.000 | 4.116 | 2.620 |
| Q7-2: AI operates more simply and is easier to understand. | 0.000 | 3.884 | 2.472 |
| Q7-3: Influence of friends and classmates around you. | 0.000 | 3.171 | 2.018 |
| Q7-4: Schools provide appropriate curricula and equipment conditions. | 0.000 | 2.923 | 1.860 |
| Q8-1: In the learning scenario. | 0.000 | 3.719 | 2.368 |
| Q8-2: In life scenarios. | 0.000 | 3.094 | 1.969 |
Independent t-test.
| Q4: Have You Ever Used an AI Program? (Mean ± Std. Deviation) | t | p | ||
|---|---|---|---|---|
| Not Used (n = 177) | Used (n = 310) | |||
| Q6/7-1: Consider personal skills and productivity gains. | 4.23 ± 0.94 | 4.12 ± 0.80 | 1.311 | 0.191 |
| Q6/7-2: AI operates more simply and is easier to understand. | 4.11 ± 0.92 | 3.88 ± 0.82 | 2.840 | 0.005 ** |
| Q6/7-3: Influence of friends and classmates around you. | 3.42 ± 1.33 | 3.17 ± 1.17 | 2.061 | 0.040 * |
| Q6/7-4: Schools provide appropriate curricula and equipment conditions. | 3.99 ± 1.01 | 2.92 ± 1.24 | 10.324 | 0.000 ** |
* p < 0.05; ** p < 0.01.
Effect-size analysis for Q6/7.
| Items | S2pooled | Cohen’s d |
|---|---|---|
| Q6/7-1: Consider personal skills and productivity gains. | 0.723 | 0.129 |
| Q6/7-2: AI operates more simply and is easier to understand. | 0.733 | 0.268 |
| Q6/7-3: Influence of friends and classmates around you. | 1.507 | 0.201 |
| Q6/7-4: Schools provide appropriate curricula and equipment conditions. | 1.345 | 0.919 |
Descriptive analysis.
| Items | N of Samples | Min | Max | Mean | Std. Deviation | Median |
|---|---|---|---|---|---|---|
| Q9-1: Text-based AI | 310 | 1.000 | 5.000 | 3.816 | 1.012 | 4.000 |
| Q9-2: Image-based AI | 310 | 1.000 | 5.000 | 2.919 | 1.142 | 3.000 |
| Q9-3: Plug-in class AI in design tool software | 310 | 1.000 | 5.000 | 2.723 | 1.226 | 3.000 |
| Q10-1: Use AI to research relevant materials, knowledge, and background | 310 | 1.000 | 5.000 | 3.794 | 0.990 | 4.000 |
| Q10-2: Use of AI to provide ideas, e.g., brainstorming and design proposals | 310 | 1.000 | 5.000 | 3.677 | 0.991 | 4.000 |
| Q10-3: Use AI to optimize design solutions and enhance details | 310 | 1.000 | 5.000 | 3.110 | 1.080 | 3.000 |
| Q10-4: Using AI to do the presentation and expression of design solutions | 310 | 1.000 | 5.000 | 2.961 | 1.140 | 3.000 |
| Q10-5: Use AI to validate and reflect on the designed solution | 310 | 1.000 | 5.000 | 2.787 | 1.254 | 3.000 |
| Q11-1: The desired result can be obtained through simple Q&A and debugging | 310 | 1.000 | 5.000 | 3.000 | 0.985 | 3.000 |
| Q11-2: The desired result can be obtained through continuous Q&A and debugging | 310 | 1.000 | 5.000 | 3.613 | 1.039 | 4.000 |
| Q12-1: Usually, they are directly adopted | 310 | 1.000 | 5.000 | 2.690 | 0.963 | 3.000 |
| Q12-2: Usually adapt and think about the results before adopting them | 310 | 1.000 | 5.000 | 3.794 | 0.886 | 4.000 |
| Q12-3: Usually, these are not adopted | 310 | 1.000 | 5.000 | 2.439 | 1.062 | 2.000 |
| Q13: Have you ever questioned or reflected on the results provided by an AI program? | 310 | 1.000 | 5.000 | 3.648 | 0.849 | 4.000 |
| Q14: Do you think you have become dependent on the AI program? | 310 | 1.000 | 5.000 | 2.819 | 0.804 | 3.000 |
ANOVA.
| Q1: What Is Your Gender? (Mean ± Std. Deviation) | F | p | ||
|---|---|---|---|---|
| Male | Female | |||
| Q9-1: Text-based AI | 4.17 ± 0.94 | 3.61 ± 1.00 | 23.182 | 0.000 ** |
| Q9-2: Image-based AI | 3.18 ± 1.14 | 2.77 ± 1.12 | 9.980 | 0.002 ** |
| Q9-3: Plug-in class AI in design tool software | 2.67 ± 1.33 | 2.76 ± 1.16 | 0.375 | 0.541 |
| Q10-1: Use AI to research relevant materials, knowledge, and background | 4.02 ± 1.00 | 3.66 ± 0.96 | 9.486 | 0.002 * |
| Q10-2: Use of AI to provide ideas, e.g., brainstorming and design proposals | 3.94 ± 0.89 | 3.53 ± 1.02 | 12.998 | 0.000 ** |
| Q10-3: Use AI to optimize design solutions and enhance details | 3.11 ± 1.10 | 3.11 ± 1.07 | 0.003 | 0.957 |
| Q10-4: Using AI to do the presentation and expression of design solutions | 2.98 ± 1.20 | 2.95 ± 1.11 | 0.062 | 0.804 |
| Q10-5: Use AI to validate and reflect on the designed solution | 2.64 ± 1.34 | 2.87 ± 1.20 | 2.481 | 0.116 |
| Q11-1: The desired result can be obtained through simple Q&A and debugging | 2.96 ± 0.98 | 3.02 ± 0.99 | 0.228 | 0.633 |
| Q11-2: The desired result can be obtained through continuous Q&A and debugging | 3.82 ± 1.03 | 3.49 ± 1.03 | 7.009 | 0.009 ** |
| Q12-1: Usually, they are directly adopted | 2.68 ± 0.99 | 2.69 ± 0.95 | 0.007 | 0.932 |
| Q12-2: Usually adapt and think about the results before adopting them | 3.96 ± 0.88 | 3.69 ± 0.88 | 6.867 | 0.009 ** |
| Q12-3: Usually, these are not adopted | 2.29 ± 1.16 | 2.53 ± 0.99 | 3.592 | 0.059 |
| Q13: Have you ever questioned or reflected on the results provided by an AI program? | 3.80 ± 0.78 | 3.56 ± 0.88 | 5.705 | 0.018 * |
| Q14: Do you think you have become dependent on the AI program? | 2.83 ± 0.83 | 2.81 ± 0.79 | 0.054 | 0.816 |
* p < 0.05; ** p < 0.01.
Results of post hoc multiple comparative analysis.
| (I)Name | (J)Name | (I)Mean | (J)Mean | D-Value | p | |
|---|---|---|---|---|---|---|
| Q9-1: Text-based AI | Male | Female | 4.167 | 3.612 | 0.554 | 0.000 ** |
| Q9-2: Image-based AI | Male | Female | 3.184 | 2.765 | 0.419 | 0.002 ** |
| Q9-3: Plug-in class AI in design tool software | Male | Female | 2.667 | 2.755 | −0.088 | 0.541 |
| Q10-1: Use AI to research relevant materials, knowledge, and background | Male | Female | 4.018 | 3.663 | 0.354 | 0.002 ** |
| Q10-2: Use of AI to provide ideas, e.g., brainstorming and design proposals | Male | Female | 3.939 | 3.526 | 0.413 | 0.000 ** |
| Q10-3: Use AI to optimize design solutions and enhance details | Male | Female | 3.114 | 3.107 | 0.007 | 0.957 |
| Q10-4: Using AI to do the presentation and expression of design solutions | Male | Female | 2.982 | 2.949 | 0.033 | 0.804 |
| Q10-5: Use AI to validate and reflect on the designed solution | Male | Female | 2.640 | 2.872 | −0.232 | 0.116 |
| Q11-1: The desired result can be obtained through simple Q&A and debugging | Male | Female | 2.965 | 3.020 | −0.055 | 0.633 |
| Q11-2: The desired result can be obtained through continuous Q&A and debugging | Male | Female | 3.816 | 3.495 | 0.321 | 0.009 ** |
| Q12-1: Usually, they are directly adopted | Male | Female | 2.684 | 2.694 | −0.010 | 0.932 |
| Q12-2: Usually adapt and think about the results before adopting them | Male | Female | 3.965 | 3.694 | 0.271 | 0.009 ** |
| Q12-3: Usually, these are not adopted | Male | Female | 2.289 | 2.526 | −0.236 | 0.059 |
| Q13: Have you ever questioned or reflected on the results provided by an AI program? | Male | Female | 3.798 | 3.561 | 0.237 | 0.018 * |
| Q14: Do you think you have become dependent on the AI program? | Male | Female | 2.833 | 2.811 | 0.022 | 0.816 |
* p < 0.05; ** p < 0.01.
ANOVA.
| Q2: What Is Your Major? (Mean ± Std. Deviation) | F | p | |||
|---|---|---|---|---|---|
| Environment Design | Visual (Graphic) Design | Industrial (Product) Design | |||
| Q9-1: Text-based AI | 3.68 ± 0.97 | 3.81 ± 1.10 | 4.13 ± 0.91 | 4.323 | 0.014 * |
| Q9-2: Image-based AI | 3.09 ± 1.11 | 2.98 ± 0.98 | 2.43 ± 1.33 | 7.928 | 0.000 ** |
| Q9-3: Plug-in class AI in design tool software | 3.04 ± 1.17 | 2.66 ± 1.15 | 2.13 ± 1.24 | 13.125 | 0.000 ** |
| Q10-1: Use AI to research relevant materials, knowledge, and background | 3.67 ± 0.95 | 3.88 ± 1.08 | 3.92 ± 0.89 | 1.943 | 0.145 |
| Q10-2: Use of AI to provide ideas | 3.58 ± 1.00 | 3.74 ± 0.89 | 3.79 ± 1.12 | 1.278 | 0.280 |
| Q10-3: Use AI to optimize design solutions and enhance details | 3.46 ± 1.02 | 3.04 ± 0.89 | 2.44 ± 1.17 | 22.373 | 0.000 ** |
| Q10-4: Using AI to do the presentation and expression of design solutions | 3.32 ± 1.11 | 2.88 ± 1.01 | 2.30 ± 1.10 | 19.968 | 0.000 ** |
| Q10-5: Use AI to validate and reflect on the designed solution | 3.28 ± 1.13 | 2.44 ± 1.26 | 2.27 ± 1.12 | 22.932 | 0.000 ** |
| Q11-1: The desired result can be obtained through simple Q&A and debugging | 3.23 ± 0.98 | 2.97 ± 0.91 | 2.52 ± 0.96 | 12.204 | 0.000 ** |
| Q11-2: The desired result can be obtained through continuous Q&A and debugging | 3.48 ± 0.95 | 3.55 ± 1.09 | 4.03 ± 1.06 | 6.818 | 0.001 ** |
| Q12-1: Usually, they are directly adopted | 2.92 ± 0.95 | 2.66 ± 0.87 | 2.22 ± 0.97 | 12.444 | 0.000 ** |
| Q12-2: Usually adapt and think about the results before adopting them | 3.70 ± 0.82 | 3.84 ± 0.92 | 3.94 ± 0.97 | 1.843 | 0.160 |
| Q12-3: Usually, these are not adopted | 2.79 ± 1.03 | 2.16 ± 1.03 | 2.11 ± 0.94 | 15.959 | 0.000 ** |
| Q13: Have you ever questioned or reflected on the results provided by an AI program? | 3.60 ± 0.85 | 3.59 ± 0.85 | 3.84 ± 0.83 | 2.058 | 0.129 |
| Q14: Do you think you have become dependent on the AI program? | 3.01 ± 0.84 | 2.73 ± 0.74 | 2.54 ± 0.74 | 9.108 | 0.000 ** |
* p < 0.05; ** p < 0.01.
Results of post hoc multiple comparative analysis.
| (I)Name | (J)Name | (I)Mean | (J)Mean | D-Value | p | |
|---|---|---|---|---|---|---|
| Q9-1: Text-based AI | Environment design | Graphic design | 3.681 | 3.811 | −0.130 | 0.312 |
| Environment design | Industrial design | 3.681 | 4.127 | −0.446 | 0.004 ** | |
| Graphic design | Industrial design | 3.811 | 4.127 | −0.316 | 0.048 * | |
| Q9-2: Image-based AI | Environment design | Graphic design | 3.092 | 2.981 | 0.111 | 0.440 |
| Environment design | Industrial design | 3.092 | 2.429 | 0.664 | 0.000 ** | |
| Graphic design | Industrial design | 2.981 | 2.429 | 0.553 | 0.002 ** | |
| Q9-3: Plug-in class AI in design tool software | Environment design | Graphic design | 3.035 | 2.660 | 0.375 | 0.014 * |
| Environment design | Industrial design | 3.035 | 2.127 | 0.908 | 0.000 ** | |
| Graphic design | Industrial design | 2.660 | 2.127 | 0.533 | 0.005 ** | |
| Q10-1: Use AI to research relevant materials, knowledge, and background | Environment design | Graphic design | 3.674 | 3.877 | −0.204 | 0.110 |
| Environment design | Industrial design | 3.674 | 3.921 | −0.247 | 0.100 | |
| Graphic design | Industrial design | 3.877 | 3.921 | −0.043 | 0.783 | |
| Q10-2: Use of AI to provide ideas | Environment design | Graphic design | 3.582 | 3.736 | −0.154 | 0.227 |
| Environment design | Industrial design | 3.582 | 3.794 | −0.212 | 0.159 | |
| Graphic design | Industrial design | 3.736 | 3.794 | −0.058 | 0.714 | |
| Q10-3: Use AI to optimize design solutions and enhance details | Environment design | Graphic design | 3.461 | 3.038 | 0.423 | 0.001 ** |
| Environment design | Industrial design | 3.461 | 2.444 | 1.017 | 0.000 ** | |
| Graphic design | Industrial design | 3.038 | 2.444 | 0.593 | 0.000 ** | |
| Q10-4: Using AI to do the presentation and expression of design solutions | Environment design | Graphic design | 3.319 | 2.877 | 0.442 | 0.002 ** |
| Environment design | Industrial design | 3.319 | 2.302 | 1.018 | 0.000 ** | |
| Graphic design | Industrial design | 2.877 | 2.302 | 0.576 | 0.001 ** | |
| Q10-5: Use AI to validate and reflect on the designed solution | Environment design | Graphic design | 3.277 | 2.443 | 0.833 | 0.000 ** |
| Environment design | Industrial design | 3.277 | 2.270 | 1.007 | 0.000 ** | |
| Graphic design | Industrial design | 2.443 | 2.270 | 0.174 | 0.353 | |
| Q11-1: The desired result can be obtained through simple Q&A and debugging | Environment design | Graphic design | 3.234 | 2.972 | 0.262 | 0.033 * |
| Environment design | Industrial design | 3.234 | 2.524 | 0.710 | 0.000 ** | |
| Graphic design | Industrial design | 2.972 | 2.524 | 0.448 | 0.003 ** | |
| Q11-2: The desired result can be obtained through continuous Q&A and debugging | Environment design | Graphic design | 3.475 | 3.547 | −0.072 | 0.583 |
| Environment design | Industrial design | 3.475 | 4.032 | −0.557 | 0.000 ** | |
| Graphic design | Industrial design | 3.547 | 4.032 | −0.485 | 0.003 ** | |
| Q12-1: Usually, they are directly adopted | Environment design | Graphic design | 2.922 | 2.660 | 0.262 | 0.029 * |
| Environment design | Industrial design | 2.922 | 2.222 | 0.700 | 0.000 ** | |
| Graphic design | Industrial design | 2.660 | 2.222 | 0.438 | 0.003 ** | |
| Q12-2: Usually adapt and think about the results before adopting them | Environment design | Graphic design | 3.695 | 3.840 | −0.145 | 0.204 |
| Environment design | Industrial design | 3.695 | 3.937 | −0.241 | 0.072 | |
| Graphic design | Industrial design | 3.840 | 3.937 | −0.097 | 0.491 | |
| Q12-3: Usually, these are not adopted | Environment design | Graphic design | 2.794 | 2.160 | 0.634 | 0.000 ** |
| Environment design | Industrial design | 2.794 | 2.111 | 0.683 | 0.000 ** | |
| Graphic design | Industrial design | 2.160 | 2.111 | 0.049 | 0.760 | |
| Q13: Have you ever questioned or reflected on the results provided by an AI program? | Environment design | Graphic design | 3.603 | 3.594 | 0.008 | 0.938 |
| Environment design | Industrial design | 3.603 | 3.841 | −0.238 | 0.064 | |
| Graphic design | Industrial design | 3.594 | 3.841 | −0.247 | 0.068 | |
| Q14: Do you think you have become dependent on the AI program? | Environment design | Graphic design | 3.014 | 2.726 | 0.288 | 0.005 ** |
| Environment design | Industrial design | 3.014 | 2.540 | 0.475 | 0.000 ** | |
| Graphic design | Industrial design | 2.726 | 2.540 | 0.187 | 0.135 |
* p < 0.05; ** p < 0.01.
ANOVA.
| Q3: What Is Your Grade Level? (Mean ± Std. Deviation) | F | p | ||||
|---|---|---|---|---|---|---|
| Fresh | Sophomore | Junior | Senior | |||
| Q9-1: Text-based AI | 3.67 ± 1.01 | 3.57 ± 0.94 | 3.67 ± 1.03 | 4.44 ± 0.84 | 13.118 | 0.000 ** |
| Q9-2: Image-based AI | 2.76 ± 1.14 | 2.91 ± 1.11 | 3.16 ± 1.30 | 2.85 ± 0.98 | 1.708 | 0.165 |
| Q9-3: Plug-in class AI in design tool software | 2.80 ± 1.14 | 3.02 ± 1.20 | 2.85 ± 1.34 | 2.13 ± 1.03 | 8.305 | 0.000 ** |
| Q10-1: Use AI to research relevant materials, knowledge, and background | 3.55 ± 0.91 | 3.49 ± 0.96 | 3.68 ± 0.94 | 4.55 ± 0.75 | 22.228 | 0.000 ** |
| Q10-2: Use of AI to provide ideas | 3.46 ± 0.94 | 3.56 ± 1.01 | 3.66 ± 1.03 | 4.08 ± 0.87 | 5.937 | 0.001 ** |
| Q10-3: Use AI to optimize design solutions and enhance details | 3.16 ± 1.14 | 3.30 ± 0.99 | 3.26 ± 1.18 | 2.66 ± 0.89 | 5.781 | 0.001 ** |
| Q10-4: Using AI to do the presentation and expression of design solutions | 3.11 ± 1.11 | 3.23 ± 1.09 | 3.04 ± 1.23 | 2.38 ± 0.93 | 9.034 | 0.000 ** |
| Q10-5: Use AI to validate and reflect on the designed solution | 3.09 ± 1.11 | 3.21 ± 1.04 | 2.92 ± 1.30 | 1.79 ± 1.07 | 24.872 | 0.000 ** |
| Q11-1: The desired result can be obtained through simple Q&A and debugging | 3.09 ± 0.91 | 3.09 ± 0.97 | 3.12 ± 1.12 | 2.66 ± 0.88 | 3.728 | 0.012 * |
| Q11-2: The desired result can be obtained through continuous Q&A and debugging | 3.29 ± 0.91 | 3.46 ± 0.98 | 3.59 ± 1.10 | 4.18 ± 0.96 | 11.314 | 0.000 ** |
| Q12-1: Usually, they are directly adopted | 2.66 ± 0.96 | 2.83 ± 0.97 | 2.63 ± 1.10 | 2.61 ± 0.78 | 0.969 | 0.408 |
| Q12-2: Usually adapt and think about the results before adopting them | 3.68 ± 0.77 | 3.49 ± 0.86 | 3.79 ± 0.97 | 4.30 ± 0.72 | 12.853 | 0.000 ** |
| Q12-3: Usually, these are not adopted | 2.49 ± 0.99 | 2.69 ± 0.97 | 2.60 ± 1.14 | 1.90 ± 1.00 | 9.009 | 0.000 ** |
| Q13: Have you ever questioned or reflected on the results provided by an AI program? | 3.54 ± 0.84 | 3.49 ± 0.84 | 3.88 ± 0.88 | 3.73 ± 0.79 | 3.554 | 0.015 * |
| Q14: Do you think you have become dependent on the AI program? | 2.91 ± 0.75 | 2.81 ± 0.75 | 2.95 ± 0.88 | 2.61 ± 0.82 | 2.618 | 0.051 |
* p < 0.05; ** p < 0.01.
Results of post hoc multiple comparative analysis.
| (I)Name | (J)Name | (I)Mean | (J)Mean | D-Value | p | |
|---|---|---|---|---|---|---|
| Q9-1: Text-based AI | Fresh | Sophomore | 3.671 | 3.567 | 0.104 | 0.485 |
| Fresh | Junior | 3.671 | 3.671 | −0.000 | 0.999 | |
| Fresh | Senior | 3.671 | 4.437 | −0.766 | 0.000 ** | |
| Sophomore | Junior | 3.567 | 3.671 | −0.105 | 0.489 | |
| Sophomore | Senior | 3.567 | 4.437 | −0.870 | 0.000 ** | |
| Senior | Junior | 3.671 | 4.437 | −0.765 | 0.000 ** | |
| Q9-2: Image-based AI | Fresh | Sophomore | 2.763 | 2.911 | −0.148 | 0.405 |
| Fresh | Junior | 2.763 | 3.164 | −0.401 | 0.032 * | |
| Fresh | Senior | 2.763 | 2.845 | −0.082 | 0.663 | |
| Sophomore | Junior | 2.911 | 3.164 | −0.253 | 0.159 | |
| Sophomore | Senior | 2.911 | 2.845 | 0.066 | 0.715 | |
| Senior | Junior | 3.164 | 2.845 | 0.319 | 0.093 | |
| Q9-3: Plug-in class AI in design tool software | Fresh | Sophomore | 2.803 | 3.022 | −0.220 | 0.235 |
| Fresh | Junior | 2.803 | 2.849 | −0.047 | 0.810 | |
| Fresh | Senior | 2.803 | 2.127 | 0.676 | 0.001 ** | |
| Sophomore | Junior | 3.022 | 2.849 | 0.173 | 0.355 | |
| Sophomore | Senior | 3.022 | 2.127 | 0.895 | 0.000 ** | |
| Senior | Junior | 2.849 | 2.127 | 0.723 | 0.000 ** | |
| Q10-1: Use AI to research relevant materials, knowledge, and background | Fresh | Sophomore | 3.553 | 3.489 | 0.064 | 0.650 |
| Fresh | Junior | 3.553 | 3.685 | −0.132 | 0.371 | |
| Fresh | Senior | 3.553 | 4.549 | −0.997 | 0.000 ** | |
| Sophomore | Junior | 3.489 | 3.685 | −0.196 | 0.168 | |
| Sophomore | Senior | 3.489 | 4.549 | −1.060 | 0.000 ** | |
| Senior | Junior | 3.685 | 4.549 | −0.864 | 0.000 ** | |
| Q10-2: Use of AI to provide ideas | Fresh | Sophomore | 3.461 | 3.556 | −0.095 | 0.529 |
| Fresh | Junior | 3.461 | 3.658 | −0.197 | 0.215 | |
| Fresh | Senior | 3.461 | 4.085 | −0.624 | 0.000 ** | |
| Sophomore | Junior | 3.556 | 3.658 | −0.102 | 0.504 | |
| Sophomore | Senior | 3.556 | 4.085 | −0.529 | 0.001 ** | |
| Senior | Junior | 3.658 | 4.085 | −0.427 | 0.009 ** | |
| Q10-3: Use AI to optimize design solutions and enhance details | Fresh | Sophomore | 3.158 | 3.300 | −0.142 | 0.388 |
| Fresh | Junior | 3.158 | 3.260 | −0.102 | 0.554 | |
| Fresh | Senior | 3.158 | 2.662 | 0.496 | 0.005 ** | |
| Sophomore | Junior | 3.300 | 3.260 | 0.040 | 0.811 | |
| Sophomore | Senior | 3.300 | 2.662 | 0.638 | 0.000 ** | |
| Senior | Junior | 3.260 | 2.662 | 0.598 | 0.001 ** | |
| Q10-4: Using AI to do the presentation and expression of design solutions | Fresh | Sophomore | 3.105 | 3.233 | −0.128 | 0.455 |
| Fresh | Junior | 3.105 | 3.041 | 0.064 | 0.722 | |
| Fresh | Senior | 3.105 | 2.380 | 0.725 | 0.000 ** | |
| Sophomore | Junior | 3.233 | 3.041 | 0.192 | 0.267 | |
| Sophomore | Senior | 3.233 | 2.380 | 0.853 | 0.000 ** | |
| Senior | Junior | 3.041 | 2.380 | 0.661 | 0.000 ** | |
| Q10-5: Use AI to validate and reflect on the designed solution | Fresh | Sophomore | 3.092 | 3.211 | −0.119 | 0.500 |
| Fresh | Junior | 3.092 | 2.918 | 0.174 | 0.347 | |
| Fresh | Senior | 3.092 | 1.789 | 1.303 | 0.000 ** | |
| Sophomore | Junior | 3.211 | 2.918 | 0.293 | 0.100 | |
| Sophomore | Senior | 3.211 | 1.789 | 1.422 | 0.000 ** | |
| Senior | Junior | 2.918 | 1.789 | 1.129 | 0.000 ** | |
| Q11-1: The desired result can be obtained through simple Q&A and debugging | Fresh | Sophomore | 3.092 | 3.089 | 0.003 | 0.983 |
| Fresh | Junior | 3.092 | 3.123 | −0.031 | 0.845 | |
| Fresh | Senior | 3.092 | 2.662 | 0.430 | 0.008 ** | |
| Sophomore | Junior | 3.089 | 3.123 | −0.034 | 0.822 | |
| Sophomore | Senior | 3.089 | 2.662 | 0.427 | 0.006 ** | |
| Senior | Junior | 3.123 | 2.662 | 0.461 | 0.005 ** | |
| Q11-2: The desired result can be obtained through continuous Q&A and debugging | Fresh | Sophomore | 3.289 | 3.456 | −0.166 | 0.283 |
| Fresh | Junior | 3.289 | 3.589 | −0.300 | 0.066 | |
| Fresh | Senior | 3.289 | 4.183 | −0.894 | 0.000 ** | |
| Sophomore | Junior | 3.456 | 3.589 | −0.133 | 0.393 | |
| Sophomore | Senior | 3.456 | 4.183 | −0.728 | 0.000 ** | |
| Senior | Junior | 3.589 | 4.183 | −0.594 | 0.000 ** | |
| Q12-1: Usually, they are directly adopted | Fresh | Sophomore | 2.658 | 2.833 | −0.175 | 0.243 |
| Fresh | Junior | 2.658 | 2.630 | 0.028 | 0.860 | |
| Fresh | Senior | 2.658 | 2.606 | 0.052 | 0.742 | |
| Sophomore | Junior | 2.833 | 2.630 | 0.203 | 0.181 | |
| Sophomore | Senior | 2.833 | 2.606 | 0.228 | 0.137 | |
| Senior | Junior | 2.630 | 2.606 | 0.025 | 0.879 | |
| Q12-2: Usually adapt and think about the results before adopting them | Fresh | Sophomore | 3.684 | 3.489 | 0.195 | 0.136 |
| Fresh | Junior | 3.684 | 3.795 | −0.110 | 0.423 | |
| Fresh | Senior | 3.684 | 4.296 | −0.612 | 0.000 ** | |
| Sophomore | Junior | 3.489 | 3.795 | −0.306 | 0.021 * | |
| Sophomore | Senior | 3.489 | 4.296 | −0.807 | 0.000 ** | |
| Senior | Junior | 3.795 | 4.296 | −0.501 | 0.000 ** | |
| Q12-3: Usually, these are not adopted | Fresh | Sophomore | 2.487 | 2.689 | −0.202 | 0.206 |
| Fresh | Junior | 2.487 | 2.603 | −0.116 | 0.490 | |
| Fresh | Senior | 2.487 | 1.901 | 0.585 | 0.001 ** | |
| Sophomore | Junior | 2.689 | 2.603 | 0.086 | 0.593 | |
| Sophomore | Senior | 2.689 | 1.901 | 0.787 | 0.000 ** | |
| Senior | Junior | 2.603 | 1.901 | 0.701 | 0.000 ** | |
| Q13: Have you ever questioned or reflected on the results provided by an AI program? | Fresh | Sophomore | 3.539 | 3.489 | 0.051 | 0.699 |
| Fresh | Junior | 3.539 | 3.877 | −0.337 | 0.015 * | |
| Fresh | Senior | 3.539 | 3.732 | −0.193 | 0.164 | |
| Sophomore | Junior | 3.489 | 3.877 | −0.388 | 0.004 ** | |
| Sophomore | Senior | 3.489 | 3.732 | −0.244 | 0.068 | |
| Senior | Junior | 3.877 | 3.732 | 0.144 | 0.303 | |
| Q14: Do you think you have become dependent on the AI program? | Fresh | Sophomore | 2.908 | 2.811 | 0.097 | 0.437 |
| Fresh | Junior | 2.908 | 2.945 | −0.037 | 0.776 | |
| Fresh | Senior | 2.908 | 2.606 | 0.302 | 0.022 * | |
| Sophomore | Junior | 2.811 | 2.945 | −0.134 | 0.287 | |
| Sophomore | Senior | 2.811 | 2.606 | 0.205 | 0.106 | |
| Senior | Junior | 2.945 | 2.606 | 0.340 | 0.011 * |
* p < 0.05; ** p < 0.01.
Subtotal.
| Items | Q4: Have You Ever Used an AI Program? | Total | |
|---|---|---|---|
| Not Used | Used | ||
| Q15-1: Role in delivering knowledge to me | 0.000 | 3.390 | 2.158 |
| Q15-2: A tool to help improve me | 0.000 | 3.813 | 2.427 |
| Q15-3: A collaborator who needs to be trained and nurtured by me | 0.000 | 3.048 | 1.940 |
| Q16-1: AI is immaturely developed and does not respond better to my needs | 3.164 | 0.000 | 1.150 |
| Q16-2: Not having enough computer knowledge | 3.836 | 0.000 | 1.394 |
| Q16-3: No better setup to support learning | 3.492 | 0.000 | 1.269 |
| Q17-1: AI is immaturely developed and does not respond better to my needs | 0.000 | 3.552 | 2.261 |
| Q17-2: Not having enough computer knowledge | 0.000 | 3.248 | 2.068 |
| Q17-3: No better setup to support learning | 0.000 | 2.790 | 1.776 |
| Q18: Does the development of AI make you anxious about your major? | 3.616 | 0.000 | 1.314 |
| Q19: Does the development of AI make you anxious about your major? | 0.000 | 3.310 | 2.111 |
Independent t-test.
| Q4: Have You Ever Used an AI Program? (Mean ± Std. Deviation) | t | p | ||
|---|---|---|---|---|
| Not Used (n = 177) | Used (n = 310) | |||
| Q16/17-1: AI is immaturely developed and does not respond better to my needs | 3.16 ± 1.28 | 3.55 ± 0.99 | −3.474 | 0.001 ** |
| Q16/17-2: Not having enough computer knowledge | 3.84 ± 1.18 | 3.25 ± 1.02 | 5.533 | 0.000 ** |
| Q16/17-3: No better setup to support learning | 3.49 ± 1.25 | 2.79 ± 1.12 | 6.181 | 0.000 ** |
| Q18/19: Does the development of AI make you anxious about your major? | 3.62 ± 1.12 | 3.31 ± 0.81 | 3.193 | 0.002 ** |
** p < 0.01.
Appendix A
The following is the complete questionnaire of this study at the questionnaire-research stage, which will hopefully help you understand this study better. This questionnaire is mainly collected online, and in order to divide the data between “not used” and “used” AI, this study has set the option of skipping questions in Q4 of the questionnaire. Among them, those who chose “not used” could only see and answer Q5, Q6, Q16, and Q18, while those who chose “used” answered Q7, Q8, Q9, Q10, Q11, Q12, Q13, Q14, Q15, Q17, and Q19.
Content of the questionnaire.
| Questions | Options | ||||
|---|---|---|---|---|---|
| Q1: What is your gender? | □Male | ||||
| □Female | |||||
| Q2: What is your major? | □Environment design | ||||
| □Visual (graphic) design | |||||
| □Industrial (product) design | |||||
| Q3: What is your current grade level? | □Fresh (enrollment in 2023) | ||||
| □Sophomore (enrollment in 2022) | |||||
| □Junior (enrollment in 2021) | |||||
| □Senior (enrollment in 2021) | |||||
| Q4: Have you ever used an AI program? | □Not used | ||||
| □Used | |||||
| Q5: Do you think it is necessary to learn about AI program? | very unnecessary | unnecessary | general | necessary | very necessary |
| □1 | □2 | □3 | □4 | □5 | |
| Q6: Which of the following factors are more likely to influence you to use an AI program? | very unimportant | unimportant | general | important | very important |
| Q6-1: Consider personal skills and productivity gains. (PE) | □1 | □2 | □3 | □4 | □5 |
| Q6-2: AI operates more simply and is easier to understand. (EE) | □1 | □2 | □3 | □4 | □5 |
| Q6-3: Influence of friends and classmates around you. (SI) | □1 | □2 | □3 | □4 | □5 |
| Q6-4: Schools provide appropriate curricula and equipment conditions. (FC) | □1 | □2 | □3 | □4 | □5 |
| Q7: Which of the following factors influenced you to go for an AI program? | very unimportant | unimportant | general | important | very important |
| Q7-1: Consider personal skills and productivity gains. (PE) | □1 | □2 | □3 | □4 | □5 |
| Q7-2: AI operates more simply and is easier to understand. (EE) | □1 | □2 | □3 | □4 | □5 |
| Q7-3: Influence of friends and classmates around you. (SI) | □1 | □2 | □3 | □4 | □5 |
| Q7-4: Schools provide appropriate curricula and equipment conditions. (FC) | □1 | □2 | □3 | □4 | □5 |
| Q8: In what situations do you typically use AI programs? | never used | rarely used | general | frequently used | very frequently used |
| Q8-1: In the learning scenario. | □1 | □2 | □3 | □4 | □5 |
| Q8-2: In life scenarios. | □1 | □2 | □3 | □4 | □5 |
| Q9: What types of AI programs do you typically use to complete coursework and design work? | never used | rarely used | general | frequently used | very frequently used |
| Q9-1: Text-based AI (ChatGPT and Ernie Bot) | □1 | □2 | □3 | □4 | □5 |
| Q9-2: Image-based AI (Midjourney and Sora) | □1 | □2 | □3 | □4 | □5 |
| Q9-3: Plug-in class AI in design tool software (ArkoAI and PS+StableDiffusion) | □1 | □2 | □3 | □4 | □5 |
| Q10: How do you typically use AI programs to complete coursework and design work? | never used | rarely used | general | frequently used | very frequently used |
| Q10-1: Use AI to research relevant materials, knowledge, and background (design-research stage). | □1 | □2 | □3 | □4 | □5 |
| Q10-2: Use AI to provide ideas, for example, brainstorming and design proposals (initial design-thinking stage). | □1 | □2 | □3 | □4 | □5 |
| Q10-3: Use AI to optimize design solutions and enhance details (design-deepening stage). | □1 | □2 | □3 | □4 | □5 |
| Q10-4: AI is used to present and express design solutions (presentation and expression of the design-solution stage). | □1 | □2 | □3 | □4 | □5 |
| Q10-5: Use AI to validate and reflect on the designed solution (design-reflection stage). | □1 | □2 | □3 | □4 | □5 |
| Q11: Which of the following are more in line with your situation of using AI programs? When dealing with coursework and design. | very inconsistent | non-compliant | general | compliant | very consistent |
| Q11-1: The desired result can be obtained through simple Q&A and debugging. | □1 | □2 | □3 | □4 | □5 |
| Q11-2: The desired result can be obtained through continuous Q&A and debugging. | □1 | □2 | □3 | □4 | □5 |
| Q12: Which processing method do you prefer for the results provided by an AI program? When dealing with coursework and design. | very inconsistent | non-compliant | general | compliant | very consistent |
| Q12-1: Usually, they are directly adopted. | □1 | □2 | □3 | □4 | □5 |
| Q12-2: Usually adapt and think about the results before adopting them. | □1 | □2 | □3 | □4 | □5 |
| Q12-3: Usually, these are not adopted. | □1 | □2 | □3 | □4 | □5 |
| Q13: Have you ever questioned or reflected on the results provided by an AI program? | not at all | no | somewhat | yes | definitely yes |
| □1 | □2 | □3 | □4 | □5 | |
| Q14: Do you think you have become dependent on the AI program? | not at all | no | somewhat | yes | definitely yes |
| □1 | □2 | □3 | □4 | □5 | |
| Q15: For you, which of the following roles does current AI prefer? | very inconsistent | non-compliant | general | compliant | very consistent |
| Q15-1: Role in delivering knowledge to me. | □1 | □2 | □3 | □4 | □5 |
| Q15-2: A tool to help improve me. | □1 | □2 | □3 | □4 | □5 |
| Q15-3: A collaborator who needs to be trained and nurtured by me. | □1 | □2 | □3 | □4 | □5 |
| Q16: Currently, the main factor that constrains you from not using AI programs? | very inconsistent | non-compliant | general | compliant | very consistent |
| Q16-1: AI is immaturely developed and does not respond better to my needs. | □1 | □2 | □3 | □4 | □5 |
| Q16-2: Not having enough computer knowledge. | □1 | □2 | □3 | □4 | □5 |
| Q16-3: No better setup to support learning. | □1 | □2 | □3 | □4 | □5 |
| Q17: Currently, the main factor that constrains you from making better use of AI programs is? | very inconsistent | non-compliant | general | compliant | very consistent |
| Q17-1: AI is immaturely developed and does not respond better to my needs. | □1 | □2 | □3 | □4 | □5 |
| Q17-2: Not having enough computer knowledge. | □1 | □2 | □3 | □4 | □5 |
| Q17-3: No better setup to support learning. | □1 | □2 | □3 | □4 | □5 |
| Q18: Does the development of AI make you anxious about your major? | not at all | no | somewhat | yes | definitely yes |
| □1 | □2 | □3 | □4 | □5 | |
| Q19: Does the development of AI make you anxious about your major? | not at all | no | somewhat | yes | definitely yes |
| □1 | □2 | □3 | □4 | □5 | |
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Abstract
The relationship between AI and design has attracted extensive academic attention and research, and the future relationship between AI and designers relies on current design students’ knowledge of AI, in addition to technological developments. To clarify the basic situation of Chinese design-college students’ use of AI software, the basic situation and status of using AI software to participate in design work, and the current relationship with AI, this study constructs a questionnaire on the status of the use of AI programs, with the help of the UTAUT model and the general program of design as a basis. The results of the research on 487 Chinese design-college students were analyzed by frequency analysis, descriptive statistics, etc., to clarify that currently more than 60% of design students have used AI programs, which are mainly used for data collection; providing ideas for design, e.g., when brainstorming; and conceptual ideas for design. Moreover, students generally believe that AI helps to improve personal skills and work efficiency, but the in-depth application and reliance on AI is relatively low; students hold anxiety about the development of AI, especially those who have not been exposed to AI. The education sector should focus on popularizing and deepening AI education, as well as helping students establish a correct concept of AI usage.
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Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer