The purpose of BSGs is to develop practical entrepreneurial skills of participants such as analysis, decision-making, organization and management by understanding the process of establishing a company (Zulfiqar et al., 2021). Participants continuously evaluate and improve group strategic, financial, human resources and other management decisions in order to apply the acquired knowledge and skills to future entrepreneurial practice (Costin et al., 2018). BSGs reflect the strategic situation of the enterprise by simulating the operation of the enterprise and the trend of the market (Toma et al., 2020).

As a game, reflecting the attributes of the game and the characteristics of game participation is another important goal of BSGs. Game attributes such as goals, rules, levels, status, competition, results, leaderboards, rewards, etc., are the attributes of the simulation game (Fox et al., 2018), which can effectively encourage participants to learn new knowledge and take new actions. Interaction, entertainment, infectiousness, ease of learning, perceived usefulness, knowledge sharing, and knowledge applicability are the characteristics of game participation, which can encourage students to be more interested in participating in it and help them think about their future career directions (Zulfiqar et al., 2021). Fun and enjoyment, as the basic characteristics of the game, have a positive relationship with students’ interest and participation, which is conducive to improving students’ decision-making confidence and motivating students to achieve their goals (Mardani et al., 2018). It is worth mentioning that the entertainment in BSGs is not simple sensory entertainment, but with education as the ultimate goal (Pando-Garcia et al., 2016). Studies have shown that using game attributes to understand whether students have a satisfactory learning experience and pleasant evaluation during the participation process can better enhance their attitudes toward entrepreneurship (Ranchhod et al., 2014). In addition, the relatively closed and controlled atmosphere provided by BSGs also helps participants to have a strong sense of self-efficacy in them (Dumblekar and Dhar, 2021).

The entrepreneurial ability of students can be obtained in entrepreneurial education. The essence of entrepreneurship education is to gain entrepreneurial experience and problem-solving methods by participating in real-world practice (Zulfiqar et al., 2021). Basic knowledge in entrepreneurship education can be achieved through traditional classroom teaching, but the learning of entrepreneurial skills and mentality requires innovative methods, such as BSGs (Zulfiqar et al., 2021). It has been more than 50 years since BSGs were set up as a university course (Goi, 2019). Meanwhile, BSGs are widely used methods in entrepreneurship education (Fox et al., 2018). For example, 1,700 colleges and universities in the United States use BSGs to improve students’ entrepreneurial skills and entrepreneurial intentions (Zulfiqar et al., 2019). Germany also attaches great importance to BSGs in the field of entrepreneurship education (Kriz and Auchter, 2016). At present, the exploration and application of BSGs in entrepreneurship education still needs to be developed (Ratten and Jones, 2021).

From the perspective of teacher teaching, many scholars believe that entrepreneurship teachers should innovate teaching methods and use simulation games in entrepreneurship education courses to truly develop students’ entrepreneurial abilities (Faria et al., 2009; Wood et al., 2009) and overall quality (Isabelle, 2020). Using BSGs as the first link in the teaching of entrepreneurship education is conducive to better guidance and assistance for teachers in subsequent teaching (Pérez-Pérez et al., 2021). The application of BSGs for teaching is an important manifestation of teachers’ use of action pedagogy. By involving students in the daily work of entrepreneurs of BSGs (Lightner et al., 2007; Page and Mukherjee, 2007), it promotes in-depth learning. Experiential learning enables better understand entrepreneurial practice and entrepreneurship (Tenenbaum et al., 2001). Immediate feedback is an important manifestation of simulation games as a reliable teaching tool, which helps reduce students’ uncertainty about the market environment (Oblinger, 2014). As a result, BSGs also put forward higher requirements for the teacher’s “guidance” role in teaching, rather than the traditional “authoritative” teaching. In addition, some scholars have pointed out that BSGs can be used as an emerging teaching method that can serve as a reference for other disciplines beyond entrepreneurship courses (Costin et al., 2018). From the perspective of students’ learning subjects, BSGs are the practice of constructivist learning theory, because they can help students construct new knowledge and better reserve knowledge in simulated learning, which can reduce the pressure of learning (Zulfiqar et al., 2021). As a tool for students’ active learning, BSGs reflect the characteristics of demonstration, science and practice, which promote the development of knowledge and skills (Ferreira et al., 2021). The requirement of teamwork in BSGs also helps students to form a good and independent learning character (Buzady and Almeida, 2019).

The current research on AI in BSGs is still relatively rare, but it is necessary to add AI to BSGs (Fuchsberger, 2016). As a new technology with high technology, interdisciplinary (Kuleto et al., 2021), wide application (Xi, 2020), strong adaptability and stability (Yang et al., 2021), its participation can promote development of learning styles in simulation games (Fuchsberger, 2016). AI is widely used in simulation environments (Rao et al., 2011; Lam et al., 2012), especially to play a guiding role in the simulation teaching of education, and has gradually developed in the direction of intelligent expert systems (Fuchsberger, 2016). Due to the rapid development of the era of big data, it is difficult for the education system to quickly obtain the latest methods and adapt to the latest needs within a certain period of time (Ifenthaler and Egloffstein, 2020). The development of related algorithms has made the application of AI more extensive (Ye et al., 2019, Ye et al., 2021; Fu et al., 2022). AI helps greatly enrich and expand human learning, knowledge, and horizons through technologies such as perception, practice, imitation, integration, and sharing (Yang et al., 2021). The future development of AI is also closely related to human communication and cooperation (Yang et al., 2021), especially in the application of higher education with unlimited development potential (Kuleto et al., 2021). Many higher education institutions have applied artificial intelligence to teaching, providing teachers with more teaching support and better promoting students’ academic achievement (Salta et al., 2021).

Artificial intelligence in games has attracted a lot of attention and learning provides an environment for AI to be applied in various fields of learning (Agung and Gaol, 2012). Integrating AI technology with the game industry will help enrich the content and form of games, meet players’ higher pursuits and expectations for games, which promote the transformation and development of games (Xi, 2020). AI can also monitor the attention level and other mental states of participants throughout the process to understand the attractiveness of different game sessions, thereby optimizing the learning experience (Ferreira et al., 2021). According to research, in participating in educational training and learning, contemporary young students are accustomed to and like to use AI technology (Salta et al., 2021). AI has begun to show its advantages in gaming. Take the AI AlphaGo as an example, in the classic game of Go, the AlphaGo learns through various algorithms and beats the human professional players (Silver et al., 2016). AI can also compete as a player’s competitor in electronic competitions for driving simulated cars (Salta et al., 2021).

The application of AI in games is reflected in the functions of changing, creating different roles, scenarios for participants (Salta et al., 2021). It provides decision support to achieve real-time competition, challenge and serve players, which promote players to develop skills and other purposes (Salta et al., 2021). The application of AI may include the following aspects: firstly, AI in BSGs is served as simulate tutors or virtual players (Bikovska et al., 2006), and AI simulates decision-making and models expert knowledge. Secondly, AI generates simulated scenarios based on initial conditions that define the game or rules set by the game leader. Thirdly, AI can provide support for the decision-making of participants. With the emergence of AI, intelligent decision-making support systems has become a trend (Brauner et al., 2019).

From the current research, the discussion of how AI can be used in BSGs is still insufficient. Therefore, it is necessary to analyze the operation process of BSGs to understand which parts are conducive to promoting entrepreneurial learning, so as to clarify the possible application of AI in BSGs.

Individual entrepreneurial attitude refers to the individual’s preference for engaging in entrepreneurial activities or participating in organizational work (Kolvereid, 1996), and also includes the degree of evaluation of the emotional preferences and practical benefits of becoming an entrepreneur (Autio et al., 2001). Attitude refers to a person’s emotional expression of a certain thing or situation, including positive and negative attitude types (Zimmerman, 2008). Attitude triggers intention, which leads to behavior (Ajzen, 1991). The importance of entrepreneurial attitude is that for students, the improvement of entrepreneurial attitude represents to a certain extent that students have a certain understanding of entrepreneurship and a positive attitude toward entrepreneurship. Many scholars also believe that the definition of entrepreneurship education should be broader, focusing on enhancing all students’ understanding of entrepreneurship after participating in entrepreneurship education, not limited to the career that students choose to pursue in the future (Jones, 2010). Therefore, the entrepreneurial attitude is a particularly important research object for the development of entrepreneurial education.

Business simulation games encourage students to form an entrepreneurial attitude (Donckels, 1991). Some scholars mentioned that in an academic environment, virtual games are used to study entrepreneurial attitude and the impact on entrepreneurship (Curry and Moutinho, 1992). Existing studies have shown that students’ feeling of benefit and happiness in participating in BSGs is beneficial to their entrepreneurial attitude (Zulfiqar et al., 2019), which encourages them actively participate in the learning of simulation games to enhance the understanding of entrepreneurial knowledge (Liu and Huang, 2015). Participating in BSGs will also make students feel the difficulty of becoming an entrepreneur, which is in line with the attitude of many entrepreneurs toward launching entrepreneurial activities in reality (Pérez-Pérez et al., 2021).

The characteristics and game attributes of BSGs are conducive to improving students’ entrepreneurial attitude. The three important characteristics of BSGs are simulation, verification, and effectiveness (Fox et al., 2018). These three aspects need to be implemented in the game process to allow students to have a sense of reality in running a business, which is beneficial to the improvement of entrepreneurial attitude and entrepreneurial intention. BSGs encourage students to think autonomously (Thanasi-Boçe, 2020), think creatively, and actively communicate and solve problems (Zulfiqar et al., 2021). Simulation promotes students to learn in action. While giving students the identity of entrepreneurs, role play in simulation also cultivate their passionate entrepreneurial attitude and practical entrepreneurial skills (Costin et al., 2018). Competitive factors in the simulation will increase students’ enthusiasm for participation, thereby enhancing entrepreneurial attitude. At the same time, the game attributes of BSGs, such as interactive, entertaining, infectious, and fun (Zulfiqar et al., 2021), not only immerse students in the process of enjoying the game (Ferreira et al., 2021), but also make students feel satisfied and happy in the learning process. The above effect helps to improve students’ attitudes toward entrepreneurship education, thereby enhancing their learning motivation and actively participating in BSGs (Ranchhod et al., 2014).

The requirement of teamwork ability in BSGs is the key to improving students’ entrepreneurial attitude. Teamwork, task management, and conflict negotiation under the conditions of game simulation can help improve students’ attitudes toward entrepreneurial management projects. In the simulation, different participants face new projects built by new companies, so they are prone to conflicts of opinions. Participants can learn about conflict management of problems through BSGs and improve their negotiation skills to obtain the best solution. Studies have shown that this ability can lead to the changes in entrepreneurial attitude and is the key to fostering students’ entrepreneurial attitude (Arias-Aranda and Bustinza-Sánchez, 2009). Therefore, the use of BSGs as a teaching method to cultivate and enhance students’ entrepreneurial attitude has important reference value.

Different from traditional BSGs, in AI-based BSGs, teachers cannot provide offline guidance, but rather serve as supervisors. Moreover, in the teaching process, students use social media to communicate, and the behavior of students is more difficult to observe. Therefore, to study the process of BSGs, it is necessary to consider the appropriate process elements.

Strengthening the research on the development process of BSGs is conducive to in-depth analysis of the connotation, influence mechanism and change process of entrepreneurial attitude. It can improve the teaching methods of BSGs to enhance students’ entrepreneurial attitude. BSGs emphasize that students learn from experience (Costin et al., 2018)and learn while playing games. In the development process, the role of teachers, students’ participation enthusiasm, failure in the game process, enterprise performance and students’ flow experience will affect the development process and the results of BSGs. That will affect students’ entrepreneurial attitude and entrepreneurship educational learning effect. Therefore, we must take these process variables into consideration.

The role of the teacher will to a certain extent affect the attitudes, methods and results of students in the process of participating in the game. In addition to giving students learning opportunities and appropriate guidance, teachers also needs to support and encourage students to improve their learning motivation, thereby enhancing their learning performance. It can be seen that the important connotation and role of the teacher are important variables that we must consider in the development of BSGs. Many scholars mentioned that teachers have diverse roles in BSGs (Robinson et al., 2016). In fact, unlike the traditional teacher role, the role of the teacher in the BSGs is not only the imparter of knowledge, but a variety of roles including supervisors, bystanders, and supporters. This requires teachers to play different roles according to the teaching process, such as acting as a bystander role in the fair competition of multiple teams in the game, and not doing too much interference in the game process. If unfair competition occurs, the teacher needs to act as a supervisor. For teams that fail to operate, teachers need to play the role of supporters. They provide timely guidance, and prompt students to recover from business failures in a timely manner, and so on. Teachers should also become the facilitators of students’ academic work, allowing students to integrate into the classroom environment and be able to independently choose appropriate learning strategies to improve learning motivation and attitudes.

Student participation not only reflects the participant’s own learning state, but also has a certain impact on the learning effect. As a student-centered teaching method, the participation of students in BSGs is particularly important. Learning participation refers to students’ efforts in learning, which shows their commitments and actions to achieve goals (Henrie et al., 2015). Student participation is closely related to teaching methods. Because the change in the form of curriculum participation is also more concerned with mobilizing students’ learning enthusiasm and participation (Hägg and Gabrielsson, 2019). In the course of BSGs, students’ active learning methods are closely related to the transformation of teaching methods. There are three typical teaching modes in higher entrepreneurship education. There are the supply mode of passive education, the demand mode of active participation, the mode that emphasizes the ability of students to use multiple resources to actively solve problems (Nabi et al., 2017). BSGs are a typical representative of the demand model, so BSGs particularly emphasize the active participation of students. On the other hand, the stronger the enthusiasms of students to participate in learning, the more conducive to the development of games, which will promote good teaching effects and improve the quality of entrepreneurial education. Students’ active participation in learning can not only mobilize students to think deeply and memorize learning materials, but also infect and enhance teachers’ teaching enthusiasm, which is conducive to enhancing the entire classroom experience (Auman, 2011). Therefore, students with low learning enthusiasm have a lower degree of participation. Although some scholars have used student participation as a research variable in BSGs, there is no uniform standard for the definition and role of participation, which also brings challenges to research. Therefore, we try to include student participation into the scope of the research variables to study its impact on students’ participation in BSGs.

Failure in the process of simulating the game will help students experience the failure experience of entrepreneurship in advance, and learn to respond better to prepare for future entrepreneurship. This is an important way for students to fully understand the process and risks of business operations and generate entrepreneurial awareness, thereby enhancing their entrepreneurial attitude. In order to study the impact of the failure of business operations on the participants and the final game results in the simulation game, we also included the failure of business operations as a research variable. The failure of business operations has become a common phenomenon in society under the current background that the new crown pneumonia affects economic development (Hwang and Choi, 2021). Similarly, business failure is also a possible situation during the game, so it is necessary to take this situation into consideration. This is also an important manifestation of the authenticity of BSGs. It is a rare learning opportunity that embodies the practical nature of entrepreneurship education (Pérez-Pérez et al., 2021). The failure of entrepreneurship will affect the attitude, confidence and business strategy of students in the process of participation to a certain extent. In the course of the game, if the business fails to simulate the operation, it will face the risk of bankruptcy. This is a simulation of the process of entrepreneurial failure, which helps students to reflect, understand the reasons for bankruptcy, and think about where they made mistakes. Failure makes it impossible for students to continue to improve the follow-up tasks in the BSGs, but it also helps them reflect on the reasons and process of wrong operations, so as to have a deeper understanding and a more complete game experience (Hou, 2015). The failures experienced in entrepreneurship learning also help students learn to manage the negative emotions after failure, and obtain the greatest wisdom of failure on the way of life growth (Pérez-Pérez et al., 2021).

The performance of a simulated enterprise is the learning result that students focus on during the process of participation. It is also an important measure of the success of student teams in entrepreneurship. In order to study the impact of different factors of BSGs on students’ entrepreneurial attitude more comprehensively, we take the performance of simulated companies into consideration. The highest performance is also a reflection of the best performing company, achieving the game goal of defeating other competitors (Peterková and Wozniaková, 2015). Research has also demonstrated that when the performance targets of simulated firms are set too high, participants’ learning may be affected (Stamatelatos and Brooks, 2020). Most scholars evaluate the performance of simulated enterprises from the aspect of financial performance of simulated enterprises. For example, Peterková and Wozniaková (2015) mentioned that the student team will receive relevant financial statement data in each simulated business cycle to compare with other teams. But at the same time he also mentioned the importance of evaluating the individual performance of the team. In addition, some scholars believe that performance should not be a reflection of competitive rankings, but should be students’ attitudes toward courses. The performance of simulated enterprises can effectively test students’ practical entrepreneurial skills, including management ability, decision-making ability, analytical ability, teamwork ability, communication and negotiation ability, etc., which reflect participants’ good control of their own entrepreneurial attitude and ability (Thanasi-Boçe, 2020). Feedback on simulated enterprise performance can also inspire students to think about such issues as the decision-making of enterprise resource allocation, the management of internal teams and the handling of external customer relations in the simulation process. It can be seen that different scholars have different definitions of the evaluation criteria and meanings of simulating enterprise performance. The performance of simulated enterprises will undoubtedly affect the learning attitude of participants and the attitude of real entrepreneurial activities in the future.

Paying attention to the flow experience is to understand the participant’s unconscious concentration on the participation in the simulation game and the impact on the game development and results, which will help to improve the deficiencies in the teaching process of BSGs. Ferreira et al. (2021) mentioned that research on BSGs design focuses more on the experience of participants. In order to study the influence of students’ different flow experiences on participating in BSGs, we included flow experience as a variable in the study. Based on the flow theory (Csikszentmihalyi, 1990), the infectiousness of the game can prompt students to focus on the process and produce a good flow experience, which can increase learning participation and concentration (Yen and Lin, 2020). “Flow” reflects the state where participants are fully focused on something physically and mentally (Csikszentmihalyi, 1975). Studies have pointed out that compared with traditional teaching methods, students are always more likely to experience flow experience in simulation games (Liu et al., 2011). Previous stock knowledge, cooperative mode of the game (Admiraal et al., 2011; Hwang et al., 2011), clarity of game rules, and difficulty in achieving game goals (Wang and Chen, 2010; Hou and Li, 2014) will affect the flow experience of the participants. According to research, participants’ different levels of flow experience will have different effects on their behavioral performance (Hou, 2015) and learning efficiency (Hou and Li, 2014).

In this study, the experimental method was used to conduct research. The experimental group and the control group were set for comparison. The experimental group and the control group measured related variables before and after the experiment, and recorded the specific process of the experimental group. The experiment time is from May 2021 to July 2021. The simulation software used in this experiment is TOP-BOSS, which was used as a competition software for business simulation business competitions. The software trains participants to analyze environmental information, deal with group relationships, and make decisions by simulating the actual business conditions of the enterprise. During the COVID-19 pandemic, the company developing the software has developed BSGs that can be run on mobile phones and tablets to accommodate teaching needs. The experimental group used the BSGs based.

The experimental group is the students participating in the BSGs training course. This course is a compulsory course, which means that students must participate in the course regardless of their original entrepreneurial intentions. Students with different entrepreneurial mentalities participating in the simulation can better observe the role of the course in entrepreneurship education. A difficult problem in the existing research on entrepreneurship education is that the causal relationship is unclear. Do students with high entrepreneurial intentions actively participating in entrepreneurial education, or can entrepreneurial education enhance entrepreneurial intentions? Compulsory courses can reflect the effect of entrepreneurship education on students with different initial states. A total of 105 students in the experimental group participated, and the students voluntarily participated in this survey. The collected results were processed, in which the students who participated in both the pre-test and the post-test were retained. And the unreasonable questionnaire results were eliminated. The data of a total of 83 people were retained.

The control group consisted of business students who did not participate in the simulation training course. The experimental group was in the same school, the same college, and the same grade, with a total of 100 students. The reason is the same school has roughly the same level of intelligence. The same college has basically the same external entrepreneurship support policies, and the influence of external factors can be controlled. The same grade means the education received is roughly the same. The collected results were processed, in which the students who participated in both the pre-test and the post-test were retained. And the unreasonable questionnaire results were eliminated. The data of a total of 83 people were retained.

The data analysis and processing of this study includes two aspects. One is to conduct a paired sample T-test on the pre-test and post-test. Entrepreneurial attitude of the control group and the experimental group are observed whether there is any change. The second is to use fsQCA fuzzy set qualitative comparative analysis to explore entrepreneurship. How do the antecedents and conditions of attitude improvement interact? Qualitative comparative analysis (QCA) has been widely used in recent years. Based on the principle of Boolean algebra, it can analyze different combinations of conditional variables and reveal complex causal relationships. The causal variables in this study include both categorical and continuous variables, so the fuzzy set qualitative comparison method (fsQCA) is used. In the variable calibration, the 95%, 50%, and 5% (Fiss, 2011) quantile values of the three variables of EA, PA, and FE are used as the threshold values of complete membership, intersection point, and complete non-membership. In the configuration analysis, the consistency threshold is set to 0.8, the case coverage threshold is 1, and the PRI value is 0.75.

Table 1 is descriptive statistics. The average value of EA2 in the control group is 22.49, which is higher than EA1, and the average value of EA2 in the experimental group is also higher than EA1, indicating that the average level of entrepreneurial attitude has increased at the two time points. Whether there is a significant change in entrepreneurial attitude still needs further verification.

A paired-sample T-test was used for the main variables of the control group and the experimental group. The results in Table 2 showed that from the results of the control group, although the mean value of EA2 was 0.468 higher than that of EA1, the difference in the mean value was not significant from the results of the T-test. It shows that for the control group, the entrepreneurial attitude of the pre-test and post-test did not change significantly. For the experimental group, the mean value of EA2 is 0.872 higher than that of EA1, which is significant at the level of 0.01. The experimental group’s entrepreneurial attitude has undergone a significant change.

A necessary analysis of a single variable can determine whether the condition is a necessary condition for the result. According to Ragin (2010), when the consistency level is higher than 0.9, this condition is a necessary condition for the result. The results in Table 3 show that the consistency of all the conditional variables is less than 0.9, indicating that the necessary conditions do not exist. It is indicating that the improvement of entrepreneurial attitude cannot be explained by a separate condition, that is, the improvement of entrepreneurial attitude is not caused by What is determined by a single fixed variable is the result of multiple factors. Therefore, it is necessary to configure and analyze the condition variables to find out the combination of multiple conditions.

Configuration analysis can reveal the adequacy of the results caused by multiple configurations. Fuzzy set analysis can obtain three solutions: complex connection, simple solution and intermediate solution. The intermediate solution is the most commonly used result in fsQCA, which is close to the theoretical reality without being too complicated. Combining simple solutions can determine the core conditions of each condition combination and marginal conditions. According to Ragin’s suggestion, the consistency threshold is set to 0.8, the case coverage threshold is set to 1, and the PRI value is set to 0.75. The results in Table 4 show that the agreement between the single solution and the overall solution is greater than 0.8, which is higher than the 0.75 suggested by Ragin, and the result is highly effective. The coverage of the overall solution is 0.390, which is relatively low, indicating that there may be other conditions that have not been considered in this study.

In this study, the consistency threshold was adjusted from 0.8 to 0.85, and the variable cross-cutting value was adjusted between -25% and 25%. The configuration quantity, configuration form, consistency and coverage rate did not change substantially, so the above research conclusions are robust.

Based on the above results, we have identified five modes for mobile-based simulation games to enhance college students’ entrepreneurial attitude, which are shown in Figure 1.

(1) Dare to challenge. Configuration 1 shows that when the teacher participates in the competition, the student’s team has good business performance and no major failures. Even if the course’s flow experience is not good, it can still effectively improve the entrepreneurial attitude. Teachers with relatively rich experience participated in the competition as participants. Some students showed a sense of competition and tried to fight against teachers in competition. According to the observation results during the competition, some student teams attach great importance to the division of labor and cooperation, so the atmosphere of cooperation is strong. They will adopt a variety of strategies to make decisions, such as imitating the teacher’s business strategy to make decisions, analyzing the strategies of competitors, and even adopting other strategies. The student team conducts an alliance against the company run by the faculty to prove the confrontation. Judging from the results of the competition, these students often rank in the forefront of competition results. This attitude of being strong when it comes to strength can effectively enhance the entrepreneurial attitude.

(2) Participation and enjoyment. Configuration 2 shows that when teachers participate in the competition, students actively participate without major failures, have a better experience of the course flow, and can improve their entrepreneurial attitude. This part of students pays special attention to participation in the competition process. Teacher participation can bring a different experience, and their own participation is high. The course experience is good. In addition, they have not encountered major failures in the business process, which may give students a kind of business operation. The feeling of relative smoothness can also improve the entrepreneurial attitude to a certain extent.

(3) Focus on experience. Configuration 3 shows that when the teacher does not participate and the students’ enthusiasm participation is not high, if the students do not experience failure, but the curriculum flow experience is better. The entrepreneurial attitude can still be effectively improved. This reflects the importance of flow experience in BSGs courses. Even if other factors are not particularly ideal, flow experience has a positive effect on improving self-efficacy. This inspired the need to pay attention to the role of flow experience in the curriculum design of BSGs. In the course design, factors such as how to match skills with challenges and ensuring the playability of the game all need to be considered. During the operation of the game, how to let students set clear goals and concentrate attention also need to be paid attention to. After the course is over, it is necessary to summarize and reflect on the effects and consequences of the flow experience, and to be able to accumulate experience in the process of repeated cycles.

(4) Focus on participation. Configuration 4 shows that when the teacher does not participate in the competition, the experience of the course flow experience is poor and the performance is not ideal, but the students actively participate. Even if they encounter a major failure, the entrepreneurial attitude is still improved. In this state, students have put in more effort, but they still encounter failures and poor business performance. This part of the students may have a “do their best” mentality, not focusing on grades and course experience, but caring about whether they work hard to participate.

According to the above analysis, we found the possible application ways of AI in BSGs, specifically including the following ways.

Artificial intelligence participates in BSGs in various capacities such as mentors or players (Bikovska et al., 2006). AI can mimic human behavior in the game (Pavlicek et al., 2014). Depending on teaching needs, AI may need to simulate participants at different levels, for example, creating comparable competitors and game modes for participants of different skill and experience levels (Dreżewski and Solawa, 2021). In the existing BSGs, there are no qualified computer players. The reason is that the strategies of BSGs are diversified, and it is difficult for existing software to respond accordingly according to the strategies of human players. AI can make corresponding strategies according to different algorithms through machine learning. The way the AI plays the player is beneficial. For example, AI can act as a guide for game novice players, guiding players to master basic game strategies. This includes not only an explanation of the game’s content, form, method, etc., but also guidance on the deficiencies of the current participants in the simulation process (Bikovska et al., 2006). AI can also act as a strong competitor, thereby simulating the situation that a company may encounter a strong competitor in reality. The simulation method is undoubtedly beneficial to the exercise of participants’ psychological quality and the improvement of strategic decision-making skills (Fuchsberger, 2016).

Artificial intelligence can provide targeted feedback on business failures. Learning from failure is an important part of entrepreneurial learning. In BSGs, it is inevitable that participants will fail in their operations. This requires participants to reflect, and thus to learn from failure. Existing teaching models rely on teachers’ guidance to repair failures. However, due to the limited ability and time of teachers, it is difficult to provide detailed guidance and analysis. If AI can analyze the failures encountered and provide targeted feedback, it will be very beneficial to repair the confidence of the participants. For example, the automated supply chain of AI can predict possible future supply chain disruptions and give early warnings through the operations of participants, so as to remind risks and intervene in time (Brauner et al., 2019). AI can find suitable operation paths for players in the game world, so as to maximize the effectiveness of the player character and smoothly pass through all obstacles (Dreżewski and Solawa, 2021). Through this learning method, participants can truly learn from failure, accumulate experience, and build confidence for the next venture.

Artificial intelligence can also start from improving the flow experience and enhance the entrepreneurial attitude of participants. The flow experience of the BSGs is very important. Even if some participants are not good in all aspects, but the flow experience is good, they can still improve their entrepreneurial attitude. AI will provide unique and compelling experiences based on the situation of different participants, not limited to the goal of seeking maximum success for participants (Dreżewski and Solawa, 2021). What’s more, AI can also detect the participants’ breathing rate and other physical data to understand their adaptability to the game process, so as to achieve the goal of maximizing the physical and mental satisfaction of the participants (Xi, 2020). AI can provide the development and transformation of different scenarios according to the different characteristics of participants through the initial definition of conditions and continuous identification of the operating behavior of the system, which is conducive to the participants’ immersive experience and promoting the flow experience (Bikovska et al., 2006). Furthermore, Players have a variety of behaviors and reactions during the game, such as cheering in the process of turning losses into profits, applauding when the rankings are announced, and so on. If AI can collect this information, it will be able to determine which aspects of the participant experience the best, so as to provide feedback for the design of the game. For example, AI can change the emotional state of participants by adjusting goals or paths (Dreżewski and Solawa, 2021), which is beneficial for participant flow experience and the improvement of entrepreneurial attitude.

Artificial intelligence can also provide timely feedback on business performance. In the process of BSGs, business performance is an indicator that participants value very much. Due to the short decision-making time and the large number of participating teams, it is difficult for teachers to provide targeted guidance for each company. It is very important for participants to learn how to analyze business decisions. If the teacher fails to answer the confusion in the business process in a timely manner, the participants may lose interest. These problems can be avoided if AI can provide feedback when participants need it. Timely feedback is beneficial for improving student engagement and achievement. Research has shown that AI can use data to understand feedback students’ growth dynamics (Ryoo and Winkelmann, 2021). Participants are able to gain a continuous decision-making experience through AI, and understand the impact of the decision on present and future situations through immediate performance to make better responses in a timely manner (Bikovska et al., 2006). AI can also set up repetitive simulations, giving participants the opportunity to re-engage in decision-making, which is consistent with real-world business environments (Bikovska et al., 2006). The logic analysis and decision optimization of AI are the functions that students think have the most development potential and the highest utilization (Fuchsberger, 2016). In addition, there are real-time follow-up and feedback requirements such as path-finding, analytical decision-making, and teamwork (Dreżewski and Solawa, 2021).

Artificial intelligence plays a role in improving student engagement. The fun of the game, the stability of the technology, the simulation degree of simulation and other factors all have an impact on the degree of participation of the students. Enhancing student engagement can start from different solutions such as the game project itself, the problems in the simulation process, and the way of teaching behavior (Larraza-Mendiluze et al., 2020). Studies have shown that participants can improve self-efficacy and enhance trust in technology when interacting with AI technology (Brauner et al., 2019). Human-machine cooperation also includes not only the vertical process of strategic analysis and decision-making, but also the horizontal aspects of data collection and analysis (Yang et al., 2021). In order to achieve the best functional effect, AI systems are also continuously improved internally as external actors and other environments change (Yang et al., 2021). During the game, AI can collect relevant data on the factors affecting student participation, and integrate this data to provide game designers with suggestions for improvement, which can promote the improvement of simulation games and the improvement of student participation.

The limitation of this article is that when using fuzzy set qualitative comparative analysis to discuss the teaching effect of mobile BSGs, the coverage of the overall solution is low, indicating that there are other factors that have not been considered in this study. The teaching process of BSGs covers a variety of factors, and future research can continue to explore the role of other factors.

Another major flaw is that several application directions of AI in BSGs proposed in this study may be technically difficult to achieve. This study considers how AI and BSGs can be combined based on the ways in which existing BSGs enhance entrepreneurial attitude. AI has not really been involved in BSGs, which may present technical difficulties. In addition, the effect of AI’s real involvement in BSGs remains to be verified.