The Role of Artificial Intelligence Autonomy in Higher Education: A Uses and Gratification Perspective
The proliferation of AI applications in education has also attracted scholars’ attention. Most recent studies focus on the perspective of the teacher and demonstrate that through the benefits provided by the autonomy of AI educators, teachers are able to free themselves from tedious teaching tasks such as homework correction, error analysis, personalized weakness analysis, and even basic knowledge teaching. In fact, autonomous AI educators can not only replace teachers in completing certain teaching tasks, but can also actively replace students in completing some learning processes, such as collecting learning materials, developing learning plans, etc. However, how students in higher education perceive this technology and whether they are willing to use such autonomous AI educators is still unknown. Considering that the student is the most central entity in the learning process, the current study attempts to investigate how different levels of AI autonomy will change students’ perceptions and intentions to use AI educators.
3. Research Model and Hypotheses Development
3.1. Categorizing the Artificial Autonomy of AI Educators
Specifically, sensing autonomy refers to the ability of AI to autonomously sense the surrounding environment. For example, AI educators can see things in the environment through a camera, hear surrounding sounds through sound sensors, sense what is happening around them through sensors, and recognize the user’s current biological status (such as their heart rate, skin electricity, blood pressure, etc.) through wearable devices. Thought autonomy refers to the ability of AI to make decisions or plans independently. For example, AI educators can determine the video and practice list based on the user’s progress, learning habits, and current status (e.g., emotions, flow, tiredness). AI educators can determine the start and end times of learning based on the user’s past preferences and habits. AI educators can also establish their next learning plan based on the user’s learning habits and current learning progress. These all reflect AI’s ability to think, reflect, and make decisions or plans independently. Action autonomy refers to the ability of AI to act, execute plans, or perform certain behavioral activities independently. For example, AI educators can autonomously play and stop the current video list. AI educators can autonomously remind users to complete exercise questions, grade test papers, and generate score analysis reports. AI educators can also execute teaching plans, teach specific chapters independently, and actively perform tutoring tasks.
3.2. Identifying the U&G Benefits of AI Educators
3.3. Hypotheses Development
3.3.1. The Sensing Autonomy and Usage Intention of AI Educators
The sensing autonomy of AI educators is positively related to usage intention due to the mediating effect of information-seeking gratification.
The sensing autonomy of an AI educator is positively related to usage intention due to the mediating effect of social interaction gratification.
The sensing autonomy of an AI educator is positively related to usage intention due to the mediating effect of entertainment gratification.
3.3.2. The Thought Autonomy and Usage Intention of AI Educators
The thought autonomy of an AI educator is positively related to usage intention due to the mediating effect of information-seeking gratification.
The thought autonomy of an AI educator is positively related to usage intention due to the mediating effect of social interaction gratification.
The thought autonomy of an AI educator is positively related to usage intention due to the mediating effect of entertainment gratification.
3.3.3. The Action Autonomy and Usage Intention of AI Educators
The action autonomy of an AI educator is positively related to usage intention due to the mediating effect of information-seeking gratification.
The action autonomy of an AI educator is positively related to usage intention due to the mediating effect of social interaction gratification.
The action autonomy of an AI educator is positively related to usage intention due to the mediating effect of entertainment gratification.
4.1. Sampling and Data Collection
Due to COVID-19, students have widely adapted to online education. An increasing number of brands and schools are trying to develop “AI teachers” products that provide AI-based intelligent learning services for students. For example, an AI educator developed by iflytek is responsible for teaching knowledge for all subjects in primary and secondary schools, interacting with students, and generating personalized mind maps. The AI educator Khanmigo, developed by Khan Academy, can teach students mathematics and computer programming. In general, online AI-based education applications are now able to cover the full scope of teaching, self-learning, and exam preparation through AI applications, generate personalized knowledge graphs for students, offer intelligent correction and rapid diagnosis, identify the weak points behind incorrect questions, and provide targeted practice exercises. In addition, several AI teachers are able to afford multi-scenario dialogues, supervise students’ learning, and accompany students in their daily lives through humorous real-time interactions.
To provide participants with examples of AI educators, this study first provided them with a video introduction (50 s) describing the services provided by AI educators. The video showcases an AI education application based on tablets from the perspective of students. In the video, students can interact with an AI educator by clicking on the screen, through a voice wake-up, or via text commands. The online teaching functions of AI virtual educators are introduced, including online course teaching, intelligently tracking students’ progress in real-time, independently analyzing knowledge gaps and visualizing mind maps, automatically developing personalized learning journeys, and actively interacting with students (e.g., answering questions, reminding students to start learning). Participants were told that this AI education application for college students has not yet been launched on the market, so the brand hoped to investigate the attitude of college students towards the AI-teacher product before the product was launched. The brand name was hidden, and no brand-related information was provided in the video. After watching the video, participants were asked to evaluate their perception and attitude towards the AI-educator product. We first conducted a pilot study by collecting 50 responses from college students to the questionnaire and made some minor modifications in terms of language and clarity. All participants in the pilot study were excluded from the main survey. A total of 673 unique responses were collected in November 2023.
4.2. Measurement Scales
4.3. The Profiles of Respondents
Firstly, 69.39% of the respondents were undergraduate students aged 17 to 22, with 51.18% being female. In total, 15.63% of the respondents were first-year students, 18.85% were second-year students, 35.33% were third-year students, and 30.19% were fourth-year students.
Secondly, 24.96% of the respondents were master’s students aged 21 to 25, with 48.21% being female. In total, 55.36% of the respondents were first-year master’s students, 32.14% are second-year master’s students, and 12.50% are third-year master’s students.
Finally, 5.65% of the respondents were doctoral students aged 22 to 29, with 50.00% being female. In total, 71.05% of the respondents were first-year doctoral students, 18.42% were second-year doctoral students, 10.53% were third-year and above doctoral students.
Overall, 50.37% of the participants were female. No participants had used AI educators, and those who had used were labeled as invalid samples. A total of 84.84% of participants had used AI applications other than AI educators, and only 15.16% of participants had not used them. Regarding experience in participating in online education, 89.01% of participants stated that they frequently participated, 6.98% of participants stated that they have participated, but not much, and 4.01% of participants had almost never participated.
Drawing on the uses and gratification theory, our study aims to analyze how the artificial autonomy of an AI educator leads to students’ usage intentions by improving user gratification. Specifically, regarding the U&G benefits, we focus on the three most salient and robust dimensions: information-seeking gratification, social interaction gratification, and entertainment gratification. Previous research has highlighted the various categories of AI autonomy and their importance; however, to our knowledge, there are few studies that classify autonomy into multiple types, as distinct influencing factors for usage intention. To this end, this study proposes a novel theoretical model that takes the sensing autonomy, though autonomy, and action autonomy of AI educators as factors of intention to use and examine the intermediary role of user gratifications (i.e., information-seeking gratification, social interaction gratification, and entertainment gratification). By doing this, our findings provide new insights into perceptions of how artificial autonomy motivates users to use an AI educator through multiple gratifications.
6.1. Theoretical Contributions
Second, we contribute to the artificial autonomy literature by disclosing its power in determining user gratification and usage. Although there are booming studies on the effects of AI design features, such as anthropomorphism, responsiveness, and personalization, very few efforts have been devoted to examining the effect of artificial autonomy. More importantly, prior studies have reached mixed conclusions on the influence of artificial autonomy. Our study provides an integrated perspective to investigate how different types of artificial autonomy affect distinct user gratification and further influence usage intention in the context of higher education, which, to some extent, can reconcile the mixed findings. Specifically, our findings show that students in higher education are motivated to use AI educators by different benefits, and the different benefits are influenced by distinctive types of artificial autonomy. For example, we find that sensing autonomy enables AI educators to fulfill social interaction and entertainment needs, but is not able to increase information-seeking gratification. The thought autonomy of AI educators increases students’ information-seeking and social interaction gratifications, but is not related to entertainment gratification. Action autonomy induces students to use AI educators through their information-seeking and entertainment motivations, but cannot motivate student usage by satisfying social interaction needs. Therefore, our findings emphasize the nonidentical effects of artificial autonomy in AI educators on students’ usage intention through the dynamic mediating paths of multiple user gratifications.
Third, our study reveals the significant power of leveraging the U&G theory to investigate the impact of AI design features on AI usage intentions. The U&G theory has a long history of development. A large number of scholars have drawn on this theory to investigate the antecedent factors and consequent outcomes of multiple gratifications. However, very few previous studies have drawn on the U&G theory to examine the role of artificial autonomy in improving AI usage. Our findings disclose the power of the U&G theory in two ways. With regard to the antecedent factors of gratification, our findings disclose different factors of distinct gratifications. Specifically, students’ information-seeking gratification is positively associated with the thought autonomy and action autonomy of AI educators. Social interaction gratification is increased by sensing autonomy and thought autonomy. Entertainment gratification is enhanced by sensing autonomy and action autonomy. Regarding the consequent outcomes of gratifications, we find that, in the context of higher education, information-seeking, social interaction, and entertainment gratifications are all positively related to AI educator usage intentions. Our findings highlight the distinct role of different types of user gratification in the effects of AI autonomous features on usage intention, which extends the extant understanding of the effects of artificial autonomy and how students’ use of AI educators is driven by different motivations in the context of higher education.
6.2. Practical Implications
Our study also has several practical implications. Although AI education is not a new concept, AI technology is still far from widespread in higher education. How AI educators should be designed to promote students’ use intention remains a challenge for suppliers and designers. While it is important for higher education schools and teachers to implement innovative technologies from a sustainable perspective, for those technologies to be deeply involved in the learning process, it is first necessary to understand how students perceive the technologies, particularly their different motivations to use them. In other words, better understanding student gratification and the intention to use an AI educator is a critical first step in implementing AI technology to effectively improve the sustainable development of higher education. Our findings highlight important areas that the suppliers and designers of AI educators need to consider, such as the autonomous design of AI educators and the gratifications that motivate students in higher education to use AI educators.
First, our study offers insights from the student perspective into how students perceive and react to AI educators with different types of artificial autonomy. Our findings provide specific guidelines for the suppliers of AI educators to consider, such as the important roles of information-seeking, social interaction, and entertainment gratifications in inducing students to use AI educators. Additionally, while all three gratifications were identified as significant benefits that should be associated with AI educators, it is important for suppliers to understand that students in higher education may pay more attention to particular gratifications in their autonomous AI educator usage. Thus, it is recommended to give the highest priority to satisfying students’ distinct needs according to the autonomous design of AI educators when suppliers do not have sufficient capacity to guarantee all gratifications.
Second, our findings identify important autonomous features of AI educators for designers to consider when designing differentiated AI educators. The findings of our study show that sensing autonomy plays a significant role in social interaction and entertainment gratifications, thought autonomy is essential for information-seeking and social interaction gratifications, while action autonomy is critical to increasing information-seeking and entertainment gratifications. When designing AI educators with different usage purposes, such as designing for social interaction with students, or designing for students seeking information, designers should consider corresponding strategies that attach different types of autonomous features to AI educators to enhance students’ usage intentions.
Third, our study provides specific guidelines for the alignment of suppliers and designers of AI educators. In some cases, the requirements proposed by the supplier cannot be met by the designer, and our findings offer possible solutions to such contradictions. For example, when designers are unable to provide the AI educator feature of thought autonomy required by suppliers, our findings suggest that designers can provide action autonomy to meet users’ information-seeking needs and provide sensing autonomy to satisfy users’ entertainment needs, to achieve similar effects as the provision of thought autonomy. Similarly, when sensing autonomy cannot be offered, our findings demonstrate that designers can provide thought autonomy to increase social interaction gratification and provide action autonomy to enhance entertainment gratification. When suppliers require a higher level of information-seeking and entertainment gratifications, which should be induced by action autonomy, our findings recommend that designers attach a sensing autonomy feature to satisfy the need for entertainment and to increase thought autonomy to improve information-seeking gratification.
6.3. Limitations and Future Directions
This study still suffers from several limitations, which provide possible directions for future research. First, our study collected data from 673 college students in China. Future research is recommended to take cultural factors into consideration and extend our research model to other countries. Secondly, this study adopted a survey method to verify the influence path of AI-educator autonomy on students’ usage intentions and the mediating role of U&G benefits. Future research may delve into the basis of this study, such as by using experimental methods to manipulate the high and low levels of artificial autonomy to measure its impact on college students’ intentions to use AI educators, and the mediating role of gratifications, or verify the generalization of our findings in field settings. Third, this study enables participants to understand the core functions and usage experience of AI educators through video-viewing, ensuring that participants have a basic understanding of AI educators. With the implementation of AI education applications in the field of higher education, future research can use scenarios based on specific AI education applications and collect data from college students who have actually used the AI educator to verify our research findings. Finally, this study did not distinguish between types of college students, such as university level and professional disciplines. Future research may compare different types of college students to explore the possible boundary conditions of our proposed model.
Disasters Expo USA, is proud to be supported by Inergency for their next upcoming edition on March 6th & 7th 2024!
The leading event mitigating the world’s most costly disasters is returning to the Miami Beach
And in case you missed it, here is our ultimate road trip playlist is the perfect mix of podcasts, and hidden gems that will keep you energized for the entire journey-