Exploring the prospects of using artificial intelligence in education

Abstract

The primary objective is to investigate the potential applications of AI in education to enhance its quality and elevate students’ knowledge levels. A comprehensive experiment was conducted, involving the integration of AI technology into the learning process of 279 students. A test was administered at the outset, categorizing students into four groups based on personality types. The results demonstrated a discernible increase in the knowledge levels of students who underwent personalized AI-enhanced learning. In the final assessments, students in the experimental group achieved higher scores in English (4.4 out of 5), computer science (4.7), economics (4.6), and anatomy (4.8), compared to the control group, which received significantly lower scores (English: 3.8, Computer Science: 3.9, Economics: 4.1, Anatomy: 4). Student feedback indicated that AI rendered the learning experience more comfortable and convenient, with an overall rating of 4.6 out of 5. For instructors, the experience was predominantly positive, with a high appraisal of the effectiveness of new methodologies, scoring 4.8 out of 5. The lowest rating, 3.8 out of 5, pertained to student engagement, possibly due to the predominant role played by AI in monitoring and controlling the learning process, leaving the teacher with limited direct observation.

Keywords:

• Artificial intelligence
• education
• innovative teaching methods
• modern technologies
• student learning

Reviewing Editor:

• Stephen Darwin, Universidad Alberto Hurtado, Chile

Subjects:

• Artificial Intelligence
• Study Skills
• Higher Education

Introduction

The development of technology impacts all of society. Such rapid progress is driven by the increased computational power of computers, improved data analysis, and data accumulation quality (Liao, 2018). In particular, advancements are evident in artificial intelligence (AI) technology. News about the development of new robots, automation in manufacturing, voice assistants, programs for automatic calculation and analysis, data processing and structuring (applied in fields such as medicine, business, manufacturing, education, and more), customer support and query processing, household and industrial AI-based technology, and autonomous task execution are becoming more frequent (Lashinsky, 2016; Woolf, 1988; Yazdani Motlagh et al., 2023). However, its integration into society still faces several limitations. Schools, colleges, universities, students, and teachers lack sufficient technical infrastructure for the active application of artificial intelligence: weak internet, insufficient computer or phone power, lack of knowledge and skills, and fear of AI due to stereotypes from mass culture; many are not ready to adopt AI or adhere to traditional teaching methods (Digital University, 2018) Consequently, contemporary AI can be considered a specialized form of technology, the core of which lies in the intellectual actions of individual spheres (Lu et al., 2017).

Artificial intelligence (AI) is actively employed by major corporations. For instance, Amazon utilizes AI to automate its delivery system (Amazon Prime Air, 2023). Facebook, on the other hand, has implemented AI for the functioning of its facial recognition technology (Taigman et al., 2014). The integration of artificial intelligence into student education is a longstanding practice in educational institutions in the United States. Innovative technologies are employed by universities and students for instructional purposes (Du Boulay, 2016; Nikpour et al., 2023). For example, Stanford University has developed the fastest robot racer, created with the assistance of artificial intelligence, which is specifically designed for use during autonomous racing events (Stanford Artificial Intelligence Laboratory, 2017). At the Massachusetts Institute of Technology (MIT), a cleaning robot has been developed and is actively utilized (MITAlumni, 2017; Ratnaparkhi et al., 2016). This technology is poised to shift heavy physical and mechanical tasks onto artificial intelligence in the future.

There exist opportunities to delegate physical tasks to artificial intelligence and machines. This prospect facilitates the optimization of production processes, rendering them more precise, straightforward, and expeditious. AI can undertake intricate mechanical and physical tasks, as well as engage in computational problem-solving, thereby alleviating humans from strenuous and monotonous labor (LeCun et al., 2015; Ahmadi & Abadi, 2020). Effectively, the concept of ‘artificial intelligence’ is construed as an intellectual system onto which humans can offload the resolution of specific tasks and the execution of functions (Rohrer & Taylor, 2007). To address a myriad of challenges, intelligent systems necessitate well-developed methods that can be regarded as artificial intelligence (Knewton, 2023).

The application of artificial intelligence in education is poised to have a positive impact on the learning experiences of diverse societal groups. For instance, for students requiring special conditions, the integration of artificial intelligence creates more opportunities for learning (Chertovskikh & Chertovskikh, 2019). The effectiveness of artificial intelligence becomes particularly apparent during emergencies when conventional teaching becomes challenging or impractical (Chetlur et al., 2014; Cumming & McDougall, 2000). For example, AI technology can be applied in situations necessitating an abrupt transition to distance learning or when working with students with special needs (Digital Literacy Index, 2017; Ahmadi, 2021). Another distinctive feature of applying artificial intelligence is the ability to personalize education and focus it on the individual needs, capabilities, and achievements of the student (Pedro et al., 2019; Ahmadi & Taghizadeh, 2019).

To effectively apply and implement artificial intelligence, specific platforms, and services have been developed. Notable among them are Moodle, Coursera, and Stepik. Creators of online courses utilize various technologies and adaptive methodologies, incorporating artificial intelligence technologies such as voice assistants, automation, and data analysis (where the system independently computes grades and student data) (Song et al., 2017). Such technology proves to be effective during the learning process due to its capacity to tailor programs to individual needs, the knowledge level, and the understanding of each student, utilizing adaptive technologies. The underlying assumption of this approach is that artificial intelligence should adapt teaching methods and materials to meet the unique needs of each student (Chertovskikh, 2019).

The scholarly contribution of this article lies in the investigation of the efficacy of ICT utilization in education and its impact on the quality of learning and the level of knowledge among students. The exploration of this issue holds significant importance as it elucidates the potential of ICT for transforming traditional teaching methods and enhancing their effectiveness. For the contemporary educational milieu grappling with the challenges of integrating cutting-edge technologies, this research may furnish concrete data and evidence regarding the benefits of ICT integration into the educational process. The derived conclusions may foster further adaptation and implementation of ICT in educational institutions, thereby fostering the enhancement of teaching quality and the preparation of future professionals.

Problem statement

For the younger generation, this is particularly relevant, as they have been applying various methodologies and technological capabilities since early childhood. The integration of artificial intelligence will be a qualitative improvement in educational methodologies, an increase in the level of personalized learning, and a focus on individual needs and capabilities. It is precisely these factors that make the integration of artificial intelligence into education so relevant and necessary. This research explores the possibilities of implementing artificial intelligence technology in the educational process and developing a new effective methodology based on the psychological needs of students.

The study aims to examine possible methodologies for integrating artificial intelligence into the educational process and to develop a scheme for individualized learning using AI.

Research Objectives:

• Examine the necessity of integrating artificial intelligence technology into the educational process using the Carnegie Learning program to identify opportunities for improving the quality of education.

• Explore the possibilities that will emerge in education after the implementation of AI.

• Develop a methodology for individualized learning based on the psychological needs of each student.

• Experiment to assess the impact of artificial intelligence integration on the learning process and the results demonstrated by students.

Literature review

Artificial intelligence in the contemporary world is regarded as a branch of engineering. Its distinctive feature lies in the creation and implementation of novel concepts to address complex problems. The electronic capabilities of computers will soon enable them to become nearly as intelligent as humans. The speed of computation, problem-solving, learning, programming capacity, and machine operation—all these aspects allow a significant portion of tasks to be delegated to the computer (Peng et al., 2023; Zhang, 2016).

Modern artificial intelligence possesses several features that impact its functioning. The first is speech recognition. The primary objective of this feature is to transform human speech into a digital format. This is applied to enable AI to comprehend tasks or questions articulated by humans (Bourlard & Morgan, 1994). The foundation of speech recognition is ‘natural language generation,’ now widely employed in voice assistants such as Apple Siri, Google Now, Amazon Alexa, and Yandex Alice (Graves et al., 2013).

The second feature is virtual and augmented reality, utilized in both simple devices and advanced systems (Riva & Wiederhold, 2015). Its foundation lies in computer-simulated three-dimensional worlds where interaction is possible like the real world. Artificial intelligence is actively employed in virtual reality, for instance, to enhance the quality of simulation and simulate resemblance to the real world (Bartsch et al., 2016; Thomas et al., 2024). Examples of virtual reality with artificial intelligence are employed by major companies such as Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, and Satisfi (Labonnote & Høyland, 2017).

The third feature is hardware, which is a crucial component in optimizing artificial intelligence. Due to the rapid growth of data volumes, artificial intelligence requires continuous learning. AI models demand substantial data and powerful computations, necessitating robust hardware acceleration for scalable data and model processing (Chetlur et al., 2014; Coccia, 2017a). Companies that can provide robust hardware and AI development include Alluviate, Cray, Google, IBM, Intel, and Nvidia (Lacey et al., 2016).

The fourth feature is platforms for quality learning. To research and classify information in the functioning of artificial intelligence, large volumes of data are essential. Platforms for processing such quantities of information are located in significant clusters and serve as the central point for the training and development of AI (Kim & Kwon, 2024; Sridharan, 2019).

Artificial intelligence is actively utilized in large corporations (robotization and program configurations), across both large and small-scale manufacturing (production automation and settings), and in the everyday lives of individuals (voice assistants and smart homes). Education has not been an exception to this trend. For instance, the concept of a ‘Digital University’ is being introduced, involving the integration of various modern technologies, informational case studies, tools, and the incorporation of digital technologies into the education system (Digital University, 2018). Over time, the implementation of online courses in Russian educational institutions has been increasing. For example, since 2017, there has been open access to 450 online courses. One such educational institution adopting this approach is the Higher School of Economics, where there are plans for a complete replacement of traditional classes with online courses (The Village, 2018).

The primary application of artificial intelligence in education lies in the implementation of adaptive learning. This is an effective method for data analysis, assessing students’ progress, collecting and exchanging information, and adapting to changes in knowledge (Knewton, 2023). This information encompasses:

• Gathering data on students’ knowledge and proficiency levels.

• A system that, based on this data, designs the level of content to be delivered.

• A level of personalization, rooted in the assessment of each student’s capabilities and the development of a learning program tailored to their needs. Such a system forecasts the level of learning quality for students and operates a personalized learning system (Chertovskikh, 2019).

Special attention must be given to the process of integrating artificial intelligence into the education of schoolchildren and students (Luckin & Holmes, 2016). Increasingly, the educational process incorporates personalized learning methods, necessitating attention to each student’s knowledge and the exploration of an individualized approach. The application of artificial intelligence for this purpose is an effective method for high-quality information processing (Becker et al., 2018; Coccia, 2017b). AI holds immense prospects in education, particularly due to its capability to process vast amounts of data and generate educational programs based on them (Holmes et al., 2018).

Artificial intelligence significantly influences the organizational framework of education, primarily impacting the operations of educational institution administrations (Chertovskikh, 2019). Tasks related to schedule formation, curriculum development, staff management, and financial management, as well as responsibilities concerning security and cybersecurity, can be delegated to AI, thereby exerting a profound influence on the shaping of the educational process system (Ward, 2018; Yim & Su, 2024).

Responsibilities for the creation of individualized learning programs, the clear structuring of subjects, and the organization of topics within subjects are entrusted to intelligent learning systems (Alkhatlan & Kalita, 2018). Leveraging knowledge in pedagogy and the proficiency levels of students, the system formulates optimal learning paths and creates lesson plans, activities, and material exploration programs. As the student’s knowledge improves, the system generates new recommendations, and hints, and adjusts the level of lesson complexity (Song et al., 2017).

The subject domain model is designed to provide information about the subject and support students during the learning process. In the realm of working with artificial intelligence, it is feasible to effectively structure one’s knowledge, separating, for instance, mathematics from history. A student can easily study materials that are compiled and categorized into distinct blocks (Zhang, 2016).

The pedagogical model incorporates the application of effective teaching methodologies gathered from research conducted by expert educators and observations in the field of education. This model encompasses teaching approaches (Bereiter & Scardamalia, 1989), developmental and learning practices (Rohrer & Taylor, 2007), cognitive load on students, and the provision of feedback (Mayer & Moreno, 2003). For instance, Vygotsky’s pedagogical model focuses on the zone of proximal development, with its primary objective being the creation of a system in which tasks and instructional materials align with students’ proficiency levels. Individualized feedback mechanisms should be established, enabling students to receive feedback as needed (Shute, 2008).

Presently, research identifies four primary applications of artificial intelligence in education:

• Language Analysis and Recognition: Utilized for the study of foreign languages and integrated into specialized applications. However, this type has several key drawbacks, including malfunctioning when dealing with individuals proficient in multiple languages and with children.

• Personalized Learning: Involves tailoring course materials to the requirements and proficiency level of a specific learner.

• Online Learning: Artificial intelligence assumes the role of error detection in students’ work, assessment, and grade assignment in online learning environments.

• Identification and Remediation of Knowledge Gaps, Adaptive Learning: Focused on identifying and addressing gaps in knowledge through adaptive learning approaches (Goetz & Cullen, 2019).

In many contemporary educational institutions in Russia, systems for integrating artificial intelligence into the educational process have already been developed. Among them are institutions such as the Higher School of Economics, the Gymnasium of RAS named after A.N. Kosygin, the University Gymnasium (boarding school) of Moscow State University named after M.V. Lomonosov, the School of Maryina Roshcha named after V.F. Orlov, and others. These institutions employ individual educational courses, video lessons, texts, artificial intelligence-driven classes, interactive modules, teaching methodologies, and online learning platforms utilizing AI, as well as computer-based data analysis (Dubov & Chulyukov, 2020).

CTI (Computer-Aided Instruction) is a direction focused on developing programs containing artificial intelligence and seeking education solutions (LeCun et al., 2015). Its foundation includes the creation of deep learning systems. For instance, the system JustTheFacts101 was developed to generate personalized educational materials based on curricula devised by teachers (Graves et al., 2013). An intriguing application with artificial intelligence is the extended Cram101, which aims to create an intelligent study guide providing knowledge in an accessible format for easy and rapid assimilation (Turbot, 2018).

Carnegie Learning is a development aimed at utilizing artificial intelligence and the psychological characteristics of students (Lu et al., 2017). All of this is designed to make learning more individualized, focusing on the personal traits of students and schoolchildren. Every action by the student is taken into account, and the system records all their results. This program is geared toward real-time learning (Carnegie Learning, 2020).

ThinkerMath is an application with a specially designed system of games and rewards, directed at children’s mathematics education and the formation and assessment of their knowledge (The Village, 2018). Everything in it is carefully designed to hold the attention of children for an extended period and provide the maximum level of quality and assimilation of knowledge. The system assesses the attention and knowledge of the student and subsequently develops a program and selects a tutor for them (Sennar, 2019).

Methods and materials

Sample and data

For the first stage of the experiment, testing was conducted to determine sociotypes and subtypes. It was carried out with students in the computer science class using the Online Test Pad service in the presence of the school psychologist and the class teacher. This test allowed us to categorize students into four groups based on subtypes and develop educational programs that would align with their psychological capabilities and needs. The Carnegie Learning platform was utilized as a means to integrate artificial intelligence. Its application was relevant for subjects such as English language, computer science, economics, anatomy, and specific subjects introduced for each group. Specifically, the first group is ‘Duel Club,’ the second group is ‘Non-standard,’ the third group is ‘Time Management,’ and the fourth group is ‘Relaxation.’ The experiment was conducted from November 2022 to June 2023.

The experiment involved 279 students aged 14–16. This age group was selected based on the consideration that these students possess sufficient experience and skills to interact with AI technology, and they are not burdened with the preparation for significant examinations. Another rationale is that children of this age already have a developing awareness of their psychological self, enhancing the reliability of the testing results. The participants were recruited from the College of Marxism, Beijing Institute of Technology.

To conduct the testing, a special committee was formed, consisting of educational institution staff, including teachers and psychologists. During the testing in the classroom, members of this committee, one teacher, and one psychologist will be present. The teacher’s responsibilities include organizational tasks (preparing computers, collecting written agreements from students and their parents), conducting an explanatory discussion to explain to the students how the testing will proceed, ensuring discipline, and maintaining order. The psychologist is required to monitor the emotional state of the students, provide assistance, and clarify unclear terms and questions in the test.

At the beginning of the testing session, the students were given explanations about how the testing would proceed and how much time it would take. Members of the parent committee could also be present in the room to observe and control the quality of the survey administration. The students were required to take the tests on the Online Test Pad website, record the results on a form, and provide their name, surname, and class. This information was necessary for later categorizing the students into four groups based on sociotype.

Measures of variables

To assess the results of the experiment, a final study was conducted, consisting of two parts: a survey among teachers and students and testing. The first part aimed to evaluate the quality of AI in education according to the opinions of teachers and students. A survey form (see Appendix 2) was sent to students and teachers via email, which they were required to fill out and answer the questions. In the form for teachers, it was necessary to include their full name, subject area, and years of teaching experience. This was done to confirm their status, knowledge of the educational process, and teaching experience. Students were not asked similar questions; they only needed to specify their class and school. All responses were sent to a specially created email, and imported into the Pandas program, where they were systematized and presented in diagrams. The goal of this survey was to determine the participants’ readiness for the integration of artificial intelligence into education and how they would assess its quality.

The second part involved testing in four subjects studied (English, economics, anatomy, and informatics). It was conducted among students who participated in the experiment (experimental group) and those who followed the standard program (control group) (see Appendix 5). The testing took place at the end of the academic year, aiming to identify differences in knowledge levels. The goal was to confirm or refute the thesis that the integration of artificial intelligence technologies and personalized learning positively influences the quality of acquired knowledge.

Procedure of data analysis

The program Pandas was employed for the synthesis of collected data. This service aids in structuring gathered information and conducting data analysis. The foundation of working with Pandas lies in the NumPy library. ANOVA data analysis was performed to determine if there were statistically significant differences between the average grades of students in different groups. In this case, this analysis was crucial to assess the effectiveness of the new artificial intelligence-based learning program compared to the traditional educational program.

Ethical issues

The agreement to participate in the experiment was signed by all students and their parents. Students could withdraw from participation in the experiment or the final testing in the second part of the study at any time. Personal data acquired during the research is confidential and not subject to disclosure.

Results

The first diagram illustrates the results of testing to determine sociotypes and subtypes. The data were collected by an expert commission at the College of Marxism, Beijing Institute of Technology (Figure 1).

Figure 1. Survey results on sociotypes and subtypes.

illustrates the results of testing to determine sociotypes and subtypes.

The test results indicated that 20% of the respondents have a dominant subtype, 30% – creative, 27% – normative, and 23% – harmonizing.

After testing, the students were divided into groups based on their subtype. Each group was assigned a name: Dominant Subtype – Alpha, Creative – Beta, Normative – Gamma, and Harmonizing – Delta. A curriculum and a separate schedule were developed for each group (see Appendix 1).

Thus, for the Alpha group, where students with a dominant personality type are located, the foundation of the program became active task execution methodologies. Their program consisted of lecture sessions immediately followed by practical exercises. Students were required to listen to video lectures and then complete interactive tasks. Special emphasis was placed on tasks focused on group work, skill development, situation analysis, role-playing, and discussions. For the dominant type, competition among themselves is characteristic, so a competitive element was added to the student’s work. For all students who completed the task, rewards were provided. For example, a student who scored the maximum number of points after completing the experiment could receive additional interactive educational materials. Students uploaded the task to the platform, and artificial intelligence assessed, assigned points, and formed a ranking of students’ success based on their performance.

The Beta group, with a creative personality type, required an emphasis on the possibilities of creative problem-solving. The foundation of their learning was centered around finding unconventional solutions to standard problems. A significant number of research and experiments (see Appendix 4) were introduced for students in this direction, which they had to devise themselves or find online and creatively play out during the lesson. Collaborative lesson planning with students, exploration of new possibilities, and materials in learning were part of their education.

Tasks aimed at developing creativity were added to their learning. For these students, it is important to channel their characteristics into a useful direction, apply unconventional thinking, and teach them to seek creative solutions.

For the comfortable learning of the Gamma group, characterized by a normative personality type, a methodology was developed that included a meticulously outlined schedule and assignment deadlines. While spontaneity may apply to the Beta group, for students in the Gamma group, a learning system with maximum clarity was necessary. Their psychological peculiarity lies in the preference for standardization and orderliness. This group tends to poorly tolerate uncertainty and abrupt changes, necessitating the creation of a highly comfortable environment and minimizing or eliminating situations that could adversely affect the students’ emotional state.

For the Gamma group, a robust schedule of subjects, individual assignments, and deadlines for submission was established. Any changes had to be communicated well in advance. The LightSkool program was employed to formulate this schedule, storing all data related to schedules and deadlines. It provided timely reminders about lesson timeframes and assignment completion. The artificial intelligence within the program monitored students’ progress, generated necessary assignments, and planned lesson sessions. Users were required to input information about their class and extracurricular activities, homework, and plans. The artificial intelligence then formulated schedules and temporal deadlines accordingly.

The final Delta group comprised students with a harmonizing subtype. Individuals of this type possess a heightened aesthetic sense, necessitating the presentation of information not only in rich and high-quality educational materials but also in visually pleasing formats. The Google AutoDraw service enables the creation of high-quality drawings and images from simple graphic sketches. These were employed to enhance the visual appeal of presentations and assignments. Students had the opportunity to craft their artworks using this tool and subsequently use them for task completion, developing and presenting practical assignments, reports, or creative works. The application of these technologies was accessible on each student’s laptop or computer.

In this format, students can assimilate information to the maximum extent. Another psychological characteristic of the group is a poor tolerance for conflicts. Therefore, the students were provided with a highly comfortable environment where conflicts were minimized, and all issues were resolved peacefully and calmly. Here, the voice assistant was again employed. The computerized instructor remains completely composed, adeptly tailoring approaches to students, and formulating conflict-free lesson plans.

Furthermore, additional subjects were introduced for the experiment participants. For the Alpha group, this was the ‘Duel Club,’ aimed at cultivating skills in discussion and debate. Participants in this group tend toward competition and defending their views, and they may easily become heated. Hence, it is crucial to teach them how to engage in discussions and control debates, expressing their viewpoints without scandal or shouting. Managed voice-controlled artificial intelligence was utilized for all of this. Students installed the Treeps app on their phones, where they selected speech and communication skill development tasks. Through the program, they engaged in virtual dialogues with AI, enhancing the required skill set.

For participants in the Beta group, a session titled ‘Non-Standard’ was introduced. The primary goal was to foster creative thinking among students, as this group exhibited the highest inclination toward it. During this lesson, students engaged in tasks aimed at promoting creativity and the development of creative thinking. These tasks included exercises on ‘Associations,’ ‘Biography,’ ‘Chameleon,’ and ‘Different Solutions’ (see Appendices 3 and 4). Additionally, students could enhance their creativity using AI-powered programs like Jukedeck, where they could create their musical compositions. The compositions created could be listened to, downloaded, or sent for further refinement.

Gamma group participants, being more inclined towards rationalization, received lessons in Time Management. These students needed to have a clear schedule and a well-thought-out daily plan. The teaching of this discipline began to impart these skills to them. Naturally, artificial intelligence technologies were employed throughout the process to assist students in developing daily plans and forming schedules. The Carnegie Learning platform was utilized for this purpose, enabling the application of data analysis technologies to structure the learning program, adapt it to the student’s needs, maintain contact with instructors, receive active responses to queries, and utilize a voice assistant for data retrieval and task execution.

The fourth group, Delta, received lessons on the subject of ‘Relax.’ Its primary objective was to teach students to react calmly to stressful situations and develop exemplary behavior in situations of heightened discomfort. This was particularly important for a group where students are highly susceptible to stress and negative influences. The role of artificial intelligence in this subject was to create suitable stress scenarios for each student and instruct them on how to navigate and cope with such situations.

For the educational process, the Carnegie Learning service was implemented, enabling the use of artificial intelligence in the study of foreign languages, computer science, economics, and anatomy. This system not only assists in supporting education, preparing lectures and practical assignments, video lessons, and communication but also in tracking students’ progress and adjusting the program to their needs. Students listened to lessons generated by the program and uploaded their assignments to the platform, where AI checked them, assigned scores, and automatically adapted the learning process to each student’s needs. On the DeepCode website, students could upload their completed work and receive recommendations for improvement. This site possesses more than a quarter of a million codes, enabling it to assess and edit completed assignments.

The education using artificial intelligence with a focus on individual student characteristics continued from November 2022 to June 2023. At the end of this period, it is possible to conclude. To assess the effectiveness of the new teaching methods, it is necessary to evaluate various aspects of the educational process and its results. Firstly, this involves assessing the quality of education from both teachers’ and students’ perspectives. A questionnaire was compiled, consisting of six points that needed to be evaluated on a five-point scale. The parameters included ‘Comfort Level during Learning,’ ‘Quality of the Teaching Program,’ ‘Interaction with Teachers and Other Students,’ ‘Effectiveness of New Teaching Methods,’ and similar aspects (see Appendix 2).

The obtained results indicate that teachers rated the quality and convenience of education using artificial intelligence quite highly. The effectiveness of the new teaching methods received a score of 4.8 out of 5, the quality of the teaching program was rated at 4.2, and the convenience of working in such a format received a score of 4.6 out of 5. The lowest rating was given to independent student work – 3.8 out of 5. The overall assessment of the application of artificial intelligence was 4.1.

Students’ evaluations also turned out to be quite high. They rated the quality of the teaching program well, giving it a score of 4.7 out of 5, and the comfort level during learning received a rating of 4.6. Slightly lower was the score for the level of interest in learning – 4.5, and the lowest score was given to interaction with teachers and other students – 4.3. The overall assessment of the implementation of AI technology in education from students was 4.4 (Table 1).

Table 1. Survey results among teachers and students.

Teachers		Students
Question	Score	Question	Score
Quality of the educational program	4.2	Comfort level during learning	4.6
Effectiveness of new methodologies	4.8	Level of interest in learning	4.5
Convenience of working in a similar format	4.6	Quality of the prepared educational program	4.7
Quality of students’ work	3.8	Interaction with teachers and other students	4.3
Evaluation of the application of AI in education	4.1	Evaluation of the application of AI during learning	4.4

The minimum value is 1, the maximum is 5.

The next stage of result analysis involved calculating the overall assessment of artificial intelligence in education from teachers and students. Four aspects were identified for the overall evaluation of AI application in education (Figure 2):

Figure 2. Overall assessment of artificial intelligence in education.

identifies four aspects for the overall evaluation of AI application in education.

• Preparation and knowledge – 4.4;

• Comfort and convenience – 4.6;

• Student interest and performance – 4.1;

• Assessment of artificial intelligence – 4.2.

From this, it can be concluded that the implementation of artificial intelligence receives a high rating from teachers and students. The undeniable advantages of new teaching methods are evident. For example, developing an educational program tailored to the individual needs of each student helps create a comfortable environment for acquiring knowledge and increases interest in learning. For teachers, this represents a simplification of their work, reducing repetitive tasks that can be delegated to artificial intelligence. This helps free up teachers, preventing emotional burnout. Teachers can dedicate themselves to professional development and acquiring knowledge rather than performing mechanical tasks (Egorova et al., 2021).

To conclude the experiment and confirm that ‘the integration of artificial intelligence improves the quality of education’ testing was conducted in four subjects: English language, computer science, economics, and anatomy. Participants included students enrolled in the new program with AI technology integration and those following the standard program. Students took three test exams designed to assess acquired knowledge (see Appendix 5). The tests were evaluated by teachers using a standard five-point scale, and the results were processed using the Pandas program.

The obtained results indicate that the assessment of English language proficiency among students enrolled in the new program was 4.4 out of 5, whereas those following the standard program received a score of 3.8. Computer science was rated at 4.7 for the experimental group and 3.9 out of 5 for students without additional educational technologies. Students whose program included AI integration achieved an overall score of 4.6 in economics, while the control group scored 4.1 out of 5. Anatomy knowledge among students participating in the experiment was rated at 4.8, compared to 4.0 for those following the regular program. Table 1 presents the results of the statistical ANOVA analysis for different subjects, comparing the experimental and control groups of students (Figure 3). The F-values indicate the level of difference in mean scores between the groups (Table 2).

Figure 3. Test results for students’ knowledge levels.

compares the experimental and control groups of students.

Table 2. Results of ANOVA data analysis for grades in different subjects in experimental and control groups.

Subject	Experimental group (N = 139)	Control group (N = 140)	F-value	p-value
English	4.4	3.8	5.67	.023
Computer Science	4.7	3.9	6.21	.018
Economics	4.6	4.1	3.12	.076
Anatomy	4.8	4.0	7.53	.012

For the subject ‘English,’ the obtained value of F = 5.67 with a statistically significant p-value of 0.023 indicates that students using the new program had higher average grades in English compared to those following the standard program. For the subject ‘Computer Science,’ the value of F = 6.21 and a statistically significant p-value of .018 also indicates a statistically significant impact of the new program on grades in computer science. For the subject ‘Economics,’ the value of F = 3.12, and although the difference between groups is not statistically significant at a significance level of 0.05, it may be practically important. For the subject ‘Anatomy,’ the value of F = 7.53 and a statistically significant p-value of .012 indicate a statistically significant positive impact of the new program on grades in anatomy. Thus, the results suggest that the new program using artificial intelligence has a significant impact on improving students’ grades in various subjects compared to the traditional educational program.

Discussion

The personalization of education, facilitated by artificial intelligence and focusing on the individual needs of each student, makes the learning process more effective. AI streamlines the work of educators by enabling the transfer of routine and bureaucratic tasks to programs incorporating this technology, which will subsequently have a positive impact on their professional development, encouraging participation in additional courses (Liao, 2018). Research emphasizes the increasing adoption of artificial intelligence, with expenditures projected to exceed 6 billion dollars by 2024 (O’Connell, 2018). Studies confirm the necessity of shifting the focus in education towards addressing the personal needs of each student, thereby enhancing the efficiency and quality of learning (Goetz & Cullen, 2019). While it might be challenging for a single teacher to tailor teaching methods and practices to each student’s needs, it becomes a straightforward task for artificial intelligence (Liao, 2018).

American studies indicate a 38% growth in the application of artificial intelligence in 2016 and a subsequent increase of 62% in 2018 (Lashinsky, 2016). Modern AI technologies encompass various tools, with three existing methods being genetic algorithms, fuzzy logic, and neural networks, each highlighted for their significance in AI applications (LeCun et al., 2015). Our conducted research corroborates the substantial impact of AI application methodologies on the educational landscape. For instance, fuzzy logic has demonstrated effectiveness in individualizing learning and tailoring programs to accommodate students, forming the basis of the personalized learning approach in this experiment.

Studies conducted at the Higher School of Economics reveal the influence of AI on the educational process, particularly in the context of educators. The integration of artificial intelligence has afforded teachers more time for research activities and professional development (Sridharan, 2019). Concurrently, research indicates that, on average, 15–17% of students attend in-person classes. Hence, introducing AI technologies becomes sensible in relieving the burden on educators and facilitating the transition of students to remote learning (The Village, 2018). All of this gains particular relevance in the contemporary societal framework. However, despite the global integration of technologies, digital literacy among Russians remains relatively low, with a rating of around 6 out of 10 (Digital Literacy Index, 2017).

Conducted research focused on assessing students’ readiness for active engagement with new technologies. This included a survey involving 150 respondents across various disciplines. Participants were tasked with identifying the pros and cons of integrating artificial intelligence into education. The research yielded ambivalent results, as respondents acknowledged that technological advancements would undoubtedly impact education but would not entirely replace traditional methodologies (UG, 2018). Our study revealed that many students highly appreciate learning with the assistance of artificial intelligence, with an AI rating of 4.4 out of 5 and a program preparedness level of 4.7 based on our survey.

The application of digital technologies, including artificial intelligence, enhances the accessibility of education. Acquiring new knowledge, studying, and enhancing professional skills becomes feasible at any time and place with minimal costs. Over time, the level and quality are expected to grow, exerting even more influence on competency. Hence, the integration of artificial intelligence plays a significant role (Chertovskikh & Chertovskikh, 2019). This is corroborated by the research results, where students rated the comfort of learning very high at 4.6 out of 5. Teachers also found it convenient to apply artificial intelligence in education, with an overall rating of 4.6 among them.

Research describes the challenges that educational institutions will encounter. It is confirmed that problems related to qualification arise, with a shortage of development skills noted in 65% of institutions, data analysis in 70%, and data processing in 71%. All these difficulties are further complicated by the inadequate implementation of artificial intelligence observed in 67% of educational establishments (Forrester Consulting, 2020). 58% of respondents pay attention to the lack of platforms and tools for the effective application of artificial intelligence, while another 50% point to the difficulties arising from AI implementation. As a result, educational institutions face increased costs for implementing artificial intelligence (Sridharan, 2019).

In this study, active implementation of personalized learning and a focus on individual student needs were emphasized. The results obtained after the final survey and testing confirmed that individualization is an essential part of education and enhances its effectiveness. Both students and educators collectively rated the integration of AI at 4.2 out of 5.

A review was conducted involving students of various ages and those pursuing computer science and technology directions. This research illustrates the level of professionalism that modern education in the fields of computer science, programming, and computer technologies should exhibit (Bosova, 2019). There is a presumption that the integration of AI into the learning process necessitates providing universities with quality infrastructure and an information environment to enhance the level of education and implement new technologies, fostering scientific research (Safuanov et al., 2019).

Research also emphasizes the challenges that arise during the digitization process and the implementation of new technologies, including artificial intelligence. For instance, educators who adhere to traditional teaching systems highlight the importance of interaction between students and their mentors (Zhang, 2016). It is noted that 77% of teachers believe that individualized learning plays a crucial role in knowledge exchange, and student engagement enhances the quality of the educational process (Hughes, 2019; Zakharova et al., 2020). In our survey, students rated their interest quite high at 4.5 out of 5. However, the opinion of teachers regarding the performance of their students was relatively low at 3.8 out of 5. This might be attributed to the difficulty experienced by traditional educators in accepting the integration of new technologies. Another drawback of implementing artificial intelligence for teachers could be the challenge of monitoring and communicating with students.

The primary positive aspects of implementing artificial intelligence in the educational process include:

• Personalization of the learning process for each student individually.

• Automation of repetitive routine tasks.

• Prompt improvement of educational materials, working in an online mode, and timely updating of content.

• Reduction of pressure on the teacher, leading to a shift in their role in the educational process (Sridharan, 2019).

For students exposed to new technologies in education, the average rating across all assessed subjects was 4.6 out of 5. In contrast, the group without innovations scored 3.9.

Conclusions

The conducted experiment in this research underscores the importance of personalized learning and focusing on the psychological characteristics of students. The test results among students confirmed that the integration of AI into the learning process enhances the quality of knowledge. The grades obtained by students in the experimental group were higher than those in the control group. For instance, the score for the computer science test for students in the AI-enhanced program was 4.7, while the control group scored 3.9. Scores for anatomy were 4.8 for the experimental group and 4 for the control group. After surveying experiment participants, it was revealed that both teachers and students highly appreciate education using artificial intelligence technology. The overall assessment of knowledge quality was 4.4, comfort and convenience during learning was rated at 4.6, student interest and performance were at 4.15, and the overall assessment of artificial intelligence in the learning process was 4.2, according to the opinions of teachers and students. It is important to mention the limitations of the current study. In developing the experimental part of the study, all measures were taken to make its results as reliable as possible. The student sample is sufficiently relevant, and the testing results can be considered accurate. The fact that artificial intelligence is a new technology for schools could influence the study results, as there might be glitches in its use during the experiment. However, this impact is not too significant and does not substantially affect the research outcomes.

Artificial intelligence (AI), like any technology, can introduce certain biases that are crucial to consider to ensure fairness, objectivity, and ethicality in its use in education. AI typically learns from large amounts of data, and if this data contains biases, the model may reproduce these biases in its decisions. The algorithms themselves used in AI can also be biased due to insufficient representativeness of the data on which they were trained or due to the selection of certain algorithm parameters. The choice of a specific model or approach to solving artificial intelligence tasks may be made with consideration of certain biases or researchers’ prior preferences, which can lead to underestimation of other possibilities or to the excessive influence of certain aspects. The selection of tasks on which AI is trained may also introduce biases, as some aspects of the learning process may be underrepresented or underestimated. Preventing biases in AI requires careful attention to the process of model training, data selection, and processing, as well as thorough analysis and validation of results. It is also important to consider diversity and involve a wide range of stakeholders in the development of systems to ensure objectivity and fairness in their functioning.

Upon reviewing other studies and conducting our experiments, it can be concluded that the integration of artificial intelligence into the learning process makes it more effective, interesting, and comfortable for both students and teachers. For the former, it is possible to create perfectly tailored learning programs focusing on their individual needs, adapting as necessary. Teachers can become less burdened by delegating many mechanical tasks to artificial intelligence.

This research can be applied subsequently to explore the possibilities of integrating artificial intelligence technology into the learning process for improving the quality of education through modern technologies. Further studies may delve into examining the impact of other aspects of AI in education. While this study primarily focused on the application of AI's personalization function, technologies could be employed in other aspects. For example, investigating the possibilities of implementing data analysis and assessment by AI to offload mechanical work and ease the burden on teachers.

Ethical approval

The research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki. The study was conducted in accordance with the ethical principles approved by the Ethics Committee of Beijing Institute of Technology.

Patient consent statement

All participants gave their written informed consent.

Data availability statement

All data generated or analysed during this study are included in this published article.

Disclosure statement

The authors report there are no competing interests to declare.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Notes on contributors

ZuoYuan Liu

ZuoYuan Liu has a PhD in Philosophy degree. ZuoYuan Liu is an Associate Professor of the College of Marxism at the Beijing Institute of Technology, Beijing, Peoples Republic of China. His research interests include education, innovative teaching methods and modern technologies. Elena Yushchik has a Candidate of Sciences in Technology degree.

Elena Yushchik

Elena Yushchik is a Head of the Department of Applied Mathematics and Computer Science at the Far Eastern State Technical Fisheries University, Vladivostok, Russian Federation. Her research interests include artificial intelligence, higher education and student learning.

 

 

Table

Group Alpha	№	Time	Monday	Tuesday	Wednesday	Thursday	Friday
	1.	8.00–8.40	Chemistry	Geometry	Artificial Intelligence in Informatics	Geometry	Algebra
	2.	8.55–9.35	Chinese Language	Literature	Algebra	History	Literature
	3.	9.50–10.30	Anatomy of Artificial Intelligence	Physics	Chemistry	‘Duel Club’ with Artificial Intelligence	English with Artificial Intelligence
	4.	10.45–11.25	Algebra	English Language with Artificial Intelligence	Chinese Language	Anatomy with Artificial Intelligence	Physics
	5.	11.40–12.20	‘Duel Club’ of Artificial Intelligence	Geography	‘Duel Club’ with Artificial Intelligence	Physics	Chinese language
	6.	12.35–13.15	Computer Science with Artificial Intelligence	Economics with Artificial Intelligence	Health and Safety	Literature	Physical Education
	7.	13.30–14.10	History	Physical Education	History

Table

Beta group	№	Time	Monday	Tuesday	Wednesday	Thursday	Friday
	1.	8.00–8.40	Algebra	Artificial Intelligence in Anatomy	Chemistry	Computer Science AI	History
	2.	8.55–9.35	Chemistry	Physics	History	Geometry	Physics
	3.	9.50–10.30	Chinese language	Artificial Intelligence in English Language	Literature	Physics	Chinese Language
	4.	10.45–11.25	Literature	Geography	Algebra	‘Non-standard’ AI	English Language AI
	5.	11.40–12.20	Artificial Intelligence in Informatics	History	Chinese Language	AI Anatomy	Literature
	6.	12.35–13.15	Physical Education	‘Non-standard’ Artificial Intelligence	Physical Education	AI Economics	Life Safety Basics
	7.	13.30–14.10					AI ‘Non-standard’

Table

Gamma group	№	Time	Monday	Tuesday	Wednesday	Thursday	Friday
	1.	8.00–8.40	History	Computer Science AI	Geography	Physics	Physics
	2.	8.55–9.35	Physics	Chinese Language	Anatomy AI	Computer Science AI	Chemistry
	3.	9.50–10.30	Geometry	Algebra	Chemistry	Geometry	Algebra
	4.	10.45–11.25	Anatomy AI	History	Time Management AI	History	Literature
	5.	11.40–12.20	Physical Education	Physics	Algebra	Chinese Language	English Language AI
	6.	12.35–13.15	Health and Safety Basics	English Language AI	Chinese Language	Physical Education	Time Management AI
	7.	13.30–14.10	Literature			Time Management AI	Economics AI

Table

Delta Group	№	Time	Monday	Tuesday	Wednesday	Thursday	Friday
	1.	8.00–8.40	Physics	History	Geometry	Literature	Computer Science AI
	2.	8.55–9.35	Algebra	Computer Science AI	Geography	Relaxation AI	Algebra
	3.	9.50–10.30	History	Chinese Language	Anatomy AI	Algebra	Chemistry
	4.	10.45–11.25	Chinese Language	Physics	Chemistry	Chinese language	History
	5.	11.40–12.20	Anatomy AI	English Language AI	Relaxation AI	Geometry	Physics
	6.	12.35–13.15	Literature	Health and Safety	Physical Education	Physics	English language AI
	7.	13.30–14.10	Economics AI	Relaxation AI			Physical Education

Appendix 2.

Students

Table

Questions	1	2	3	4	5
Comfort Level During Learning
Level of interest in learning
Quality of the prepared educational program
Interaction with teachers and other students
Evaluation of the application of AI during learning

Teachers.

Table

Questions	1	2	3	4	5
Quality of the educational program
Effectiveness of new methodologies
Convenience of working in a similar format
Quality of students’ work
Evaluation of the application of AI in education

Appendix 3.

Fireworks in a glass

Materials: two tablespoons of vegetable oil and food coloring.

Procedure: Add food coloring to the oil and then pour this mixture into water. You can observe the spectacle. The essence is that something lighter than water will float on the surface, while the heavy coloring will begin to settle and create patterns in the water.

Tornado in a jar

Materials: a water-filled jar and dish soap.

Procedure: Add dish soap to the jar of water, then tightly close the lid and shake well. This will create a tornado right in your jar.

Elephant toothpaste

Materials: measuring cup, hydrogen peroxide, dish soap, food coloring, and yeast.

Procedure: Mix hydrogen peroxide, dish soap, and coloring in the measuring cup. Then add a bit of yeast. The resulting chemical reaction will release a lot of foam.

Appendix 4.

Associations

The main essence of the task is to choose any word and come up with as many associations as possible for it. For example, we take the word ‘watermelon’ and choose associations like ‘summer,’ ‘August,’ ‘sea,’ ‘warmth,’ and so on.

Biography

In this exercise, students were shown various photos of people and had to invent a life story for each person. They were encouraged to reflect on where the person was born, how they grew up, who they studied with, what they do, and what their life story might be.

Chameleon

Students were asked to take a statement, such as ‘Megalodon is alive!’ and come up with arguments for and against this statement.

Different solutions

When a person faces various problems, they need to think about more than one solution. This exercise aims to develop this skill. By doing so, one can enhance creativity and learn to find different ways out of challenging situations. When you encounter a problem, don’t settle for just one solution. Think about how it can be solved in different ways. This way, you’ll be hitting two birds with one stone—developing creativity and finding the best solutions to save time and resources.

Appendix 5.

English

Table

When Margaret smiles, you can see her
a) Toes	b) knees	c) ankles	d) teeth
All men have chest and only women have
a) ust	b) elbow	c) back	d) hips
A place where you can eat on a train.
a) ining hall	b) ining suite	c) dining room	d) ining car
The place at a port, airport, or frontier where officials check incoming goods, travelers, or luggage.
a) Customs	b) inspection	c) security	d) overseer
They are not usually on the American table.
a) Napkins	b) toothpicks	c) knives	d) glasses
Most English people are polite and fond of phrases like ‘Thank you’ and___________.
a) Things happen	b) I’m sorry	c) By the way	d) In short
The organization in America for training boys and for developing their character.
a) Girl Scouts	b) Boy Scouts	c) Future Farmers	d) the Green
Which of the following adjectives cannot be used for describing volcanoes?
a) Active	b) alive	c) extinct	d) dormant
A hot spring in which water intermittently boils, sending a tall column of water and steam into the air is called__________.
a) Gusher	b) fountain	c) gale	d) geyser
The British are famous as a nation of______________ lovers
a) animal	b) pasta	c) tango	d) work
They are not usually on the American table.
a) Napkins	b) glasses	c) toothpicks	d) knives
The weather in Great Britain is very___________.
a) Changeable	b) steady	c) bright	d) dry
A votary to a religion founded in the U.S. in 1830 by Joseph Smith, Jr
a) Catholic	b) Mormon	c) Christian	d) Protestant
How do we call the place where people stand when they are waiting for a train?
a) Platform	b) land	c) islet	d) coach station
The best way not to lose your way is to take a with you.
a) Passport	b) map	c) timetable	d) watch

Computer science.

Table

The sequence of actions allowed for the performer is
a) program	b) algorithm	c) command	d) command system
A graphical way of describing an algorithm is…
a) program	b) flowchart	c) algorithm	d) verbal-stepwise record
Separate instruction to the performer is…
a) program	b) algorithm	c) command	d) order
Artificial languages for writing algorithms to execute them on computers…
a) Coding	b) algorithm	c) programming	d) programming language
By what word is a variable declared?
a) const	b) var	c) begin	d) end
Mark the principles followed in the creation and acquisition of CAD…
a) development	b) design	c) integration	d) compatibility
How many types of devices for converting projections can be distinguished?
a) 5	b) 6	c) 4	d) 3
Dynamic and kinematic schemes are…
a) models of structure	b) models of functioning	c) models of states	d) models of temporal and spatiotemporal relations
Design where individual transformations are carried out by interaction between a person and a computer is…
a) automated design	b) automatic design	c) mechanical design	d) software design
A web page (HTML document) is…
a) A text file with the extension txt or doc	b) A graphic file with the extension gif or jpg	c) A text file with the extension htm or html	d) A binary file with the extension com or exe
Hypertext is…
a) Very large-sized text	b) Structured text with possible transitions through highlighted labels	c) Text that uses a large font	d) Text into which objects with a large amount of information are inserted
What is the most common cause of the blue screen?
a) RAM	b) ROM	c) Hard drive	d) System unit
Indicate the correct types of cloud storage…
a) Object storage, File storage, Block storage	b) Block storage, Folder storage, Disk storage	c) Network storage, Server storage	d) Structural storage, Internal storage
What is the purpose of the Button component?
a) Command button	b) Quick button	c) Border	d) Picture
Programs in which commands are executed sequentially, one after another, are called…
a) Linear	b) Branching	c) Cyclical	d) Auxiliary

Anatomy.

Table

Which organ’s functions in the human body can be compared to the work of a filter?
a) liver	b) kidney	c) bladder	d) stomach
Name the gland responsible for the growth hormone.
a) epiphysis	b) pituitary gland	c) thyroid	d) parathyroid
A highly contagious viral disease characterized by abundant rash, often leaving scars behind.
a) smallpox	b) tuberculosis	c) flu	d) common cold
‘Cold sores’ refer to:
a) herpes	b) lichen	c) chickenpox	d) smallpox
Which of the following functions is not characteristic of blood?
a) protective	b) supportive	c) transport	d) connective
A prominent biologist and pathologist. In 1983, he discovered the phenomenon of phagocytosis.
a) I.I. Mechnikov	b) A.I. Oparin	c) C. Linnaeus	d) J.B. Lamarck
Which vitamin’s content in the human body is replenished through the procedure depicted in the photo?
a) A	b) B	c) C	d) D
What is the medical term for the displacement of bones in the joint cavity?
a) bruise	b) fracture	c) sprain	d) dislocation
What medical term refers to an injury with a disruption of bone integrity?
a) fracture	b) bruise	c) dislocation	d) sprain
What remains in the blood if its formed elements are removed?
a) serum	b) lymph	c) water	d) plasma
In which process do platelets participate?
a) blood clotting	b) oxygen transport	c) bacterial destruction	d) nutrient transport
What do cells with similar structures and functions form?
a) tissues	b) organs	c) organ systems	d) a single organism
In the human nervous system, interneurons transmit nerve impulses
a) from a motor neuron to the brain	b) from sensory neurons to working organs	c) from the spinal cord to the brain	d) from the brain to motor neurons
Which neurons convert stimuli into nerve impulses?
a) sensory	b) interneurons	c) motor
The peripheral nervous system includes:
a) nerve nodes	b) pons	c) cerebellum	d) motor nerves

Economics.

Table

Economics is needed for
a) Saving money	b) Satisfying people’s needs in a market environment	c) Living prosperously
The main problem of economics is that
a) Human desires are limited	b) Resources are limitless	c) People always have to make choices when using limited resources
Microeconomics is a branch of economic science that studies economic activities at the level of
a) Industry in a region	b) Country	c) Global community
The problem of choice exists
a) Only for the government	b) Universal problem	c) Only for those individuals who need to save money
Which of the following includes the main questions of economics?
a) What? How? Whom?	b) What? Who? How?	c) What? How? For whom?
In which economic system are all decisions made by central authorities?
Traditional	Command	Market
Free prices and competition are the basic elements of
a) Traditional	b) Market	c) Any economic system
Factors of production are?
a) Labor, capital, resources	b) Labor, land, machinery	c) Land, money, labor
The limited availability of labor factors is associated with
a) The geography of the country	b) The previous development of the country	c) The limited number of able-bodied people
As labor productivity increases, the volume of goods and services produced
a) Increases	b) Decreases	c) Does not change
With an increase in the number of consumers in the market, demand
a) Increases	b) Decreases	c) Does not change
The change in which factor does not cause a shift in the demand curve?
a) Consumer preferences	b) Income size	c) Price of the product
In which economic system is the role of the consumer not significant
a) In traditional	b) In command	c) In market
The quantity of a good that producers would like to sell at a specific price is called
a) Supply	b) Quantity supplied	c) Elasticity of supply
In the case of an excess of supply in the market, there is
a) Shortag	b) Equilibrium	c) Surplus