Comparative Analysis of the Results of Teaching AI and Robotics for School Students

Authors: Javlonbek Abdujalilov

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Abstract:

Keywords: AI, Robotics, Students, Fourth industrial revolution

I. INTRODUCTION

The Fourth Industrial Revolution (4IR) is marked by the fusion of technologies that blur the lines between the physical, digital, and biological spheres. AI and robotics are among the key drivers of this revolution, transforming industries, workplaces, and everyday life. As we move deeper into this era, there is a growing need to equip students with the knowledge and skills necessary to navigate and thrive in a world shaped by AI and automation. Educational institutions must adapt to this new reality by incorporating AI and robotics into their curricula.

AI and robotics are no longer niche areas of study, they are becoming integral to various industries, including healthcare, manufacturing, finance, and education itself [1]. Therefore, early exposure to AI and robotics in the school setting is essential for developing foundational knowledge that students can build upon in higher education and their future careers. Teaching these technologies in schools has many advantages.

A. Enhancement of Computational Thinking

Research by this author defines computational thinking as a fundamental skill for all learners, not just computer scientists [2]. Teaching AI and robotics helps students develop logical thinking, algorithmic design, and the ability to break complex problems into manageable parts.

B. Promotion of Interdisciplinary Learning

AI and robotics are interdisciplinary by nature, combining knowledge from mathematics, engineering, computer science, and even ethics [2]. As a result, these fields promote integrated learning and connect academic concepts to real-world applications.

Development of 21st Century Skills: Research by this author found that robotics education improves students’ creativity, teamwork, and problem-solving skills [3]. These competencies are crucial for success in a rapidly evolving global economy.

This paper presents a comparative analysis of the effectiveness of teaching AI and robotics in school settings, based on an experiment conducted at the 129th school in Tashkent during the 2023-2024 academic year.

II. LITERATURE REVIEW

The incorporation of AI and robotics into educational curricula has garnered significant attention in recent years. Studies on technological pedagogical content knowledge emphasize the need for innovative teaching methodologies to effectively integrate AI and robotics [4]. Several international programs, such as AI for K-12 (AI4K12) in the United States, have highlighted the importance of early exposure to AI concepts to develop computational thinking and problem-solving skills.

Existing literature suggests that students exposed to AI and robotics demonstrate improved learning outcomes in STEM subjects. For instance, research by author shows that students who engage with robotics develop better critical thinking and teamwork skills compared to their peers [5]. However, there remains a gap in the research regarding the direct impact of these subjects on long-term academic performance and career readiness, particularly in non-Western contexts like Uzbekistan [6].

Several initiatives and frameworks have been developed globally to introduce AI and robotics into school curricula, with varying levels of complexity and success. The AI4K12 initiative in the United States is one such effort that aims to create national guidelines for teaching AI to K-12 students [2]. The framework outlines five big ideas in AI: perception, representation and reasoning, learning, natural interaction, and societal impact. AI4K12 encourages educators to integrate these concepts progressively from elementary through high school, ensuring that students gain both theoretical and practical experience in AI.

In a similar effort, the International Society for Technology in Education (ISTE) has developed a set of standards for integrating technology in education, with a focus on digital literacy and innovation [7]. Robotics, in particular, has gained popularity as an engaging and effective way to introduce students to these concepts. FIRST Robotics, a popular global program, encourages students to design, build, and program robots to solve specific challenges. Research by author shows that participation in robotics programs improves students’ technical skills and fosters a growth mindset, resilience, and collaboration [6].

In South Korea, AI and robotics have been integral to the STEAM education movement, which incorporates science, technology, engineering, arts, and mathematics into the national curriculum. Research by author indicates that early exposure to AI through project-based learning has increased students’ engagement and motivation in STEM subjects [8]. Such approaches are seen as key to developing the workforce needed for the 4IR.

While the benefits of teaching AI and robotics are well-documented, there are several challenges to its implementation.Ertmer and Ottenbreit-Leftwich indicates that one of the main barriers to the adoption of AI and robotics in education is the lack of teacher confidence and expertise in these areas [9]. Teachers need adequate professional development and training to effectively deliver these subjects, especially in regions where access to cutting-edge technology is limited.

The importance of long-term comparative analysis in evaluating the sustained impact of integrating AI and robotics into education is crucial. Despite the settings focused on short-term, Farias et al. [14] observed a development the knowledge of content and instruction between student and coaches during a year-long study, in which they became increasingly self-assisted in activities. Short-term results include immediate improvements in problem-solving and teamwork, while long-term outcomes focus on sustained skill development, deeper conceptual understanding, and changes in career aspirations. This differentiation helps understand the incremental benefits of integrating AI and robotics over time. Specific tailored strategies `are designed for leveraging open-source tools such as Orange3 for data visualization and fostering partnerships with international organizations to overcome resource limitations.

Hovewer, incorporating a technology into existing school curricula can be challenging [13]. Challenges such as limited resources, teacher training gaps, and infrastructure inadequacies, particularly in developing countries like Uzbekistan, pose significant barriers. There is often limited room for new subjects in already packed curricula. The author argues that while robotics is typically introduced as an extracurricular activity or elective course, integrating it into the core curriculum requires systemic change in education policy and practice [3].

Access to Resources and Infrastructure: A study by author found that successful implementation of robotics education depends heavily on the availability of resources, such as hardware (robots, sensors) and software (programming platforms) [5]. Schools in under-resourced areas may struggle to provide students with the tools necessary for hands-on learning experiences, further exacerbating educational inequality.

Numerous studies have evaluated the impact of AI and robotics education on students’ academic performance and engagement
Improved Learning Outcomes in STEM: Research by authors found that students who participated in robotics education demonstrated better understanding and retention of STEM concepts compared to their peers. These students also showed improved performance in math and science standardized tests [10].

Engagement and Motivation: Educational Robotics have been shown to increase student motivation and engagement, particularly for students who might otherwise be disinterested in traditional STEM subjects [11]. The interactive and hands-on nature of robotics makes learning more dynamic and relevant to students’ lives.

Closing the Gender Gap: Robotics programs, when implemented effectively, have been shown to encourage more female students to pursue careers in STEM fields [1]. Studies by author found that when girls are provided with robotics education, they demonstrate increased interest and self-efficacy in STEM subjects [12].

However, while these findings are encouraging, there is still a need for more empirical studies that evaluate the long-term impact of AI and robotics education on students’ future career choices and academic success. In the context of Uzbekistan, there is limited research on the introduction of AI and robotics into school curricula, making this study particularly valuable.

III. METHODOLOGY

The experiment was conducted at the 129th general education institution in Tashkent, Uzbekistan during the 2nd and 3rd quarters of the 2023-2024 academic year. The study involved 48 students from the 10th and 11th grades. The students were divided into two groups: the experimental group (24 students) and the control group (24 students).

A phased approach was used for conducting longitudinal study, including periodic data collection at one-year intervals and teacher training programs. The definition of ‘long-term’ in this context refers to observations extending over multiple academic years (three or more years), allowing for a comprehensive understanding of sustained pedagogical shifts.

For long-term observation it incorporates an iterative feedback mechanism to ensure alignment with the research problem, aiming to evaluate the sustained impact of integrating AI and robotics into education.

The experimental group received instruction with a comprehensive pedagogical approach centered on AI and robotics. The current approach jointly provided theoretical concepts of AI, machine learning, and robotics with practical, hands-on activities and real-world applications. Proposed instructional design aimed to develop engagement and contributed for understanding of difficult topics. The control group continued with the traditional Informatics curriculum, which included only one class hour topics for each AI and robotics.

The experiment group studied how to design and simulate different sensors shown on Fig.1.

Blacksmith Circuit configuration can be applied in different real-life applications shown on Fig.2.

Orange3 Data Processing and Handling shown on Fig.3. Orange is an open-source component-based visual programming toolkit. The software is used for data visualization, machine learning, data mining, and conducting data analysis. The software components are called widgets, and they offer a range of features like simple data visualization, subset selection, and pre-processing. Users can also make practical evaluations of learning algorithms and predictive modeling.

This study utilized a combination of interviews, surveys and observational checklists to gather comprehensive data. To assess students’ knowledge and skills before and after the intervention we have employed pre- and post-tests. The survey was conducted to gather feedback from students regarding their interest in AI and robotics and their perception of the teaching methodology.

Three Informatics teachers at the school evaluated the teaching methods and provided qualitative feedback.
A comparative analysis was conducted between the pre- and post-test results of the experimental and control groups. The data were analyzed using descriptive statistics to measure differences in performance, and qualitative feedback from teachers and students was thematically analyzed to understand the effectiveness of the teaching approach.

IV. EXPERIMENT

The performance of students according to the results of experimental work organized in 129-school of Tashkent during 2023-2024 academic year.

Performance of students	Experimental group				Control group
	Number of students at the beginning of the experiment	%	Number of students at the end of the experiment	%	Number of students at the beginning of the experiment	%	Number of students at the end of the experiment	%
High	2	8	8	33	2	8	2	8
Medium	11	46	12	50	8	33	10	42
Low	11	46	4	17	14	59	12	50
Total	24	100	24	100	24	100	24	100

At the beginning of the experiment, the number of experimental group students are 24. They participated in the experimental group and in the evaluation results were 2 (8%) high level students, 11 (46.0%) medium students, 11 (46.0%) low level students, and by the end of the experiment students’ performance results were improved, (33.0%) 8 students obtained high marks, showed an increase of 23.0%, middle 12 (50%) increased by 4%, low level 4 (17.0%) showed a decrease of 29.0%.

At the beginning of the experiment, the control group students were 24. They participated in the control group and their performance were 2 (8,0%) high level students, medium 8 (33,0%), low 14 (59,0%), and by the end of the experiment, high 2 (8,0 %) by 0.0 % changed, the middle 10 (42,0 %) showed an increase of 9,0 % and the low level 14 (59,0%) showed a decrease of 9,0 % (Table 1.).

We have used Pearson’s chi-squared test to define the effectiveness of AI and robotics instruction based on the following research questions:

Does the AI and robotics instructional topics significantly improve the digital competencies of 10th- and 11th-grade students compared to the existing traditional theory based curriculum? We have defined the following hypotheses:

Null Hypothesis (H₀): The teaching method (AI & robotics vs. traditional) does not affect the improvement of digital competencies (the two variables are independent).

Alternative Hypothesis (Hₐ): The teaching method influences the improvement of digital competencies (the two variables are dependent).

Group	Improved	Not Improved	Total
Experimental (AI & Robotics)	14	10	24
Control (Traditional)	4	20	24
Total	18	30	48

In the following the calculation of Expected Frequencies (E)
The expected frequency for each cell is calculated using:

Group	Improved	Not Improved	Total
Experimental (AI & Robotics)	9	15	24
Control (Traditional)	9	15	24
Total	28	20	48

In the following the calculation of Chi-Squared Statistic (χ²)
The formula for the chi-squared statistic is:

where (II-table) Observed frequency and (III-table) Expected frequency. For each cell, the contributions to χ² are calculated as follows:

Summing these contributions gives: χ² = 2,7 + 1,6 + 2,7 + 1,6 = 8,6

Determine Degrees of Freedom (df)，The degrees of freedom are calculated as:

df= (Rows-1) (Column-1); df = (2 – 1) × (2 – 1) = 1

In the following we found the Critical Value and P-Value

At a significance level (α) of 0.05 and df = 1, the critical value from the chi-squared distribution table is χ²_critical = 3.841
P-Value: From the chi-squared distribution, the p-value corresponding to χ² = 8,6 and df = 1 is: p = 0.019

Since χ² = 8,6 > χ²_critical = 3.841 and p = 0.019 < 0.05:

We reject the null hypothesis (H₀). The teaching method (AI and robotics) significantly influences the improvement of digital competencies among students.

V. RESULTS AND DISCUSSION

Before the experiment, both the experimental and control groups were assessed through a pre-test. The results indicated that there was no significant difference in the knowledge, skills, and competencies of students in both groups, as their initial test scores were similar. This provided a solid baseline for comparing the impact of the new teaching methodology.

At the conclusion of the experiment, the experimental group showed a significant improvement in their test scores compared to the control group. The results are as follows: The average score of the experimental group increased by 18-20% compared to their pre-test results. The control group showed only marginal improvement, with an average increase of 5-7%.

The questionnaires revealed that students in the experimental group were more engaged and motivated to learn. Many expressed an interest in pursuing further studies in AI and robotics. Additionally, the teachers noted that the experimental group’s problem-solving and teamwork abilities had noticeably improved. Three teachers who participated in the evaluation process provided positive feedback on the new methodology. They noted that the integration of AI and robotics into the Informatics curriculum made the subject more dynamic and relevant to modern technological advancements. The teachers recommended further implementation of the methodology across other schools in the region.

The comparative analysis of the control and experimental groups demonstrated the clear benefits of teaching AI and robotics. The experimental group outperformed the control group by 13-15% in final tests, highlighting the positive impact of introducing AI and robotics in the school curriculum. The increase in knowledge and skills in the experimental group underscores the importance of adopting new technologies in education to better prepare students for the demands of the 4IR.

This study highlights the importance of integrating AI and robotics into school curricula as part of the broader educational reforms needed in the 4IR. The experiment conducted at the 129th school in Tashkent provides empirical evidence that teaching
AI and robotics significantly enhances students’ academic performance, engagement, and readiness for future careers in STEM fields.

The findings from this experiment indicate that AI and robotics should be incorporated into the national curriculum to prepare students for the future job market. Further studies are needed to explore the long-term impacts and scalability of this approach across different schools and regions.

The success of this experimental study calls for the expansion of such innovative teaching methodologies, not only in Uzbekistan but globally, to better equip future generations with the skills required in the modern technological landscape.

Reference

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