Role of Artificial Intelligence to Enhance Learning Competencies at Secondary School Students

One of the most important parts of every community is its educational system. It has significant links to and effects on all other economic spheres. As a result of its importance, education is a must-have for all social groups notwithstanding barriers. For instance, many scholars are interested in the difficulty that the education sector has had during COVID-19. Access to education, barriers to entering physical classrooms, and economic concerns are only a few examples of the perennial social dilemmas. There are, and will be, a lot of ways to fix things, but our research is centered on one technological approach: artificial intelligence (AI). The educational system is not immune to the societal shifts brought on by AI. Many nations, like Singapore, Malaysia, and South Korea, have been driven by technological advancements to increase their use of technology in the classroom. Education's bright future is inextricably linked to the development of new technology. More sophisticated machinery will help the educational industry meet the new difficulties and take advantage of new possibilities. Economists, political scientists, military consultants, security analysts, and educators are all taking an interest in the AI industry. The effects of three artificial intelligence (AI) applications on classroom instruction are examined here: smart learning (SL), social robots (SR), and tutoring systems (TS).

Artificial intelligence (AI) refers to computer systems that can mimic human intellect. The difference between human and animal intelligence is that one has awareness and emotions while the other has not. "Making a machine behave in ways that would be called intelligent if a human were so behaving" is how John MsCarthy, the guy credited with coining the phrase "artificial intelligence" in 1955, described it. In 1950, Alan Turing popularized the idea that computers may eventually mimic human thought. He anticipated that in the not-too-distant future, computers will do computations that people just couldn't be bothered with. The underlying concern is how binary computations will have human meanings, given that computing devices operate on binary numbers. Commonly, when people talk about artificial intelligence, they mean robots that can mimic human intelligence by carrying out tasks like learning and problem solving. An artificial agent is a machine that can learn from experience and make choices that increase the likelihood of success in achieving certain objectives. Since the activities they are doing have become their normal labor and routine technology, they are often omitted from AI, but have been given the moniker artificial intelligence impact as time and development in the domains of AI have progressed. Artificial intelligence (AI) enables highly developed robots to effectively comprehend human speech. Helping robots in complex strategic games, autonomous vehicles, and similar technologies all fall under the umbrella term "artificial intelligence." Knowledge representation, planning, learning, processing, and object usage are at the heart of artificial intelligence research. Statistics-based modelling and computational intelligence are only two examples of the many methods used to advance the field of artificial intelligence. Artificial intelligence (AI) has far-

Six hundred and five high school students were polled using a modified survey instrument to delve into students' understanding of AI education's most foundational concepts. The poll was modified from one created by Huang, who surveyed Chinese students in grades K-12 who had taken artificial intelligence classes. Due to contextual and cultural variations, the survey is aimed towards their K-12 equivalent in India in order to determine the competences needed to be AI literate in schools. While the poll was conducted following a successful session of experimental teaching, it should be noted that AI education has not been introduced to grades K-12 in Africa and India. The skills needed to understand relatively new areas of study, such artificial intelligence courses, were included in the hardcopy survey. Because it offers greater flexibility, this scale has been embraced because it is more likely to reflect people's objective reality. It was also emphasized that the "faculty of reason" of the participants may be effectively tapped by using a 7-point scale since it provides a more detailed explanation of the theme. Where there are no standardized metrics for measuring concepts like resources and capacities, as Mikalef and Krogstie

The modified survey was used to collect information from the pupils. There were 614 students who began the survey and 605 who finished it. The fact that the survey was presented after the lesson allowed for a more thorough response from students. As can be seen in Table 4.1, the final data set includes answers from students of varying ages and academic levels. There are somewhat more women than men involved (50.2 vs. 49.8%). There is a roughly equal number of male and female participants in the research.

The coefficients and variation of the routes in the overall structural model are shown in Table 4.5. Overall, cognitive ability is the best predictor of collaborative success, whereas expertise with human collaboration tools has the greatest bearing on AI knowledge. Each hypothesis is confirmed. In this research, we employed the WarpPLS program to put our partial least square (PLS) route modeling theories to the test. Research models with many components and numerous elements may be more easily analyzed using a structural equation modelling (SEM) strategy. Figure 4.1 displays the hypothesis tests using route of self-learning competence and a high level of self-learning competence. HTC SLC = 0.30 and t = 7.53 show a positive correlation between human competence with collaboration tools and self-learning competence, whereas HTC Content = 0.29 and t = 7.28 show a positive correlation between human competence with collaboration tools and the contents of AI. Cognitive ability and AI content were shown to have a strong relationship. In this model, individual intelligence is seen as the most important factor in determining a group's success. Not only that, but the R2 for Skill Competence was 6%, the R2 for Team Competence was 10%, the R2 for AI Contents was 13%, and the R2 for Learning Competence was 21%. All the observed discrepancies between the model and reality are statistically insignificant.

To verify the variations in core abilities needed to acquire AI in an African environment across gender and school type, we conducted a multigroup study using WarpPLS Bootstrapping. In this research, the multigroup path coefficient shows that men and women see the world differently. Male students are more likely to believe that cognitive and teamwork skills impact AI literacy, whereas female students are more likely to believe that cognitive and skill competences are necessary to acquire AI. Both the beta coefficient for human collaboration tools competence and the measures of cognitive competence and self-learning competence show that women do better than males. Women have a higher association with AI content than men do when it comes to human collaboration tool competence. Men have a stronger correlation with AI subject knowledge than women do (β= 0.09/ β= 0.17). All the ways matter regardless of a person's sex, except for the cognitive skill element of AI for women. Women's intelligence does not indicate their ability to understand material presented in AI classes. According to this method of categorizing educational institutions, students at private schools are more likely to believe that their level of cognitive and teamwork competence has an effect on their AI literacy than students at public schools, while students at public schools are more likely to believe that they need cognitive and self-learning competencies in order to learn AI. Except for the cognitive competency components of AI in private schools, all the other routes are relevant. Students at for-profit educational institutions do not demonstrate a correlation between IQ and AI course topic comprehension. This analysis compared male and female students as well as those attending public and private schools to see whether there are any differences in the suggested model. Overall, both sexes, both private and public schools, and all structural model routes are statistically significant. Two-tailed hypothesis testing revealed that the transition from CC to CAI was statistically significant only among males and in public school settings. Females and private schools both had t-test values below the threshold of 1.96, at 1.65 and 1.75 respectively.

This research adds to the body of knowledge by emphasizing the significance of students' prior knowledge and skills in the classroom, particularly as they relate to AI instruction. Since competency goes beyond cognitive characteristics and includes skills and interpersonal traits that are suited to the environment, it is important to investigate the function of learners' competencies in order to successfully design materials that will engage them. This article highlights the value of cooperation and the necessity of human-tool collaboration in AI education. This discovery highlights the value of student collaboration in keeping up with the rapid speed of technological change. There are no discernible disparities between sexes or between different types of schools, as shown by the multigroup study. Gender disparities in AI learning have only been the subject of a small number of research thus far. This research contributes to the existing body of knowledge on the gender and schooling-related disparities in AI learning. There are, of course, certain caveats to this work that should not be overlooked despite the fact that the findings of this research shed some light on the link between several essential competences for comprehending the content of AI and AI literacy. To begin, A limited number of pupils from five separate Indian secondary schools make up our sample. It is possible that the conclusion drawn from the

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