Artificial Intelligence Awareness Among School Students

Artificial Intelligence Awareness Among School Students

Rafat Fatima

(Ph.D. Research Scholar) Department of Computer Science Sona Devi University, Jharkhand

Abstract – Based on existing research methods, this paper depends on the concept of artificial intelligence and its impact on society and various fields, health industry, education sector etc. The research is done by qualitative and quantitative analysis of the data and statistics in turn which results in how Artificial Intelligence has become a fast growing name in technology. In this survey we found out that many Individuals are aware of that they are encountering artificial intelligence (AI) in daily life, such as Robotic Mop, Movie recommendations, customer service chatbots and product recommendations based on previous purchases etc. Simultaneously, only three-in-ten adults are able to correctly identify all ten uses of AI asked about in the survey, underscoring the developing nature of public understanding.

Keyword: Artificial Intelligence, Higher education, awareness, progressive

1. INTRODUCTION

Artificial Intelligence (AI) refers to the ability of a human- made interface (machine or application to mimic human like intelligence i.e. reasoning and integration of knowledge

Artificial intelligence (AI) has many different definitions; some see it as the created technology that allows computers and machines to function intelligently. Some see it as the machine that replaces human labor to work for men a more effective and speedier result. Others see it as a system with the ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation .

Despite the different definitions, the common understanding of AI is that it is associated with machines and computers to help humankind solve problems and facilitate working processes. In short, it is an intelligence designed by humans and demonstrated by machines. The term AI is used to describe these functions of human-made tool that emulates the cognitive abilities of the natural intelligence of human minds .

Along with the rapid development of cybernetic technology in recent years, AI has been seen almost in all our life circles, and some of that may no longer be regarded as AI because it is so common in daily life that we are much used to it such as optical character recognition or the Siri (speech

interpretation and recognition interface) of information searching equipment on computer .

Artificial Intelligence in our Life- Awareness

Artificial Intelligence (AI) is becoming a part of our daily lives. Most of us are aware and some of us are not. We have done a survey among teachers, parents and students of school to know the awareness about AI.

How we did this

We organize the reports of the findings of the entire research process undertake.. Data analysis was done after the investigator constructed the Artificial Intelligence quotient scale with five-point scale. The self-constructed Artificial Intelligence quotient scale was administered on 2100 Higher Secondary School students of English medium college for data collection after prior approval from the authorities. The data collected through administration of the tool on selected samples were raw. This collected data was coded first and then scoring was done. The data was tabulated, organized and analyzed according to the variables of the study for drawing sound conclusions and valid generalizations. This chapter gives a detailed description of drawing inferences and generalizations through testing hypotheses which has been presented with the help of tables, graphs and narrations as below.

4.2 DEMOGRAPHIC PROFILE OF RESPONDENTS

Table4. 1 School type of the respondents

Figure 4. 1 School type of the respondents

Table 4.1 provides a distribution of respondents based on the type of school they attend, distinguishing between private and government institutions. Out of a total of 2,100 respondents, a significant majority1,260 individuals, accounting for 60%are enrolled in private schools. This indicates a clear preference or trend toward private education among the surveyed population. It suggests that private schools may be perceived as offering better infrastructure, teaching quality, or academic opportunities, which could explain their higher enrollment numbers.

On the other hand, 840 respondents, which make up 40% of the total, are from government schools. While this is a smaller proportion compared to private school respondents, it still represents a substantial segment of the student population. The 40% attendance at government schools could be influenced by factors such as affordability, accessibility in rural or semi-urban areas, or the presence of government schemes like mid- day meals and free textbooks.

The data reflects a division in the education sector, potentially highlighting socio-economic disparities. Students attending private schools might come from families with higher income levels, while those in government schools may belong to economically weaker sections. Overall, this table helps us understand the

educational landscape in terms of school preferences and may serve as a basis for deeper analysis on quality of education, resource allocation, or policy planning.

Table4. 2 Gender of the respondents

Figure 4. 2 Gender of the respondents

Table 4.2 presents a breakdown of the respondents based on gender. Out of the total 2,100 respondents, 1,155 are male, which constitutes 55% of the sample population. This shows that male respondents form a slight majority in the survey. Their dominant representation may reflect either a higher male enrollment in the educational institutions surveyed or a gender imbalance in participation in the study.

Female respondents number 945, making up 45% of the total. Although slightly lower in proportion, this still represents a significant part of the respondent base, indicating a relatively balanced gender representation. However, the 10% gap between male and female respondents may suggest gender-based disparities in school attendance, accessibility to education, or cultural factors that influence participation rates.

The nearly balanced distribution allows for comparative gender-based analysis in subsequent sections of the study. It also points toward ongoing efforts in achieving gender equity in education, though some room for improvement still remains.

Table4. 3 Stream of Education of the respondents

Figure . 3 Stream of Education of the respondents

Table 4.3 shows the distribution of respondents according to their chosen stream of education: Arts, Science, and Commerce. Among the 2,100 total respondents, the largest proportion900 students, or 42.86%are enrolled in the Arts stream. This indicates a strong preference for Arts subjects, which may be attributed to a wide range of career options, perceived accessibility, or less intense academic requirements compared to Science.

Science stream students make up the second-largest group, with 750 respondents accounting for 35.71% of the sample. This suggests that a significant number of students are still attracted to science-related careers, possibly due to the potential for professional opportunities in medicine, engineering, and research fields. The Science stream typically demands a higher academic performance, and its sizable representation here reflects a healthy interest in technical and scientific education.

Commerce is the least represented stream, with 450 students making up 21.43% of the respondents. While smaller in number, this still indicates a substantial

interest in business, finance, and economics-related careers. The lower percentage may be due to limited awareness, fewer institutions offering Commerce, or competitive entry criteria.

Overall, the table highlights diverse educational interests among students, with Arts being the most popular, followed by Science and Commerce. These preferences could be influenced by personal interests, socio- economic backgrounds, career aspirations, and availability of resources in different schools.

Table4. 4 Area of the respondents

Figure 4. 4 Area of the respondents

Table 4.4 illustrates the geographical distribution of the respondents based on whether they reside in rural or urban areas. Out of a total of 2,100 respondents, 1,260 individuals, or 60%, are from rural areas. This indicates that the majority of the respondents come from rural backgrounds. The higher percentage of rural respondents could be due to various factors, such as a larger rural population, the focus of the study on rural communities, or a broader representation of rural students in the surveyed group. This could also suggest that rural areas may have different educational experiences or challenges compared to urban areas, which might be relevant for further analysis.

In contrast, 840 respondents (40%) come from urban areas, which is a smaller proportion compared to their rural counterparts. While still significant, the urban group is less represented, potentially highlighting the

different social and economic dynamics of city-based students compared to those from rural backgrounds. Urban areas typically have more access to resources such as well-equipped schools, extracurricular activities, and better infrastructure, which may influence the educational experiences and opportunities available to students in these areas.

This table provides important insights into the geographical diversity of the respondents, which is crucial for understanding how location might affect educational outcomes, access to facilities, and overall opportunities for students. The rural-urban distribution also offers valuable context for evaluating potential disparities in the quality of education and support systems available in different regions.

Table4. 5 Medium of Education of the respondents

Figure 4. 5 Medium of Education of the respondents

Table 4.5 provides an analysis of the medium of education chosen by the respondents, categorizing them into those who receive instruction in English and those in Hindi. Out of the 2,100 total respondents, 1,380 individuals (or 65.71%) are educated in English. This reflects a strong preference for English as the medium of instruction, which is often associated with access to higher education, global opportunities, and a perceived better standard of learning. The dominance of English- speaking schools may also suggest the influence of urbanization or socio-economic factors, where families might prioritize English-medium education for its perceived advantages in the professional and academic

arenas.

On the other hand, 720 respondents (34.29%) receive their education in Hindi. While this is a smaller proportion compared to those in English-medium schools, it still represents a significant portion of the population. Hindi, as a widely spoken language in India, remains a key medium of education in many rural and semi-urban regions where local languages might also play a role in the schooling system. The preference for Hindi as a medium of instruction may reflect cultural and regional factors, where students and their families feel more comfortable with or value their native language in the education system.

This table illustrates the linguistic diversity within the educational system and may provide insight into regional, cultural, and socio-economic preferences regarding language use in schools. It can also serve as a basis for understanding how the medium of instruction might influence the educational experience, access to resources, and the future academic and professional prospects of students.

Table4. 6 Socio Economic Status of the respondents

Figure 4. 6 Socio Economic Status of the respondents

Table 4.6 presents the socio-economic status (SES) distribution of the respondents, categorizing them into high, upper, middle, and low socio-economic classes. Out of a total of 2,100 respondents, 945 individuals (45%) belong to the middle socio-economic class, making it the largest group in this distribution. This suggests that the majority of respondents come from families that experience a moderate level of income and access to resources. The middle class is often characterized by a mix of both financial stability and moderate access to educational and professional opportunities, which might influence students' educational experiences and aspirations. The upper socio-economic class represents 525 respondents (25%) of the sample, indicating that a significant proportion of students come from more affluent families. This group likely has access to better resources, such as private education, extracurricular activities, and technology, which can contribute to better educational outcomes. The representation of the upper socio-economic class may also suggest a higher level of investment in education, with parents possibly prioritizing higher-quality schools and institutions for their children. The high socio- economic class is represented by 315 respondents (15%), which is a smaller but still notable proportion. This group is typically characterized by significant wealth and resources, likely contributing to enhanced educational opportunities, such as access to international schools, specialized programs, and global exposure. Their relatively smaller percentage in the total sample may reflect the generaldemographic distribution of extremely wealthy families.

Finally, 315 respondents (15%) come from the low socio-economic class, the smallest group in the sample. Students from this group may face challenges related to financial constraints, limited access to educational resources, and potential socio-economic barriers that can impact their academic performance and opportunities. This category reflects the struggles faced by lower- income families in accessing quality education and may highlight disparities in the education system.

Overall, this table provides a comprehensive view of the socio-economic landscape of the respondents, offering insights into the educational opportunities and challenges faced by students from various economic backgrounds. It highlights the need for targeted interventions to bridge socio-economic gaps and ensure equitable access to quality education across different income levels.

CONCLUSION

In order to study the levels of AIQ, a self-constructed AIQ scale was prepared and it was administered on under graduate students included as sample. The responses of the students were evaluated and presented below.

Table4. 8 Levels of Artificial Intelligence Quotient in Terms of Score

Figure 4. 8 Levels of Artificial Intelligence Quotient in Terms of Score

Table 4.8 provides an insightful breakdown of the Artificial Intelligence Quotient (AIQ) levels within a sample group of 2100 individuals, categorizing them into three distinct groups: High, Average, and Low. Each category is represented by both the number of individuals falling into that group and the percentage of the total population they comprise.

The "High" Artificial Intelligence Quotient category consists of 630 individuals, which represents 30% of the total group. This indicates that a significant portion of the population exhibits a high level of Artificial Intelligence awareness, intelligence, or sensitivity. These individuals are likely to demonstrate advanced characteristics associated with high AIQ, such as a profound understanding of Artificial Intelligence concepts, heightened empathy, and a strong ability to live in alignment with their Artificial Intelligence values. A 30% figure suggests that while the group is not the majority, it is still a considerable portion of the overall population, reflecting a meaningful presence of

individuals who possess a higher degree of Artificial Intelligence intelligence.

The largest group, comprising 60% of the population, falls into the "Average" Artificial Intelligence Quotient category. This group includes 1260 individuals, making it the majority. People in this category likely exhibit a standard level of Artificial Intelligence intelligence.

They may have a basic understanding of Artificial Intelligence concepts and exhibit Artificial Intelligence traits such as empathy and self-awareness, but not to the same extent as those in the High category. This suggests that most individuals in the sample possess a moderate level of Artificial Intelligence quotient, which may be sufficient for personal development, but they may not engage deeply with Artificial Intelligence practices or transcendence in the way that the High AIQ group does.

Lastly, the "Low" Artificial Intelligence Quotient group, which represents only 10% of the population (or 210 individuals), indicates that a smaller portion of the sample demonstrates a lower level of Artificial Intelligence intelligence. Individuals in this category may struggle with aspects of self-awareness, empathy, or alignment with Artificial Intelligence or moral values.

A low AIQ score may reflect a more materialistic or practical worldview, where Artificial Intelligence principles are less integrated into daily life. The relatively small size of this groupcomprising only 10% of the total populationsuggests that low Artificial Intelligence intelligence is not as prevalent in this particular sample, though it still represents a significant proportion of individuals.

Overall, the distribution of Artificial Intelligence Quotient levels in this table highlights that the majority of individuals fall into the "Average" category, with a substantial minority exhibiting a high level of Artificial Intelligence intelligence, and a small group with a low level.

This suggests that Artificial Intelligence awareness is a widely distributed trait in the population, but deeper Artificial Intelligence insight is less common.

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