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The thematic analysis of focus group discussions revealed students' enthusiasm for AI, recognizing its potential to transform education. However, significant concerns about data privacy and job displacement were also prevalent. Students expressed uncertainty about how their personal data would be used and protected, aligning with broader societal concerns about data security (Bostrom & Yudkowsky, 2014). The fear of job displacement highlighted the need for educational institutions to prepare students for an AI-driven job market, reflecting findings from Brynjolfsson and McAfee (2017). The call for clear ethical guidelines and greater transparency from AI developers underscored the necessity of ethical frameworks to ensure responsible AI deployment. These qualitative insights enriched the quantitative findings, providing a deeper understanding of the ethical considerations that influence student acceptance of AI.

Table 4 The thematic analysis of focus group discussions revealed students' enthusiasm for AI, recognizing its potential to transform education. However, significant concerns about data privacy and job displacement were also prevalent. Students expressed uncertainty about how their personal data would be used and protected, aligning with broader societal concerns about data security (Bostrom & Yudkowsky, 2014). The fear of job displacement highlighted the need for educational institutions to prepare students for an AI-driven job market, reflecting findings from Brynjolfsson and McAfee (2017). The call for clear ethical guidelines and greater transparency from AI developers underscored the necessity of ethical frameworks to ensure responsible AI deployment. These qualitative insights enriched the quantitative findings, providing a deeper understanding of the ethical considerations that influence student acceptance of AI.

Related Figures (9)

Figure 1. Shows the Theory of Planned Behavior (Ajzen, 1991). This study examined how these factors influence students' acceptance of AI technologies in their educational environment. Ethical theories such as Deontology (Kant, 1785) and Utilitarianism (Mill, 1861) provided the moral foundation for analyzing ethical concerns related to AI, guiding the exploration of issues like data privacy, fairness, and accountability.
Figure 1. Shows the Theory of Planned Behavior (Ajzen, 1991). This study examined how these factors influence students' acceptance of AI technologies in their educational environment. Ethical theories such as Deontology (Kant, 1785) and Utilitarianism (Mill, 1861) provided the moral foundation for analyzing ethical concerns related to AI, guiding the exploration of issues like data privacy, fairness, and accountability.
Figure 2. Shows the Ethical Theories
Figure 2. Shows the Ethical Theories
Impact Factor: 4.843 ISSN: 0976-7797 The conceptual framework integrates TPB and Ethical Theories to understand the complex interplay of factors influencing student perceptions and ethical concerns regarding AI. This framework posits that attitudes towards AI, subjective norms, perceived behavioral control, and ethical considerations interact to shape student acceptance of AI technologies. Aldrin Sebastian Valerio, Cognizance Journal of Multidisciplinary Studies, Vol.4, Issue.6, June 2024, pg. 408-418
Impact Factor: 4.843 ISSN: 0976-7797 The conceptual framework integrates TPB and Ethical Theories to understand the complex interplay of factors influencing student perceptions and ethical concerns regarding AI. This framework posits that attitudes towards AI, subjective norms, perceived behavioral control, and ethical considerations interact to shape student acceptance of AI technologies. Aldrin Sebastian Valerio, Cognizance Journal of Multidisciplinary Studies, Vol.4, Issue.6, June 2024, pg. 408-418
The descriptive statistics highlighted that 75% of students had a high level of awareness about the basic concepts of AI, and 40% were familiar with specific AI applications in education. This demonstrated that while students generally understood AI, there was still a gap in their knowledge about its specific uses in educational settings. Positive attitudes towards AI were evident, with 68% of students recognizing its potential benefits in enhancing learning experiences. However, significant ethical concerns were noted, particularly regarding data privacy (55%) and job displacement (55%). These concerns indicated that while students were optimistic about AI's capabilities, they were also wary of its implications, aligning with broader societal concerns about AI (Bostrom & Yudkowsky, 2014). The mean scores for perceived behavioral control (3.8) and subjective norms (3.5) suggested that students felt moderately confident in their ability to use AI and perceived some social pressure to adopt it.
The descriptive statistics highlighted that 75% of students had a high level of awareness about the basic concepts of AI, and 40% were familiar with specific AI applications in education. This demonstrated that while students generally understood AI, there was still a gap in their knowledge about its specific uses in educational settings. Positive attitudes towards AI were evident, with 68% of students recognizing its potential benefits in enhancing learning experiences. However, significant ethical concerns were noted, particularly regarding data privacy (55%) and job displacement (55%). These concerns indicated that while students were optimistic about AI's capabilities, they were also wary of its implications, aligning with broader societal concerns about AI (Bostrom & Yudkowsky, 2014). The mean scores for perceived behavioral control (3.8) and subjective norms (3.5) suggested that students felt moderately confident in their ability to use AI and perceived some social pressure to adopt it.
Table 2: Demographic Characteristics of the Sample
Table 2: Demographic Characteristics of the Sample
The demographic data showed that the majority of students were aged 22-25 (50%), with a balanced representation of males (44%) and females (56%). Most students came from the Arts (40%) and Sciences (30%) disciplines, with fewer from Engineering (20%) and other fields (10%). The distribution across year levels was relatively even, with a slight majority of juniors (30%). Notably, 64% of students had no prior experience with AI, indicating that most were encountering AI in an academic context for the first time. This demographic spread ensured a diverse representation of student experiences and perspectives, enhancing the generalizability of the findings.
The demographic data showed that the majority of students were aged 22-25 (50%), with a balanced representation of males (44%) and females (56%). Most students came from the Arts (40%) and Sciences (30%) disciplines, with fewer from Engineering (20%) and other fields (10%). The distribution across year levels was relatively even, with a slight majority of juniors (30%). Notably, 64% of students had no prior experience with AI, indicating that most were encountering AI in an academic context for the first time. This demographic spread ensured a diverse representation of student experiences and perspectives, enhancing the generalizability of the findings.
Table 3. Shows the Regression Analysis Predicting Student Acceptance of AI The regression analysis revealed that attitudes towards AI (8 = 0.45, p < 0.01) and perceived behavioral control (B = 0.37, p < 0.01) were the strongest predictors of student acceptance of AI, supporting the Theory of Planned Behavior (Ajzen, 1991). Subjective norms also had a significant but moderate influence (B = 0.29, p < 0.05), indicating the role of social influence in AI adoption. Demographic variables such as age, gender, academic discipline, year level, and prior experience with AI were also significant predictors. Younger students, males, those in science and engineering disciplines, seniors, and students with prior AI experience were more likely to accept AI technologies. These findings underscored the importance of demographic factors in shaping AI acceptance and highlighted the need for tailored educational strategies to address the specific concerns and needs of different student groups.
Table 3. Shows the Regression Analysis Predicting Student Acceptance of AI The regression analysis revealed that attitudes towards AI (8 = 0.45, p < 0.01) and perceived behavioral control (B = 0.37, p < 0.01) were the strongest predictors of student acceptance of AI, supporting the Theory of Planned Behavior (Ajzen, 1991). Subjective norms also had a significant but moderate influence (B = 0.29, p < 0.05), indicating the role of social influence in AI adoption. Demographic variables such as age, gender, academic discipline, year level, and prior experience with AI were also significant predictors. Younger students, males, those in science and engineering disciplines, seniors, and students with prior AI experience were more likely to accept AI technologies. These findings underscored the importance of demographic factors in shaping AI acceptance and highlighted the need for tailored educational strategies to address the specific concerns and needs of different student groups.
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