Neo-Luddite Concerns about Artificial Intelligence
Description
Data Description The dataset comprises quantitative survey data collected from 354 12th-grade high school students enrolled in six different types of public high schools located in the city center of Eskişehir, Türkiye. The data were gathered during the 2024–2025 academic year from students who were actively preparing for the 2025 Higher Education Institutions Examination (YKS). The dataset was generated using a structured 70-item questionnaire administered in person after obtaining official permission from the Provincial Directorate of National Education. The survey includes two main sections. The first section contains 20 items capturing students’ socio-demographic characteristics, such as school type and background variables relevant to career decision-making. The second section consists of 50 five-point Likert-type items designed to measure students’ perceptions of artificial intelligence, levels of Neo-Luddite fear related to AI-driven technological change, and the perceived influence of these factors on their career choice decisions. Sampling was conducted using a combination of simple random sampling and criterion sampling, with the primary inclusion criterion being enrollment in the 12th grade. The resulting dataset provides a cross-sectional snapshot of students’ attitudes and perceptions at a single point in time. Preliminary analysis indicated that the data were normally distributed based on the Kolmogorov–Smirnov test. The dataset is suitable for descriptive and inferential statistical analyses, including frequency distributions, means, independent samples t-tests, and one-way ANOVA, all of which were conducted using SPSS version 27.0. The data are available from the authors upon reasonable request.
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This study employed a cross-sectional descriptive survey, a quantitative research approach designed to capture the opinions of a large population on a specific topic. In such studies, data are collected from a sample rather than the entire population, and responses are used to define characteristics or patterns within the population (Fraenkel et al., 2012). Cross-sectional studies provide a snapshot of participants at a single point in time, enabling researchers to examine relationships between variables without manipulation (Creswell, 2014). In this study, high school students were surveyed to explore how Neo-Luddite fear, originating from artificial intelligence developments, influences their career choice decisions. The study population consisted of 12th-grade students attending six high schools in the city center of Eskişehir: one Social Sciences High School, one Science High School, one Anatolian High School, one Anatolian Fine Arts High School, one Anatolian Health Vocational High School, and one Girls’ Vocational and Technical Anatolian High School. A sample of 354 students was selected using simple random and criterion sampling. Simple random sampling ensures that each individual has an equal chance of being selected, eliminating bias, while criterion sampling selects participants who meet predetermined criteria, allowing for in-depth insights (Patton, 2002; Creswell, 2014). The main criterion was that students were 12th graders preparing for the 2025 YKS exam, making them directly relevant to career choice decisions.Data were collected using a 70-item survey. 20 items captured socio-demographic information, and 50 Likert-type items assessed the impact of artificial intelligence and Neo-Luddite fear on students’ career choices. After obtaining the necessary permission from the Provincial Directorate of National Education to collect data, schools in the sample were visited. After providing the necessary explanations to the students, a survey was administered. The Kolmogorov-Smirnov test was performed to determine whether the data were normally distributed, and it was determined that they were. SPSS 27.0 was used to analyze the data, and frequency, percentage, mean, t-test, and ANOVA calculations were performed. Data will be made available on reasonable request.
Institutions
- Anadolu Universitesi