<p>Identifying valid and reliable instruments measuring attitudes toward Artificial Intelligence (AI) and examining attitudinal gaps are becoming increasingly important as they may inform ethical and appropriate development, adoption, and regulation of AI technologies. In this study, we validated the 4-item AI Attitude Scale (AIAS-4) in a large sample of Norwegian university students (<i>n</i> = 2329). Using confirmatory factor analysis, Cronbach’s alpha and McDonald’s Omega, we found the AIAS-4 to be both valid and reliable. Further, through multiple regression analysis, we identified the students’ gender, age, and study discipline to be significant predictors of positive attitudes toward AI technology in our sample. Male students were more positive than female students and students between 21 and 25 were more positive than students under 21 and students over 25 years. As a novel discovery, we found that the students from the faculty of engineering and natural sciences were more positive than students from the faculty of health sciences and from the faculty of teacher education, culture, and sports, but not significantly more positive than students from the faculty of economics and social sciences. Our findings are discussed in light of previous research and, although limited by restricted generalizability, potential response bias, and the inherent limitations of the cross-sectional study design, they contribute new knowledge to the field of AI in higher education and society.</p>

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Validating the AI attitude scale (AIAS-4) and exploring attitudinal differences in a large sample of Norwegian university students

  • Anja Møgelvang,
  • Simone Grassini

摘要

Identifying valid and reliable instruments measuring attitudes toward Artificial Intelligence (AI) and examining attitudinal gaps are becoming increasingly important as they may inform ethical and appropriate development, adoption, and regulation of AI technologies. In this study, we validated the 4-item AI Attitude Scale (AIAS-4) in a large sample of Norwegian university students (n = 2329). Using confirmatory factor analysis, Cronbach’s alpha and McDonald’s Omega, we found the AIAS-4 to be both valid and reliable. Further, through multiple regression analysis, we identified the students’ gender, age, and study discipline to be significant predictors of positive attitudes toward AI technology in our sample. Male students were more positive than female students and students between 21 and 25 were more positive than students under 21 and students over 25 years. As a novel discovery, we found that the students from the faculty of engineering and natural sciences were more positive than students from the faculty of health sciences and from the faculty of teacher education, culture, and sports, but not significantly more positive than students from the faculty of economics and social sciences. Our findings are discussed in light of previous research and, although limited by restricted generalizability, potential response bias, and the inherent limitations of the cross-sectional study design, they contribute new knowledge to the field of AI in higher education and society.