Analyzing the effect of artificial intelligence and data science lectures on motivation and career development

Abstract

As social expectations of data science and artificial intelligence (AI) increase and their application to various industrial fields advances, greater emphasis is being placed on data science and AI related ed-ucation. In this study, we ascertained the impact of data science and AI education on learners' motivation and career development by analyzing a lecture-style course offered as part of mathematical and data science education at Tokyo City University, Japan. The course analyzed was Data Science Literacy 1 (DS1). The analysis period spanned three academic years, from 2020 to 2022. The analyzed items were three motivational factors derived from expectancy-value theory, namely intrinsic value, attainment utility value, and expectations for success. A fourth factor, career development, was also analyzed. We collected data pertaining to the four factors, that is, the three abovementioned motivational factors and career development, through questionnaires administered to DS1 learners. Regarding data analysis, learners were classified according to the values (high vs. low) they reported for each of the four factors of interest at the beginning of the course. These were compared to the values reported at the end of the course. Results showed increases in all the motivation factors. The increasing trend was particularly pronounced among learners who initially reported low values. Although trends differed from year to year, the results suggest that data science and AI education can positively impact motivational factors and related career devel-opment.