ARTIFICIAL INTELLIGENCE IN SOCIAL SCIENCE RESEARCH: MAPPING ITS EXTENT, OPPORTUNITIES, AND CONSTRAINTS IN HIGHER EDUCATION CONTEXTS

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly transformed research practices across disciplines, including the social sciences. In higher education contexts, AI tools are increasingly being utilized to support literature review, data analysis, and academic writing. However, the extent of their use, along with associated opportunities and constraints, remains underexplored—particularly within university classrooms. This study aimed to map the extent to which AI is integrated into social science research practices and to examine its perceived benefits and limitations among university students. The study was guided by the following objectives; (1) to assess the extent of AI utilization in social science research at the university level; (2) to identify the perceived opportunities of AI in enhancing research efficiency and quality; (3) to examine the constraints and challenges associated with AI use in academic research; (4) to analyze differences in AI usage based on demographic variables (e.g., gender, level of study).A quantitative descriptive survey design was employed. The sample consisted of N = 240 university students enrolled in social science disciplines (Education, Sociology, Psychology) selected through stratified random sampling. Data were collected using a structured questionnaire comprising three subscales: AI Usage (10 items), Opportunities (8 items), and Constraints (8 items), measured on a 5-point Likert scale. The instrument demonstrated acceptable reliability (Cronbach’s α = 0.87). Data were analyzed using descriptive statistics (mean, standard deviation, percentages) and inferential statistics, including independent samples t-test and one-way ANOVA. Correlation analysis (Pearson r) was also conducted to examine relationships among variables. The results indicated a moderate to high level of AI usage in social science research (M = 3.68, SD = 0.74). Perceived opportunities were significantly high (M = 4.02, SD = 0.65), highlighting AI’s role in improving research efficiency and data analysis capabilities. However, constraints were also notable (M = 3.51, SD = 0.71), particularly concerning over-reliance and ethical concerns. A significant positive correlation was found between AI usage and perceived opportunities (r = 0.62, p < .01), while a moderate correlation existed between AI usage and constraints (r = 0.41, p < .05). Gender differences were insignificant (t = 1.12, p > .05), whereas level of study showed significant differences (F = 4.36, p < .05), with postgraduate students reporting higher AI usage. The findings suggest that AI is becoming an integral tool in social science research, enhancing productivity and access to information. However, concerns regarding academic integrity, critical thinking decline, and ethical usage persist. The coexistence of high opportunities and notable constraints reflects a transitional phase in AI adoption within higher education. AI holds substantial potential to transform social science research in university settings. While its integration is evident and beneficial, it requires structured guidance to mitigate associated risks and ensure responsible use.

 It is recommended that universities develop AI usage guidelines, integrate AI literacy programs into curricula, and promote ethical research practices. Additionally, faculty training should be conducted to effectively incorporate AI tools into teaching and research supervision.