A COMPARATIVE STUDY OF PSYCHOLOGICAL FLEXIBILITY, COGNITIVE FLEXIBILITY, VEDIC PERSONALITY AND ATTITUDES TOWARD ARTIFICIAL INTELLIGENCE AMONG PSYCHOLOGY AND COMPUTER SCIENCE STUDENTS
The rapid proliferation of artificial intelligence (AI) is redefining professional landscapes, prompting urgent questions about how individuals from different disciplinary backgrounds perceive and adapt to this technological shift. This study undertakes a comparative investigation of psychological flexibility, cognitive flexibility, Vedic personality traits, and attitudes toward AI among professionals from psychology and computer science, two fields with contrasting philosophical and functional orientations. Adopting a cross-sectional, quantitative design, data were collected from a purposive sample of 205 professionals actively engaged in their respective domains. Standardized instruments were used to measure each construct, and statistical techniques such as one-way ANOVA and Pearson’s correlation were applied to examine both group differences and inter-variable associations. Gender was also considered as a factor potentially influencing psychological dispositions and attitudes toward AI. The findings revealed significant disciplinary and gender-based differences. Psychology professionals displayed higher psychological flexibility and more reflective attitudes toward AI compared to those in computer science. Additionally, psychological flexibility and cognitive flexibility were positively correlated, suggesting a synergistic relationship between emotional adaptability and cognitive versatility. Vedic personality traits, particularly sattva and rajas, also demonstrated notable influence, with sattvic traits aligning positively with openness to AI, while rajasic traits were more associated with ambivalence or resistance. Ultimately, the study highlights how internal psychological dynamics interact with external technological forces, shaping the future of work, thought, and identity across disciplines.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-01-01
- DOI
- https://doi.org/10.5281/zenodo.16926687
- Primary Topic
- Geochemistry and Geologic Mapping
- Type
- article
- Field-Weighted Citation Impact
- 0.00