To build and implement an inclusive technology, the AI workforce must be more diverse. The need for qualified technology professionals is growing along with AI’s development, making inclusion even more important. The study has two primary research objectives: (i) To identify and understand the factors associated with student(female) decisions about whether to take up AI/ML/analytics/DS courses, and (ii) To suggest ways and methods that stakeholders (educational institutions) can adopt to motivate and facilitate students to take up AI/ML/analytics/DS courses. The present study is based on the Social Cognitive Theory (SCT) structure (Bandura, 1986). The data was collected using four focus group discussions, and thematic analysis method was used for data analysis. The study provided in-depth analysis into the inclinations and inhibitions of students (male and female) to take up AI courses. The results also highlight the gender-based differences in the rational and reasons for choosing or giving-up AI courses as a career choice. The study findings reveal four broad themes: (a) personal, (b) contextual, (c) outcome expectations and (d) cultural. The study aims to identify and offer strategies that educational institutions and other stakeholders may use to encourage and enable women to enrol in courses in artificial intelligence, data analytics, and decision sciences