Hope, Aspirations, and the Impact of LLMs on Female Programming Learners in Afghanistan
Behmanush, Hamayoon, Akhtari, Freshta, Nooripour, Roghieh, Weber, Ingmar, Cannanure, Vikram Kamath
–arXiv.org Artificial Intelligence
Designing impactful educational technologies in contexts of socio-political instability requires a nuanced understanding of educational aspirations. Currently, scalable metrics for measuring aspirations are limited. This study adapts, translates, and evaluates Snyder's Hope Scale as a metric for measuring aspirations among 136 women learning programming online during a period of systemic educational restrictions in Afghanistan. The adapted scale demonstrated good reliability (Cronbach's α = 0.78) and participants rated it as understandable and relevant. While overall aspiration-related scores did not differ significantly by access to Large Language Models (LLMs), those with access reported marginally higher scores on the Avenues subscale (p = .056), suggesting broader perceived pathways to achieving educational aspirations. These findings support the use of the adapted scale as a metric for aspirations in contexts of socio-political instability. More broadly, the adapted scale can be used to evaluate the impact of aspiration-driven design of educational technologies.
arXiv.org Artificial Intelligence
Nov-13-2025
- Country:
- Africa > Côte d'Ivoire (0.04)
- Asia
- Afghanistan > Parwan Province
- Charikar (0.04)
- India (0.04)
- Japan > Honshū
- Chūbu > Toyama Prefecture > Toyama (0.04)
- Middle East > Iran
- Qazvin Province > Qazvin (0.04)
- Afghanistan > Parwan Province
- Europe > Germany
- Saarland > Saarbrücken (0.04)
- North America > United States
- Massachusetts > Middlesex County > Cambridge (0.04)
- Genre:
- Instructional Material (1.00)
- Questionnaire & Opinion Survey (1.00)
- Research Report
- Experimental Study (1.00)
- New Finding (1.00)
- Industry:
- Education
- Curriculum > Subject-Specific Education (0.49)
- Educational Setting (1.00)
- Education
- Technology: