Hope, Aspirations, and the Impact of LLMs on Female Programming Learners in Afghanistan

2025-11-09

AI Summary

This study addresses the challenge of measuring educational aspirations among female programming learners in Afghanistan’s sociopolitical instability and examines their association with large language model (LLM) usage. Methodologically, it pioneers the cross-cultural adaptation, translation, and validation of Snyder’s Hope Scale for this population—yielding a culturally appropriate, psychometrically sound instrument (Cronbach’s α = 0.78). Data were collected via online surveys and analyzed by subscale. Results reveal a marginally significant increase (p = .056) in LLM users’ scores on the “Pathways” subscale, suggesting LLMs may broaden perceived feasible learning trajectories. The study contributes both an aspiration-driven assessment framework tailored to marginalized learners and the first application of hope psychology theory to the intersection of human–computer interaction and digital education equity.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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 {alpha} = 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.

Problem

• Measuring educational aspirations in socio-political instability contexts
• Evaluating impact of LLMs on female programming learners in Afghanistan
• Developing scalable metrics for aspiration-driven educational technology design

Innovation

• Adapted Snyder's Hope Scale for aspiration measurement
• Evaluated scale reliability with 136 female learners
• Assessed LLM access impact on educational pathways