Development and Validation of Komal & Qureshi AI Dependency Scale (KQAIDS 2025)
DOI:
https://doi.org/10.47941/jts.3814Keywords:
AI, Dependency, Development, ValidationAbstract
Purpose: The main purpose of this study was to develop and validate a scale for artificial intelligence dependency. AI has enabled the machines to think, learn and solve problems just like human but over reliance on AI has introduced many perils like decreased creativity, problem solving skills, competence and emotional dependency.
Methodology: A mixed research design was used in this study consisting of two phases, qualitative phase for item generation and quantitative for establishing psychometric properties of the scale. Purposive sampling was used (N=300). To determine and confirm the factor structure EFA and CFA were conducted.
Findings: EFA revealed three factors structure (emotional dependency, relatedness thwarted and competence thwarted) with 14 items for the scale confirmed by CFA with good reliability (α=.74). It’s convergent and discriminant validity was also established.
Unique Contribution to Theory, Practice and Policy: This study contributes theoretically by encompassing Self-Determination Theory into the evolving field of human AI collaboration and conceptualizing AI dependency through psychological aspects of emotional dependency, relatedness thwarted, and competence thwarted. Practically, KQAIDS provides a validated tool to recognize maladaptive AI dependency patterns and design targeted interventions. At the policy level, the findings show the need for guidelines endorsing responsible AI use in educational settings, stressing AI as a helpful tool rather than a substitution for human abilities. It is recommended that educational institutes incorporate AI literacy programs, endorse critical thinking and self-regulation skills, and start monitoring frameworks for healthy AI engagement.
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