Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Artificial Intelligence in Educational Decision Sciences Journal of Education Psychology and Social Development
Kamal Singh Kunwar
Tribhuvan University, Nepal
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Algorithmic Governance in Education: A Framework for AI-Driven Decision Systems, Inequality and Policy Accountability Kamal Singh Kunwar
Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i1.40

Abstract

Purpose – This article develops a mechanism-based conceptual framework to explain how artificial intelligence (AI)-enabled decision systems are reshaping governance processes and distributive outcomes in contemporary education systems. It addresses a key gap in the existing scholarship: the absence of an integrated analytical lens linking algorithmic decision-making with institutional accountability and inequality in education. Methods – This study adopts a theory-building approach grounded in cross-disciplinary synthesis, drawing on insights from Artificial Intelligence, Decision Science, and Public Policy. Through a structured analytical method, it advances a multilevel framework that explains how AI-driven decisions are produced, interpreted, and implemented within institutional contexts. The analysis focuses on causal mechanisms rather than technical system design, positioning the contributions within governance and policy analysis.Findings – Four interrelated mechanisms are identified: (1) algorithmic bias transmission rooted in data and model construction; (2) institutional mediation shaping the interpretation and use of algorithmic outputs; (3) policy distortion arising from uneven or selective implementation; and (4) the reproduction or amplification of inequality across educational settings. Together, these mechanisms illustrate how AI systems interact with institutional structures to influence their outcomes. Research implications – This study provides a structured basis for analyzing accountability and inequality in AI-enabled education, while offering indicative directions for improving transparency and governance in policy contexts.Originality – This study introduces the Unified Algorithmic Governance Framework (UAGF), which integrates data processes, algorithmic decision-making, institutional dynamics, and socio-educational outcomes into a single analytical model. Unlike existing work on AI ethics and educational data governance, the framework emphasizes the interaction between technical systems and institutional processes in producing distributive effects.
Childhood Socialization in Post-Conflict Societies: Intergenerational Transmission of Trauma and Its Impact on Learning Behavior in Nepal Kamal Singh Kunwar
Journal of Education Psychology and Social Development Vol 2, No 1 (2026): June 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/jepsi.v2i1.686

Abstract

Armed conflict can produce enduring psychosocial consequences that extend across generations and influence children’s educational development. This study examined the relationship between intergenerational trauma and learning behavior in post-conflict Nepal, with particular attention to the mediating roles of childhood socialization, including parenting style, emotional climate, and social trust. A convergent parallel mixed-methods design was employed. Quantitative data were collected from 400 students aged 10–16 years in conflict-affected districts and analyzed using confirmatory factor analysis and structural equation modeling. Qualitative data were obtained through semi-structured interviews with 25 students, parents, and teachers and analyzed thematically. The structural model demonstrated an acceptable fit to the data. Intergenerational trauma was negatively associated with emotional climate, parenting style, and social trust, while all three socialization dimensions were positively associated with students’ attention, motivation, and academic engagement. Emotional climate emerged as the strongest mediating pathway, followed by parenting style and social trust. Although intergenerational trauma retained a significant direct negative association with learning behavior, the combined indirect effect through childhood socialization was stronger. The qualitative findings supported these statistical relationships by revealing persistent household tension, inconsistent parental support, reduced trust, and lower classroom participation among students from trauma-affected families. These findings indicate that the educational consequences of intergenerational trauma are largely transmitted through the relational and emotional environments in which children develop. The study highlights the need for trauma-informed educational policies, teacher preparation, family-based support, and school-linked psychosocial services in post-conflict Nepal.
Co-Authors