Biography
Dr. Arpan Shrivastava is an Associate Professor with more than 15 years of experience in higher education, academic administration, research, and industry. He holds a PhD in Management, with research focusing on special economic zones and export performance, and an MBA in International Business.
Conference contributions 1
RESPONSIBLE AI AND EDUCATIONAL TRANSFORMATION IN THE GLOBAL SOUTH:
Student trust, assessment integrity, and institutional governance in higher education
Artificial Intelligence (AI) is rapidly transforming higher education through adaptive learning systems, automated assessment tools, predictive analytics, and AI-supported pedagogical practices. While these technologies offer opportunities for personalized learning, instructional efficiency, and institutional innovation, their adoption also raises significant concerns related to assessment integrity, student trust, ethical governance, digital inequality, and institutional readiness, particularly within the Global South. Existing research on AI in higher education has largely focused on technologically advanced contexts, with limited attention given to the socio-economic, infrastructural, and governance challenges experienced by institutions in developing educational ecosystems. Addressing this gap, the present study examines the role of responsible AI practices in shaping educational transformation within higher education institutions in the Global South. Drawing upon the Technology Acceptance Model (TAM), Institutional Theory, and Responsible AI governance frameworks, the study develops a conceptual model investigating the relationships among AI transparency, institutional readiness, perceived fairness, AI literacy, assessment integrity, and student trust in AI-mediated educational environments. The study further explores how institutional governance mechanisms and ethical AI practices influence student engagement, perceptions of fairness, and acceptance of AI-supported assessment systems. A quantitative research design employing Structural Equation Modeling (SEM) is proposed to examine the hypothesized relationships using data collected from university students and faculty members across selected Global South higher education institutions. The study contributes theoretically by integrating responsible AI governance with educational transformation and student trust literature within underrepresented Global South contexts. Practically, the findings are expected to provide strategic insights for universities, policymakers, and educational administrators regarding ethical AI adoption, transparent assessment practices, faculty AI literacy, and inclusive institutional governance. The paper ultimately proposes a human-centered AI governance framework capable of balancing technological innovation with educational equity, trust, academic integrity, and sustainable institutional transformation in higher education.