Biography
Dr. Parul Kulshrestha is an Assistant Professor of Human Resource Management at Chitkara Business School, Chitkara University, Punjab. She holds a Ph.D. in Management and an MBA in Business Management from Jiwaji University, Gwalior. With over 15 years of experience in teaching, research, academic administration, and student mentoring. Dr. Kulshrestha has published several research papers, book chapters, and academic contributions in the areas of human resource management. She has actively participated in more than 40 national and international conferences, seminars, workshops, faculty development programmes, and research methodology training programmes. Dr. Kulshrestha served as the Organising Secretary for the G20 University Connect programme held on 24 August 2023, a Government-funded project. Her research interests focus on sustainable development, inclusive growth, responsible technology adoption, AI in human resource management, digital transformation, and developing future-ready workforce capabilities.
Conference contributions 4
BUILDING RESPONSIBLE AI LITERACY AMONG UNIVERSITY STUDENTS:
A conceptual framework for higher education
Artificial intelligence (AI) has rapidly transformed higher education, creating unprecedented opportunities for teaching, learning, and research while simultaneously raising complex ethical, social, and governance challenges. Although AI literacy has emerged as a critical competency for university students, existing studies predominantly emphasize technical knowledge and AI adoption, with limited attention to integrating ethical reasoning, responsible behaviour, social responsibility, and institutional support into a unified framework. This conceptual paper addresses this gap by proposing the Responsible AI Literacy Development Framework (RAIL Framework) for higher education. Adopting an integrative literature review approach, the study synthesizes contemporary scholarly literature and international policy frameworks, including UNESCO, the European Commission, and the OECD, to identify the core dimensions of responsible AI literacy. The proposed framework conceptualizes responsible AI literacy as a developmental process beginning with institutional enablers including governance, policies, curriculum integration, and faculty capacity which foster students' AI foundations, cognitive competencies, ethical competencies, responsible AI behaviour, and social responsibility, ultimately leading to responsible AI literacy and the development of future-ready graduates. By integrating educational, ethical, and institutional perspectives into a single developmental model, the framework extends existing AI literacy research beyond technical competence and positions responsible AI literacy as a multidimensional capability essential for higher education. The paper contributes to theory by offering a comprehensive conceptual framework and provides practical implications for curriculum design, institutional governance, faculty development, and policy formulation aimed at preparing ethically informed, socially responsible, and AI-ready graduates in an increasingly AI-driven society.
STUDENTS' ADOPTION OF GENERATIVE AI IN HIGHER EDUCATION:
Examining opportunities, challenges, and transformative learning
The rapid advancement of Generative Artificial Intelligence (GenAI) has significantly transformed the higher education landscape, creating new opportunities for enhancing teaching and learning while simultaneously raising important ethical and academic concerns. As students increasingly integrate GenAI tools into their academic activities, understanding the factors influencing their adoption and the resulting educational outcomes has become a critical area of inquiry. This study examines students' adoption of Generative AI in higher education by exploring the influence of perceived opportunities and perceived challenges on adoption behavior and its subsequent impact on transformative learning. Drawing upon the Technology Acceptance Model and Transformative Learning Theory, the study conceptualizes perceived opportunities through dimensions such as improved productivity, personalized learning, and faster information access, while perceived challenges encompass ethical concerns, accuracy concerns, and academic integrity issues. A quantitative research design is proposed, targeting university students who actively engage with Generative AI tools for academic purposes. Data will be collected through a structured questionnaire and analyzed using Structural Equation Modeling (SEM) to test the proposed relationships. The study posits that perceived opportunities positively influence students' adoption of Generative AI, whereas perceived challenges negatively affect adoption intentions. Furthermore, the adoption of Generative AI is expected to contribute positively to transformative learning by fostering critical reflection, enhanced knowledge acquisition, and adaptive learning experiences. The findings are anticipated to provide valuable insights for educators, policymakers, and higher education institutions seeking to promote the responsible and effective integration of Generative AI in academic environments. By addressing both the benefits and challenges associated with GenAI adoption, the study contributes to the emerging discourse on technology-enabled educational transformation and responsible AI implementation in higher education.
DIGITAL INEQUALITY IN THE AGE OF ARTIFICIAL INTELLIGENCE:
A structural equation modelling approach to educational equity and social inclusion
Purpose: This research explores the impact of digital divide on educational equality and social inclusion in AI-based educational environments through the influence of AI literacy, participation in AI-based education, inappropriate algorithm, and information asymmetry, and inclusive AI governance. Method: The study employed a quantitative and cross-sectional approach and distributed a structured questionnaire to 400 respondents comprising students, teachers, faculty, administrators, and educational technology experts in Punjab, India. The theoretical framework used for this research was developed based on Digital Divide Theory, the Capability Approach, the Technology Acceptance Model, Algorithmic Fairness Theory, and Responsible AI Governance Theory. Data collected was analyzed using SPSS and AMOS 28.0 software through CFA (Confirmatory Factor Analysis) and CB-SEM (Covariance-Based Structural Equation Modelling). Results: Results show that digital divide adversely affects educational equality and social inclusion, while AI literacy, involvement in AI-based education, and inclusive governance are positively related to educational equality. Originality: Utilizing a blend of technology, education, and governance, this research provides a better understanding of responsible artificial intelligence (AI) use and also delivers useful advice to decision-makers in policy and educational institutions as well as technical developers who want to establish inclusive, fair, and sustainable AI education.
DIGITAL INEQUALITY IN THE AGE OF AI:
Challenges for education and social inclusion
Learning, work and communication have been significantly transformed through Artificial Intelligence (AI) creating new possibilities for social and economic growth. However, there is an unequal access to AI resources and therefore, this unevenness will increase already existing problems with digital inequality. This interdisciplinary research looks at how digital inequality affects educational attainment and social inclusion in this new AI world. For example, Learning Management Systems (LMS), Adaptive Learning Technologies (ALT), and Intelligent Tutoring Systems (ITS) offer individualized ways to facilitate personalized learning. However, marginalized populations will not have access to digital resources, adequate training or AI knowledge making them unable to benefit from technological advances in education and social services. The examination of educational, technological, sociological and public policy perspectives shows the impact of algorithmic bias, inequity of data, exclusionary language and varying access to technology, as well as how these factors contribute to enlarging opportunity gaps and increasing social exclusion in education. Furthermore, the study addresses the ethical and socially responsible aspects of these technologies by stressing inclusive design, equitable access to AI, transparent governance, cultural responsiveness in the development of the technology, and establishing a common language across various disciplines. This study presents findings through a primary literature review and an analysis of recently developed policy frameworks and thus, demonstrates successful strategies for digital equity, such as: AI literacy training for teachers and students, creating and implementing equitable educational policy, supporting public-private partnerships to implement and promote digital equity, and creating human-centered AI development. Furthermore, the findings indicate that addressing digital inequity is necessary for establishing digital equity, so that AI will be used as a means of empowerment rather than exclusion in digital society. The conclusion of this study is a set of recommendations for creating equitable, inclusive, and socially responsible AI ecosystems that promote educational opportunity and social engagement for all members of society.