Biography
Dr. Rohit Kumar Pal is a Global Post-Doctoral Researcher at Lincoln University College, Malaysia, with multidisciplinary expertise spanning environmental science, human resource management, educational leadership, sustainability, and artificial intelligence. He holds a Professional Doctorate in Education Management and Leadership from the European International University, Paris, an MBA in Human Resource Management, an M.Sc. in Life Sciences from Presidency University, Kolkata, and a Postgraduate Diploma in Sustainability Science. He has been associated with the R.I.C.E. Adamas Group, Kolkata, as a professional trainer since 2017 and is a Certified Educator of the National Geographic Society. Dr. Pal serves on the editorial and reviewer boards of several national and international journals and has authored numerous research articles, books, and book chapters published by reputed publishers, including Springer and IGI Global. His research focuses on Education 4.0 and 5.0, sustainable development, artificial intelligence, organizational behavior, environmental governance, and technology-enabled learning. He is also actively involved in academic content development, scientific peer review, and interdisciplinary research collaborations, contributing to the advancement of sustainable and innovative educational practices.
Conference contributions 2
BEYOND B.ED AND NET/SET:
Evaluating teaching quality and experience recognition in India's coaching-to-academia transition
India's coaching industry has emerged as a significant parallel educational ecosystem, producing successful candidates in competitive examinations such as NEET, JEE, UPSC, and other national-level entrance tests. Many teaching professionals in this sector possess highly prestigious qualifications from institutions such as Indian Institutes of Technology (IITs) and Indian Institutes of Management (IIMs), alongside formal credentials including B.Ed and UGC NET/SET. Despite their proven teaching effectiveness and domain expertise, many are rejected from school, college, and university teaching positions due to limited recognition of coaching-sector experience. This study examines whether teaching quality can be measured solely through formal credentials such as B.Ed and NET/SET or whether practical teaching outcomes and experience should receive greater institutional recognition. The study analyzes data collected from 552 teachers and 337 educational administrators across India. Findings reveal that 45.1% of applicants were rejected despite substantial teaching experience, while administrative hiring policies remained heavily credential-oriented. Statistical analysis demonstrates a significant disconnect between practical teaching effectiveness and formal recruitment standards. Administrators acknowledged regulatory constraints linked to affiliation norms, API scores, and statutory compliance requirements. The research argues for a balanced evaluation framework integrating teaching outcomes, domain expertise, and professional experience alongside formal qualifications. The study contributes to policy discussions on teacher recruitment, educational quality assessment, and experience-equivalency frameworks within India's evolving education system.
WHEN ASSISTANCE REPLACES PRACTICE:
An empirical examination of AI reliance, cognitive offloading, and the homework–exam performance gap among competitive-exam candidates: evidence from a 100-students of competitive-exam preparation sector
Generative AI can produce polished written responses with little effort, creating a distinction between assisted performance and competence. This study examines whether frequent AI use for descriptive homework is associated with a widening gap between homework performance and supervised, offline examination performance among competitive-exam candidates. The dataset contains 100 student records classified into low, moderate, and heavy AI-use groups, together with homework and exam scores, AI dependence, awareness, and faculty satisfaction. Homework performance rises from 62.15% in the low-use group to 73.41% in the moderate-use group and 91.21% in the heavy-use group, whereas supervised exam performance falls from 62.08% to 41.41% and then to 5.86%. Very large group differences are observed; because exam-score variances violate homogeneity, Welch's ANOVA is also reported. AI-use frequency correlates positively with homework performance (r = .938) but negatively with exam performance (r = −.955), while perceived AI dependence correlates negatively with exam performance (r = −.956). The findings are interpreted through cognitive offloading, retrieval practice, metacognition, automation bias, and self-regulated learning. The pattern is consistent with a mechanism in which AI substitutes for generative and retrieval work that normally develops independent writing skill, producing an illusion of competence when assisted output is mistaken for personal mastery. However, the source file describes the dataset as synthetic and the design is observational; therefore, the results do not constitute causal experimental proof that AI decreases human cognitive ability. They provide an empirical demonstration of a performance-assistance gap and a framework for randomized longitudinal research.