GENERATIVE AI IN EDUCATION AND RESEARCH: A SYSTEMATIC MAPPING REVIEW OF CURRENT APPLICATIONS, EMERGING CHALLENGES, ETHICAL CONSIDERATIONS, AND FUTURE DIRECTIONS ACROSS LEARNING CONTEXTS

Abstract

Generative artificial intelligence (AI) has quickly revolutionized educational and research processes, especially since the introduction of massive language models. This paper provides a comprehensive mapping evaluation of 85 publications (2019-2025) to examine existing uses, obstacles, ethical considerations, and future research objectives for generative AI in education. The findings show a large increase in research production, with peak publications found between 2023 and 2024, indicating a high-growth period of scholarly interest. Bibliometric data suggest that journal articles account for 60% of the literature, indicating an increase in research maturity. According to the application study, personalized learning and tutoring are the most popular (38%), followed by content development (28%) and research support tools (24%), with AI-based mentoring remaining limited (10%). Major obstacles cited include concerns about academic integrity (42%), correctness and reliability (35%), and technical limits (23%). According to ethical analysis, the most crucial difficulties are prejudice in AI systems (30%) and privacy concerns (28%), followed by intellectual property (25%) and accountability hurdles (17%). The sentiment study suggests that 62% of respondents have a positive perception, with 25% neutral and 13% negative, showing cautious adoption and existing worries. According to topic modelling, research is predominantly focused on teaching and learning (32%), followed by academic support (27%), with gaps in pedagogical integration (30%) and governance frameworks (28%). Overall, the study indicates that while generative AI has significant potential to improve education, its deployment necessitates resolving ethical, technical, and pedagogical issues through organized policy and future research