Impact of generative artificial intelligence in education: Opportunities, challenges, and strategies

Abstract

Due to the exponential growth of generative artificial intelligence (GenAI) technologies, especially large language models, like ChatGPT, Claude, and Gemini, unprecedented shifts in education have occurred in nearly every part of the world. Although adoption rates have risen exponentially. It has been demonstrated that a fifth of these technologies bear extensive and diverse implications on the efficiency of teaching and learning, academic honesty, and the attainment of learning outcomes. The present study concerns three core issues: to begin with, the empirical models used to assess the GenAI, and its difference effects in educational settings are nonexistent; secondly, there is a lack of insight into the implementation-based strategies to maintain the balance between innovation and academic integrity; and finally, the policies within the measurement of equitable access and ethical practices lack empirical justification. A mixed-methods design with educational institutions based in various countries, as well as qualitative, GenAI implementation frameworks was used. Evidence shows that GenAI integration is significantly associated with better personalized learning improvements (β = 0.67, p < 0.001) and increased pedagogical efficiency (42% of administrative tasks were reduced), but at the same time, it is associated with significant issues, such as academic dishonesty (88% of students used GenAI to complete assessments) and equity (equity). The analysis has identified five key success factors in the success of GenAI implementation that are extensive faculty training programs, strong ethical guidelines, dynamic assessment practices, technology support, and alignment among policies.