Generative Artificial Intelligence in Education: A Systematic Literature Review on Instructional Materials Development

This study presents a systematic literature review (SLR) on the use of Generative Artificial Intelligence (GenAI) in developing instructional materials across formal educational contexts. Data were retrieved from the Scopus database using a structured search strategy following PRISMA 2020 guidelines. A total of 31 studies met the inclusion criteria, with 71% conducted in higher education and 29% in K-12. Thematic categorization shows that instructional materials developed with GenAI are dominated by textual and written materials, while the most frequently used tools are large language models (LLMs)/text generation such as ChatGPT and Copilot. The findings indicate that GenAI offers strong potential to accelerate the development and diversify the formats of instructional materials, with reported benefits including time, workload, and cost efficiency, support for educators, personalization and adaptability, improved quality and range of materials, as well as positive impacts on learning processes and outcomes. However, key challenges remain, particularly related to output quality, human readiness, technical-operational constraints, ethical issues, as well as negative impacts on learning processes and outcomes. Despite several limitations, the study highlights the importance of strengthening educators’ AI literacy and skills, institutional policy support, and further empirical research regarding the use of GenAI in education.