The swift development of Artificial Intelligence (AI), namely the introduction of Large Language Models (LLMs), is drastically altering various industries and necessitating a major change in the way software engineering is taught. To equip upcoming software engineers with the knowledge and abilities to function in this AI-powered environment, curriculum and pedagogical techniques must be critically reevaluated. To better understand the integration of AI and LLMs into software engineering education, this study gives a thorough and critical analysis of the literature, looking at existing models, pedagogical frameworks, and enduring issues. We explore various approaches utilized by educational establishments, including as specialized AI and LLM courses, incorporating modules into pre-existing curricula, and utilizing open-source LLM materials. Our analysis, which is based on case studies and research data, thoroughly assesses how well these strategies enable software engineers to comprehend, make use of, and ethically create AI and LLMs. Key obstacles to the successful integration of AI and LLM are also identified by our analysis, including the inexperienced status of LLM educators, resource limitations, potential biases in AI and LLM algorithms, and insufficient instructor knowledge. Building on these discoveries, we provide solid answers to these problems and suggest interesting avenues for further study to improve the integration of AI and LLM. In the end, this study advocates for a multimodal strategy to get future software engineers ready for the impending AI and LLM future and secure their place in this quickly changing field.

Hello World! This is Chandan, a Beihang University postgraduate, who harnesses deep learning’s power for a greener future. Driven by passion and nature’s wisdom, I seek AI’s potential to uplift society and the planet. Let’s journey together into academia and make the world bloom with intelligent solutions.