A Unified Generative AI Framework for Adaptive and Multimodal STEM Education

The fast development of Generative Artificial Intelligence (GAI) has provided new possibilities of reforming Science, Technology, Engineering, and Mathematics (STEM) education through generating adaptive, multimodal, and customized learning conditions. Nevertheless, the current methods of STEM learning still fall short on numerous issues such as the lack of individualization, engagement in complicated concepts, and poor preparation of students to meet the needs of the new industry. Conventional pedagogical frameworks are unable to scale personalized education, whereas previous AI-based tools have no contextual awareness of situations, multimodal interactivity, or long-term learner modeling. Even though further development of GAI-based systems in recent years shows the promise of producing content and/or explaining concepts, it is not investigated in the context of systematic STEM teaching. To fill these gaps, this paper will provide a thorough review and cohesive framework of GAI-enabled STEM education to increase understanding, interest, and employability level among undergraduate students. The paper methodically reviews multimodal GAI applications such as visual simulations, audio, code generation, and interactive problem-solving and assesses how these contribute to enhancing cognitive growth and skills learning. Moreover, the paper identifies the shortcomings of present-day GAI-based tools, presents developing gaps in research, including reliability, bias, pedagogical correspondence, and long-term learning effect, and suggests a framework for introducing GAI in STEM education. This literature review shows that properly designed GAI-driven systems can greatly contribute to enhancing conceptual clarity, inquiry-based learning, and industry-relevant skills in combination with data-driven personalization and ongoing evaluation. The results put GAI as a potential jump towards the better development of human talent in STEM areas but also highlight the necessity of longitudinal research, ethics, and sounder pedagogical research to achieve its potential. This study further proposes a unified adaptive GAI framework for personalized multimodal STEM learning and validates its effectiveness through controlled simulation-based learner interaction analysis. The results demonstrate consistent improvements in comprehension, engagement, and task performance.