In recent years, the field of education has witnessed a growing interest in leveraging artificial intelligence (AI) to better understand and address complex phenomena. However, the application of AI in educational research often overlooks the importance of incorporating a phenomenological way of knowing. This study proposes a novel framework that combines the power of generative AI techniques with a phenomenological approach to explore and comprehend complex educational phenomena.
Drawing on insights from phenomenology, which emphasizes the subjective experiences and lived realities of individuals, this research seeks to transcend the limitations of traditional AI approaches that predominantly focus on objective measurements and quantitative data. Instead, it aims to capture the rich and nuanced aspects of complex educational phenomena by integrating AI-generated models within a phenomenological context.
The proposed framework introduces the concept of “spheres” as a way to organize and represent the multidimensional nature of educational phenomena. Spheres encompass various dimensions, such as cognitive, affective, sociocultural, and contextual aspects, which interact and shape the educational experiences of learners. By employing generative AI techniques, the framework enables the creation of AI-generated models that can simulate and explore these multi-layered spheres, providing new insights into the complexity of educational phenomena.
This study highlights the potential of the proposed framework through a case study in the context of collaborative problem-solving in online learning environments. By utilizing generative AI models, the research team generated virtual scenarios that capture the diverse range of factors influencing collaborative problem-solving experiences. Through an iterative process, these models were refined and validated using qualitative data collected from learners’ reflections and interviews.
The findings of this research demonstrate the value of incorporating a phenomenological way of knowing in AI-driven educational research. By bridging the gap between AI and phenomenology, this framework offers a promising avenue for gaining deeper insights into the complex educational phenomena that shape learners’ experiences.
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