Ungrading in Education: Leveraging Activity Theoretic Frameworks and ChatGPT for Hermeneutic Research Methods

Potential Abstract:
Abstract: In recent years, the concept of ungrading has gained traction in educational settings as a means to foster intrinsic motivation, self-regulation, and authentic learning experiences among students. This study explores the intersection of ungrading practices with activity theoretic frameworks, leveraging the use of AI-powered language models such as ChatGPT to facilitate hermeneutic research methods. By adopting a qualitative approach, we examine how the integration of these theoretical and technological tools can enhance pedagogical practices and student outcomes within higher education contexts. Through the lens of activity theory, we analyze the dynamics of classroom interactions, the distribution of agency among participants, and the mediating artifacts that shape the learning environment. Additionally, we investigate the role of ChatGPT in supporting educators’ reflective practices, facilitating feedback mechanisms, and promoting meaningful dialogue with students in the absence of traditional grading structures. The hermeneutic research method employed in this study allows for a deep exploration of the interpretive processes that underlie educational practices, shedding light on the complex interplay between pedagogy, technology, and student engagement. Our findings suggest that a synergistic approach to ungrading, informed by activity theory and supported by ChatGPT, holds promise for transforming teaching and learning experiences in the digital age.

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