Potential Abstract:
Recent advancements in artificial intelligence, particularly large language models (LLMs), have revolutionized various industries, including education. This study delves into the sociopolitical implications of utilizing LLMs in Massive Open Online Courses (MOOCs) through a semiotic analysis lens. By examining the intricate interplay between technology, language, and power dynamics within the educational sphere, this research aims to shed light on how LLMs influence learners’ experiences and knowledge construction in online learning environments.
Drawing on critical theoretical frameworks, this study unpacks the sociopolitical dimensions embedded in the design, implementation, and use of LLMs in MOOCs. Through a close examination of the semiotic elements present in course materials, interactions, and assessments, we aim to elucidate how these technologies shape and are shaped by broader societal structures and power relations. By highlighting the complexities of integrating LLMs in education, we seek to foster critical discussions on equity, inclusion, and agency in digital learning spaces.
This research contributes to the growing body of literature on technology-enhanced education by offering a nuanced understanding of the sociopolitical dynamics at play when deploying LLMs in MOOCs. By centering semiotic analysis as a methodological approach, this study provides a comprehensive framework for examining the multifaceted implications of technology in educational settings. Ultimately, this work underscores the importance of critically evaluating the role of LLMs in shaping the future of online learning and calls for a more thoughtful and informed approach to integrating AI technologies in education.
Potential References:
- A Sociolinguist’s Look at the “Language” in Large Language Models
- Textual Analysis
- The expected AI as a sociocultural construct and its impact on the discourse on technology
- Can machines (do) language?
- At the intersection of humanity and technology: a technofeminist intersectional critical discourse analysis of gender and race biases in the natural language processing …
