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All Journal Budapest International Research and Critics in Linguistics and Education Journal (Birle Journal)
Müller, Thomas
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Humanities Education Languange, Linguistic, Communication & Media Social Sciences Other

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The Complementary Educator: A Framework for Integrating Large Language Models into Higher Education Pedagogy Müller, Thomas
Budapest International Research and Critics in Linguistics and Education (BirLE) Journal Vol 1, No 1 (2012): Budapest International Research and Critics in Linguistics and Education, Februa
Publisher : BIRCU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birle.v1i1.8158

Abstract

The rapid integration of large language models (LLMs) into education has sparked debates about technological substitution versus human-AI complementarity. This study introduces the Complementary Educator Framework, a human-centered model that repositions educators as strategic directors, LLMs as capacity-multiplying assistants, and students as critical co-creators. Grounded in sociocultural learning theory and hybrid intelligence paradigms, the framework was developed through iterative design informed by existing literature on AI-augmented pedagogy and Bloom’s revised taxonomy. Visual diagrams explicate four core dimensions: (1) educator orchestration and context provision, (2) LLM task automation, (3) student critical engagement, and (4) multi-level operationalization. Findings demonstrate that strategic task reallocation offloads lower-order cognitive demands, enabling educators to allocate approximately 70% of time to higher-order functions such as mentorship and complex discourse. The framework’s novelty lies in its integrated, multi-stakeholder approach that preserves human relational and ethical primacy while harnessing AI efficiency. Results affirm enhanced capacity, deepened criticality, and ethical integration when implemented with institutional support. In conclusion, the framework offers a sustainable pathway for AI augmentation that safeguards educational essence. Recommendations emphasize phased institutional adoption, comprehensive faculty development, and equity-focused policies. This model advances balanced AI integration, ensuring technology serves rather than supplants human pedagogical judgment.
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