A Comparative Study of Reliability, Bias, and Learning Gains in Claude and chatGPT in Higher Education

Author(s)	Ms. Pallavi Chopra, Ms. Pallavi Sood
Country	India
Abstract	Generative artificial intelligence is fundamentally reshaping contemporary instructional structures. This study provides a direct empirical comparison between Claude and ChatGPT to analyze their viability as virtual teaching assistants. By investigating output precision, systemic bias, and student performance metrics, this research reveals distinct behavioral configurations. Claude demonstrates higher factual accuracy and lower demographic bias within STEM subjects. Conversely, ChatGPT proves more effective in humanities instruction due to its narrative fluency and expressive delivery. Notably, while both systems accelerate short-term information retrieval, an over-reliance on automated tools correlates with diminished long-term analytical resilience and critical problem-solving skills. Based on these outcomes, we offer actionable strategic frameworks for administrators and educators to ensure responsible AI integration.
Keywords	Generative AI, Educational Technology, Large Language Models, STEM Education, ChatGPT, Claude, Algorithmic Bias, Student Outcomes, Curriculum Design
Field	Computer
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-22
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.79058

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