Beyond ChatGPT: How DeepSeek R1 may transform academia and libraries?

Purpose This paper aims to examine the evolution of DeepSeek R1 as an advanced alternative to ChatGPT in academic and library contexts. It highlights DeepSeek R1’s potential to enhance research methodologies, optimize search and metadata processing and refine content development. Additionally, it explores the ethical implications of integrating next-generation AI models in academia, focusing on bias, privacy and academic integrity. Design/methodology/approach The study employs a comparative analysis of DeepSeek R1 and ChatGPT, evaluating their capabilities in academic applications through structured interviews and technical assessments. Key areas analyzed include natural language processing, reasoning capabilities and code generation. The research also incorporates an ethical framework to assess the implications of AI-driven academic support tools. Findings DeepSeek R1 outperforms ChatGPT in several academic domains, particularly in reasoning, computational efficiency and metadata organization. Its open-source architecture allows for greater customization, making it more adaptable for academic institutions. However, the study also identifies limitations, such as content filtering restrictions and ethical concerns related to AI dependence and data privacy. The findings suggest that DeepSeek R1 can serve as a powerful complement to existing AI tools in academia. Research limitations/implications Given the rapid development of AI models, this study’s findings may require future validation as newer versions of DeepSeek R1 and ChatGPT emerge. Further research is recommended to explore long-term AI adoption in academic settings and assess evolving ethical concerns. Practical implications This research provides valuable insights for academic institutions and libraries on integrating AI-driven tools. It offers practical recommendations for leveraging DeepSeek R1 to enhance academic workflows, while ensuring ethical AI use through transparency, bias mitigation and privacy safeguards. Originality/value This paper contributes to the discourse on AI in academia by providing an in-depth comparative analysis of DeepSeek R1 and ChatGPT. It offers a structured evaluation of AI’s evolving role in academic research and library services, emphasizing ethical considerations and best practices for AI adoption in educational institutions.

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Publication Details

Journal
Library Hi Tech News
Published
2025-04-01
DOI
https://doi.org/10.1108/lhtn-01-2025-0024
Citations
5
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
6.80
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article

Beyond ChatGPT: How DeepSeek R1 may transform academia and libraries?

Brady Lund, Nishith Reddy Mannuru, Sai Pranathi Karedla, Ravi Varma Kumar Bevara et al.
5 citations
Library Hi Tech News
COVID-19 diagnosis using AI
6.80
article

Beyond ChatGPT: How DeepSeek R1 may transform academia and libraries?

Brady Lund, Nishith Reddy Mannuru, Sai Pranathi Karedla, Ravi Varma Kumar Bevara, Aashrith Mannuru
article en
5 citations

Abstract

Purpose This paper aims to examine the evolution of DeepSeek R1 as an advanced alternative to ChatGPT in academic and library contexts. It highlights DeepSeek R1’s potential to enhance research methodologies, optimize search and metadata processing and refine content development. Additionally, it explores the ethical implications of integrating next-generation AI models in academia, focusing on bias, privacy and academic integrity. Design/methodology/approach The study employs a comparative analysis of DeepSeek R1 and ChatGPT, evaluating their capabilities in academic applications through structured interviews and technical assessments. Key areas analyzed include natural language processing, reasoning capabilities and code generation. The research also incorporates an ethical framework to assess the implications of AI-driven academic support tools. Findings DeepSeek R1 outperforms ChatGPT in several academic domains, particularly in reasoning, computational efficiency and metadata organization. Its open-source architecture allows for greater customization, making it more adaptable for academic institutions. However, the study also identifies limitations, such as content filtering restrictions and ethical concerns related to AI dependence and data privacy. The findings suggest that DeepSeek R1 can serve as a powerful complement to existing AI tools in academia. Research limitations/implications Given the rapid development of AI models, this study’s findings may require future validation as newer versions of DeepSeek R1 and ChatGPT emerge. Further research is recommended to explore long-term AI adoption in academic settings and assess evolving ethical concerns. Practical implications This research provides valuable insights for academic institutions and libraries on integrating AI-driven tools. It offers practical recommendations for leveraging DeepSeek R1 to enhance academic workflows, while ensuring ethical AI use through transparency, bias mitigation and privacy safeguards. Originality/value This paper contributes to the discourse on AI in academia by providing an in-depth comparative analysis of DeepSeek R1 and ChatGPT. It offers a structured evaluation of AI’s evolving role in academic research and library services, emphasizing ethical considerations and best practices for AI adoption in educational institutions.

Library Hi Tech NewsVol. 42(4)
University of North Texas (US), The University of Texas at Dallas (US)
Openalex Percentile: Top 3%
COVID-19 diagnosis using AI
6.80
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