ChatGPT in research and practice: Insights into applications, challenges, and future prospects

Abstract

The emergence of ChatGPT, a sophisticated language model developed by OpenAI, has revolutionized the fields of artificial intelligence (AI) and natural language processing (NLP). This study offers a comprehensive overview of current research directions related to ChatGPT, analyzing its applications, challenges, and prospects. This most up-to-date study extracts and analyzes keywords and abstracts from 7,455 research articles in the Scopus database, providing a thorough overview using text mining techniques. The evaluation, conducted through descriptive statistical analysis of keyword frequency, co-occurrence, and related metrics, highlights current research issues, indicating that efficacy evaluation remains an ongoing area of investigation. Distinct from previous studies, this research extends the findings by classifying the overarching impact of ChatGPT across all fields, leveraging deep learning algorithms through Generative Artificial Intelligence (Gen AI), utilizing BERT Base Multilingual Uncased for sentiment analysis. Concurrently, it contrasts these results with those obtained from traditional analysis using the Vader Lexicon machine learning approach, revealing a consistent and overwhelmingly positive impact that significantly outweighs negative biases. While ChatGPT demonstrates transformative potential across diverse sectors, ranging from enhancing business operational strategies and content innovation to streamlining programming tasks and fostering personalized learning or clinical simulations, results also underscore critical concerns regarding academic integrity, biased AI responses, and diminished critical thinking. The metrics evaluation using Gen AI and machine learning shows good performance; The findings further suggest that Gen AI opens more promising avenues for enhancing bibliometric analysis, providing a robust framework for evaluating academic documents specifically, and advancing natural language processing methodologies more broadly.

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