Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review

Abstract

This study aims to explore ChatGPT’s (GPT-3.5 version) functionalities, including reinforcement learning, diverse applications, and limitations. ChatGPT is an artificial intelligence (AI) chatbot powered by OpenAI’s Generative Pre-trained Transformer (GPT) model. The chatbot’s applications span education, programming, content generation, and more, demonstrating its versatility. ChatGPT can improve education by creating assignments and offering personalized feedback, as shown by its notable performance in medical exams and the United States Medical Licensing Exam. However, concerns include plagiarism, reliability, and educational disparities. It aids in various research tasks, from design to writing, and has shown proficiency in summarizing and suggesting titles. Its use in scientific writing and language translation is promising, but professional oversight is needed for accuracy and originality. It assists in programming tasks like writing code, debugging, and guiding installation and updates. It offers diverse applications, from cheering up individuals to generating creative content like essays, news articles, and business plans. Unlike search engines, ChatGPT provides interactive, generative responses and understands context, making it more akin to human conversation, in contrast to conventional search engines’ keyword-based, non-interactive nature. ChatGPT has limitations, such as potential bias, dependence on outdated data, and revenue generation challenges. Nonetheless, ChatGPT is considered to be a transformative AI tool poised to redefine the future of generative technology. In conclusion, advancements in AI, such as ChatGPT, are altering how knowledge is acquired and applied, marking a shift from search engines to creativity engines. This transformation highlights the increasing importance of AI literacy and the ability to effectively utilize AI in various domains of life.

Keywords: Artificial intelligence; Literacy; Reproducibility of results; Search engine; Writing.

Fig. 1.

Diagram of the reinforcement learning (RL) process from human feedback. Modified from Learning from human preferences [Internet]. OpenAI; 2017 [cited 2023 Jan 26]. Available from: https://openai.com/blog/deep-reinforcement-learning-from-human-preferences/ [3].

Fig. 2.

A diagram illustrating the 3 steps of the training method: (1) supervised fine-tuning (SFT), (2) reward model (RM) training, and (3) reinforcement learning via proximal policy optimization (PPO) on this reward model. Blue arrows indicate that this data is used to train one of the models. In Step 2, boxes A–D are samples from the models that get ranked by labelers. From Ouyang L, et al. Training language models to follow instructions with human feedback. arXiv [Preprint] 2022 Mar 4. https://doi.org/10.48550/arXiv.2203.02155 [13].

Fig. 3.

Artificial intelligence landscape. From Le Cunff AL. AI and I: the age of artificial creativity [Internet]. Ness Labs; c2022 [cited 2023 Jan 30]. Available from: https://nesslabs.com/artificial-creativity [14].