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In the field of education, the significance of AI chatbots is threefold (Farazouli et al., 2023). Firstly, AI chatbots can personalize the learning experience for each student by adapting to their unique needs and learning styles. Secondly, AI chatbots can provide students with anytime, anywhere access to learning resources, thus increasing the efficiency of the education process. Finally, AI chatbots can help educators monitor students' progress and identify areas where they need additional support.
Despite their potential benefits, the use of AI chatbots in education is also associated with ethical concerns (Babu & Sharmila, 2023). One of the main ethical issues is the replacement of human-to-human interactions with AI chatbots in the classroom. This can lead to a loss of human connection and social skills in students, as well as a decrease in critical thinking skills since AI chatbots may provide ready-made answers instead of challenging students to think for themselves (Kooli, 2023). Additionally, AI chatbots may be biased or limited by their training data, leading to unintentional discrimination or exclusion of certain groups of students.
Sentimental analysis is a technique that can be employed to assess students' emotional states and monitor their progress in ChatGPT-based educational systems (Sudheesh et al., 2023). By analyzing textual data, sentimental analysis can determine the sentiment or emotional tone of the text. This analysis can help educators identify patterns or trends in sentiment across large datasets and provide insights into how individuals or groups are feeling or reacting to particular events or topics (M. Kim, 2021). Sentimental analysis can also be used to assess students' emotional states and monitor their progress throughout the learning process.
The research gap in this field lies in addressing the ethical concerns associated with the use of AI chatbots in education while leveraging their potential benefits (Jeyaraman et al., 2023). One approach to address this gap is to develop ChatGPT models that prioritize human-to-human interactions in the classroom while also leveraging the benefits of AI chatbots (Cai et al., 2023). Further research is needed to explore the effectiveness of sentimental analysis in assessing students' emotional states and monitoring their progress, as well as to ensure that ChatGPT models are bias-free and do not discriminate against any group of students (Sharma & Sharma, 2023).
This study utilizes NVivo 12 Plus to examine the role of AI chatbots in education, ethical considerations, and sentimental analysis regarding the utilization of ChatGPT in education. Through an accumulation of pertinent data, including articles, reports, research papers, news articles, interviews, and social media platforms, the research project is established (Heyman & Heyman, 2023). The data is subsequently categorized into nodes or groups linked to the aforementioned topics (Fan et al., 2022). The textual data is subject to a range of textual analysis techniques, such as word frequency analysis, theme analysis, and coding. These analytical methods yield relevant information and patterns. Additionally, sentimental analysis is conducted on the text data using NVivo 12 Plus' sentimental analysis feature to identify positive, negative, or neutral sentiment related to the topic (Kaushal & Yadav, 2023). Based on these findings, conclusions are drawn regarding the role of AI chatbots in education, ethical issues regarding ChatGPT usage, and sentimental analysis. The results are shared through collaboration with other researchers using NVivo 12 Plus' exportation of projects or presentation decks.