February 17th, 2023
Author: Le Thanh Binh - UPP Global Technology
In recent times, the subject of ChatGPT has gained considerable attention on social media platforms. This article provides an overview of ChatGPT and its model architecture, which built upon Transformers architecture. After that, ChatGPT was situated within the framework of ongoing Artificial Intelligence in Education (AIEd) research, talk about applications geared toward student-facing, teacher-facing, and system-facing, and assess potential and risks. In conclusion, we provide some suggestions for students, professors, and higher education institutions as a way to wrap up the essay.
GPT (Generative Pre-trained Transformer) is a family of natural language processing (NLP) models. These models are designed to generate human-like text by predicting the next word in a sentence or generating an entire sentence given some initial input. GPT models are pre-trained using unsupervised learning techniques, which means that they are trained on large amounts of text data without any human supervision or labeling. For specific tasks, GPT can be fine-tuned for apply in a wide range of applications, including summarization (Grail, Q., Perez, J., & Gaussier, E. (2021), sentiment analysis (Zhang, L., Fan, H., Peng, C., Rao, G., & Cong, Q. (2020)), language translation (Sawai, R., Paik, I., & Kuwana, A. (2021)), and even writing entire news articles (Dale, R. (2021)).
At UPP Technology, we can offer the involvement of human experts in fine-tuning GPT models for diverse tasks that would benefit your organization.
On November 30, 2022, ChatGPT was introduced as a prototype and rapidly gained popularity due to its ability to provide detailed and articulate responses across various domains of knowledge. Developed using a InstructGPT model that was trained using supervised techniques, ChatGPT offers real-time conversations and offers advice and solutions to complex problems. Additionally, since ChatGPT is designed to emulate human conversations, a number of experts have observed that it can be difficult to distinguish between interacting with a machine program and communicating with an actual human, particularly if we are not aware of it in advance. (Cotton, D., Cotton, P., & Shipway, J. R. 2023)
We can outline some notable characteristics of ChatGPT in comparison to other chatbot products that are currently on the market (generated by ChatGPT):
In this portion, we will delve into the specific applications and technologies of ChatGPT that can be utilized to enhance the education system.
In context of applying existing artificial intelligence in education, we can divide as 3 main parts: Students-facing applications, Teacher-facing applications, System-facing applications.
Regarding student learning, ChatGPT can be beneficial by assisting with:
ChatGPT can be used for reducing workloads from teachers such as: Automating assessment, creating education content (quizz, homework), provide real-time feedback when teacher is not in-charge., etc. Some of most popular applications include:
Some AI-based applications that focus on the system side of education can offer academic administrators and managers high-level data, such as the patterns of attrition in different schools or institutions. Nonetheless, this aspect has been given the least amount of attention.
However, with the appearence of ChatGPT and GPT-3, we can expect a future of more opportunities for reseachers and scientists in this application.
If AI can produce the answers, there is less incentive for students to acquire knowledge, skills and independent learning. Relying too heavily on the chatbot can result in negative consequences for the student, such as a weak grasp of the subject matter, reduced motivation to study, and an inability to independently research or explore alternative resources. Additionally, without seeking other sources of information for comparison, the student may be at risk of receiving inaccurate or misleading information.
Plagiarism is also a concernable problem. It is difficult to distinguish between a student’s own writing and the responses generated by a chatbot application, mostly in case students can easily access to ChatGPT for asking. (Cotton, D., Cotton, P., & Shipway, J. R. 2023)
OpenAI itselft notes that the bot may sometimes provide plausible-sounding but incorrect or nonsensical answers and may exhibit biased behavior. This could lead to misunderstandings or errors on the part of students who rely solely on the system for information. As a result, it is important to use ChatGPT as a supplement to traditional learning methods rather than a replacement for them, to ensure that students develop critical thinking skills, problem-solving abilities, and creativity.
To address the issue of plagiarism and improve accessment process, there are several strategies that teachers can implement, including:
I suggest that students should consider the following recommendations:
Tools such as GPT-3 and ChatGPT are bringing AI to the threshold of being a routine aspect of daily life. While the effects of this advancement on society are only beginning to emerge, the potential for substantial impact is enormous. In my opinion, AI will play a key role in shaping the future of education by facilitating communication between individuals, promoting creative pursuits, decreasing tedious homework in favor of more collaborative and imaginative group assignments.
Cotton, D., Cotton, P., & Shipway, J. R. (2023, January 10). Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT.
RudolphA, Jürgen. "Journal of Applied Learning & Teaching." Journal of Applied Learning & Teaching 5, no. 1 (2022).
Azaria, A. (2022). ChatGPT usage and limitations. Preprint. DOI: 10.13140/RG.2.2.26616.11526
Deng, J., & Lin, Y. (2022). The benefits and challenges of ChatGPT: An overview. Frontiers in Computing and Intelligent Systems, 2(2), 81-83.
Zhai, X. (2022). ChatGPT user experience: Implications for education.
Grail, Q., Perez, J., & Gaussier, E. (2021, April). Globalizing BERT-based transformer architectures for long document summarization. In Proceedings of the 16th conference of the European chapter of the Association for Computational Linguistics: Main volume (pp. 1792-1810).
Zhang, L., Fan, H., Peng, C., Rao, G., & Cong, Q. (2020, August). Sentiment analysis methods for hpv vaccines related tweets based on transfer learning. In Healthcare (Vol. 8, No. 3, p. 307). MDPI.
Sawai, R., Paik, I., & Kuwana, A. (2021). Sentence Augmentation for Language Translation Using GPT-2. Electronics, 10 (24), 3082.
Dale, R. (2021). GPT-3: What’s it good for?. Natural Language Engineering , 27 (1), 113-118.