Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review

Advancements in generative artificial intelligence (AI) and large language models (LLMs) have fueled the development of many educational technology innovations that aim to automate the often time-consuming and laborious tasks of generating and analysing textual content (e.g., generating open-ended questions and analysing student feedback survey) (Kasneci et al., 2023; Wollny et al., 2021; Leiker et al., 2023). LLMs are generative artificial intelligence models that have been trained on an extensive amount of text data, capable of generating human-like text content based on natural language inputs. Specifically, these LLMs, such as Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) and Generative Pre-trained Transformer (GPT) (Brown et al., 2020), utilise deep learning and self-attention mechanisms (Vaswani et al., 2017) to selectively attend to the different parts of input texts, depending on the focus of the current tasks, allowing the model to learn complex patterns and relationships among textual contents, such as their semantic, contextual, and syntactic relationships (Min et al., 2021; Liu et al., 2023). As several LLMs (e.g., GPT-3 and Codex) have been pre-trained on massive amounts of data across multiple disciplines, they are capable of completing natural language processing tasks with little (few-shot learning) or no additional training (zero-shot learning) (Brown et al., 2020; Wu et al., 2023). This could lower the technological barriers to LLMs-based innovations as researchers and practitioners can develop new educational technologies by fine-tuning LLMs on specific educational tasks without starting from scratch (Caines et al., 2023; Sridhar et al., 2023). The recent release of ChatGPT, an LLMs-based generative AI chatbot that requires only natural language prompts without additional model training or fine-tuning (OpenAI, 2023), has further lowered the barrier for individuals without technological background to leverage the generative powers of LLMs. Although educational research that leverages LLMs to develop technological innovations for automating educational tasks is yet to achieve its full potential (i.e., most works have focused on improving model performances (Kurdi et al., 2020; Ramesh and Sanampudi, 2022)), a growing body of literature hints at how different stakeholders could potentially benefit from such innovations.