Towards Automatic Boundary Detection for Human-AI Hybrid Essay in Education

Human-AI collaborative writing has been greatly facilitated with the help of modern large language models (LLM), e.g., ChatGPT. While admitting the convenience brought by technology advancement, educators also have concerns that students might leverage LLM to partially complete their writing assignment and pass off the human-AI hybrid text as their original work. Driven by such concerns, in this study, we investigated the automatic detection of Human-AI hybrid text in education, where we formalized the hybrid text detection as a boundary detection problem, i.e., identifying the transition points between human-written content and AI-generated content. We constructed a hybrid essay dataset by partially removing sentences from the original student-written essays and then instructing ChatGPT to fill in for the incomplete essays. Then we proposed a two-step detection approach where we (1) Separated AI-generated content from human-written content during the embedding learning process; and (2) Calculated the distances between every two adjacent prototypes (a prototype is the mean of a set of consecutive sentences from the hybrid text in the embedding space) and assumed that the boundaries exist between the two prototypes that have the furthest distance from each other. Through extensive experiments, we summarized the following main findings: (1) The proposed approach consistently outperformed the baseline methods across different experiment settings; (2) The embedding learning process (i.e., step 1) can significantly boost the performance of the proposed approach; (3) When detecting boundaries for single-boundary hybrid essays, the performance of the proposed approach could be enhanced by adopting a relatively large prototype size, leading to a $22$\% improvement (against the second-best baseline method) in the in-domain setting and an $18$\% improvement in the out-of-domain setting.Comment: 9 pages including references, 2 figure

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

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (e.g., question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency, and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (e.g., GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models

CFD simulation of flow and mixing characteristics in a stirred tank agitated by improved disc turbines

To reduce the power consumption and improve the mixing performance in stirred tanks, two improved disc turbines namely swept-back parabolic disc turbine (SPDT) and staggered fan-shaped parabolic disc turbine (SFPDT) are developed. After validation of computational fluid dynamics (CFD) model with experimental results, CFD simulations are carried out to study the flow pattern, mean velocity, power consumption, pumping capacity and mixing efficiency of the improved and traditional impellers in a dished-bottom tank under turbulent flow conditions. The results indicate that compared with the commonly used parabolic disc turbine (PDT), the power number of proposed SPDT and SFPDT impellers is reduced by 43% and 12%, and the pumping efficiency is increased by 68% and 13%, respectively. Furthermore, under the same power consumption (0-700 W center dot m(-3)), the mixing performance of both SPDT and SFPDT is also superior to that of Rushton turbine and PDT. (c) 2022 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved

CFD simulation of flow and mixing characteristics in a stirred tank agitated by improved disc turbines

To reduce the power consumption and improve the mixing performance in stirred tanks, two improved disc turbines namely swept-back parabolic disc turbine (SPDT) and staggered fan-shaped parabolic disc turbine (SFPDT) are developed. After validation of computational fluid dynamics (CFD) model with experimental results, CFD simulations are carried out to study the flow pattern, mean velocity, power consumption, pumping capacity and mixing efficiency of the improved and traditional impellers in a dished-bottom tank under turbulent flow conditions. The results indicate that compared with the commonly used parabolic disc turbine (PDT), the power number of proposed SPDT and SFPDT impellers is reduced by 43% and 12%, and the pumping efficiency is increased by 68% and 13%, respectively. Furthermore, under the same power consumption (0-700 W center dot m(-3)), the mixing performance of both SPDT and SFPDT is also superior to that of Rushton turbine and PDT. (c) 2022 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved

RAD51 Inhibition Shows Antitumor Activity in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the major type of liver cancer, causes a high annual mortality worldwide. RAD51 is the critical recombinase responsible for homologous recombination (HR) repair in DNA damage. In this study, we identified that RAD51 was upregulated in HCC and that RAD51 silencing or inhibition reduced the proliferation, migration, and invasion of HCC cells and enhanced cell apoptosis and DNA damage. HCC cells with the combinatorial treatments of RAD51 siRNA or inhibitor and sorafenib demonstrated a synergistic effect in inhibiting HCC cell proliferation, migration, and invasion, as well as inducing cell apoptosis and DNA damage. Single RAD51 silencing or sorafenib reduced RAD51 protein expression and weakened HR efficiency, and their combination almost eliminated RAD51 protein expression and inhibited HR efficiency further. An in vivo tumor model confirmed the RAD51 inhibitor’s antitumor activity and synergistic antitumor activity with sorafenib in HCC. RNA-Seq and gene set enrichment analysis (GSEA) in RAD51-inactivated Huh7 cells indicated that RAD51 knockdown upregulated cell apoptosis and G1/S DNA damage checkpoint pathways while downregulating mitotic spindle and homologous recombination pathways. Our findings suggest that RAD51 inhibition exhibits antitumor activities in HCC and synergizes with sorafenib. Targeting RAD51 may provide a novel therapeutic approach in HCC

Can large language models write reflectively

Generative Large Language Models (LLMs) demonstrate impressive results in different writing tasks and have already attracted much attention from researchers and practitioners. However, there is limited research to investigate the capability of generative LLMs for reflective writing. To this end, in the present study, we have extensively reviewed the existing literature and selected 9 representative prompting strategies for ChatGPT – the chatbot based on state-of-art generative LLMs to generate a diverse set of reflective responses, which are combined with student-written reflections. Next, those responses were evaluated by experienced teaching staff following a theory-aligned assessment rubric that was designed to evaluate student-generated reflections in several university-level pharmacy courses. Furthermore, we explored the extent to which Deep Learning classification methods can be utilised to automatically differentiate between reflective responses written by students vs. reflective responses generated by ChatGPT. To this end, we harnessed BERT, a state-of-art Deep Learning classifier, and compared the performance of this classifier to the performance of human evaluators and the AI content detector by OpenAI. Following our extensive experimentation, we found that (i) ChatGPT may be capable of generating high-quality reflective responses in writing assignments administered across different pharmacy courses, (ii) the quality of automatically generated reflective responses was higher in all six assessment criteria than the quality of student-written reflections; and (iii) a domain-specific BERT-based classifier could effectively differentiate between student-written and ChatGPT-generated reflections, greatly surpassing (up to 38% higher across four accuracy metrics) the classification performed by experienced teaching staff and general-domain classifier, even in cases where the testing prompts were not known at the time of model training