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

Few-photon single ionization of cold rubidium in the over-the-barrier regime

Photoionization of the rubidium (Rb) atoms cooled in a magneto-optical trap, characterized by the coexistence of the ground 5$S_{1/2}$ and the excited 5$P_{3/2}$ states, is investigated experimentally and theoretically with the 400 nm femtosecond laser pulses at intensities of $I=3\times10^9$ W/cm$^2$ - $4.5\times10^{12}$ W/cm$^2$. Recoil-ion momentum distribution (RIMD) of Rb$^+$ exhibits rich ring-like structures and their energies correspond to one-photon ionization of the 5$P_{3/2}$ state, two-photon and three-photon ionizations of the 5$S_{1/2}$ state, respectively. With the increasing of $I$, we find that experimental signals near zero-momentum (NZM) in RIMDs resulted from the 5$P_{3/2}$ state enhance dramatically and its peaked Rb$^+$ momenta dwindle obviously while that from the 5$S_{1/2}$ state is maintained. Meanwhile, the ion-yield ratio of the 5$S_{1/2}$ over the 5$P_{3/2}$ states varies from $I$ to $I^{1.5}$ as $I$ increases. These features indicate a transition from perturbative ionization to strong-perturbative ionization for the 5$P_{3/2}$ state. Numerical simulations by solving the time-dependent Schr\"odinger equation (TDSE) can qualitatively explain the measurements of RIMD, photoion angular distributions, as well as ion-yield ratio. However, some discrepancies still exist, especially for the NZM dip, which could stem from the electron-electron correlation that is neglected in the present TDSE simulations since we have adopted the single-active-electron approximation

Evaluating the American Heart Association/American College of Cardiology Guideline—Recommended and Contemporary Pretest Probability Models in a Mixed Asian Cohort: The Contribution of Coronary Artery Calcium

BACKGROUND: Most pretest probability (PTP) tools for obstructive coronary artery disease (CAD) were Western -developed. The most appropriate PTP models and the contribution of coronary artery calcium score (CACS) in Asian populations remain unknown. In a mixed Asian cohort, we compare 5 PTP models: local assessment of the heart (LAH), CAD Consortium (CAD2), risk factor-weighted clinical likelihood, the American Heart Association/American College of Cardiology and the European Society of Cardiology PTP and 3 extended versions of these models that incorporated CACS: LAH (CACS), CAD2 (CACS), and the CACS-clinical likelihood. METHODS AND RESULTS: The study cohort included 771 patients referred for stable chest pain. Obstructive CAD prevalence was 27.5%. Calibration, area under the receiver-operating characteristic curves (AUC) and net reclassification index were evaluated. LAH clinical had the best calibration (χ 2 5.8; P=0.12). For CACS models, LAH (CACS) showed least deviation between observed and expected cases (χ 2 37.5; P&lt;0.001). There was no difference in AUCs between the LAH clinical (AUC, 0.73 [95% CI, 0.69-0.77]), CAD2 clinical (AUC, 0.72 [95% CI, 0.68-0.76]), risk factor-weighted clinical likelihood (AUC, 0.73 [95% CI: 0.69-0.76) and European Society of Cardiology PTP (AUC, 0.71 [95% CI, 0.67-0.75]). CACS improved discrimination and reclassification of the LAH (CACS) (AUC, 0.88; net reclassification index, 0.46), CAD2 (CACS) (AUC, 0.87; net reclassification index, 0.29) and CACS-CL (AUC, 0.87; net reclassification index, 0.25). CONCLUSIONS: In a mixed Asian cohort, Asian-derived LAH models had similar discriminatory performance but better calibration and risk categorization for clinically relevant PTP cutoffs. Incorporating CACS improved discrimination and reclassification. These results support the use of population-matched, CACS-inclusive PTP tools for the prediction of obstructive CAD.</p

3D a-Fe2O3 nanorods arrays@graphene oxide nanosheets as sensing materials for improved gas sensitivity

Hybridizing nanostructured metal oxides with grapheme oxide (GO) is highly desirable for the improvement of gas sensing performance of gas sensors. Herein, we develop an in situ fluorine directed solution process combined with heat treatment to grow highly ordered, porous α-Fe2O3 nanorods arrays (NRAs) onto graphene oxide (GO) sheets, resulting in flexible three-dimensional nanostructures. The α-Fe2O3 NRAs have heights of ∼50 nm and widths of 10–20 nm, which are densely and vertically attached to both sides of GO sheets, thereby generating an interesting “blanket-like” geometry. When tested as sensing materials for gas sensors, the nanocomposite displays a high sensitivity of 19.14 toward 50 ppm acetone at a work temperature of 220 °C, which was 5.4 times of that of α-Fe2O3. Even after 50 days, a stable sensitivity as high as 18.9 toward 50 ppm acetone could be maintained. The improved high sensitivity, fast response and recovery, and extraordinary stability of GO/α-Fe2O3 can be attributed to the highly ordered, porous structure and the synergistic effect between α-Fe2O3 and GO.</p

TMIE Is an Essential Component of the Mechanotransduction Machinery of Cochlear Hair Cells

SummaryHair cells are the mechanosensory cells of the inner ear. Mechanotransduction channels in hair cells are gated by tip links. The molecules that connect tip links to transduction channels are not known. Here we show that the transmembrane protein TMIE forms a ternary complex with the tip-link component PCDH15 and its binding partner TMHS/LHFPL5. Alternative splicing of the PCDH15 cytoplasmic domain regulates formation of this ternary complex. Transducer currents are abolished by a homozygous Tmie-null mutation, and subtle Tmie mutations that disrupt interactions between TMIE and tip links affect transduction, suggesting that TMIE is an essential component of the hair cell’s mechanotransduction machinery that functionally couples the tip link to the transduction channel. The multisubunit composition of the transduction complex and the regulation of complex assembly by alternative splicing is likely critical for regulating channel properties in different hair cells and along the cochlea’s tonotopic axis

Manganese@Albumin Nanocomplex and Its Assembled Nanowire Activate TLR4-Dependent Signaling Cascades of Macrophages

The immunomodulatory effect of divalent manganese cations (Mn2+), such as activation of the cGAS-STING pathway or NLRP3 inflammasomes, positions them as adjuvants for cancer immunotherapy. In this study, it is found that trace Mn2+ ions, bound to bovine serum albumin (BSA) to form Mn@BSA nanocomplexes, stimulate pro-inflammatory responses in human- or murine-derived macrophages through TLR4-mediated signaling cascades. Building on this, the assembly of Mn@BSA nanocomplexes to obtain nanowire structures enables stronger and longer-lasting immunostimulation of macrophages by regulating phagocytosis. Furthermore, Mn@BSA nanocomplexes and their nanowires efficiently activate peritoneal macrophages, reprogramme tumor-associated macrophages, and inhibit the growth of melanoma tumors in vivo. They also show better biosafety for potential clinical applications compared to typical TLR4 agonists such as lipopolysaccharides. Accordingly, the findings provide insights into the mechanism of metalloalbumin complexes as potential TLR agonists that activate macrophage polarization and highlight the importance of their nanostructures in regulating macrophage-mediated innate immunity.Trace Mn2+ bound to bovine serum albumin (BSA) to form Mn@BSA nanocomplexes can potentiate pro-inflammatory responses through TLR4-mediated signaling cascades. Furthermore, assembly of the resultant Mn@BSA nanocomplexes into larger nanowires enables robust and long-duration interactions with membrane-bound TLR4s via phagocytosis, boosting the immunostimulation of macrophages.imagePeer reviewe

Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions

BACKGROUND: Emergency readmission poses an additional burden on both patients and healthcare systems. Risk stratification is the first step of transitional care interventions targeted at reducing readmission. To accurately predict the short- and intermediate-term risks of readmission and provide information for further temporal risk stratification, we developed and validated an interpretable machine learning risk scoring system. METHODS: In this retrospective study, all emergency admission episodes from January 1st 2009 to December 31st 2016 at a tertiary hospital in Singapore were assessed. The primary outcome was time to emergency readmission within 90 days post discharge. The Score for Emergency ReAdmission Prediction (SERAP) tool was derived via an interpretable machine learning-based system for time-to-event outcomes. SERAP is six-variable survival score, and takes the number of emergency admissions last year, age, history of malignancy, history of renal diseases, serum creatinine level, and serum albumin level during index admission into consideration. FINDINGS: A total of 293,589 ED admission episodes were finally included in the whole cohort. Among them, 203,748 episodes were included in the training cohort, 50,937 episodes in the validation cohort, and 38,904 in the testing cohort. Readmission within 90 days was documented in 80,213 (27.3%) episodes, with a median time to emergency readmission of 22 days (Interquartile range: 8-47). For different time points, the readmission rates observed in the whole cohort were 6.7% at 7 days, 10.6% at 14 days, 13.6% at 21 days, 16.4% at 30 days, and 23.0% at 60 days. In the testing cohort, the SERAP achieved an integrated area under the curve of 0.737 (95% confidence interval: 0.730-0.743). For a specific 30-day readmission prediction, SERAP outperformed the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, and Emergency department visits in past six months) and the HOSPITAL score (Hemoglobin at discharge, discharge from an Oncology service, Sodium level at discharge, Procedure during the index admission, Index Type of admission, number of Admissions during the last 12 months, and Length of stay). Besides 30-day readmission, SERAP can predict readmission rates at any time point during the 90-day period. INTERPRETATION: Better performance in risk prediction was achieved by the SERAP than other existing scores, and accurate information about time to emergency readmission was generated for further temporal risk stratification and clinical decision-making. In the future, external validation studies are needed to evaluate the SERAP at different settings and assess their real-world performance. FUNDING: This study was supported by the Singapore National Medical Research Council under the PULSES Center Grant, and Duke-NUS Medical School