Background: Type 2 diabetes (T2D) is a prevalent chronic disease with a
significant risk of serious health complications and negative impacts on the
quality of life. Given the impact of individual characteristics and lifestyle
on the treatment plan and patient outcomes, it is crucial to develop precise
and personalized management strategies. Artificial intelligence (AI) provides
great promise in combining patterns from various data sources with nurses'
expertise to achieve optimal care. Methods: This is a 6-month ancillary study
among T2D patients (n = 20, age = 57 +- 10). Participants were randomly
assigned to an intervention (AI, n=10) group to receive daily AI-generated
individualized feedback or a control group without receiving the daily feedback
(non-AI, n=10) in the last three months. The study developed an online
nurse-in-the-loop predictive control (ONLC) model that utilizes a predictive
digital twin (PDT). The PDT was developed using a transfer-learning-based
Artificial Neural Network. The PDT was trained on participants self-monitoring
data (weight, food logs, physical activity, glucose) from the first three
months, and the online control algorithm applied particle swarm optimization to
identify impactful behavioral changes for maintaining the patient's glucose and
weight levels for the next three months. The ONLC provided the intervention
group with individualized feedback and recommendations via text messages. The
PDT was re-trained weekly to improve its performance. Findings: The trained
ONLC model achieved >=80% prediction accuracy across all patients while the
model was tuned online. Participants in the intervention group exhibited a
trend of improved daily steps and stable or improved total caloric and total
carb intake as recommended.

2 型糖尿病患者通过人工智能和个性化反馈改善每日步数、总卡路里和总碳水化合物摄入，并提供预测控制模型以精确管理病情。

临床试验中使用转学习预测数字孪生的护士参与的精准 2 型糖尿病管理人工智能

Nurse-in-the-Loop Artificial Intelligence for Precision Management of  Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive  Digital Twin

Providing consistent, individualized feedback to teachers on their
instruction can improve student learning outcomes. Such feedback can especially
benefit novice instructors who teach on online platforms and have limited
access to instructional training. To build scalable measures of instruction, we
fine-tune RoBERTa and GPT models to identify five instructional talk moves
inspired by accountable talk theory: adding on, connecting, eliciting, probing
and revoicing students' ideas. We fine-tune these models on a newly annotated
dataset of 2500 instructor utterances derived from transcripts of small group
instruction in an online computer science course, Code in Place. Although we
find that GPT-3 consistently outperforms RoBERTa in terms of precision, its
recall varies significantly. We correlate the instructors' use of each talk
move with indicators of student engagement and satisfaction, including
students' section attendance, section ratings, and assignment completion rates.
We find that using talk moves generally correlates positively with student
outcomes, and connecting student ideas has the largest positive impact. These
results corroborate previous research on the effectiveness of accountable talk
moves and provide exciting avenues for using these models to provide
instructors with useful, scalable feedback.

用 RoBERTa 和 GPT 模型 fine-tune 五个教学对话技巧，来帮助在线平台上受限于教学培训的初级教师提供一致而个性化的反馈，研究结果表明教师的对话技巧与学生的学习成果有正向相关性，其中连接学生的想法对学生的积极影响最大。

衡量五个负责任对话举措以实现规模化教学改进

Measuring Five Accountable Talk Moves to Improve Instruction at Scale

Generative AI and large language models hold great promise in enhancing
programming education by automatically generating individualized feedback for
students. We investigate the role of generative AI models in providing human
tutor-style programming hints to help students resolve errors in their buggy
programs. Recent works have benchmarked state-of-the-art models for various
feedback generation scenarios; however, their overall quality is still inferior
to human tutors and not yet ready for real-world deployment. In this paper, we
seek to push the limits of generative AI models toward providing high-quality
programming hints and develop a novel technique, GPT4Hints-GPT3.5Val. As a
first step, our technique leverages GPT-4 as a ``tutor'' model to generate
hints -- it boosts the generative quality by using symbolic information of
failing test cases and fixes in prompts. As a next step, our technique
leverages GPT-3.5, a weaker model, as a ``student'' model to further validate
the hint quality -- it performs an automatic quality validation by simulating
the potential utility of providing this feedback. We show the efficacy of our
technique via extensive evaluation using three real-world datasets of Python
programs covering a variety of concepts ranging from basic algorithms to
regular expressions and data analysis using pandas library.

使用生成式 AI 模型和大规模语言模型来生成编程教育中的个性化反馈，为学生提供编程提示以帮助他们解决程序中的错误。通过使用 GPT-4 作为 “导师” 模型和符号信息来提高生成质量，然后通过使用 GPT-3.5 作为 “学生” 模型来验证提示质量，我们开发了一种名为 GPT4Hints-GPT3.5Val 的新技术，通过对三个实际数据集进行广泛评估展示了我们技术的有效性。