Accelerating nurse training without impacting the quality of education, by leveraging LWM (large whatever models) to provide individual feedback to students and help teachers how to optimize teaching.

High-quality education and training of nurses are of utmost importance to keep high standards in medical care. Nevertheless, as the covid pandemic has shown quite impressively, there are too few healthcare professionals available. Therefore, education and training of nurse students, or adapting the training of nurses is challenged to accelerate, to have manpower of nurses available when it is required. Still, accelerating training often comes with reduced quality, which can easily lead to bad qualifications and, in the worst case, to a lethal outcome.
Thus, in nurse training a pressing question is, how to optimize and with it accelerate training without suffering in quality.
One of the significant questions for teachers in training nurse students is to understand the state of a student’s education. Are some students in need of more repetitions? which students can proceed to the next level, who is ready to get in contact with actual patients? In this regard, optimization of training means to individualize, not only individualize the training of students but also individualize the feedback and information a teacher gets about their way of teaching.

We believe this to be a field where Artificial Intelligence (AI) and more specifically the application of foundational models (LLMs large language models, paired with other methods of machine learning) can provide real support.

In the first part of this microproject, together with Nurse-Teachers of the University of Southampton, we want to define and design an LWM that fits the requirements of nurse training. For this, 2-3 nurse teachers from Southampton will visit DFKI in order to get a feeling for systems that are available, and also what applications are feasible. In turn, researchers of DFKI will visit the nurse training facilities in Southampton to get a better picture of how nurse training is conducted. At the end of this first phase of the microproject, an LWM (large whatever model) is defined (existing LLMs combined with additional features and data sources, as required).

In the second phase, this LWM will be implemented and tested against videos of recorded training sessions. Specific focus will be set on:
• How to understand the action of a particular person?
• Actions taken by the trainee, are they correct or false? What would have been the correct action?
• Which teaching efforts work and which do not as much?
• Which useful suggestions and feedback can be provided to the trainees and teachers?

Depending on the outcome of this microproject, in a follow-up project, an online LWM system could be installed at the facilities of the University of Southampton, where the effects of direct feedback on teaching and performance, could be evaluated.

Output

1) Definition and design of the LWM will be documented and if possible published in an adequate scientific journal
2) Developed algorithms and results will be published at a scientific conference (AI and possibly also medical)
3) The developed LWM will be made available to be used in a follow-up project

Project Partners

Primary Contact

Agnes Grünerbl, DFKI, EI

A graduate level educational module (12 lectures + 5 assignments) covering basic principles and techniques of Human-Interactive Robot Learning.

Human-Interactive Robot Learning (HIRL) is an area of robotics that focuses on developing robots that can learn from and interact with humans. This educational module aims to cover the basic principles and techniques of Human-Interactive Robot Learning. This interdisciplinary module will encourage graduate students (Master/PhD level) to connect different bodies of knowledge within the broad field of Artificial Intelligence, with insights from Robotics, Machine Learning, Human Modelling, and Design and Ethics. The module is meant for Master’s and PhD students in STEM, such as Computer Science, Artificial Intelligence, and Cognitive Science.
This work will extend the tutorial presented in the context of the International Conference on Algorithms, Computing, and Artificial Intelligence (ACAI 2021) and will be shared with the Artificial Intelligence Doctoral Academy (AIDA). Moreover, the proposed lectures and assignments will be used as teaching material at Sorbonne University, and Vrije Universiteit Amsterdam.
We plan to design a collection of approximately 12 1.5-hour lectures, 5 assignments, and a list of recommended readings, organized along relevant topics surrounding HIRL. Each lecture will include an algorithmic part and a practical example of how to integrate such an algorithm into an interactive system.
The assignments will encompass the replication of existing algorithms with the possibility for the student to develop their own alternative solutions.

Proposed module contents (each lecture approx. 1.5 hour):
(1) Interactive Machine Learning vs Machine Learning – 1 lecture
(2) Interactive Machine Learning vs Interactive Robot Learning (Embodied vs non-embodied agents) – 1 lecture
(3) Fundamentals of Reinforcement Learning – 2 lectures
(4) Learning strategies: observation, demonstration, instruction, or feedback
– Imitation Learning, Learning from Demonstration – 2 lectures
– Learning from Human Feedback: evaluative, descriptive, imperative, contrastive examples – 3 lectures
(5) Evaluation metrics and benchmarks – 1 lecture
(6) Application scenarios: hands-on session – 1 lecture
(7) Design and ethical considerations – 1 lecture

Output

Learning objectives along Dublin descriptors:
(1) Knowledge and understanding;
– Be aware of the human interventions in standard machine learning and interactive machine learning.
– Understand human teaching strategies
– Gain knowledge about learning from feedback, demonstrations, and instructions.
– Explore ongoing works on how human teaching biases could be modeled.
– Discover applications of interactive robot learning.
(2) Applying knowledge and understanding;
– Implement HIRL techniques that integrate different types of human input
(3) Making judgments;
– Make informed design choices when building HIRL systems
(4) Communication skills;
– Effectively communicate about own work both verbally and in a written manner
(5) Learning skills;
– Integrate insights from theoretical material presented in the lecture and research papers showcasing state-of-the-art HIRL techniques.

Project Partners

Primary Contact

Mohamed Chetouani, ISIR, Sorbonne University