Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP)
Authors: Aditi Ramachandran, Sarah Strohkorb Sebo, Brian Scassellati8050-8057
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This approach is validated through a between-subjects field study, which involved 4th grade students (n = 28) interacting with a social robot solving long division problems over five sessions. Students who received help from a robot using the AT-POMDP policy demonstrated significantly greater learning gains than students who received help from a robot with a fixed help action selection policy. Our results demonstrate that this robust computational framework can be used effectively to deliver diverse and personalized tutoring support over time for students. |
| Researcher Affiliation | Academia | Aditi Ramachandran,* Sarah Strohkorb Sebo,* Brian Scassellati Computer Science, Yale University New Haven, Connecticut, USA {aditi.ramachandran, sarah.sebo, brian.scassellati}@yale.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code used to create AT-POMDP is available at: https://github.com/ScazLab/AT-POMDP |
| Open Datasets | No | The paper mentions deriving parameters from 'a previous robot-student tutoring data set (Ramachandran, Litoiu, and Scassellati 2016)' but does not provide concrete access information (link, DOI, or explicit statement of public availability) for this or any other dataset used in the experiments. |
| Dataset Splits | No | The paper describes a field study with pre-test and post-test evaluations, but it does not describe specific training, validation, or testing dataset splits, nor does it refer to standard splits for any public dataset. |
| Hardware Specification | No | The paper mentions the 'Nao robot' and a 'tablet device' as part of the tutoring system, but it does not specify any hardware details (e.g., GPU, CPU models, or memory) used for running the AT-POMDP policy computation or for any training. |
| Software Dependencies | No | The paper mentions 'ROS architecture' but does not provide specific version numbers for ROS or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the setup of the user study (experimental conditions, help actions, procedure) but does not provide specific experimental setup details for the computational model, such as hyperparameters (e.g., learning rate, batch size) or training configurations for the AT-POMDP itself. |