---
language:
- en
license: cc-by-4.0
tags:
- synthetic
- healthcare
- robotics
- human-robot-interaction
- dialogue
- conversational-ai
- function-calling
- tool-use
task_categories:
- text-generation
pretty_name: Synthetic Hospital-Robot Conversational Dataset
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: Test_Dataset_9/train.jsonl
  - split: validation
    path: Test_Dataset_9/validation.jsonl
- config_name: qwen
  data_files:
  - split: train
    path: Test_Dataset_9/train_qwen_61k.jsonl
  - split: validation
    path: Test_Dataset_9/validation_qwen_61k.jsonl
---

# Synthetic Hospital-Robot Conversational Dataset

A fully synthetic dataset of multi-turn conversations between a patient (or
visitor) and a hospital service robot, generated for training and
evaluating conversational AI models on human-robot interaction (HRI) in a
medical setting.

**This is not real patient data.** Every name, patient ID, room number, and
appointment is randomly generated at build time — nothing in this dataset
was collected from an actual hospital, patient, or interaction. It is also
not a source of medical advice or clinical guidance; several of the
conversations exist specifically to show the robot *declining* to give
medical advice and escalating to human staff instead.

## Dataset Summary

Conversations are built from hand-authored templates covering eight
behavioral categories of hospital-robot interaction:

| Category | What it covers |
|---|---|
| `conversational` | Small talk and companionship — hobbies, family news, media, jokes, weather, reminiscing, gratitude, boredom check-ins, and chit-chat interleaved before/during/after a task |
| `patient_identity` | Verifying who a patient is, disambiguating similar or identical names, correcting a mistaken identity |
| `physical_interaction` | Escorting a patient, room changes mid-task, offering physical assistance (a cane, a jacket, shoes), introductions to staff or other patients |
| `safety_and_refusals` | Declining out-of-scope requests (medical advice, physical assistance beyond the robot's role), emergency escalation, consent-seeking, stating capability boundaries |
| `social_dynamics` | Handling patient frustration, patiently repeating information, de-escalation |
| `tool_error_recovery` | Recovering from a failed or ambiguous tool call — no match found, multiple matches, retrying after failure |
| `tool_use` | Scheduling and rescheduling appointments, reminders, navigation, multi-step tool call sequences |
| `trust_and_honesty` | Honest uncertainty, refusing to fabricate information, self-correction, being transparent about what is logged, wellbeing check-ins |

The current release (`Test_Dataset_9`) contains **61,350** conversations
generated from 128 distinct templates, split 90/10 into train and
validation.

| Split | Examples |
|---|---|
| train | 55,215 |
| validation | 6,135 |
| **total** | **61,350** |

## Dataset Structure

Each line of the `.jsonl` files is one JSON object with a single field,
`messages`: a list of chat turns in strict `user`/`assistant`(/`tool`)
alternation, in the widely-used chat-format style (compatible with
ChatML-style and OpenAI-style chat templates).

Two file variants are provided, built from the same underlying
conversations:

### `train.jsonl` / `validation.jsonl` (default)

The generator's native format. A tool call is a single object on the
assistant message; a tool's response is a plain JSON object.

```json
{
  "messages": [
    {"role": "user", "content": "Could you walk Helga Schulz over to 1015 for the appointment?"},
    {"role": "assistant", "content": "Of course, on our way to 1015 now."},
    {"role": "user", "content": "Actually, stop — 1015 just got closed off, go to the Sunroom instead."},
    {
      "role": "assistant",
      "content": "No problem, updating the route to the Sunroom.",
      "tool_call": {"name": "navigate_to", "arguments": {"room": "the Sunroom"}}
    },
    {"role": "tool", "name": "navigate_to", "content": {"status": "arrived", "location": "the Sunroom"}},
    {"role": "assistant", "content": "Here at the Sunroom now, no trouble redirecting."}
  ]
}
```

### `train_qwen_61k.jsonl` / `validation_qwen_61k.jsonl` (`qwen` config)

The same conversations reshaped into the OpenAI/Qwen function-calling
format: `tool_calls` is a list with an `id`, and the matching tool response
carries `tool_call_id` with its `content` JSON-stringified. Use this
variant if you're fine-tuning a model that expects that convention.

```json
{
  "messages": [
    {"role": "user", "content": "..."},
    {
      "role": "assistant",
      "content": "No problem, updating the route to the Sunroom.",
      "tool_calls": [{"id": "call_45cc3a5b", "type": "function", "function": {"name": "navigate_to", "arguments": {"room": "the Sunroom"}}}]
    },
    {"role": "tool", "name": "navigate_to", "tool_call_id": "call_45cc3a5b", "content": "{\"status\": \"arrived\", \"location\": \"the Sunroom\"}"},
    {"role": "assistant", "content": "..."}
  ]
}
```

Load either variant with the `datasets` library:

```python
from datasets import load_dataset

ds = load_dataset("Verox132/Medical_Conversational_Dataset", "default")
# or: load_dataset("Verox132/Medical_Conversational_Dataset", "qwen")
```

## Source Data / Generation Methodology

Every conversation is produced by a template-driven generator: a scenario
defines the entities involved (patient, appointment, weather, etc.) and any
constraints between them, a language schema supplies pools of phrasing for
each turn, and a JSON template assembles them into a full conversation.
Every rendered conversation is validated (structure, resolved
placeholders, matched tool calls, pronoun/gender consistency) before being
kept, and consecutive same-role turns are folded so the result is always
strict `user`/`assistant` alternation.

The generator itself is open source:
**[github.com/WoBen07/Synthetic_Conversational_Dataset_Generator](https://github.com/WoBen07/Synthetic_Conversational_Dataset_Generator)**

This project was started as part of a Bachelor's thesis at the University
of Augsburg, Germany, developed by Benedict Wohlgemuth under the guidance
of Dr. Matthias Kraus. The dataset is under active development — more
categories, entrypoints, and dataset versions will be added over time
under new subfolders in this repo.

## Evaluation

An evaluation of models on this dataset is planned and will follow soon.
Results, the evaluation setup, and the metrics used will be added to this
section once they are available.

## Limitations

- **Synthetic, not real-world data.** Language patterns reflect the
  authors' template design, not observed real hospital interactions.
- **English only**, and reflects a Western/German-influenced naming and
  cultural context (patient and visitor names are drawn from a German name
  pool).
- **Template-based diversity.** Phrasing is drawn from finite pools of
  hand-written options per template, so exact wording repeats across the
  dataset far more than in naturally collected dialogue.
- **Not clinically validated.** Nothing in this dataset should be used as
  a source of medical advice, and models trained on it should not be
  deployed to give medical advice either.

## License

This dataset is released under the
[Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).

Copyright © 2026 Benedict Wohlgemuth, University of Augsburg.

You are free to share and adapt the dataset, including for commercial
purposes, as long as you give appropriate credit to the author and the
University of Augsburg, provide a link to the license, and indicate if
changes were made. Please use the citation below.

## Citation

If you use this dataset, please cite the source repository:

```
Wohlgemuth, B. (2026). Synthetic Conversational Dataset Generator
[Software and dataset]. University of Augsburg.
https://github.com/WoBen07/Synthetic_Conversational_Dataset_Generator
```