STAQ: Synthetic Technology Assistance Queries

STAQ (Synthetic Technology Assistance Queries, pronounced "stack") is a dataset of technology help-seeking queries written in the communication style of older adults, each paired with a AI-generated paraphrase that clarifies the intended meaning. Synthetic — generated by a large language model and grounded in real diary-study data, not collected directly from real users.

Key facts about the dataset

Field	Value
Number of instances	514
File format	CSV
Fields	id, query, rephrased_query, category, expert_rating
Language	English
Generation model	GPT-4o (few-shot prompting)
License	CC BY 4.0
Canonical copy	Zenodo (see Archived at)
Mirror	Hugging Face: hhshomee2/STAQ
Archived at	Zenodo — DOI [10.5281/zenodo.21323107]
Associated paper	Sharifi et al., ASSETS '26 — DOI 10.1145/3797867.3829031
Version [v1] — [July 12, 2026]

Contents

• staq.csv — the dataset (514 rows).
• README.md — this file.
• DATASHEET.md — generation details, example pairs, provenance, and evaluation.
• DATA_STATEMENT.md — language variety and assumed population.

What's in the data

Each row is one older-adult-style query, its paraphrase, the communication characteristic it exhibits, and (for a sampled subset) an expert plausibility rating. The columns are:

Column	Description
id	Unique instance identifier.
query	Technology help-seeking query in the communication style of older adults.
rephrased_query	AI-generated (GPT-4o) paraphrase clarifying the intended meaning.
category	Communication characteristic. Exactly one of: verbosity, over-specification, under-specification, incompleteness.
expert_rating	Expert plausibility rating of how likely an older adult would phrase the query, coded 0 = unlikely, 1 = likely, and 2 = maybe/possibly. Populated only for the 50-query face-validity sample; unrated rows are marked N/A. See Limitations.

The 514 instances are distributed across four categories, shown below:

Category	Count
under-specification	154
verbosity	120
over-specification	120
incompleteness	120

Two illustrative pairs (query → rephrased_query):

• Under-specification: "I want to send pictures to my grandson but my Gmail is just not doing it." → "How can I successfully send images through email using Gmail?"
• Incompleteness: "Hey, my Facebook is not working like before. How do I make it normal?" → "How can I revert to the previous version or settings of my Facebook application as it is not behaving as expected currently?"

How it was generated

Queries were generated with GPT-4o using few-shot prompting. Each prompt included three example pairs — an original query exhibiting one of four communication characteristics (verbosity, over-specification, under-specification, or incompleteness) and a corresponding expert-written paraphrase — and instructed the model to produce one new example in the same casual, conversational style, output as JSON. To avoid ageist stereotypes, the model was not asked to emulate a generic "older adult"; instead, generation was anchored in real, unstructured queries from a formative diary study, requiring the model to mirror the specific phrasing, expressions of uncertainty, and informal tone observed in that data. The full prompt and example pairs are in the paper's appendix.

Linguistic properties and evaluation

Synthetic queries averaged 58.89 tokens; paraphrases averaged 23.75 tokens (range 13–174). Type-token ratio was 0.858 for the older-adult-style queries versus 0.937 for paraphrases — closely matching the real-world data (0.842 vs 0.890).

The dataset was evaluated on two dimensions. For fidelity, sentence embeddings (SBERT) of synthetic and real queries were compared with Principal Component Analysis (PCA); the distributions overlapped strongly, indicating the synthetic queries capture the semantic structure of the real data. For face validity, two experts in aging and technology independently rated a random sample of 50 queries; 40 were rated "likely," 1 "possibly," and 9 "unlikely," with high inter-rater reliability (Cohen's kappa = 0.83).

Quick start

from datasets import load_dataset
ds = load_dataset("hhshomee2/STAQ")

# or, from the CSV directly:
import pandas as pd
df = pd.read_csv("staq.csv")
print(len(df))                         # expected: 514
print(df["category"].value_counts())   # under-specification 154, others 120 each
print(df["expert_rating"].notna().sum())  # expected: 50 (rated sample)

Intended use

STAQ is intended as a scalable, training-oriented resource for building age-inclusive AI systems — for example, fine-tuning or evaluating models that help articulate or clarify technology-support requests from older adults. It is meant to complement, never replace, participatory design and human evaluation with older adults.

Out of scope

• Do not treat these queries as a substitute for real older adults' behavior or as evidence about any individual or group.
• Do not use STAQ to replace human evaluation or participatory research.

Limitations

• Synthetic, not real. Queries are model-generated. Although grounded in and validated against real diary-study data, they may not capture the full range of how older adults actually communicate.
• Population framing. The underlying diary study recruited participants aged 60 or older who used a mobile device (smartphone or tablet) at least weekly and were proficient in English. All participants were community-dwelling adults residing in the United States. The data does not represent older adults as a monolith.
• Language and cultural scope. English only; phrasing and examples may not transfer to other languages or cultural contexts.
• Model-encoded bias. Generation was anchored in real data specifically to reduce ageist stereotyping, but residual bias from GPT-4o cannot be ruled out.
• Partial expert ratings. expert_rating is populated only for the random 50-query face-validity sample described above; the remaining rows are unrated. Do not treat missing ratings as a low rating.

License

Released under the Creative Commons Attribution 4.0 International license (CC BY 4.0). You are free to share and adapt the data with attribution.

How to cite

If you use STAQ, please cite the paper. The dataset is also archived on Zenodo with its own DOI (see the "Archived at" row above) if you need to cite the data directly.

@inproceedings{sharifi2026staq,
author = {Sharifi, Hasti and Shomee, Homaira Huda and Lamar, Melissa and Medya, Sourav and Chattopadhyay, Debaleena},
title = {Helping the Helper: {LLM}-Assisted Problem Articulation for Older Adults Seeking Technology Support},
year = {2026},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3797867.3829031},
doi = {10.1145/3797867.3829031},
booktitle = {Proceedings of the 28th International ACM SIGACCESS Conference on Computers and Accessibility},
articleno = {61},
numpages = {22},
keywords = {Older adults, Large language models, Synthetic data, Technology support},
location = {Vila Nova de Gaia, Portugal},
series = {ASSETS '26}
}

Versioning and changelog

• [1.0.0] — [date] — Initial release accompanying the ASSETS '26 paper.

Related documentation

• Datasheet — generation details, example pairs, provenance, and evaluation.
• Data statement — language variety and assumed population.

Contact

Debaleena Chattopadhyay — debchatt@uic.edu — ORCID