Digital Life 2025 (Dataset)

Dataset summary: A de-identified open dataset (n=1,003) examining how digitally active adults experience everyday life in increasingly technology-mediated environments, including digital behaviour, cognitive load, fatigue, attention, trust, values alignment, and interactions with emerging AI technologies across six English-speaking countries.

Measures include patterns of digital technology use, mental fatigue and energy, attention and distraction, trust in digital systems, values alignment, perceived wellbeing, attitudes toward technology, and demographic variables. The dataset provides a broad behavioural baseline that underpins multiple Human Clarity Institute research programmes and reports.

Part of the Human Clarity Institute's Human–AI Experience Data Series.

Framework

HRL domain(s): Attention & Cognitive Load, Trust & Epistemic Stability, Meaning & Behavioural Alignment

Registry construct alignment: Attention Capacity, Cognitive Load, Trust Calibration, Behavioural Alignment, Meaning Coherence

Listed constructs reflect longitudinal, registry-mapped item alignment and do not represent the full thematic scope of this dataset.

Dataset Availability

Participant-level dataset downloads are temporarily unavailable while the Human Clarity Institute completes an independent privacy and data governance review.

This reflects our ongoing commitment to responsible data stewardship, contemporary de-identification standards, and participant privacy.

Study methodology, summary findings, benchmark statistics, and citation information remain publicly available through this website and the permanent Zenodo record.

Researchers, universities, and organisations interested in accessing a dataset or discussing research collaboration are welcome to contact info@humanclarityinstitute.com.

Citation & Dataset Record

The Zenodo DOI provides the permanent scholarly record for this dataset, including version history, metadata, and citation information.

Citation

APA

Human Clarity Institute. (2025). Digital Life 2025 (Dataset). Human Clarity Institute. https://doi.org/10.5281/zenodo.17393880

BibTeX

@dataset{hci_ai_human_2025,
  author    = {Human Clarity Institute},
  title     = {Digital Life 2025 (Dataset)},
  year      = {2025},
  doi       = {10.5281/zenodo.17393880},
  url       = {https://humanclarityinstitute.com/datasets/digital-life-2025/},
  license   = {CC-BY-4.0}
}

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

You are free to share, adapt, and build upon publicly available HCI datasets for any purpose, including commercial use, provided appropriate credit is given to the Human Clarity Institute.

Full licence text: https://creativecommons.org/licenses/by/4.0/

Data Summaries

Explore the key findings generated from this foundational dataset.

Digital Trust → Key Findings

Digital Fatigue & Energy → Key Findings

Methodology

This dataset forms part of the Human Clarity Institute’s Human–AI Experience research programme, examining how people navigate trust, authenticity, credibility, focus, fatigue, values alignment, and uncertainty in digitally mediated environments shaped by AI systems, online platforms, and rapidly circulating information. The study uses a cross-sectional online survey design and provides a broad behavioural baseline for understanding digital life across multiple HCI research streams.

Data were collected via the Prolific research platform on 10 September 2025 from adults across the United Kingdom, United States, Canada, Australia, Ireland, and New Zealand. Participants provided informed consent for their de-identified survey responses to be publicly released for research purposes.

Sampling & Participants

Final sample: 1,003 participants
Sampling countries: United Kingdom, United States, Canada, Australia, Ireland, New Zealand
Eligibility: Adults (18+) fluent in English
Recruitment platform: Prolific

The resulting dataset should be interpreted as a non-probability convenience sample and is not intended to represent national populations.

Data Integrity

All percentages reported on this page are calculated from valid responses in the cleaned dataset. Percentages are rounded to the nearest whole number for readability. Unless otherwise stated, summary percentages combine respondents selecting 5–7 on the 7-point agreement scale.

Public release datasets undergo structured de-identification prior to publication. Direct identifiers, participant IDs, timestamps, geographic variables, and participant free-text responses are removed or transformed where appropriate to reduce re-identification risk while preserving research utility.

This dataset is exploratory and descriptive in nature. It does not support causal inference and results should be interpreted as observed patterns within the survey sample.

Data use and reuse terms are outlined in our Data Use & Disclaimer.