Correction
Frontiers in Computer Science ·
Volume 8 - 2026 | https://doi.org/10.3389/fcomp.2026.1951219
Correction: Establishing reference points for artificial social agent evaluation: the ASAQ representative set 2025
- Siska Fitrianie1*corresponding author
- Amal Abdulrahman1
- Merijn Bruijnes2
- Willem-Paul Brinkman1
- 1Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Delft University of Technology, Delft, Netherlands
- 2Faculty of Law, Economics and Governance, Utrecht University, Utrecht, Netherlands
Abstract
Keywords
There was a mistake in Table 1 as published. In column “Phase: Establishing Dataset – ASAQ Score – Long”, an error occurred in the calculation of the ASAQ score for the Long version, specifically, items were wrongly included for some underlying constructs. The corrected Table 1 appears below.
Table 1. Summary of participants' usage of the 13 ASAs participated between November 30 and December 13, 2023 (n = 1,253).
| Agent | Phase | Phase | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Screening agent population set | Establishing dataset | ||||||||||
| ASAQ score | |||||||||||
| Total users (%) | Today | This week | This month | Last half year | This year | More than a year ago | Never | n | Long | Short | |
| Alexa | 655 (52.27) | 118 | 94 | 81 | 94 | 98 | 170 | 598 | 67 | 7 | 9 |
| Bard | 448 (35.75) | 37 | 90 | 143 | 53 | 94 | 31 | 805 | 66 | 9 | 10 |
| Bing | 644 (51.40) | 64 | 140 | 166 | 77 | 115 | 82 | 609 | 68 | 9 | 9 |
| ChatGPT | 1,121 (89.47) | 295 | 362 | 240 | 106 | 102 | 16 | 132 | 67 | 4 | 8 |
| CoPilot | 203 (16.20) | 17 | 53 | 48 | 24 | 33 | 28 | 1,050 | 67 | 11 | 13 |
| A dog (animal) | 854 (68.16) | 546 | 91 | 53 | 25 | 28 | 111 | 399 | 66 | 30 | 32 |
| Eliza | 28 (2.23) | 1 | 2 | 3 | 2 | 4 | 16 | 1,225 | 68 | −27 | −30 |
| Google assistant | 1,066 (85.08) | 180 | 273 | 240 | 108 | 150 | 115 | 187 | 67 | 4 | 6 |
| IKEA | 335 (26.74) | 3 | 13 | 38 | 57 | 86 | 138 | 918 | |||
| Replika | 131 (10.45) | 3 | 5 | 9 | 24 | 37 | 53 | 1,122 | |||
| Roomba | 340 (27.13) | 24 | 73 | 48 | 40 | 56 | 99 | 913 | 64 | −3 | 0 |
| Siri | 896 (71.51) | 163 | 191 | 142 | 85 | 123 | 192 | 357 | 66 | 6 | 8 |
| Xiaoice | 100 (7.98) | 5 | 8 | 16 | 9 | 35 | 27 | 1,153 |
The reported % of total any-use reported for each ASA, and when this use last occurred. We present the ASAQ score only for the ASAs we measured (n = 666).
A correction has been made to section 3, Data description, paragraph 3. The sentence previously stated: “… ranging from −30 (Eliza) to +30 (the dog) …”. It has been changed to: “… ranging from −27 (Eliza) to +30 (the dog) …”
A correction has been made to section 4, Recommendation for future use, Paragraph 1: The sentence previously stated: “… ranging from −30 (Eliza) to +30 (the dog) …”. It has been changed to: “… ranging from −27 (Eliza) to +30 (the dog) …”
A correction has been made to section 2, Method, Paragraph 5. The paragraph has been changed to: “The resulting dataset included participants from the two phases: Phase 1 (n = 1,253) and a subset of these participants in Phase 2 (n = 666). The majority of participants identified as male (Phase 1: 54.6%; Phase 2: 57.8%), followed by female (Phase 1: 45%; Phase 2: 42%). The mean age was similar across both Phases (Phase 1: M = 30, SD = 9.2; Phase 2: M = 29.8, SD = 9.2), with the largest age groups being 18–25 (Phase 1: 39%; Phase 2: 39.8%) and 26–35 (Phase 1: 39.7%; Phase 2: 39.8%). Education levels were comparable between groups, with the highest proportions holding an undergraduate degree (Phase 1 and Phase 2: 41.4%) or a graduate degree (Phase 1: 25%; Phase 2: 23.9%). Socioeconomic status, assessed via the MacArthur Scale (Adler et al., 2000) (1 = lowest, 10 = highest), was distributed across the scale, with the largest proportions in the middle ranges (e.g., at level 6, Phase 1: 25.9% at level 6; Phase 2: 28.4%). Geographically (based on the United Nations Regional Groups (United Nations, 2024), most participants resided in Western Europe (Phase 1: 46.8%; Phase 2: 42.9%), followed by Africa (Phase 1: 21.1%; Phase 2: 23%) and Eastern Europe (Phase 1: 18.2%; Phase 2: 20.1%). Smaller proportions were from Latin America and the Caribbean (Phase 1: 11.7%; Phase 2: 12.2%), with limited presentation from the United States (Phase 1: 1.2%; Phase 2: 0.6%), and other regions. Users of this dataset might select sub-datasets based on these characteristics to study specific groups.”
There was a mistake in the supplementary file Data Sheet 1.pdf published with the original version of this paper. The file has now been replace.
The original version of this article has been updated.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Footnotes
Edited and reviewed by: Kostas Karpouzis, Panteion University, Greece
Citation
Fitrianie, S., et al. (2026). Correction: Establishing reference points for artificial social agent evaluation: the ASAQ representative set 2025. Frontiers in Computer Science, Volume 8. https://doi.org/
- Received
- Accepted
- Published
- Volume
- 8 - 2026
Copyright
Copyright © 2026 Fitrianie, Abdulrahman, Bruijnes and Brinkman.
License: https://creativecommons.org/licenses/by/4.0/
*Correspondence: Siska Fitrianie
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