LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models
Open accessAbstract
There is growing interest in understanding how people interact with large language models (LLMs) and whether such models elicit dependency or even addictive behaviour. Validated tools to assess the extent to which individuals may become dependent on LLMs are scarce and primarily build on classic behavioral addiction symptoms, adapted to the context of LLM use. We view this as a conceptual limitation, as the LLM-human relationship is more nuanced and warrants a fresh and distinct perspective. To address this gap, we developed and validated a new 12-item questionnaire to measure LLM dependency, referred to as LLM-D12. The scale was based on the authors' prior theoretical work, with items developed accordingly and responses collected from 526 participants in the UK. Exploratory and confirmatory factor analyses, performed on separate halves of the total sample using a split-sample approach, supported a two-factor structure: Instrumental Dependency (six items) and Relationship Dependency (six items). Instrumental Dependency reflects the extent to which individuals rely on LLMs to support or collaborate in decision-making and cognitive tasks. Relationship Dependency captures the tendency to perceive LLMs as socially meaningful, sentient, or companion-like entities. The two-factor structure demonstrated excellent internal consistency and clear discriminant validity. External validation confirmed both the conceptual foundation and the distinction between the two subscales. The psychometric properties and structure of our LLM-D12 scale were interpreted in light of the emerging view that dependency on LLMs does not necessarily indicate dysfunction but may still reflect reliance levels that could become problematic in certain contexts.
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Self-developed measures may focus on relational aspects or aspects associated with strong emotional attachment but not measuring indicators of dependence, supported by theoretical considerations regarding the adoption of cognitive tools.
“or on relational aspects or aspects associated with strong emotional attachment105, but not measuring indicators of dependence, supported by theoretical considerations regarding the adoption of cognitive tools.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [105]
The Design section explicitly says LLM-D12 was developed to measure dependency on LLMs and that its items reflect symptoms of dependency. Its relational subscale does address social and emotional bonds, but the claim that it does not measure indicators of dependence is contrary to the paper’s stated purpose (page unavailable in our copy). (LLM, conf=0.86)
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Experimental — accuracy not yet measuredSome scales do not differentiate between functional and relational overreliance, potentially confounding different constructs.
“some scales do not differentiate between functional and relational overreliance, potentially confounding different constructs105.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [105]
Section 4.3 finds that instrumental and relationship dependency are distinct constructs, and Section 3.5.2 reports that a one-factor model fit significantly worse than a two-factor model. This supports the concern about combining the constructs, but the passages do not establish that some existing scales fail to differentiate them. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMeasures of AI dependence consist either of adaptations from other scales of behavioral addiction to technologies (e.g., social media, smartphone, or internet addiction) or self-developed measures.
“Measures of “AI dependence” consist either of adaptations from other scales of behavioral addiction to technologies (e.g., social media, smartphone, or internet addiction)100–102, or self-developed measures103–106.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [105]
Dependence on LLM-CBs can be classified into an instrumental component (cognitive overreliance) and a relational component (pseudosocial dependence).
“Dependence on LLM-CBs can be classified into an instrumental component (cognitive overreliance) and a relational component (pseudosocial dependence)105.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [105]
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Zahid, I. A., Joudar, S. S., Albahri, A. S., Albahri, O. S., Alamoodi, A. H., Santamaría, J., & Alzubaidi, L. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200431 · Cited by 25 · see citing works
The neural basis of delayed gratification
Gao, Z., Wang, H., Lu, C., Lu, T., Froudist‐Walsh, S., Chen, M., Wang, X., Hu, J., & Sun, W. (2021) · Science Advances · doi:10.1126/sciadv.abg6611 · Cited by 24 · see citing works
Gößwein, E., & Liebherr, M. (2025) · SAGE Open · doi:10.1177/21582440241311126 · Cited by 15 · see citing works
Self-citationLiang, C., Zhou, P., & Xie, Y. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2496429 · Cited by 10 · cited 1× · see citing works
“Anthropomorphisation, already well-documented in human-computer interaction, increases the likelihood of emotional bonding and perceived companionship [14].”
A bonus task boosts people's willingness to offload cognition to an algorithm
Wahn, B., & Schmitz, L. (2024) · Cognitive Research Principles and Implications · doi:10.1186/s41235-024-00550-0 · Cited by 4 · see citing works
Jensen, P. G., & Neele, T. (2023) · International Journal on Software Tools for Technology Transfer · doi:10.1007/s10009-023-00699-x · Cited by 2 · see citing works
Cook, K. S., Cheshire, C., Rice, E. R. W., et al. (2013) · Handbooks of Sociology and Social Research · doi:10.1007/978-94-007-6772-0_3
Flagged first, then by citations
Research works printed but not linked: 13
- R. Heersmink, "Use of large language models might affect our cognitive skills," Nat Hum Behav, vol. 8, no. 5, pp. 805-806, May 2024, doi: 10.1038/S41562-024-01859-Y;SUBJMETA=179,4007,438;KWRD=ETHICS,PHILOSOPHY. · DOI
- Z. Elyoseph, D. Hadar-Shoval, K. Asraf, and M. Lvovsky, "ChatGPT outperforms humans in emotional awareness evaluations," Front Psychol, vol. 14, p. 1199058, May 2023, doi: 10.3389/FPSYG.2023.1199058/BIBTEX. · DOI
- M. Chandra et al., "Longitudinal Study on Social and Emotional Use of AI Conversational Agent," arXiv preprint arXiv:2504.14112, Apr. 2025, Accessed: May 05, 2025. [Online]. Available: https://arxiv.org/pdf/2504.14112 · arXiv
- J. Coda-Forno et al., "Inducing anxiety in large language models can induce bias," Apr. 2023, Accessed: May 05, 2025. [Online]. Available: https://arxiv.org/pdf/2304.11111 · arXiv
- M. Kosinski, "Evaluating large language models in theory of mind tasks," Proc Natl Acad Sci U S A, vol. 121, no. 45, p. e2405460121, Nov. 2024, doi: 10.1073/PNAS.2405460121/SUPPL_FILE/PNAS.2405460121.SAPP.PDF. · DOI
- S. C. Yu, H. R. Chen, and Y. W. Yang, "Development and validation the Problematic ChatGPT Use Scale: a preliminary report," Current Psychology, vol. 43, no. 31, pp. 26080-26092, Aug. 2024, doi: 10.1007/S12144-024-06259-Z/TABLES/4. · DOI
- B. Waber and N. J. Fast, "Is GenAI"s Impact on Productivity Overblown?," Harvard Business Review .
- G. Northoff, "Is the self a higher-order or fundamental function of the brain? The "basis model of self-specificity" and its encoding by the brain"s spontaneous activity," Cogn Neurosci, vol. 7, no. 1-4, pp. 203-222, Oct. 2016, doi: 10.1080/17588928.2015.1111868. · DOI
- C. Sindermann et al., "Assessing the Attitude Towards Artificial Intelligence: Introduction of a Short Measure in German, Chinese, and English Language," KI -Kunstliche Intelligenz, vol. 35, no. 1, pp. 109-118, Mar. 2021, doi: 10.1007/S13218-020-00689- 0/TABLES/5. · DOI
- G. W. Cheung, H. D. Cooper-Thomas, R. S. Lau, and L. C. Wang, "Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations," Asia Pacific Journal of Management, vol. 41, no. 2, pp. 745- 783, Jun. 2024, doi: 10.1007/S10490-023-09871-Y/TABLES/7. · DOI
- J. C. Nunnally and I. H. Bernstein, "Psychometric theory," Published in 1994 in New York by McGraw-Hill, pp. 9-11, 1994, Accessed: Apr. 30, 2025. [Online]. Available: https://lib.ugent.be/catalog/rug01:000331515
- J. Henseler, C. M. Ringle, and M. Sarstedt, "A new criterion for assessing discriminant validity in variance-based structural equation modeling," J Acad Mark Sci, vol. 43, no. 1, pp. 115-135, Jan. 2015, doi: 10.1007/S11747-014-0403-8/FIGURES/8. · DOI
- R. Heersmink, "The Internet, Cognitive Enhancement, and the Values of Cognition," Minds Mach (Dordr), vol. 26, no. 4, pp. 389-407, Dec. 2016, doi: 10.1007/S11023-016-9404-3/METRICS. · DOI
Cited by
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Goh, A. Y. H., Hartanto, A., & Majeed, N. M. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100845 · Cited by 65 · see citing works
Yankouskaya, A., Almourad, M. B., Liebherr, M., Al-Beyahi, F., Xu, G., & Ali, R. (2026) · AI & Society · doi:10.1007/s00146-026-02858-5 · Cited by 8 · see citing works
Self-citationLoneliness and AI chatbot dependence in young adults: the mediating role of perceived social support
Uğuz, Ö., Doğan, S., & Gungor, D. (2026) · BMC Psychology · doi:10.1186/s40359-026-04916-5 · Cited by 7 · see citing works
Artificial intelligence dependency in college students: a critical narrative review
He, X., Li, S., Cha, R., Wang, L., Shen, Y., Wu, R., Wu, L., Gong, X., & Liu, J. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1808268 · Cited by 7 · see citing works
A scoping review on the mental health harms of LLM-based chatbots
Diel, A., Torous, J., Cuijpers, P., Kleesiek, J., Nensa, F., Weber, N., Faust, F., Lalgi, T. J., Mellis, F. S., Teufel, M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-03054-x · Cited by 4 · see citing works
“Research on cognitive overreliance suggests that a neurocognitiveshiftcausedbytheadaptationofLLM-CBsascognitivetools (analogoustocalculators,searchengines,orautocorrectors)mayleadto increasedtaskef fi ciency and certain “ supervisory ” skills at the cost of a decrease in cognitive capabilities…”
InfluentialMeasuring Large Language Models Dependency: Validating the Arabic Version of the LLM-D12 Scale
Alshakhsi, S. A. A., Yankouskaya, A., Liebherr, M., & Ali, R. (2026) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-026-11386-9 · Cited by 4 · see citing works
Self-citationRizvi, S. W. F., Yankouskaya, A., Alshakhsi, S. A. A., Al‐Thani, D., & Ali, R. (2025) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm67465.2025.11391082 · Cited by 2 · see citing works
Self-citationBig tech, the state, or you? Who is responsible for generative AI addiction?
Stahl, B. C., Ali, R., Hitcham, L., Liebherr, M., Murray, R., Vallejos, E. P., & Yankouskaya, A. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2663509 · Cited by 2 · see citing works
Self-citationLiebherr, M., Yankouskaya, A., Alshakhsi, S. A. A., Montag, C., & Ali, R. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01781-4 · Cited by 2 · see citing works
Self-citationBeyond utility: Relational conflicts in human-AI relationships
Skjuve, M., Larsen, A. G., Følstad, A., & Brandtzaeg, P. B. (2026) · Int. J. Hum. Comput. Stud. · doi:10.1016/j.ijhcs.2026.103883 · Cited by 1 · see citing works
Pika: Designing a social-support agent to improve drivers’ experience in gig work
Xiang, W., Xin-li, C., Guo, T., Zhou, M., & Chen, S. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103721 · Cited by 1 · see citing works
Bao, T. G., El-Haj, M., Alshakhsi, S. A. A., García‐Cabot, A., Ali, R., & Yankouskaya, A. (2026) · Discover Psychology · doi:10.1007/s44202-026-00829-x · Cited by 1 · see citing works
Self-citationDiel, A., Hölsken, S., Christoph, J., Lalgi, T. J., Lena, H., Martin, T. C., & Bäuerle, A. (2026) · Mental Health & Prevention · doi:10.1016/j.mhp.2026.200503 · Cited by 1 · see citing works
Rahman, M. M., Ali, R., & Yankouskaya, A. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7141-3_2 · Cited by 1 · see citing works
Self-citationWorkaholism is associated with dependency on Large Language Models in a cross-national studyPreprint
Barajeeh, B., Kuhail, M. A., Yankouskaya, A., Yang, H., Wang, X., Tina Yunsi, M. A., Alshakhsi, S. A. A., Liebherr, M., & Ali, R. (2026) · Research Square · doi:10.21203/rs.3.rs-8467589/v1
Self-citationRizvi, S. W. F., Yankouskaya, A., Alshakhsi, S. A. A., Al‐Thani, D., & Ali, R. (2025) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm67465.2025.11391019
Self-citationZhang, J., Yao, Z., Kang, D., Rasiah, R., & Gao, N. (2026) · BMC Psychology · doi:10.1186/s40359-026-04246-6 · Cited by 1 · see citing works
Investigating LLMs Dependency vs Privacy-Risk Tolerance: A Research Proposal
Hamdi, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7144-4_35
梁, 昊. (2026) · Advances in Psychology · doi:10.12677/ap.2026.164199
Gamification for Inclusive Large Language Models
Samimi-Dehkordi, L., Kolahdouz-Rahimi, S., & Fatima, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6063-8.ch003
Exploring Global Patterns of Social Media Disorder: The Role of Sociodemographic Factors
Rizvi, S. W. F., Yankouskaya, A., Alshakhsi, S. A. A., Rashid5, M. M., & Ali, R. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7141-3_6
Self-citationMunira, S., & Manikonda, L. (2026) · ACM Web Science Conference (WebSci) · doi:10.1145/3795513.3807437 · Cited by 1 · see citing works
Tanaka, M., Furutani, K., Alshakhsi, S. A. A., Yankouskaya, A., & Ali, R. (2026) · Research Square · doi:10.21203/rs.3.rs-9832900/v1
Self-citationDiel, A., Weber, N., Beckord, J., Lalgi, T. J., Jansen, C., Skoda, E., Dörrie, N., Musche, V., Robitzsch, A., Teufel, M., et al. (2026) · JMIR AI · doi:10.2196/104297
When Greater Frequency Is Not Better: Patterns of Generative AI Use in Higher Education
Tarasova, K., Павлович, Т. Д., & Евгеньевна, И. А. (2026) · Высшее образование в России · doi:10.31992/0869-3617-2026-35-6-127-148
Watanabe, Y., Nakayama, M., Takemura, K., & Uchida, Y. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100365
Yankouskaya, A., Kuhail, M. A., Yang, H.-B., Wang, X.-K., Chen, J., Ma, T. Y., Alshakhsi, S., Xu, G.-D., & Ali, R. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2707709
Self-citationIs generative AI use causing mental health problems?
Montag, C., & Becker, B. (2026) · BMC Global and Public Health · doi:10.1186/s44263-026-00320-8
Dwomoh, O. O., Aigbavboa, C. O., Kaku, G., Addae, M., & Asare, F. (2026) · African Journal Of Applied Research · doi:10.26437/rckb7c17
Validation of the generative artificial intelligence dependency scale among Chinese college students
Deng, Y., Wen, J., 庞爱莲, Li, Y., & Zhang, Y. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107760
Distributed Dependency Intervention to Mitigate AI Companion Overreliance
Yokoyama, T., Matsuda, I., Yang, F., Yamada, S., Sato, S., Ohki, T., & Nishigaki, M. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-38477-5_14
A Design Framework for Calibrated Emotional Anthropomorphism in Conversational AI
Leucas, D. (2026) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw71585.2026.11712082
- Authors
- Yankouskaya, A., Babiker, A., Rizvi, S., Alshakhsi, S., Liebherr, M., & Ali, R.
- Affiliations
- Bournemouth University (GB)
- Hamad bin Khalifa University (QA)
- University of Duisburg-Essen (DE)
- Venue
- ACM Transactions on the Web
- Published
- 2025-09-03
- ISSN
- 1559-1131 (ISSN-L)
- 1559-114X
- OpenAlex
- W4413955382
- Cited by
- 32
- References
- 89
- Citations by year
- 2026: 28 · 2025: 4
- FWCI
- 55.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- ACM Transactions on the Web (publishedVersion, other-oa)
- Bournemouth University Research Online (Bournemouth University) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Qatar Foundation
- Fonds National de la Recherche Luxembourg
- Qatar National Research Fund
- Qatar National Library
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- large language models, scale development, psychometric validation, two-factor structure, behavioral addiction, AI companionship
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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