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Generative artificial intelligence (GenAI) use and dependence: an approach from behavioral economics

Robayo-Pinzon, Oscar, Rojas-Berrio, Sandra, Camargo, Jorge E., Foxall, Gordon R.·2025·Frontiers in Public Health
DOI 10.3389/fpubh.2025.1634121PMC PMC12364812
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Abstract

Objective: This study aims to explore the perceived dependence on Generative Artificial Intelligence (GenAI) tools among young adults and examine the relative reinforcing value of AI chatbots use compared to monetary rewards, applying a behavioral economics approach. Participants/methods: A total of 420 university students from Bogotá, Colombia, participated in an online survey. The study employed a Multiple Choice Procedure (MCP) to assess the relative reinforcement between different durations of GenAI use (1, 2, and 4 weeks) and monetary rewards, which varied in amount and delay. Additionally, an adapted AI Dependence Scale evaluated levels of dependence on AI tools. Data analysis included repeated measures ANOVA to examine the effects of reward magnitude and delay on choices, and correlations to assess the relationship between perceived dependence and reinforcement values. Results: Participants reported low average dependence on AI tools (mean AI Dependence Scale score = 65.6), with no significant gender differences. MCP findings indicated significant differences in crossover points across varying durations or delays for AI chatbots use, suggesting a higher relative value of use for the option to use AI chatbots immediately. The average reinforcement value for AI use versus monetary rewards did significantly vary with reward magnitude. On the other hand, significant differences were found in the levels of perceived dependence on AI, according to the average daily time of AI tool use. Conclusion: The results suggest that young adults exhibit low perceived dependence on GenAI tools but show differential reinforcement values based on usage duration or delay conditions. This behavioral economics approach provides novel insights into decision-making patterns related to AI chatbots use, emphasizing the need for further research to understand the psychological and social factors influencing dependence on AI technologies.

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Crossref · 10.3389/fpubh.2025.1634121

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Figure 1 Mean multiple choice procedure crossover points of each of the six versions.
Figure 1 Mean multiple choice procedure crossover points of each of the six versions.CC BY · PubMed Central
Figure 2 Differences in the average crossover points according to the magnitude of the reinforcer.
Figure 2 Differences in the average crossover points according to the magnitude of the reinforcer.CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 37

  1. Doğan, 2025, AI in higher education: risks and opportunities from the academician perspective, Eur J Educ, 60, e12863, 10.1111/ejed.12863 · DOI
  2. Maphosa, 2023, Artificial intelligence in higher education: a bibliometric analysis and topic modeling approach, Appl Artif Intell, 37, 10.1080/08839514.2023.2261730 · DOI
  3. Zhang, 2024, Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior, Int J Educ Technol High Educ, 21, 34, 10.1186/s41239-024-00467-0 · DOI · Paper page
  4. Khanna, 2015, A study of today’s a.I. Through Chatbots and rediscovery of machine intelligence. Int J u- e- Serv, Sci Technol, 8, 277, 10.14257/ijunesst.2015.8.7.28 · DOI · Paper page
  5. Huang, 2024, Exploring the effect of attachment on technology addiction to generative AI Chatbots: a structural equation modeling analysis, Int J Hum Comput Interact, 41, 9440, 10.1080/10447318.2024.2426029 · DOI · Paper page
  6. Turel, 2011, Integrating technology addiction and use: an empirical investigation of online auction users, MIS Q, 35, 1043, 10.2307/41409972 · DOI · Paper page
  7. Şahin, 2017, The predictive level of social media addiction for life satisfaction: a study on university students, Turk Online J Educ Technol, 16, 120
  8. Dalvi-Esfahani, 2020, Social media addiction and empathy: moderating impact of personality traits among high school students, Telemat Info, 2021, 101516, 10.1016/j.tele.2020.101516 · DOI
  9. Pillai, 2024, Students’ adoption of AI-based teacher-bots (T-bots) for learning in higher education, Inf Technol People, 37, 328, 10.1108/ITP-02-2021-0152 · DOI · Paper page
  10. Hyde, 2024, Tools or fools: are we educating managers or creating tool-dependent robots?, J Manag Educ, 48, 708, 10.1177/10525629241230357 · DOI
  11. Acuff, 2018, Applying behavioral economic theory to problematic internet use: an initial investigation, Psychol Addict Behav, 32, 846, 10.1037/adb0000404 · DOI
  12. West, 2021, Problematic smartphone use: the role of reward processing, depressive symptoms and self-control, Addict Behav, 122, 107015, 10.1016/j.addbeh.2021.107015 · DOI
  13. Robayo-Pinzon, 2021, Does excessive use of smartphones and apps make us more impulsive? An approach from behavioural economics, Heliyon, 7, e06104, 10.1016/j.heliyon.2021.e06104 · DOI
  14. Bickel, 2014, The behavioral economics of substance use disorders: reinforcement pathologies and their repair, Annu Rev Clin Psychol, 10, 641, 10.1146/annurev-clinpsy-032813-153724 · DOI · Paper page
  15. Benson, 2009, Effects of reinforcer magnitude and alternative reinforcer delay on preference for alcohol during a multiple-choice procedure, Drug Alcohol Depend, 100, 161, 10.1016/j.drugalcdep.2008.09.005 · DOI
  16. Griffiths, 1993, Multiple-choice procedure: an efficient approach for investigating drug reinforcement in humans, Behav Pharmacol, 4, 3, 10.1097/00008877-199302000-00001 · DOI
  17. Griffiths, 1996, Validation of the multiple-choice procedure for investigating drug reinforcement in humans, Exp Clin Psychopharmacol, 4, 97, 10.1037/1064-1297.4.1.97 · DOI
  18. Amlung, 2014, Understanding the effects of stress and alcohol cues on motivation for alcohol via behavioral economics, Alcohol Clin Exp Res, 38, 1780, 10.1111/acer.12423 · DOI
  19. Madden, 2010, A delay-discounting primer, 11, 10.1037/12069-001 · DOI
  20. Yi, 2006, The combined effects of delay and probability in discounting, Behav Process, 73, 149, 10.1016/j.beproc.2006.05.001 · DOI
  21. Macedo, 2022, Delay discounting in aging: the influence of cognitive and psychological variables, Behav Neurosci, 136, 392, 10.1037/bne0000518 · DOI
  22. Gershman, 2020, Rationally inattentive intertemporal choice, Nat Commun, 11, 1, 10.1038/s41467-020-16852-y · DOI
  23. Prelec, 1991, Decision making over time and under uncertainty: a common approach, Manag Sci, 37, 770, 10.1287/mnsc.37.7.770 · DOI
  24. Arfer, 2015, The predictive accuracy of intertemporal-choice models, Br J Math Stat Psychol, 68, 326, 10.1111/bmsp.12049 · DOI
  25. Foxall, 2012, When loss rewards: the near-miss effect in slot machine gambling, Anal Gambl Behav, 6, 5
  26. Billieux, 2012, Problematic use of the mobile phone: a literature review and a pathways model, Curr Psychiatr Rev, 8, 299, 10.2174/157340012803520522 · DOI · Paper page
  27. Robayo-Pinzon, 2023, Social media sites users’ choice between utilitarian and informational reinforcers assessed using temporal discounting, Front Public Health, 11, 960321, 10.3389/fpubh.2023.960321 · DOI
  28. Mohamed, 2025, Facebook addiction among medical students: prevalence and consequences, Middle East Curr Psychiatry, 32, 7, 10.1186/s43045-025-00501-5 · DOI
  29. Yang, 2025, I tweet, therefore I am: a systematic review on social media use and disorders of the social brain, BMC Psychiatry, 25, 95, 10.1186/s12888-025-06528-6 · DOI
  30. Li, 2025, The longitudinal relationships of problematic social media use, self-transcendence values and school adaptation: a two-wave study, BMC Psychol, 13, 67, 10.1186/s40359-025-02356-1 · DOI
  31. Bassett, 2020, Initial validation of the multiple-choice procedure for measuring video game playing, Addict Res Theory, 28, 314, 10.1080/16066359.2019.1650350 · DOI
  32. Butler, 2018, Using the multiple-choice procedure to measure the relative reinforcing efficacy of gambling: initial validity evidence among college students, J Gambl Stud, 34, 513, 10.1007/s10899-017-9716-0 · DOI
  33. Tutgun-ünal, 2015, Development of the social media addiction scale, AJIT-e Bilişim Teknol Online Derg, 6, 51, 10.5824/1309-1581.2015.4.004.x · DOI
  34. Buono, 2017, Delay discounting of video game players: comparison of time duration among gamers, Cyberpsychol Behav Soc Netw, 20, 104, 10.1089/cyber.2016.0451 · DOI
  35. Correia, 2006, Use of a multiple-choice procedure with college student drinkers, Psychol Addict Behav, 20, 445, 10.1037/0893-164X.20.4.445 · DOI
  36. Krishnamurti, 2020, Spending money to make change: association of methamphetamine abstinence and voucher spending among contingency management pilot participants in South Africa, J Subst Abus Treat, 112, 60, 10.1016/j.jsat.2020.01.014 · DOI
  37. Foxall, 2016, Addiction as consumer choice: Exploring the cognitive dimension, 222, 10.4324/9780203794876 · DOI

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Metadata

Authors
Robayo-Pinzon, O., Rojas-Berrio, S., Camargo, J. E., & Foxall, G. R.
Venue
Frontiers in Public Health
Publisher
Frontiers Media SA
Published
2025-08-06
Volume
13
ISSN
2296-2565
DOI
10.3389/fpubh.2025.1634121
PMC
PMC12364812
Cited by
19
References
7
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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