SavedAdd your journal

Journals / İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi / 2026 / Cilt: 25 Sayı: 56

The Mediating Role of Loneliness in the Relationship Between Depression and Dependence on AI Chatbots Among Emerging Adults

Beliren Yetişkinlerde Depresyon ve Yapay Zekâ Sohbet Botlarına Bağımlılık İlişkisinde Yalnızlığın Aracı Rolü

Cengizhan Aynacı(SAKARYA ÜNİVERSİTESİ)0000-0001-6536-6642
Pages
881–913
Publication Date
2026-09-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings were evaluated within the framework of the Compensatory Internet Use Theory and the I-PACE model; it was concluded that depression increases feelings of loneliness and that this situation can lead individuals toward patterns of dependence on artificial intelligence chatbots.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aimed to examine the relationship between depression and addiction to AI chatbots, as well as the mediating role of loneliness in this relationship, among a sample of 830 emerging adults aged 18–25.
📊
Key Findings & Data:The data were collected using self-report scales that assess levels of depression, loneliness, and dependence on AI chatbots; correlation and regression analyses, along with the Hayes PROCESS Macro Model 4, were used to analyze the data.
💡
Conclusion & Impact:The findings were evaluated within the framework of the Compensatory Internet Use Theory and the I-PACE model; it was concluded that depression increases feelings of loneliness and that this situation can lead individuals toward patterns of dependence on artificial intelligence chatbots.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Bağımlılık · yapay zekâ sohbet botu bağımlılığı · depresyon

Abstract

This study aimed to examine the relationship between depression and addiction to AI chatbots, as well as the mediating role of loneliness in this relationship, among a sample of 830 emerging adults aged 18–25. The data were collected using self-report scales that assess levels of depression, loneliness, and dependence on AI chatbots; correlation and regression analyses, along with the Hayes PROCESS Macro Model 4, were used to analyze the data. Correlation analysis revealed positive and significant relationships among the three variables. An examination of the regression analyses revealed that depression significantly predicted artificial intelligence dependence (R² = .041) and loneliness (R² = .128), while loneliness significantly predicted the use of artificial intelligence chatbots (R² = .022). The results of the bootstrap-based mediation analysis showed that loneliness played a significant partial mediating role in the relationship between depression and dependence on AI chatbots, and that the indirect effect accounted for 15,46% of the total effect. The findings were evaluated within the framework of the Compensatory Internet Use Theory and the I-PACE model; it was concluded that depression increases feelings of loneliness and that this situation can lead individuals toward patterns of dependence on artificial intelligence chatbots. Based on these findings, it is recommended that, at the micro level, psychologists, psychological counselors, and social workers address feelings of loneliness and AI usage patterns together in their assessment processes; at the mezzo level, universities and social service organizations develop awareness programs and early screening protocols; and at the macro level, the ethical and legal frameworks regulating AI applications and social work professional standards be updated.

Özet

Bu araştırma, 18-25 yaş aralığındaki 830 beliren yetişkinden oluşan bir çalışma grubu üzerinde depresyonun yapay zekâ sohbet botlarına bağımlılık ile ilişkisini ve bu ilişkide yalnızlığın aracı rolünü incelemeyi amaçlamıştır. Veriler, depresyon, yalnızlık ve yapay zekâ sohbet botlarına bağımlılık düzeylerini değerlendiren özbildirim ölçekleri aracılığıyla toplanmış; verilerin analizinde korelasyon ve regresyon analizleri ile Hayes PROCESS Macro Model 4 kullanılmıştır. Korelasyon analizi, üç değişken arasında pozitif yönlü ve anlamlı ilişkiler bulunduğunu ortaya koymuştur. Regresyon analizleri incelendiğinde, depresyonun yapay zekâ bağımlılığını (R²= .041) ve yalnızlığı (R²= .128) anlamlı biçimde yordadığı; yalnızlığın ise yapay zekâ sohbet botları kullanımını anlamlı düzeyde yordadığı (R²= .022) görülmüştür. Bootstrap tabanlı aracılık analizi sonuçları, depresyon ile yapay zekâ sohbet botlarına bağımlılık arasındaki ilişkide yalnızlığın anlamlı bir kısmi aracı rol oynadığını ve dolaylı etkinin toplam etkiye oranının %15,46 olduğunu göstermiştir. Bulgular, Telafi Edici İnternet Kullanımı Kuramı ve I-PACE modeli çerçevesinde değerlendirilmiş; depresyonun yalnızlık duygularını artırdığı ve bu durumun bireyleri yapay zekâ sohbet botlarına yönelik bağımlılık örüntülerine yönlendirebildiği sonucuna ulaşılmıştır. Elde edilen bulgular doğrultusunda mikro düzeyde psikolog, psikolojik danışman ve sosyal hizmet uzmanlarının değerlendirme süreçlerinde yalnızlık ve YZ kullanım örüntülerini birlikte ele alması; mezzo düzeyde üniversite ve sosyal hizmet kuruluşlarının farkındalık programları ile erken tarama protokolleri geliştirmesi; makro düzeyde ise YZ uygulamalarını düzenleyen etik-yasal çerçevelerin ve sosyal hizmet mesleki standartlarının güncellenmesi önerilmektedir.

🔖

Cite This Article

Aynacı, C. (2026). The Mediating Role of Loneliness in the Relationship Between Depression and Dependence on AI Chatbots Among Emerging Adults. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, Cilt: 25 Sayı: 56, 881–913. https://doi.org/10.46928/iticusbe.1957632

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (64)

  1. Achterbergh, L., Pitman, A., Birken, M., Pearce, E., Sno, H., & Johnson, S. (2020). The experience of loneliness among young people with depression: A qualitative meta-synthesis of the literature. BMC Psychiatry, 20, 4151-23. https://doi.org/10.1186/s12888-020-02818-3
  2. Ait Baha, T., El Hajji, M., Es-Saady, Y., & Fadili, H. (2023). The power of personalization: A systematic review of personality-adaptive chatbots. SN Computer Science, 4, 661. https://doi.org/10.1007/s42979-023-02092-6
  3. Arnett, J. J. (1994). Are college students adults? Their conceptions of the transition to adulthood. Journal of Adult Development, 1, 213–224. https://doi.org/10.1007/BF02277582
  4. Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469–480. https://doi.org/10.1037/0003-066X.55.5.469
  5. Arnett, J. J. (2001). Conceptions of the transition to adulthood: Perspectives from adolescence through midlife. Journal of Adult Development, 8(2), 133–143. https://doi.org/10.1023/A:1026450103225
  6. Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., & Smith, D. M. (2023). Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Internal Medicine, 183(6), 589–596. https://doi.org/10.1001/jamainternmed.2023.1838
  7. Boucher, E. M., Harake, N. R., Ward, H. E., Stoeckl, S. E., Vargas, J., Minkel, J., et al. (2021). Artificially intelligent chatbots in digital mental health interventions: A review. Expert Review of Medical Devices, 18(S1), 37–49. https://doi.org/10.1080/17434440.2021.2013200
  8. Boughedda, S. (2026, February 6). Chinese AI progress has been “astounding” over past year, Barclays says. Investing.com. https://uk.investing.com/news/stock-market-news/chinese-ai-progress-has-been-astounding-over-past-year-barclays-says-4493868
  9. Bozkurt, S. (2023). PROCESS makro ile aracılık, düzenleyicilik ve durumsal aracılık etki analizleri (SPSS uygulamalı). Ekin Yayınları.
  10. Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. (2019). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience & Biobehavioral Reviews, 104, 1–10. https://doi.org/10.1016/j.neubiorev.2019.06.032
  11. Cacioppo, J. T., Cacioppo, S., & Boomsma, D. I. (2014). Evolutionary mechanisms for loneliness. Cognition and Emotion, 28(1), 13–221. https://doi.org/10.1080/02699931.2013.837379
  12. Chaturvedi, R., Verma, S., Das, R., & Dwivedi, Y. K. (2023). Social companionship with artificial intelligence: Recent trends and future avenues. Technological Forecasting and Social Change, 193, 1226341-20. https://doi.org/10.1016/j.techfore.2023.122634
  13. Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, 1023831-17. https://doi.org/10.1016/j.ijinfomgt.2021.102383
  14. Çaparlar, C. Ö., & Dönmez, A. (2016). Bilimsel araştırma nedir, nasıl yapılır. Turk J Anaesthesiol Reanim, 44(4), 212-218. https://doi.org/10.5152/TJAR.2016.34711
  15. Denecke, K., Abd-Alrazaq, A., & Househ, M. (2021). Artificial intelligence for chatbots in mental health: Opportunities and challenges. In M. Househ, E. Borycki, & A. Kushniruk (Eds.), Multiple perspectives on artificial intelligence in healthcare (pp. 115–128). Springer International Publishing. https://doi.org/10.1007/978-3-030- 47346-6_6
  16. Desai, P., Ng, T. K. S., Krueger, K. R., Wilson, R. S., Evans, D. A., & Rajan, K. B. (2025). Social engagement, depressive symptoms, and loneliness, and longitudinal cognitive decline in a population-based cohort of older adults. The American Journal of Geriatric Psychiatry: Open Science, Education, and Practice, 6, 39–48. https://doi.org/10.1016/j.osep.2025.02.004
  17. Doğan, T., Çötok, N. A., & Tekin, E. G. (2011). Reliability and validity of the Turkish version of the UCLA Loneliness Scale (ULS-8) among university students. Procedia - Social and Behavioral Sciences, 15, 2058–2062. https://doi.org/10.1016/j.sbspro.2011.04.053
  18. Dünya Sağlık Örgütü. (2025). From loneliness to social connection. https://iris.who.int/bitstream/handle/10665/381746/9789240112360-eng.pdf
  19. Erzen, E., & Çikrikci, Ö. (2018). The effect of loneliness on depression: A meta-analysis. International Journal of Social Psychiatry, 64(5), 427–435. https://doi.org/10.1177/0020764018776349
  20. Ge, L., Yap, C. W., Ong, R., & Heng, B. H. (2017). Social isolation, loneliness and their relationships with depressive symptoms: A population-based study. PLoS ONE, 12(8), e01821451-13. https://doi.org/10.1371/journal.pone.0182145
  21. Gürbüz, S. (2019). Sosyal bilimlerde aracı, düzenleyici ve durumsal etki analizleri. Seçkin Yayıncılık.
  22. Hartanto, A., Quek, F. Y., Tng, G. Y., & Yong, J. C. (2021). Does social media use increase depressive symptoms? A reverse causation perspective. Frontiers in Psychiatry, 12, 6419341-5. https://doi.org/10.3389/fpsyt.2021.641934
  23. Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: A theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine, 40(2), 218–227. https://doi.org/10.1007/s12160-010-9210-8
  24. Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). The Guilford Press.
  25. Heffer, T., Good, M., Daly, O., MacDonell, E., & Willoughby, T. (2019). The longitudinal association between social-media use and depressive symptoms among adolescents and young adults: An empirical reply to
  26. Twenge et al. (2018). Clinical Psychological Science, 7(3), 462–470. https://doi.org/10.1177/2167702618812727
  27. Holt-Lunstad, J. (2022). Social connection as a public health issue: The evidence and a systemic framework for prioritizing the “social” in social determinants of health. Annual Review of Public Health, 43, 193–213. https://doi.org/10.1146/annurev-publhealth-052020-110732
  28. Hu, B., Mao, Y., & Kim, K. J. (2023). How social anxiety leads to problematic use of conversational AI: The roles of loneliness, rumination, and mind perception. Computers in Human Behavior, 145, 1-10107760. https://doi.org/10.1016/j.chb.2023.107760
  29. Ji, W., Ding, L., Li, X., Wu, Y., & Li, H. (2026). Why is problematic social media use more prevalent among Chinese adolescents with probable depression than their non-depressed peers? A longitudinal examination of the mediating roles of peer acceptance and loneliness. Addictive Behaviors, 177, 108640. https://doi.org/10.1016/j.addbeh.2026.108640
  30. Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior, 31, 351–354. https://doi.org/10.1016/j.chb.2013.10.059
  31. Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 1019251-33. https://doi.org/10.1016/j.tele.2022.101925
  32. Koverola, M., Kunnari, A., Sundvall, J., & Laakasuo, M. (2022). General attitudes towards robots scale (GAToRS): A new instrument for social surveys. International Journal of Social Robotics, 14, 1559–1581. https://doi.org/10.1007/s12369-022-00880-3
  33. Lai, L., Pan, Y., Xu, R., & Jiang, Y. (2025). Depression and the use of conversational AI for companionship among college students: The mediating role of loneliness and the moderating effects of gender and mind perception. Frontiers in Public Health, 13, 15808261-9. https://doi.org/10.3389/fpubh.2025.1580826
  34. Lasgaard, M., Goossens, L., & Elklit, A. (2011). Loneliness, depressive symptomatology, and suicide ideation in adolescence: Cross-sectional and longitudinal analyses. Journal of Abnormal Child Psychology, 39(1), 137–150. https://doi.org/10.1007/s10802-010-9442-x
  35. Latikka, R., Bergdahl, J., Savolainen, I., Celuch, M., & Oksanen, A. (2026). Individual and well-being factors associated with social chatbot usage: A six-country study. Journal of Social and Personal Relationships, 43(4), 1055–1079. https://doi.org/10.1177/02654075251392956
  36. Liang, L., Zhou, D., Yuan, C., Shao, A., & Bian, Y. (2016). Gender differences in the relationship between internet addiction and depression: A cross-lagged study in Chinese adolescents. Computers in Human Behavior, 63, 463–470. https://doi.org/10.1016/j.chb.2016.04.043
  37. Luo, J., Wong, N. M. L., Zhang, R., et al. (2025). A network analysis of rumination on loneliness and the relationship with depression. Nature Mental Health, 3, 46–57. https://doi.org/10.1038/s44220-024-00350-x
  38. Maples, B., Cerit, M., Vishwanath, A., & Pea, R. (2024). Loneliness and suicide mitigation for students using GPT-3-enabled chatbots. NPJ Mental Health Research, 3(1), 1-64. https://doi.org/10.1038/s44184-023-00047-6
  39. Meriluoto, J. (2026, January 18). ChatGPT statistics 2026. Superlines. https://www.superlines.io/articles/chatgpt-statistics
  40. Misiak, B., Liśkiewicz, P., & Samochowiec, J. (2025). Temporal patterns of loneliness and their associations with mental health outcomes: Observations from a longitudinal study. European Psychiatry, 68(1), e941-12. https://doi.org/10.1192/j.eurpsy.2025.10055
  41. Montag, C., Spapé, M., & Becker, B. (2025). Can AI really help solve the loneliness epidemic? Trends in Cognitive Sciences, 29(10), 869–871. https://doi.org/10.1016/j.tics.2025.08.002
  42. Nakagomi, A., Akutsu, Y., Yasuoka Jensen, M., Imamura, K., Watanabe, K., Sasaki, N., & Tabuchi, T. (2026). AI companions and well-being: Moderation by social connectedness and loneliness. Technology in Society, 85, 103229. https://doi.org/10.1016/j.techsoc.2026.103229
  43. Özdemir, H. D., & Dağdeviren, H. N. (2024). Construct validity study of the Turkish form of the short Beck Depression Inventory. Eurasian Journal of Family Medicine, 13(2), 61-67. https://doi.org/10.33880/ejfm.2024130203
  44. Peng, C., Zhang, S., Wen, F., et al. (2025). How loneliness leads to conversational AI usage intention: The roles of anthropomorphic interface and parasocial interaction. Current Psychology, 44, 8177–8189. https://doi.org/10.1007/s12144-024-06809-5
  45. Phang, J., Lampe, M., Ahmad, L., Agarwal, S., Fang, C. M., Liu, A. R., Danry, V., Lee, E., Chan, S. W., & Pataranutaporn, P. (2025). Investigating affective use and emotional well-being on ChatGPT (arXiv:2504.03888). arXiv. https://arxiv.org/abs/2504.03888
  46. Prizeman, K., McCabe, C., & Weinstein, N. (2025). Internalized stigma is a predictor of mental health secrecy and loneliness in young people with clinical depression symptoms: A longitudinal study. Journal of Clinical Psychology, 81(3), 545–556. https://doi.org/10.1002/jclp.2378
  47. Puukko, K., Hietajärvi, L., Maksniemi, E., Alho, K., & Salmela-Aro, K. (2020). Social media use and depressive symptoms: A longitudinal study from early to late adolescence. International Journal of Environmental Research and Public Health, 17(16), 1-185921. https://doi.org/10.3390/ijerph17165921
  48. Qualter, P., Vanhalst, J., Harris, R., Van Roekel, E., Lodder, G., Bangee, M., et al. (2015). Loneliness across the life span. Perspectives on Psychological Science, 10(2), 250–264. https://doi.org/10.1177/1745691615568999
  49. Richardson, T., Elliott, P., & Roberts, R. (2017). Relationship between loneliness and mental health in students. Journal of Public Mental Health, 16(2), 48–54. https://doi.org/10.1108/JPMH-03-2016-0013
  50. Rodríguez-Martínez, A., Amezcua-Aguilar, T., Cortés-Moreno, J., & Jiménez-Delgado, J. J. (2023). Qualitative analysis of conversational chatbots to alleviate loneliness in older adults as a strategy for emotional health. Healthcare, 12(1), 621-30. https://doi.org/10.3390/healthcare12010062
  51. Rostami, M., & Navabinejad, S. (2023). Artificial empathy: User experiences with emotionally intelligent chatbots. AI Tech and Behavioral & Social Sciences, 1(3), 19–27. https://doi.org/10.61838/kman.aitech.1.3.4
  52. Sharma, A., Lin, I. W., Miner, A. S., Atkins, D. C., & Althoff, T. (2023). Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nature Machine Intelligence, 5(1), 46–57. https://doi.org/10.1038/s42256-022-00593-2
  53. Skjuve, M., Følstad, A., Fostervold, K. I., & Brandtzaeg, P. B. (2021). My chatbot companion: A study of human-chatbot relationships. International Journal of Human-Computer Studies, 149, 1026011-12. https://doi.org/10.1016/j.ijhcs.2021.102601
  54. Sorin, V., Brin, D., Barash, Y., Konen, E., Charney, A., Nadkarni, G., & Klang, E. (2024). Large language models and empathy: Systematic review. Journal of Medical Internet Research, 26, e52597. https://doi.org/10.2196/52597 Tabachnick, B. G., & Fidell, L. S. (2018). Using multivariate statistics (7th ed.). Pearson.
  55. Tam, K. W., Zhang, D., Li, Y., et al. (2025). Meaning in life: Bidirectional relationship with depression, anxiety, and loneliness in a longitudinal cohort of older primary care patients with multimorbidity. BMC Geriatrics, 25, 1951-13. https://doi.org/10.1186/s12877-025-05762-7
  56. Van As, B. A. L., Imbimbo, E., Franceschi, A., Menesini, E., & Nocentini, A. (2022). The longitudinal association between loneliness and depressive symptoms in the elderly: A systematic review. International Psychogeriatrics, 34(7), 657–669. https://doi.org/10.1017/S1041610221000399
  57. Velagaleti, S. B., Choukaier, D., Nuthakki, R., Lamba, V., Sharma, V., & Rahul, S. (2024). Empathetic algorithms: The role of AI in understanding and enhancing human emotional intelligence. Journal of Electrical Systems, 20, 2051–2060. https://doi.org/10.52783/jes.1806
  58. Vzorin, G. D., Bukinich, A. M., Sedykh, A. V., Vetrova, I. I., & Sergienko, E. A. (2024). The emotional intelligence of the GPT-4 large language model. Psychology in Russia: State of the Art, 17(2), 85–99. https://doi.org/10.11621/pir.2024.0206
  59. Wiederhold, B. K. (2024). The rise of AI companions and the quest for authentic connection. Cyberpsychology, Behavior, and Social Networking, 27(8), 524–526. https://doi.org/10.1089/cyber.2024.0309
  60. Xie, Z. L., Gan, K. P., Li, J., et al. (2025). The impact of loneliness on depression among college students: The mediating role of problematic internet use and the moderating role of perceived social support. BMC Public Health, 25, 25341-13. https://doi.org/10.1186/s12889-025-23759-0
  61. Yıldız Durak, H., & Balıkçı, H. C. (2025). Yapay zekâ sohbet botu bağımlılık ölçeğinin Türkçeye uyarlanması. In Proceeding book of the 3rd International Conference on Trends in Advanced Research (ICTAR 2025) (pp. 1–6). All Sciences Academy. https://as-proceeding.com/index.php/ictar/home
  62. Yin, Y., Jia, N., & Wakslak, C. J. (2024). AI can help people feel heard, but an AI label diminishes this impact. Proceedings of the National Academy of Sciences, 121(14), e2319112121. https://doi.org/10.1073/pnas.2319112121
  63. Zhang, X., Yin, M., Zhang, M., Li, Z., & Li, H. (2025). The development and validation of an artificial intelligence chatbot dependence scale. Cyberpsychology, Behavior, and Social Networking, 28(2), 126–131. https://doi.org/10.1089/cyber.2024.0240
  64. Zhao, W., Zhao, Y., Lu, X., Wang, S., Tong, Y., & Qin, B. (2023). Is ChatGPT equipped with emotional dialogue capabilities? (arXiv:2304.09582). arXiv. https://arxiv.org/abs/2304.09582