The influence of learning motivation and procrastination on ChatGPT dependence
DOI:
https://doi.org/10.5944/ried.45497Keywords:
motivation, dependence, ChatGPT, procrastination, artificial intelligence, higher educationAbstract
In a short period of time, generative artificial intelligences such as ChatGPT have become a widely used support tool for students' learning processes, altering how many approach academic tasks. Their immediacy and accessibility make them attractive alternatives to traditional study resources, to the extent that some students may have even developed a certain degree of dependence on these tools. The present study investigates the impact of learning motivation and procrastination on ChatGPT dependence. A total of 467 university students from the field of education participated by completing a series of validated scales measuring different types of motivation defined in Self-Determination Theory (extrinsic motivation, intrinsic motivation, and amotivation), procrastination, and ChatGPT dependence. The indirect effects of the mediation analyses revealed that students with lower levels of intrinsic motivation (β = -.076; LL = -.121; UL = -.037) and higher levels of amotivation (β = .090; LL = .041; UL = .144) were more likely to procrastinate frequently, with procrastination emerging as a significant factor contributing to greater ChatGPT dependence. Similarly, results indicated that students with high extrinsic motivation (without procrastination serving as a mediator) were more prone to develop greater dependence on ChatGPT (β = .122; p = .022). These findings highlight the importance of implementing strategies that foster intrinsic motivation and self-regulation, helping students use generative AI-based tools appropriately while developing essential competencies that could be at risk from excessive use of these tools, such as critical thinking and problem-solving.
Downloads
References
Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M. K., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities & Social Sciences Communications, 10(1), 311. https://doi.org/10.1057/s41599-023-01842-4
Ahmad, S. F., Rahmat, M. K., Mubarik, M. S., Alam, M. M., & Hyder, S. I. (2021). Artificial intelligence and its role in education. Sustainability, 13(22), 12902. https://doi.org/10.3390/su132212902
Ali, K., García, A., & Vadsariya, A. (2024). Impact of the AI dependency revolution on both physical and mental health. Journal of Strategic Innovation and Sustainability, 19(2), 70-81. https://doi.org/10.33423/jsis.v19i2.7006
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing.
Ayele, N. (2024). The usage of new AI technologies by college students and its influence on learning and dependence [Manuscrito, Caldwell University Archives]. JSTOR Community Collections. https://www.jstor.org/stable/community.37312365
Baird, A., & Maruping, L. M. (2021). The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts. MIS Quarterly, 45(1), 315-341. https://doi.org/10.25300/MISQ/2021/15882
Benavides-Nieto, A., Romero-López, M., Quesada-Conde, A. B., & Alba, G. (2017). Basic executive functions in early childhood education and their relationship with social competence. Procedia - Social and Behavioral Sciences, 237, 471-478. https://doi.org/10.1016/j.sbspro.2017.02.092
Bolden, J., & Fillauer, J. P. (2020). “Tomorrow is the busiest day of the week”: Executive functions mediate the relation between procrastination and attention problems. Journal of American College Health, 68(8), 854–863. https://doi.org/10.1080/07448481.2019.1626399
Bucur, A. M., Cosma, A., & Dinu, L. P. (2021). Early risk detection of pathological gambling, self-harm and depression using BERT [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2106.16175
Carroll, N., Lang, M., & Connolly, C. (2025). An extended community of inquiry framework supporting students in online and digital education. Innovations in Education and Teaching International, 62(2), 369-385. https://doi.org/10.1080/14703297.2024.2326658
Chai, C. S., Chiu, T. K. F., Wang, X., Jiang, F., & Lin, X.-F. (2023). Modeling Chinese secondary school students’ behavioral intentions to learn artificial intelligence with the theory of planned behavior and self-determination theory. Sustainability, 15, 605. https://doi.org/10.3390/su15010605
Chen, N.-S., Yin, C., Isaias, P., & Psotka, J. (2020). Educational big data: Extracting meaning from data for smart education. Interactive Learning Environments, 28(2), 142-147. https://doi.org/10.1080/10494820.2019.1635395
Cheon, S. H., Reeve, J., & Vansteenkiste, M. (2020). When teachers learn how to provide classroom structure in an autonomy-supportive way: Benefits to teachers and their students. Teaching and Teacher Education, 90, 103004. https://doi.org/10.1016/j.tate.2019.103004
Chiu, T. K. F. (2024). A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: A case of ChatGPT. Educational Technology Research & Development. https://doi.org/10.1007/s11423-024-10366-w
Chiu, T. K. F., & Chai, C.-S. (2020). Sustainable curriculum planning for artificial intelligence education: A self-determination theory perspective. Sustainability, 12(14), 5568. https://doi.org/10.3390/su12145568
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Coppock, A., Leeper, T. J., & Mullinix, K. J. (2018). Generalizability of heterogeneous treatment effect estimates across samples. Proceedings of the National Academy of Sciences, 115(49), 12441-12446. https://doi.org/10.1073/pnas.1808083115
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210, 104967. https://doi.org/10.1016/j.compedu.2023.104967
Del Cisne, M., Romero, J., Sancho, D., & Romero, A. (2024). Consecuencias de la dependencia a la inteligencia artificial en habilidades críticas y aprendizaje autónomo en los estudiantes. Ciencia Latina Revista Científica Multidisciplinar, 8(2), 2368-2382. https://doi.org/10.37811/cl_rcm.v8i2.10678
Delgado, N., Campo Carrasco, L., Sainz de la Maza, M., & Etxabe-Urbieta, J. M. (2024). Application of artificial intelligence (AI) in education: Benefits and limitations of AI as perceived by primary, secondary, and higher education teachers. Revista Electrónica Interuniversitaria de Formación del Profesorado, 27(1), 207-224. https://doi.org/10.6018/reifop.577211
Donoghue, G. M., & Hattie, J. A. C. (2021). A meta-analysis of ten learning techniques. Frontiers in Education, 6, 581216. https://doi.org/10.3389/feduc.2021.581216
Duru, E., & Balkis, M. (2017). Procrastination, self-esteem, academic performance, and well-being: A moderated mediation model. International Journal of Educational Psychology, 6(2), 97-119. https://doi.org/10.17583/ijep.2017.2584
Estrada-Araoz, E. G., Mamani-Roque, M., Quispe-Aquise, J., Manrique-Jaramillo, Y. V., & Cruz-Laricano, E. O. (2025). Academic self-efficacy and dependence on artificial intelligence in a sample of university students. Sapienza: International Journal of Interdisciplinary Studies, 6(1), e25008. https://doi.org/10.51798/sijis.v6i1.916
Feng, M., Xia, A., & Xia, X. (2023). The association between ChatGPT usage and college students’ online learning burnout: The mediating role of self-control. In X. Chen & Q. Liu (Eds.), Proceedings of the International Conference of Educational Innovation through Technology (pp. 209-212). IEEE. https://doi.org/10.1109/EITT61659.2023.00046
Fernández-Río, J., Sanz, N., Fernández-Cando, J., & Santos, L. (2017). Impact of a sustained cooperative learning intervention on student motivation. Physical Education and Sport Pedagogy, 22(1), 89-105. https://doi.org/10.1080/17408989.2015.1123238
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage.
Filgona, J., Sakiyo, J., Gwany, D. M., & Okoronka, A. U. (2020). Motivation in learning. Asian Journal of Education and Social Studies, 10(4), 16-37. https://doi.org/10.9734/ajess/2020/v10i430273
Ganesalingam, K., Yeates, K. O., Taylor, H. G., Chertkoff, N., Stancin, T., & Wade, S. (2011). Executive functions and social competence in young children 6 months following traumatic brain injury. Neuropsychology, 25(4), 466-476. https://doi.org/10.1037/a0022768
Ganotice, F. A., Mendoza, N. B., Dizon, J. I. W. T., Shen, X., Lee, J. C.-Y., Chan, E., Luk, P., Manio, M. M., He, Q., Khoo, U. S., Lam, M. P. S., Chan, S. C., Chow, A. Y. M., Wang, N., & Tipoe, G. L. (2024). Students’ motivation and engagement in interprofessional education: The mediating role of peer relatedness. Medical Education Online, 29(1), 2430593. https://doi.org/10.1080/10872981.2024.2430593
García, J. A., Carcedo, R. J., & Castaño, J. L. (2019). The influence of feedback on competence, motivation, vitality, and performance in a throwing task. Research Quarterly for Exercise and Sport, 90(2), 172-179. https://doi.org/10.1080/02701367.2019.1571677
Garon-Carrier, G., Boivin, M., Guay, F., Kovas, Y., Dionne, G., Lemelin, J. P., Séguin, J. R., Vitaro, F., & Tremblay, R. E. (2016). Intrinsic motivation and achievement in mathematics in elementary school: A longitudinal investigation of their association. Child Development, 87(1), 165-175. https://doi.org/10.1111/cdev.12458
Grund, A., & Fries, S. (2018). Understanding procrastination: A motivational approach. Personality and Individual Differences, 121, 120-130. https://doi.org/10.1016/j.paid.2017.09.035
Guay, F., Stupnisky, R., Boivin, M., Japel, C., & Dionne, G. (2019). Teachers’ relatedness with students as a predictor of students’ intrinsic motivation, self-concept, and reading achievement. Early Childhood Research Quarterly, 48(3), 215-225. https://doi.org/10.1016/j.ecresq.2019.03.005
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson Education International.
Hen, M., & Goroshit, M. (2018). General and life-domain procrastination in highly educated adults in Israel. Frontiers in Psychology, 9, 1173. https://doi.org/10.3389/fpsyg.2018.01173
Hong, J.-C., & Chen, N. (2024). Effects of achievement motivation and ChatGPT self-efficacy on ChatGPT addiction and ChatGPT continuance intention [Preprint]. SSRN. https://doi.org/10.2139/ssrn.5064583
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modeling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
Huang, S., Lai, X., Ke, L., Li, Y., Wang, H., Zhao, X., Dai, X., & Wang, Y. (2024). AI technology panic—Is AI dependence bad for mental health? A cross-lagged panel model and the mediating roles of motivations for AI use among adolescents. Psychology Research and Behavior Management, 17, 1087-1102. https://doi.org/10.2147/PRBM.S440889
Jara, I., & Ochoa, J. M. (2020). Usos y efectos de la inteligencia artificial en educación. Banco Interamericano de Desarrollo. https://doi.org/10.18235/0002380
Jho, M. Y., & Chae, M. O. (2014). Impact of self-directed learning ability and metacognition on clinical competence among nursing students. Journal of Korean Academic Society of Nursing Education, 20(4), 513-522. https://doi.org/10.5977/jkasne.2014.20.4.513
Johnson, J. L., & Bloom, A. M. (1995). An analysis of the contribution of the five factors of personality to variance in academic procrastination. Personality and Individual Differences, 18(1), 127-133. https://doi.org/10.1016/0191-8869(94)00109-6
Kachgal, M. M., Hansen, L. S., & Nutter, K. J. (2001). Academic procrastination prevention/intervention: Strategies and recommendations. Journal of Developmental Education, 25(1), 14-24. https://www.jstor.org/stable/42775842
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451
Klingbeil, A., Grützner, C., & Schreck, P. (2024). Trust and reliance on AI—An experimental study on the extent and costs of overreliance on AI. Computers in Human Behavior, 160, 108352. https://doi.org/10.1016/j.chb.2024.108352
Klingsieck, K. B. (2013). Procrastination: When good things don’t come to those who wait. European Psychologist, 18, 24-34. https://doi.org/10.1027/1016-9040/a000138
Koike, D. A., & Pearson, L. (2005). The effect of instruction and feedback in the development of pragmatic competence. System, 33(3), 481-501. https://doi.org/10.1016/j.system.2005.06.008
Kok, J. K. (2016). The relationships between procrastination and motivational aspects of self-regulation. Jurnal Psikologi Malaysia, 30(1), 30-39.
Korteling, J. E., van de Boer-Visschedijk, G. C., Blankendaal, R. A. M., Boonekamp, R. C., & Eikelboom, A. R. (2021). Human-versus artificial intelligence. Frontiers in Artificial Intelligence, 4, 622364. https://doi.org/10.3389/frai.2021.622364
Kotera, Y., Conway, E., & Green, P. (2023). Construction and factorial validation of a short version of the Academic Motivation Scale. British Journal of Guidance & Counselling, 51(2), 274-283. https://doi.org/10.1080/03069885.2021.1903387
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024). The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives. Computers and Education: Artificial Intelligence, 6, 100221. https://doi.org/10.1016/j.caeai.2024.100221
Legood, A., Lee, A., Schwarz, G., & Newman, A. (2018). From self-defeating to other defeating: Examining the effects of leader procrastination on follower work outcomes. Journal of Occupational and Organizational Psychology, 91, 430-439. https://doi.org/10.1111/joop.12205
Li, Y., Zhang, L., Zhang, R., Xu, T., & Feng, T. (2021). The neural basis linking achievement motivation with procrastination: Left precuneus connectivity with right anterior cingulate cortex. Personality and Social Psychology Bulletin, 48, 1382-1392. https://doi.org/10.1177/01461672211040677
Liu, M., Ren, Y., Nyagoga, L. M., Stonier, F., Wu, Z., & Yu, L. (2023). Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools. Future in Educational Research, 1(1), 72-101. https://doi.org/10.1002/fer3.10
Ljubin-Golub, T., Rijavec, M., & Olčar, D. (2020). Student flow and burnout: The role of teacher autonomy support and student autonomous motivation. Psychological Studies, 65, 145-156. https://doi.org/10.1007/s12646-019-00539-6
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7(1), 83-104. https://doi.org/10.1037/1082-989X.7.1.83
Maldonado-Sánchez, M., Aguinaga-Villegas, D., Nieto-Gamboa, J., Fonseca-Arellano, F., Shardin-Flores, L., & Cadenillas-Albornoz, V. (2019). Learning strategies for the development of the autonomy of secondary school students. Propósitos y Representaciones, 7(2), 415–439. https://doi.org/10.20511/pyr2019.v7n2.290
Morales-García, W. C., Sairitupa-Sánchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024). Development and validation of a scale for dependence on artificial intelligence in university students. Frontiers in Education, 9, 1323898. https://doi.org/10.3389/feduc.2024.1323898
Morandín-Ahuerma, F. (2022). What is artificial intelligence? International Journal of Research Publication and Reviews, 3(12), 1947-1951. https://doi.org/10.55248/gengpi.2022.31261
Mulatsih, S. (2011). The use of scaffolding technique to improve the students’ competence in writing genre-based texts. Parole: Journal of Linguistics and Education, 2(1), 101-109.
Namaziandost, E., Shatalebi, V., & Nasri, M. (2019). The impact of cooperative learning on developing speaking ability and motivation toward learning English. Journal of Language and Education, 5(3), 83-101. https://doi.org/10.17323/jle.2019.9809
Naseer, A., Rafaqat, N., & Amjad, M. (2025). Psychological impacts of AI dependence: Assessing the cognitive and emotional costs of intelligent systems in daily life. Review of Applied Management and Social Sciences, 8(1), 291-307. https://doi.org/10.47067/ramss.v8i1.458
Neitzel, C., & Stright, A. D. (2003). Mothers’ scaffolding of children’s problem solving: Establishing a foundation of academic self-regulatory competence. Journal of Family Psychology, 17(1), 147-159. https://doi.org/10.1037/0893-3200.17.1.147
Paraso, Y. M., Sedon, S. B., & Mahilum, D. Z. E. (2024). Student’s artificial intelligence (AI) dependency: The lived experience of STEM students at Tongantongan National High School. International Journal of All Research Writings, 6(4), 34-43.
Reeve, J., & Cheon, S. H. (2021). Autonomy-supportive teaching: Its malleability, benefits, and potential to improve educational practice. Educational Psychologist, 56(1), 54-77. https://doi.org/10.1080/00461520.2020.1862657
Rodríguez, J., Marín, I., & Espejo, R. (2024). Is artificial intelligence use related to self-control, self-esteem, and self-efficacy among university students? Education and Information Technologies, 30, 2507-2524. https://doi.org/10.1007/s10639-024-12906-6
Roebers, C. M., Cimeli, P., Röthlisberger, M., & Neunschwander, R. (2012). Executive functioning, metacognition, and self-perceived competence in elementary school children: An explorative study on their interrelations and their role for school achievement. Metacognition and Learning, 7, 151-173. https://doi.org/10.1007/s11409-012-9089-9
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Siste, K., Assandi, P., Irawati, R., Setiawan, M. W., & Wiguna, T. (2019). Internet addiction: A new addiction? Medical Journal of Indonesia, 28(1), 82-91. https://doi.org/10.13181/mji.v28i1.2752
Stojanov, A. (2024). Achievement goal motivation and reliance on ChatGPT for learning. In C. Pracana & M. Wang (Eds.), Psychological Applications and Trends (pp. 547–551). inScience Press. https://doi.org/10.36315/2024inpact127
Swargiary, K. (2024). The impact of ChatGPT on student learning outcomes: A comparative study of cognitive engagement, procrastination, and academic performance [Preprint]. SSRN. https://doi.org/10.2139/ssrn.4914743
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53-55. https://doi.org/10.5116/ijme.4dfb.8dfd
Van Bergen, E., Hart, S. A., Latvala, A., Vuoksimaa, E., Tolvanen, A., & Torppa, M. (2022). Literacy skills seem to fuel literacy enjoyment, rather than vice versa. Developmental Science, 26(3), e13325. https://doi.org/10.1111/desc.13325
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Wang, Z., Liu, J., & Dong, R. (2018). Intelligent auto-grading system. In Proceedings of the 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) (pp. 430-435). IEEE. https://doi.org/10.1109/CCIS.2018.8691244
Wu, F., & Fan, W. (2016). Academic procrastination in linking motivation and achievement-related behaviours: A perspective of expectancy-value theory. Educational Psychology, 37(6), 1-17. https://doi.org/10.1080/01443410.2016.1202901
Wypych, M., Matuszewski, J., & Dragan, W. Ł. (2018). Roles of impulsivity, motivation, and emotion regulation in procrastination—Path analysis and comparison between students and non-students. Frontiers in Psychology, 9, 891. https://doi.org/10.3389/fpsyg.2018.00891
Xia, Q., Chiu, T. K. F., Lee, M., Sanusi, I. T., Dai, Y., & Chai, C. S. (2022). A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education. Computers & Education, 189, 104582. https://doi.org/10.1016/j.compedu.2022.104582
Yan, B., & Zhang, X. (2022). What research has been conducted on procrastination? Evidence from a systematical bibliometric analysis. Frontiers in Psychology, 13, 809044. https://doi.org/10.3389/fpsyg.2022.809044
Ye, J. H., Zhang, M., Nong, W., Wang, L., & Yang, X. (2024). The relationship between inert thinking and ChatGPT dependence: An I-PACE model perspective. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12966-8
Yockey, R. D. (2016). Validation of the short form of the Academic Procrastination Scale. Psychological Reports, 118(1), 171-179. https://doi.org/10.1177/0033294115626825
Yu, S.-C., Chen, H.-R., & Yang, Y.-W. (2024). Development and validation the Problematic ChatGPT Use Scale: A preliminary report. Current Psychology, 43, 26080–26092. https://doi.org/10.1007/s12144-024-06259-z
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11, 28. https://doi.org/10.1186/s40561-024-00316-7
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Héctor Galindo-Domínguez, Martín Sainz-de-la-Maza, Lucía Campo, Daniel Losada-Iglesias

This work is licensed under a Creative Commons Attribution 4.0 International License.
The articles that are published in this journal are subject to the following terms:
1. The authors grant the exploitation rights of the work accepted for publication to RIED, guarantee to the journal the right to be the first publication of research understaken and permit the journal to distribute the work published under the license indicated in point 2.
2. The articles are published in the electronic edition of the journal under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can copy and redistribute the material in any medium or format, adapt, remix, transform, and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
3. Conditions for self-archiving. Authors are encouraged to disseminate electronically the OnlineFirst version (assessed version and accepted for publication) of its articles before publication, always with reference to its publication by RIED, favoring its circulation and dissemination earlier and with this a possible increase in its citation and reach among the academic community.

