logo

Articles

Vol. 4 No. 1 (2025): Generative AI and education

Large Language Model Chatbots in education: Exploring literature insights on their impact and influence on learning behaviours

DOI:
https://doi.org/10.21428/8c225f6e.540b41b5
Submitted
13 August 2026
Published
15-08-2026

Abstract

Since the launch of ChatGPT-3.5 in 2022, Large Language Model (LLM) chatbots have garnered significant attention in educational contexts, sparking debates about their potential benefits and drawbacks. While existing research primarily focuses on academic outcomes, there remains a notable gap in understanding how these tools influence key learning behaviours such as resilience, effort, perseverance, and challenge-seeking. This paper begins by introducing LLM chatbots and providing an overview of current literature on their impact in education, highlighting both their advantages and limitations. Underscoring a gap in research, this paper draws attention to the overlooked potential of these chatbots to influence learning behaviours which are valuable for academic and lifelong success. By examining emerging research on LLM chatbot capabilities and their impact on the learning experience through the lens of established psychological theories, such as Social Cognitive Theory and Self-Determination Theory, this paper discusses the potential influence of LLM chatbots on key learning behaviours, alongside practical and psychological challenges that may hinder their effectiveness. Bringing attention to this important, yet under-researched relationship, this paper advocates for empirical studies to explore the relationship between LLM chatbots and learning behaviours, as this understanding is vital for the ethical and effective integration of these technologies in educational settings.

References

  1. Abbas, M., Jam, F.A. & Khan, T.I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, 10. https://doi.org/10.1186/s41239-024-00444-7
  2. Abdillah, H. Z., Partino, P., & Madjid, A. (2023). Enhancing Student Well-being through AI Chat GPT in the Smart Education University Learning Environment: A Preliminary Review of Research Literature. In: E3S Web of Conferences, 440, 05005. https://doi.org/10.1051/e3sconf/202344005005
  3. Abuhamdeh, S., & Csikszentmihalyi, M. (2012). The importance of challenge for the enjoyment of intrinsically motivated, goal-directed activities. Personality and Social Psychology Bulletin, 38(3), 317-330. https://doi.org/10.1177/0146167211427147
  4. Alexander, R. J. (2008). Towards Dialogic Teaching: rethinking classroom talk (4th Edition). Dialogos.
  5. Alneyadi, S., & Wardat, Y. (2023). ChatGPT: Revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in Emirates schools. Contemporary Educational Technology, 15(4), ep448. https://doi.org/10.30935/cedtech/13417
  6. Antony, S., & Ramnath, R. (2023). A Phenomenological Exploration of Students' Perceptions of AI Chatbots in Higher Education. IAFOR Journal of Education, 11(2), 7-38. https://doi.org/10.22492/ije.11.2.01
  7. Baker, R. S., & Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32, 1052–1092. https://doi.org/10.1007/s40593-021-00285-9.
  8. Bandura, A. (1977). Social learning theory. Prentice-Hall.
  9. Bastani, H., Bastani, O., Sungu, Alp., Ge, H., & Kabakcı, Ö., & Mariman, R. (2024) Generative AI Can Harm Learning. The Wharton School Research Paper. Accessible at: https://ssrn.com/abstract=4895486. https://doi.org/10.2139/ssrn.4895486
  10. Baten, E., Vansteenkiste, M., De Muynck, G. J., De Poortere, E., & Desoete, A. (2020). How can the blow of math difficulty on elementary school children’s motivational, cognitive, and affective experiences be dampened? The critical role of autonomy-supportive instructions. Journal of Educational Psychology, 112(8), 1490–1505. https://doi.org/10.1037/edu0000444.
  11. Bittmann, (2021). When problems just bounce back: about the relation between resilience and academic success in German tertiary education. SN Social Sciences, 1(2), 65. https://doi.org/10.1007/s43545-021-00060-6
  12. Burnette, J. L., O'boyle, E. H., VanEpps, E. M., Pollack, J. M., & Finkel, E. J. (2013). Mind-sets matter: a meta-analytic review of implicit theories and self-regulation. Psychological Bulletin, 139(3), 655. https://doi.org/10.1037/a0029531
  13. Cambridge Dictionary. (n.d.-a). Perseverance. Cambridge University Press. Accessible at : https://dictionary.cambridge.org/dictionary/english/perseverance?q=perseverance
  14. Cambridge Dictionary. (n.d.-b). Resilience. Cambridge University Press. Accessible at: https://dictionary.cambridge.org/dictionary/english/resilience
  15. Cassidy, S. (2015). Resilience building in students: The role of academic self-efficacy. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.01781
  16. Chang, C. Y., Hwang, G. J., & Gau, M. L. (2022). Promoting students' learning achievement and self-efficacy: A mobile chatbot approach for nursing training. British Journal of Educational Technology, 53(1), 171-188. https://doi.org/10.1111/bjet.13158
  17. Chiu, T. K., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot. Interactive Learning Environments, 1-17. https://doi.org/10.1080/10494820.2023.2172044
  18. Collins English Dictionary. (n.d.). Effort. Collins. Accessible at: https://www.collinsdictionary.com/dictionary/english/effort
  19. Dahlkemper, M. N., Lahme, S. Z., & Klein, P. (2023). How do physics students evaluate artificial intelligence responses on comprehension questions? A study on the perceived scientific accuracy and linguistic quality of ChatGPT. Physical Review Physics Education Research, 19(1). https://doi.org/10.1103/physrevphyseducres.19.010142
  20. Dai, Y., Lai, S., Lim, C. P., & Liu, A. (2023). ChatGPT and its impact on research supervision: Insights from Australian postgraduate research students. Australasian Journal of Educational Technology, 39(4), 74-88. https://doi.org/10.14742/ajet.8843
  21. 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
  22. Das, S. R., & Madhusudan, J. V. (2024). Perceptions of Higher Education Students towards ChatGPT Usage. International Journal of Technology in Education, 7(1), 86-106. https://doi.org/10.46328/ijte.583
  23. Deci, E. L. & Ryan, R. M. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 25(1), 54-67. https://doi.org/10.1006/ceps.1999.1020
  24. Deneen, C. (2024). AI and assessment in higher education: Problems, possibilities, and pathways. Pacific Journal of Technology Enhanced Learning, 6(1), 28. https://doi.org/10.24135/pjtel.v6i1.199
  25. Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2024). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224
  26. Desierto, A., De Maio, C., O’Rourke, J., & Sharp, S. (2018). Deep or Surface? The learning approaches of enabling students in an Australian public university. Paper presented at the STARS (Students Transitions Achievement Retention and Success) Conference, Auckland, New Zealand. Accessible at: https://www.researchgate.net/publication/353837764_Deep_or_Surface_The_learning_approaches_of_enabling_students_in_an_Australian_public_university
  27. Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. https://doi.org/10.1037/0022-3514.92.6.1087
  28. Dunlosky, J., Badali, S., Rivers, M. L., & Rawson, K. A. (2020). The role of effort in understanding educational achievement: Objective effort as an explanatory construct versus effort as a student perception. Educational Psychology Review, 32, 1163-1175. https://doi.org/10.1007/s10648-020-09577-3
  29. Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040–1048. https://doi.org/10.1037//0003-066x.41.10.1040
  30. Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.
  31. ElSayary, A. (2023). An investigation of teachers' perceptions of using ChatGPT as a supporting tool for teaching and learning in the digital era. Journal of Computer Assisted Learning, 40(3), 931–945. Portico. https://doi.org/10.1111/jcal.12926
  32. Elyoseph, Z., Hadar-Shoval, D., Asraf, K., & Lvovsky, M. (2023). ChatGPT outperforms humans in emotional awareness evaluations. Frontiers in Psychology, 14, https://doi.org/10.3389/fpsyg.2023.1199058
  33. Escalante, J., Pack, A. & Barrett, A. (2023). AI-generated feedback on writing: insights into efficacy and ENL student preference. International Journal of Educational Technology in Higher Education, 20, 57. https://doi.org/10.1186/s41239-023-00425-2
  34. Essel, H. B., Vlachopoulos, D., Essuman, A. B., & Amankwa, J. O. (2024). ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs). Computers and Education: Artificial Intelligence, 6, 100198. https://doi.org/10.1016/j.caeai.2023.100198
  35. Fryer, L. K., Ainley, M., Thompson, A., Gibson, A., & Sherlock, Z. (2017). Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners. Computers in Human Behavior, 75, 461-468. https://doi.org/10.1016/j.chb.2017.05.045
  36. Fryer, L. K., Thompson, A., Nakao, K., Howarth, M., & Gallacher, A. (2020). Supporting selfefficacy beliefs and interest as educational inputs and outcomes: Framing AI and Human partnered task experiences. Learning and Individual Differences, 80, 101850. https://doi.org/10.1016/j.lindif.2020.101850
  37. Gattupalli, S., Maloy, R. W., & Edwards, S. (2023) Comparing Teacher-Written and AIGenerated Math Problem Solving Strategies for Elementary School Students: Implications for Classroom Learning. College of Education Working Papers and Reports Series. 5. https://doi.org/10.7275/8sgx-xj08
  38. Gupta, & Chen, Y. (2022). Supporting Inclusive Learning Using Chatbots? A Chatbot-Led Interview Study. Journal of Information Systems Education, 33(1), 98–108. https://jise.org/Volume33/n1/JISE2022v33n1pp98-108.html
  39. Haque, M. R., & Rubya, S. (2023). An overview of chatbot-based mobile mental health apps: insights from app description and user reviews. JMIR mHealth and uHealth, 11(1), e44838. https://doi.org/10.2196/44838
  40. Harter, S. (1981). A new self-report scale of intrinsic versus extrinsic orientation in the classroom: Motivational and informational components. Developmental Psychology, 17(3), 300–312. https://doi.org/10.1037//0012-1649.17.3.300
  41. Hasanein, A. M., & Sobaih, A. E. E. (2023). Drivers and Consequences of ChatGPT Use in Higher Education: Key Stakeholder Perspectives. European Journal of Investigation in Health, Psychology and Education, 13(11), 2599–2614. https://doi.org/10.3390/ejihpe13110181
  42. Hassija, V., Chamola, V., Mahapatra, A. et al. Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognition and Computing, 16, 45–74 (2024). https://doi.org/10.1007/s12559-023-10179-8
  43. Hawanti, S., & Zubaydulloevna, K. M. (2023). AI chatbot-based learning: alleviating students' anxiety in english writing classroom. Bulletin of Social Informatics Theory and Application, 7(2), 182-192. https://doi.org/10.31763/businta.v7i2.659
  44. Hayat, A. A., Shateri, K., Amini, M., & Shokrpour, N. (2020). Relationships between academic self-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: a structural equation model. BMC Medical Education, 20, 1-11. https://doi.org/10.1186/s12909-020-01995-9
  45. Hmoud, M., Swaity, H., Hamad, N., Karram, O., & Daher, W. (2024). Higher Education Students’ Task Motivation in the Generative Artificial Intelligence Context: The Case of ChatGPT. Information, 15(1), 33. https://doi.org/10.3390/info15010033
  46. Holmes, W. (2023). The unintended consequences of artificial intelligence and education. Education International.
  47. Hsu, Huang, H.-L., Hwang, G.-J., & Chen, M.-S. (2023). Effects of Incorporating an Expert Decision-making Mechanism into Chatbots on Students’ Achievement, Enjoyment, and Anxiety. Educational Technology & Society, 26(1), 218–231. https://doi.org/10.30191/ETS.202301_26(1).0016
  48. Impact Research. (2024). AI Chatbots in Schools: Findings from a Poll of K-12 Teachers, Students, Parents, and College Undergraduates. Walton Family Foundation. Accessible at: https://8ce82b94a8c4fdc3ea6d-b1d233e3bc3cb10858bea65ff05e18f2.ssl.cf2.rackcdn.com/bf/24/cd3646584af89e7c668c7705a006/deck-impact-analysis-national-schools-tech-tracker-may-2024-1.pdf
  49. Karumbaiah, S., Ocumpaugh, J., & Baker, R. S. (2021). Context Matters: Differing Implications of Motivation and Help-Seeking in Educational Technology. International Journal of Artificial Intelligence in Education, 32(3), 685–724. https://doi.org/10.1007/s40593-021-00272-0
  50. Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
  51. Kaya, S., & Karakoc, D. (2022). Math Mindsets and Academic Grit: How are They Related to Primary Math Achievement? European Journal of Science and Mathematics Education, 10(3), 298–309. https://doi.org/10.30935/scimath/11881
  52. Khan, S. (2024). Brave New Words: How AI Will Revolutionize Education (and Why That’s a Good Thing). Penguin Books.
  53. Khurma, O. A., Albahti, F., Ali, N., & Bustanji, A. (2024). AI ChatGPT and student engagement: Unraveling dimensions through PRISMA analysis for enhanced learning experiences. Contemporary Educational Technology, 16(2), ep503. https://doi.org/10.30935/cedtech/14334
  54. Kikerpill, K., & Siibak, A. (2023). App-hazard disruption: An empirical investigation of media discourses on ChatGPT in educational contexts. Computers in the Schools, 40(4), 334-355. https://doi.org/10.1080/07380569.2023.2244941
  55. Kleitman, S., Jackson, S. A., Zhang, L. M., Blanchard, M. D., Rizvandi, N. B., & Aidman, E. (2022). Applying Evidence-Centered Design to Measure Psychological Resilience: The Development and Preliminary Validation of a Novel Simulation-Based Assessment Methodology. Frontiers in Psychology, 12, 717568. https://doi.org/10.3389/fpsyg.2021.717568
  56. Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for Language Teaching and Learning. RELC Journal, 54(2), 537-550. https://doi.org/10.1177/00336882231162868
  57. Kreijkes, P., Kewenig, V., Kuvalja, M., Lee, M., Vitello, S., Hofman, J., Sellen, A., Rintel, S., Goldstein, D., Rothschild, D., Tankelevitch, L., & Oates, T. (2025). Effects of LLM Use and Note-Taking On Reading Comprehension and Memory: A Randomised Experiment in Secondary Schools. Accessible at: https://ssrn.com/abstract=5095149. http://dx.doi.org/10.2139/ssrn.5095149
  58. Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2022). Interacting with educational chatbots: A systematic review. Education and Information Technologies, 28(1), 973–1018. https://doi.org/10.1007/s10639-022-11177-3
  59. Kumar, H., Rothschild, D. M., Goldstein, D. G., & Hofman, J. M. (2023). Math Education with Large Language Models: Peril or Promise?. Accessible at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4641653
  60. Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: systematic literature review. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00426-1
  61. Lee, H. Y., Chen, P. H., Wang, W. S., Huang, Y. M., & Wu, T. T. (2024). Empowering ChatGPT with guidance mechanism in blended learning: effect of self-regulated learning, higher-order thinking skills, and knowledge construction. International Journal of Educational Technology in Higher Education, 21(1), 1-28. https://doi.org/10.1186/s41239-024-00447-4
  62. Lee, Y. F., Hwang, G. J., & Chen, P. Y. (2022). Impacts of an AI-based chatbot on college students’ after-class review, academic performance, self-efficacy, learning attitude, and motivation. Educational Technology Research and Development, 70(5), 1843- 1865. https://doi.org/10.1007/s11423-022-10142-8
  63. Lehmann, M., Cornelius, P. B., & Sting, F. J. (2024). AI Meets the Classroom: When Does ChatGPT Harm Learning?. arXiv preprint arXiv:2409.09047. https://doi.org/10.2139/ssrn.4941259
  64. Li-Fang, Z. (2006). A Research on Relations among the Academic Self-concept, Achievement Goal Orientation and Learning Persistence of College Students. Chinese Journal of Clinical Psychology, 14(2), 192–193. Accessible at: https://scispace.com/papers/a-research-on-relations-among-the-academic-self-concept-1et03l0heq
  65. Lodge, J., Howard, S., Bearman, M., Dawson, P., & Associates (2023). Assessment Reform for The Age of Artificial Intelligence. Tertiary Education Quality and Standards Agency. Accessible at : https://www.teqsa.gov.au/sites/default/files/2023-09/assessment-reform-age-artificial-intelligence-discussion-paper.pdf
  66. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed. An argument for AI in Education, 18. Accessible at : https://www.researchgate.net/profile/Rosemary-Luckin/publication/299561597_Intelligence_Unleashed_An_argument_for_AI_in_Education/links/5812782608ae1f5510c2aa4d/Intelligence-Unleashed-An-argument-for-AI-in-Education.pdf
  67. Luo, W., He, H., Liu, J., Berson, I. R., Berson, M. J., Zhou, Y., & Li, H. (2024). Aladdin’s Genie or Pandora’s Box for early childhood education? Experts chat on the roles, challenges, and developments of ChatGPT. Early Education and Development, 35(1), 96-113. https://doi.org/10.1080/10409289.2023.2214181
  68. Ma, Q. (2021). The role of teacher autonomy support on students’ academic engagement and resilience. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.778581
  69. Marton, F., & Saljo, R. (1976). On qualitative differences in learning: I. Outcome and process. British Journal of Educational Psychology, 46(1), 4–11. https://doi.org/10.1111/j.2044-8279.1976.tb02980.x
  70. Mei, Q., Xie, Y., Yuan, W., & Jackson, M. O. (2024). A Turing test of whether AI chatbots are behaviorally similar to humans. Proceedings of the National Academy of Sciences, 121(9). https://doi.org/10.1073/pnas.2313925121
  71. Mercer, N., Wegerif, R., & Major, L. C. (Eds.). (2019). The Routledge international handbook of research on dialogic education. Routledge Meyer, Turner & Spencer.
  72. Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: A guidance for policymakers. UNESCO Publishing. Accessible at : https://books.google.co.uk/books?hl=en&lr=&id=yyE7EAAAQBAJ&oi=fnd&pg=PA3&dq=UNESCO+AI+and+Education+Guidance+for+Policymakers&ots=cBibH7riP5&sig=Laoqq_-Xf87ATaQAfR3BhymVAwc&redir_esc=y#v=onepage&q=UNESCO%20AI%20and%20Education%20Guidance%20for%20Policymakers&f=false
  73. Mierke, K., Scheidtmann, S., & Ibrahimova, D. (2017). Task-order autonomy fosters flow, self-efficacy, cognitive performance, and challenge-seeking in test situations. Journal of Business and Media Psychology, 8, 20-27. Accessible at: https://journal-bmp.de/wp-content/uploads/3_set_them_free.pdf
  74. Mintz, J., Holmes, W., Liu, L., & Perez-Ortiz, M. (2023). Artificial Intelligence and K-12 Education: Possibilities, Pedagogies and Risks. Computers in the Schools, 40(4), 325–333. https://doi.org/10.1080/07380569.2023.2279870
  75. Mogavi, R. H., Deng, C., Kim, J. J., Zhou, P., Kwon, Y. D., Metwally, A. H. S., ... & Hui, P. (2024). ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions. Computers in Human Behavior: Artificial Humans, 2(1), 100027. https://doi.org/10.1016/j.chbah.2023.100027
  76. Mouratidis, A., Michou, A., Demircioğlu, A. N., & Sayil, M. (2018). Different goals, different pathways to success: Performance-approach goals as direct and mastery-approach goals as indirect predictors of grades in mathematics. Learning and Individual Differences, 61, 127-135. https://doi.org/10.1016/j.lindif.2017.11.017
  77. National 4-H Council & Hart Research. (2024). Youth AI use & understanding survey. Accessible at : https://www.4-h.org/wp-content/uploads/2024/02/27162629/Hart-Research-Youth-AI-Survey-Results42.pdf?_ga=2.151063673.1761332902.1724919083-558076093.1724919083
  78. Ng, D.T.K., Leung, J.K.L., Su, J. et al. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Education Technology Research and Development, 71, 137–161. https://doi.org/10.1007/s11423-023-10203-6
  79. Novianti, I. (2025). An exploratory study on the challenges of AI technology in education and its practical recommendations. International Journal of Social Science Research and Review, 8(3), 26–37. https://doi.org/10.47814/ijssrr.v8i3.2481
  80. OECD (2019), PISA 2018 Results (Volume II): Where All Students Can Succeed, PISA, OECD Publishing. Accessible at: https://doi.org/10.1787/b5fd1b8f-en.
  81. Phung, T., Pădurean, V.-A., Cambronero, J., Gulwani, S., Kohn, T., Majumdar, R., Singla, A., & Soares, G. (2023). Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors. Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 2, 41–42. https://doi.org/10.1145/3568812.3603476
  82. Polyportis, A. (2024). A longitudinal study on artificial intelligence adoption: understanding the drivers of ChatGPT usage behavior change in higher education. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1324398
  83. Porter, T., Molina, D. C., Blackwell, L., Roberts, S., Quirk, A., Duckworth, A. L., & Trzesniewski, K. (2020). Measuring Mastery Behaviours at Scale: The Persistence, Effort, Resilience, and Challenge-Seeking (PERC) Task. Journal of Learning Analytics, 7(1), 5-18. https://doi.org/10.18608/jla.2020.71.2
  84. Pratschke, B. M. (2024). Generative AI and education: digital pedagogies, teaching innovation and learning design. Springer.
  85. Sharples, M. (2023). Towards social generative AI for education: theory, practices and ethics. Learning: Research and Practice, 9(2), 159–167. https://doi.org/10.1080/23735082.2023.2261131
  86. Shieh, E., Vassel, F. M., Sugimoto, C., & Monroe-White, T. (2024). Laissez-Faire Harms: Algorithmic Biases in Generative Language Models. arXiv preprint arXiv:2404.07475. Accessible at: https://arxiv.org/abs/2404.07475
  87. Singh, M. (2024). Byju’s founder says his edtech startup, once worth $22B, is now ‘worth zero’. TechCrunch. Accessible at: https://techcrunch.com/2024/10/17/byjus-founder-says-his-edtech-startup-once-worth-22b-is-now-worth-zero/
  88. Skinner, B. F. (1953). Science and Human Behavior. Macmillan.
  89. Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips. Science, 333(6043), 776–778. https://doi.org/10.1126/science.1207745
  90. Strachan, J. W. A., Albergo, D., Borghini, G., Pansardi, O., Scaliti, E., Gupta, S., Saxena, K., Rufo, A., Panzeri, S., Manzi, G., Graziano, M. S. A., & Becchio, C. (2024). Testing theory of mind in large language models and humans. Nature Human Behaviour, 8(7), 1285–1295. https://doi.org/10.1038/s41562-024-01882-z
  91. Sufyan, N. S., Fadhel, F. H., Alkhathami, S. S., & Mukhadi, J. Y. (2024). Artificial intelligence and social intelligence: preliminary comparison study between AI models and psychologists. Frontiers in Psychology, 15, 1353022.
  92. Sulik, M. J., Finch, J. E., & Obradović, J. (2020). Moving beyond executive functions: Challenge preference as a predictor of academic achievement in elementary school. Journal of Experimental Child Psychology, 198, 104883.
  93. Sun, D., Boudouaia, A., Zhu, C. et al. (2024). Would ChatGPT-facilitated programming mode impact college students’ programming behaviors, performances, and perceptions? An empirical study. International Journal of Educational Technology in Higher Education, 21, 14. https://doi.org/10.1186/s41239-024-00446-5
  94. Terblanche, Molyn, J., Williams, K., & Maritz, J. (2023). Performance matters: students’ perceptions of Artificial Intelligence Coach adoption factors. Coaching : an International Journal of Theory, Research & Practice, 16(1), 100–114. https://doi.org/10.1080/17521882.2022.2094278
  95. Tinto, V. (2022). Exploring the Character of Student Persistence in Higher Education: The Impact of Perception, Motivation, and Engagement. In: Reschly, A.L., Christenson, S.L. (eds). Handbook of Research on Student Engagement. Springer. https://doi.org/10.1007/978-3-031-07853-8_17
  96. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00237-x
  97. Tuomi, I. (2019). The Impact of Artificial Intelligence on Learning, Teaching, and Education: Policies for the Future. JRC Science for Policy Report. European Commission. Accessible at: https://publications.jrc.ec.europa.eu/repository/handle/JRC113226
  98. Vasconcelos, M. A. R., & dos Santos, R. P. (2023). Enhancing STEM learning with ChatGPT and Bing Chat as objects to think with: A case study. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2296. https://doi.org/10.29333/ejmste/13313
  99. Vassel, F.-M., Shieh, E., Sugimoto, C. R., & Monroe-White, T. (2024). The Psychosocial Impacts of Generative AI Harms. Proceedings of the AAAI Symposium Series, 3(1), 440–447. https://doi.org/10.1609/aaaiss.v3i1.31251
  100. Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press.
  101. Walter, Y. (2024). Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00448-3
  102. Watson, S. (2025). Emergent discourses in generative AI in education and society (Pre-review). CUP. https://rgdoi.net/10.13140/RG.2.2.29773.14562
  103. Wegerif, R. (2019). Dialogic education. In Oxford Research Encyclopedia of Education. Accessible at: https://oxfordre.com/education/display/10.1093/acrefore/9780190264093.001.0001/acrefore-9780190264093-e-396
  104. Wegerif, R., & Major, L. (2024). The theory of educational technology: towards a dialogic foundation for design. Routledge.
  105. Wu, T.-T., Lee, H.-Y., Li, P.-H., Huang, C.-N., & Huang, Y.-M. (2023). Promoting Self-Regulation Progress and Knowledge Construction in Blended Learning via ChatGPT-Based Learning Aid. Journal of Educational Computing Research, 61(8), 3–31. https://doi.org/10.1177/07356331231191125
  106. Xenofontos, C., & Mouroutsou, S. (2023). “Resilience” in Mathematics Education Research: A Systematic Review of Empirical Studies. Scandinavian Journal of Educational Research, 67(7), 1041–1055. https://doi-org.ezp.lib.cam.ac.uk/10.1080/00313831.2022.2115132
  107. Xu, X., Wang, X., Zhang, Y., & Zheng, R. (2024). Applying ChatGPT to tackle the side effects of personal learning environments from learner and learning perspective: An interview of experts in higher education. PLOS ONE, 19(1), e0295646. https://doi.org/10.1371/journal.pone.0295646
  108. Yang, S.-H., Chen, Y.-E., Wang, J.-H., & Chen, G.-D. (2023). Integrating a Chatbot and the Concepts of Grit and Growth Mindset into a Mobile Game-Based Learning System. Innovative Technologies and Learning, 187–196. https://doi.org/10.1007/978-3-031-40113-8_18
  109. Yildiz Durak, H. (2023). Conversational agent-based guidance: examining the effect of chatbot usage frequency and satisfaction on visual design self-efficacy, engagement, satisfaction, and learner autonomy. Education and Information Technologies, 28(1), 471-488. https://doi.org/10.1007/s10639-022-11149-7
  110. Yilmaz, R. & Karaoglan Yilmaz, F.G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/j.caeai.2023.100147.
  111. Yin, M., Heard, H. E., Szabo, J., & Ankoor, N. (2021). Social and Emotional Skills of 15-Year-Old Students in the Houston Independent School District. Houston Education Research Consortium. Accessible at: https://files.eric.ed.gov/fulltext/ED617735.pdf
  112. Young, J, R. (2024). An Education Chatbot Company Collapsed. Where Did the Student Data Go? In EdSurge. Accessible at: https://www.edsurge.com/news/2024-07-15-an-education-chatbot-company-collapsed-where-did-the-student-data-go
  113. Zamfiroiu, A., Vasile, D., & Savu, D. (2023). ChatGPT–A Systematic Review of Published Research Papers. Informatica Economica, 27(1), 5-16. https://doi.org/10.24818/issn14531305/27.1.2023.01
  114. Zhu, C., Sun, M., Luo, J., Li, T., & Wang, M. (2023). How to harness the potential of ChatGPT in education?. Knowledge Management & E-Learning, 15(2), 133-152. https://doi.org/10.34105/j.kmel.2023.15.008