Human-Centered AI in Practice: Peer Facilitators as Technostress Reducers in a Mixed-Methods LMS Study
DOI:
https://doi.org/10.15294/rji.v4i2.64092Keywords:
AI anxiety, Generative artificial intelligence, Human-centered AI, Peer facilitation, Zone of proximal developmentAbstract
Purpose: While the rapid integration of generative artificial intelligence (AI) offers significant pedagogical potential, it often outpaces the psychological scaffolding that anxious learners require, creating a severe affective barrier known as technostress. To address this critical gap, this study investigates whether structured peer facilitation, embedded within a Learning Management System (LMS) based AI course, can effectively reduce undergraduate students' AI anxiety and strengthen their conceptual understanding.
Methods/Study design/approach:
An explanatory sequential mixed-methods design was conducted involving 33 undergraduate students encountering generative AI instruction for the first time. Quantitative data encompassing AI anxiety, conceptual understanding, and technical self-efficacy were collected pre- and post-intervention, followed by analysis using Wilcoxon signed-rank tests and Spearman's rank correlation. Subsequently, qualitative data from open-ended evaluations were examined through reflexive thematic analysis to contextualize and deeply explain the quantitative shifts.
Result/Findings:
The intervention yielded a statistically significant reduction in AI anxiety (Z = -2.151, p = .032) alongside a significant improvement in conceptual understanding (Z = -1.990, p = .047). Although technical self-efficacy and career projection improved, the changes did not reach statistical significance. Furthermore, facilitator evaluation exhibited a significant negative correlation with anxiety reduction (rs = -.389). Thematic analysis confirmed that peer facilitators effectively acted as technical translators and technostress reducers, establishing the psychological safety necessary for learning.
Novelty/Originality/Value:
This research uniquely repositions peer mediation as the vital affective "more capable other" within Vygotsky's Zone of Proximal Development, prioritizing the resolution of emotional barriers before cognitive engagement with AI tools. The study extends Human-Centered AI (HCAI) theory by delivering robust empirical evidence that pairing generative AI modules with human peer facilitation creates a synergistic learning ecosystem. This provides educators with a scalable, practical solution for mitigating technostress in modern digital education.
References
[1] M. Firat, “What ChatGPT means for universities: Perceptions of scholars and students,” Journal of Applied Learning and Teaching, vol. 6, no. 1, pp. 57–63, Jan. 2023, doi: 10.37074/jalt.2023.6.1.22.
[2] O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education – where are the educators?,” International Journal of Educational Technology in Higher Education, vol. 16, no. 1, p. 39, 2019, doi: 10.1186/s41239-019-0171-0.
[3] A. Bozkurt et al., “Speculative Futures on ChatGPT and Generative Artificial Intelligence (AI): A Collective Reflection from Educational Landscape," Asian Journal of Distance Education, vol. 18, no. 1, p. 53, 2023, [Online]. Available: http://www.asianjde.com/
[4] J. Su and W. Yang, “Unlocking the Power of ChatGPT: A Framework for Applying Generative AI in Education,” ECNU Review of Education, vol. 6, no. 3, pp. 355–366, Aug. 2023, doi: 10.1177/20965311231168423.
[5] I. Celik, M. Dindar, H. Muukkonen, and S. Järvelä, “The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research,” TechTrends, vol. 66, no. 4, pp. 616–630, 2022, doi: 10.1007/s11528-022-00715-y.
[6] T. K. F. Chiu, “Future research recommendations for transforming higher education with generative AI,” Computers and Education: Artificial Intelligence, vol. 6, p. 100197, 2024, doi: https://doi.org/10.1016/j.caeai.2023.100197.
[7] F. Ouyang and P. Jiao, “Artificial intelligence in education: The three paradigms,” Computers and Education: Artificial Intelligence, vol. 2, p. 100020, 2021, doi: https://doi.org/10.1016/j.caeai.2021.100020.
[8] L. Cai, M. M. Msafiri, and D. Kangwa, “Exploring the impact of integrating AI tools in higher education using the Zone of Proximal Development,” Educ. Inf. Technol. (Dordr)., vol. 30, no. 6, pp. 7191–7264, 2025, doi: 10.1007/s10639-024-13112-0.
[9] J. Xu and S. Zhang, “The Routledge handbook of sociocultural theory and second language development,” Int. J. Biling. Educ. Biling., vol. 25, no. 1, pp. 376–379, Jan. 2022, doi: 10.1080/13670050.2019.1652559.
[10] R. Hikmawan, D. Rohendi, J. Septiadi, and M. A. F. Barri, “Development of SEKAPAI: An AI-Based Scaffolding Platform for Programming Education,” JUSIFO (Jurnal Sistem Informasi), vol. 11, no. 2, pp. 85–96, Dec. 2025, doi: 10.19109/jusifo.v11i2.30467.
[11] J. Kim, K. Merrill, K. Xu, and D. D. Sellnow, “My Teacher Is a Machine: Understanding Students’ Perceptions of AI Teaching Assistants in Online Education,” Int. J. Hum. Comput. Interact., vol. 36, no. 20, pp. 1902–1911, Dec. 2020, doi: 10.1080/10447318.2020.1801227.
[12] Y.-Y. Wang and Y.-S. Wang, “Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior,” Interactive Learning Environments, vol. 30, no. 4, pp. 619–634, Apr. 2022, doi: 10.1080/10494820.2019.1674887.
[13] L. Alzahrani, “Analyzing Students’ Attitudes and Behavior Toward Artificial Intelligence Technologies in Higher Education,” International Journal of Recent Technology and Engineering (IJRTE), vol. 11, no. 6, pp. 65–73, Mar. 2023, doi: 10.35940/ijrte.F7475.0311623.
[14] F. Saleem, E. Chikhaoui, and M. I. Malik, “Technostress in students and quality of online learning: role of instructor and university support,” Front. Educ. (Lausanne)., vol. Volume.9 2024, [Online]. Available: https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2024.1309642
[15] M. A. F. Barri, A. P. Sari, and S. Widodo, “Reducing Cognitive Load in Micro-School Systems through Frugal Interaction Design,” Journal of Applied Informatics and Computing, vol. 10, no. 3, pp. 2305–2312, 2026.
[16] Y.-Y. Wang and Y.-W. Chuang, “Artificial intelligence self-efficacy: Scale development and validation,” Educ. Inf. Technol. (Dordr)., vol. 29, no. 4, pp. 4785–4808, 2024, doi: 10.1007/s10639-023-12015-w.
[17] E. Brivio et al., “Preventing Technostress Through Positive Technology,” Front. Psychol., vol. Volume 9. 2018, [Online]. Available: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2018.02569
[18] A. R. Akese, H. L. Shaikh, S. Sheraz, and F. A. Mughal, “The Psychological Effects of Remote Learning on Students: Examining Social Isolation, Anxiety, and Self-Discipline,” Review of Applied Management and Social Sciences, vol. 7, no. 4, pp. 319–332, Nov. 2024, doi: 10.47067/ramss.v7i4.383.
[19] L. Gehreke, H. Schilling, and S. Kauffeld, “Effectiveness of peer mentoring in the study entry phase: A systematic review,” Review of Education, vol. 12, no. 1, p. e3462, Apr. 2024, doi: https://doi.org/10.1002/rev3.3462.
[20] B. Sichterman, O. Noroozi, J. Boetje, S. van Ginkel, H. Khosravi, and J. Versendaal, “Supporting peer learning with artificial intelligence: A systematic literature review,” Innovations in Education and Teaching International, vol. 62, no. 5, pp. 1648–1664, Sep. 2025, doi: 10.1080/14703297.2025.2530118.
[21] K. J. Topping, “Peer Assessment : Learning by Judging and Discussing the Work of Other Learners,” Interdisciplinary Education and Psychology, vol. 1, p. 7, Oct. 2017.
[22] L. Zheng, X. Zhang, and P. Cui, “The role of technology-facilitated peer assessment and supporting strategies: a meta-analysis,” Assess. Eval. High. Educ., vol. 45, no. 3, pp. 372–386, Apr. 2020, doi: 10.1080/02602938.2019.1644603.
[23] J. W. Creswell and J. D. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 6th ed. SAGE Publications, 2022.
[24] J. W. Creswell and C. N. Poth, Qualitative Inquiry and Research Design: Choosing Among Five Approaches, 4th ed. SAGE Publications, 2017.
[25] J. W. Creswell and V. L. Plano Clark, Designing and Conducting Mixed Methods Research, 4th ed. SAGE Publications, 2021.
[26] G. Peacock, “A sequential explanatory mixed-methods study of UK higher education teacher experiences when building rapport with international students online,” SN Social Sciences, vol. 2, no. 10, p. 214, 2022, doi: 10.1007/s43545-022-00533-2.
[27] J. Schoonenboom and R. B. Johnson, “How to Construct a Mixed Methods Research Design,” KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie, vol. 69, no. 2, pp. 107–131, 2017, doi: 10.1007/s11577-017-0454-1.
[28] A. P. Field, Discovering Statistics Using IBM SPSS Statistics, 6th ed. SAGE Publications, 2024.
[29] V. Braun and V. Clarke, “One size fits all? What counts as quality practice in (reflexive) thematic analysis?,” Qual. Res. Psychol., vol. 18, no. 3, pp. 328–352, Jul. 2021, doi: 10.1080/14780887.2020.1769238.
[30] W. Xu, M. J. Dainoff, L. Ge, and Z. Gao, “Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI,” Int. J. Hum. Comput. Interact., vol. 39, no. 3, pp. 494–518, Feb. 2023, doi: 10.1080/10447318.2022.2041900.
