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Human-Centered AI in Practice: Peer Facilitators as Technostress Reducers in a Mixed-Methods LMS Study

Recursive Journal of Informatics · Vol. 4 No. 2 (2026) · hlm. 149–155

DOI
10.15294/rji.v4i2.64092
ISSN
2986-6588, 2963-5551
Terbit
2026-09-30
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

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.

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16 penulis · 35 tautan kepenulisan bersama · 22 lintas organisasi

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  • Indonesia University of Education
  • Yogyakarta State University
  • Universitas Islam Negeri Ar-Raniry
  • Universiti Sains Malaysia
  • Universitas Muhammadiyah Purworejo
  • Universitas Negeri Surabaya
  • Sebelas Maret University
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Rekan penulisOrganisasiArtikel bersama
Putu SudiraYogyakarta State University2
Muhammad Isfa' LanaIndonesia University of Education1
Afika RiantiIndonesia University of Education1
Ahmad FauziUniversitas Islam Negeri Ar-Raniry1
Amara Seviany AgustinIndonesia University of Education1
Md Baharuddin Bin Abdul RahmanUniversiti Sains Malaysia1
Suyitno SuyitnoUniversitas Muhammadiyah Purworejo1
Warju WarjuUniversitas Negeri Surabaya1
Pardjono PardjonoYogyakarta State University1
Farid MutohhariYogyakarta State University1
Fajar Danur IsnantyoSebelas Maret University1
Taufik RidwanIndonesia University of Education1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/rji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Human-Centered AI in Practice: Peer Facilitators as Technostress Reducers in a Mixed-Methods LMS Study</dc:title>
	<dc:creator xml:lang="en">Pambudi, Ilham Agung</dc:creator>
	<dc:creator xml:lang="en">Majid, Nuur Wachid Abdul</dc:creator>
	<dc:creator xml:lang="en">Barri, Muhamad Akda Fathul</dc:creator>
	<dc:creator xml:lang="en">Sari, Ayu Permata</dc:creator>
	<dc:subject xml:lang="en">AI anxiety</dc:subject>
	<dc:subject xml:lang="en">Generative artificial intelligence</dc:subject>
	<dc:subject xml:lang="en">Human-centered AI</dc:subject>
	<dc:subject xml:lang="en">Peer facilitation</dc:subject>
	<dc:subject xml:lang="en">Zone of proximal development</dc:subject>
	<dc:description xml:lang="en">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.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2026-09-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/rji/article/view/64092</dc:identifier>
	<dc:identifier>10.15294/rji.v4i2.64092</dc:identifier>
	<dc:source xml:lang="en">Recursive Journal of Informatics; Vol. 4 No. 2 (2026): September 2026; 149-155</dc:source>
	<dc:source>2986-6588</dc:source>
	<dc:source>2963-5551</dc:source>
	<dc:source>10.15294/j2r8b957</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/rji/article/view/64092/11626</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Recursive Journal of Informatics</dc:rights>
</oai_dc:dc>