TRV-2026-0424Version 1 · Certified
Reason for this version
Certified into the record
Canonical text (the exact bytes fingerprinted)
TRUVACE RECORD VERSION record: TRV-2026-0424 version: 1 kind: certified reason: Certified into the record timestamp: 2026-07-20T10:42:27.760356Z status: published lens: g_space sector: education headline: Trust and attitude towards AI as pathways to creativity: a TAM Model study of EFL students’ digital literacy and AI acceptance dek: Abstract The integration of artificial intelligence (AI) into educational settings, particularly in language learning, necessitates a deeper understanding of the determinants of student trust. This study aims to investigate how digital literacy and trust in AI shape the attitudes, behavioral intentions, and creativity of English as a Foreign Language (EFL) students. This study is grounded in the technology acceptance model (TAM) and employed a two-stage survey methodology. Study 1 utilized a survey methodology w… gain_title: In EFL education, higher digital literacy and trust in AI increased perceived ease of use and usefulness, strengthened attitudes and intentions to continue using AI, and supported creative language learning. problem_title: (none) trace_subject: (none) gain_reading: In EFL education, higher digital literacy and trust in AI increased perceived ease of use and usefulness, strengthened attitudes and intentions to continue using AI, and supported creative language learning. gain_evidence: digital literacy significantly enhances perceived ease of use and perceived usefulness | trust positively influenced attitudes and intentions to use AI, with implications for creative language learning problem_reading: (none) problem_evidence: (none) quick_read: By December 2025, researchers surveyed EFL students in two stages to understand AI acceptance. The first survey of 460 students linked digital literacy to higher perceived ease of use and usefulness and greater trust in AI, which in turn shaped attitudes and intentions to use AI for language learning. The second survey of 640 students split trust into Human-like Trust and Functionality Trust and found differentiated effects on continued use and relational engagement. The findings matter because they suggest design choices for educational AI can influence not just adoption but creative language outcomes. Building reliable functionality may sustain use, while empathic, benevolent interaction may deepen engagement. What remains uncertain from the abstract alone is how these self-reported intentions translate into measured learning gains over time and across different EFL contexts. limitation: tag: Evidence-backed gain key_points: Study 1 with n = 460 EFL students found digital literacy enhances perceived ease of use and usefulness, which fosters trust in AI. | Study 2 with n = 640 EFL students separated trust into Human-like Trust (benevolence and integrity) and Functionality Trust (competence). | Functionality Trust had stronger effect on behavioral intentions for continued use, while Human-like Trust was more critical for relational engagement. rundown: The work used a two-stage survey grounded in the technology acceptance model. Study 1 tested links from digital literacy to perceived ease of use and usefulness to trust, then to attitudes and intentions. Study 2 expanded the model by treating trust as multidimensional, distinguishing benevolence and integrity from competence, and comparing their specialized effects on continued use versus relational engagement. sources: - peer_reviewed | Humanities and Social Sciences Communications | https://doi.org/10.1057/s41599-025-06362-x | 2025-12-09 prev: 0000000000000000000000000000000000000000000000000000000000000000
- sha256
- e23ed82102582ab076c0460eb230b3eba69fb85392a904861322254a64c54b6c
- previous
- 0000000000000000000000000000000000000000000000000000000000000000
Verify this record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0424 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.
ace