Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0129
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-13T08:30:52.540800Z
status: published
lens: trace
sector: education
headline: Negotiating Ethical Coexistence with AI: A Grounded Theory of Japanese Students’ Conditional Trust, Agency, and the Development of Critical AI Literacy
dek: Generative Artificial Intelligence (GenAI) is increasingly reshaping how students learn, reason, and create, raising pressing questions about agency, trust, and moral responsibility. While information systems (IS) research has emphasized organizational governance and design ethics, less is known about how students develop ethical awareness in everyday AI use. This study applies a systematic grounded theory lens to a mixed-format survey of 69 Japanese university students. We develop a process model of how student…
gain_title: Japanese university students using GenAI develop conditional trust and reflexive evaluation through an ongoing process of moral calibration.
problem_title: Japanese university students using GenAI experience ethical anxiety and heightened awareness of power and risk that complicates agency and moral responsibility.
trace_subject: negotiating ethical coexistence with GenAI among Japanese university students
gain_reading: Japanese university students using GenAI develop conditional trust and reflexive evaluation through an ongoing process of moral calibration.
gain_evidence: conditional trust | ethical learning as an ongoing process of moral calibration rather than a static belief or competence
problem_reading: Japanese university students using GenAI experience ethical anxiety and heightened awareness of power and risk that complicates agency and moral responsibility.
problem_evidence: from recognition of power and risk to ethical anxiety, reflexive evaluation, and conditional trust | raising pressing questions about agency, trust, and moral responsibility | Generative Artificial Intelligence (GenAI) is increasingly reshaping how students learn, reason, and create
quick_read: By July 2026, researchers had surveyed 69 Japanese university students about everyday GenAI use and analyzed responses with systematic grounded theory. They found students moved through an iterative moral trajectory from recognizing power and risk to feeling ethical anxiety, then to reflexive evaluation and conditional trust, captured in the NECoAI model.

This matters because it reframes AI literacy in education as ongoing moral calibration rather than a static skill, suggesting human-centered instruction should support reflection. It remains uncertain how this trajectory varies outside Japan, beyond 69 students, or with different GenAI tools and tasks.
limitation: Findings are based on a small, culturally specific sample of 69 Japanese university students using a mixed-format survey and grounded theory, limiting generalizability beyond that population and method.
tag: Automated dual reading
key_points: Study used systematic grounded theory lens on mixed-format survey of 69 Japanese university students | Analysis identified iterative moral trajectory from recognition of power and risk to ethical anxiety, reflexive evaluation, and conditional trust | Authors propose grounded model Negotiating Ethical Coexistence with AI (NECoAI)
rundown: The authors applied a systematic grounded theory lens to survey responses to build a process model of ethical sensemaking in everyday AI use, rather than organizational governance.

The resulting NECoAI model frames ethical learning not as a fixed competence but as iterative calibration, extending responsible AI research in IS by centering use-time sensemaking.
sources:
- peer_reviewed | Journal of the Association for Information Systems | https://aisel.aisnet.org/pacis2026/is_education/is_education/15 | 2026-07-05
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

2732c14d2c6a8fe75b1b90f362ef9f55e89fc01400bc4d12460d1bacf59f66e7

previous

0000000000000000000000000000000000000000000000000000000000000000