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ARXIV:2604.10978 · AI ETHICS & EDUCATION · SUBMITTED 14 APR · 16:52 UTC · FRESHNESS STALE
ARXIV:2604.10978AI ETHICS & EDUCATIONSUBMITTED 14 APR · 16:52 UTCFRESHNESS STALEYiran Du · Huimin He · arXiv
This study investigates student AI use concealment intention in higher education using SEM and fsQCA, revealing enabling and inhibitory pathways related to stigma, self-efficacy, and psychological safety.
Opportunity summary
Pain This study investigates student AI use concealment intention in higher education using SEM and fsQCA, revealing enabling and inhibitory pathways related to stigma, self-efficacy, and psychological safety.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
This study investigates student AI use concealment intention in higher education using SEM and fsQCA, revealing enabling and inhibitory pathways related to stigma, self-efficacy, and psychological safety. Drawing on data from 1346 university students,…
This study investigates students' AI use concealment intention in higher education by integrating the cognition-affect-conation (CAC) framework with a dual-method approach combining structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Drawing on…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. The enabling pathway shows that perceived stigma, perceived risk, and perceived policy uncertainty increase fear of negative evaluation, which in turn promotes concealment. Code…
AI Ethics & Education moved forward this cycle; last verified April 2026. Public score 2.0/10. Production flags indicate code availability.
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This study investigates student AI use concealment intention in higher education using SEM and fsQCA, revealing enabling and inhibitory pathways related to stigma, self-efficacy, and psychological safety.
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Paper Pack
10.48550/arXiv.2604.10978This study investigates student AI use concealment intention in higher education using SEM and fsQCA, revealing enabling and inhibitory pathways related to stigma, self-efficacy, and psychological safety.
Abstract
This study investigates students' AI use concealment intention in higher education by integrating the cognition-affect-conation (CAC) framework with a dual-method approach combining structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Drawing on data from 1346 university students, the findings reveal two opposing mechanisms shaping concealment intention. The enabling pathway shows that perceived stigma, perceived risk, and perceived policy uncertainty increase fear of negative evaluation, which in turn promotes concealment. In contrast, the inhibitory pathway demonstrates that AI self-efficacy, perceived fairness, and perceived social support enhance psychological safety, thereby reducing concealment intention. SEM results confirm the hypothesised relationships and mediation effects, while fsQCA identifies multiple configurational pathways, highlighting equifinality and the central role of fear of negative evaluation across conditions. The study contributes to the literature by conceptualising concealment as a distinct behavioural outcome and by providing a nuanced explanation that integrates both net-effect and configurational perspectives. Practical implications emphasise the need for clear institutional policies, destigmatisation of appropriate AI use, and the cultivation of supportive learning environments to promote transparency.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 2.0
PROBLEM
This study investigates student AI use concealment intention in higher education using SEM and fsQCA, revealing enabling and inhibitory pathways related to stigma, self-efficacy, and psychological safety. Drawing on data from 1346 university students, the findings reveal two opp...
METHOD
This study investigates students' AI use concealment intention in higher education by integrating the cognition-affect-conation (CAC) framework with a dual-method approach combining structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Drawi...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. The enabling pathway shows that perceived stigma, perceived risk, and perceived policy uncertainty increase fear of negative evaluation, which in turn promotes concealment. Code availability is flagged in...
WHY NOW
AI Ethics & Education moved forward this cycle; last verified April 2026. Public score 2.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
This study investigates student AI use concealment intention in higher education using SEM and fsQCA, revealing enabling and inhibitory pathways related to stigma, self-efficacy, and psychological safety. Drawing on data from 1346 university students, the findings reveal two opposing mechanisms shaping concealment intention.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
This study investigates students' AI use concealment intention in higher education by integrating the cognition-affect-conation (CAC) framework with a dual-method approach combining structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Drawing on data from 1346 university students, the findings reveal two opposing mechanisms shaping concealment intention.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. The enabling pathway shows that perceived stigma, perceived risk, and perceived policy uncertainty increase fear of negative evaluation, which in turn promotes concealment. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Ethics & Education moved forward this cycle; last verified April 2026. Public score 2.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This study investigates student AI use concealment intention in higher education using SEM and fsQCA, revealing enabling and inhibitory pathways related to stigma, self-efficacy, and psychological safety.
Segment
AI Ethics & Education
Adoption evidence
No public code link in the paper record yet
Commercial read
2.0/10 public viability
Direct
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2/3 checks · 67%
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status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Evidence
0 references, 3 sources, 50% evidence coverage.
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Buyer clarity
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No budget owner is verified for this paper.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Regulatory load
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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People
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Regulatory need unclassified.
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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