ECySA 2026 · Article Reviews

5 articles · 12 criteria
Paper #1036

Students Awareness in Cyber

Hawassa University, Ethiopia

Overview

Background
Digital technologies in HEI; students face phishing, malware, data breaches.
Problem
Gap between confidence and actual cyber hygiene; misconceptions & overconfidence.
Method
Descriptive cross-sectional quantitative survey.
Data
N=459 (208 F, 251 M, 20 disabilities); stratified random sampling; 4 campuses.
Results
Confidence AMS=3.65; Risk perception AMS=3.52; Practices AMS=3.12; 79% no formal training; 58% self-study.
Findings
Confidence > practice; urgent need for targeted, context-specific awareness programs.

⚠️ Weaknesses

Self-reported data; single-institution; cross-sectional design limits causality.

🔮 Future Work

Develop & test targeted awareness programs; longitudinal follow-up; multi-institution replication.

Evaluation

CriterionScore
Accept with minor comments Total: 0 / 60
Paper #1037

AI in Education and Research

Ethical & institutional challenges of generative AI

Overview

Background
Generative AI (ChatGPT, etc.) used for drafting, summarising, tutoring in HEI.
Problem
Obscured authorship, verification complexity, credibility risks, impeded critical thinking.
Method
Not explicitly detailed in abstract; appears to be survey/qualitative research.
Data
"Considerable number of students" — sample not specified.
Results
Students concerned about accuracy, addiction, reduced critical thinking, academic dishonesty.
Findings
Advocates policy-driven adoption; human supervision; transparency norms; ethical integration.

⚠️ Weaknesses

Methodology & sample size not clearly stated; abstract-only submission limits depth.

🔮 Future Work

Detailed empirical study; develop institutional AI policies; assess impact on learning outcomes.

Evaluation

CriterionScore
Accept with major comments Total: 0 / 60
Paper #1035

Sovereignty as a Liability

Closing the Governance & Liability Vacuum in Ethiopia

Overview

Background
Ethiopia's "Digital Ethiopia 2025" strategy; rapid adoption of AI, digital ID, automated services.
Problem
"Governance and Liability Vacuum" — traditional legal codes inadequate for AI harms.
Method
Legal analysis; policy review; conceptual framework development.
Data
Ethiopian Civil Code (1960), Proclamation 1321/2024, Proclamation 958/2016.
Results
Identifies 3 critical legal challenges: AI Liability Chasm, Administrative Law Transformation, High-Risk Compliance Zone.
Findings
Proposes S.A.I.L. Framework; recommends a Center of Excellence for Digital GRC.

⚠️ Weaknesses

Conceptual/legal analysis with limited empirical data; implementation details of S.A.I.L. not fully elaborated.

🔮 Future Work

Operationalise S.A.I.L. Framework; stakeholder consultations; comparative analysis with other jurisdictions.

Evaluation

CriterionScore
Accept with minor comments Total: 0 / 60
Paper #1034

Navigating Multi-Stakeholder Trust Dynamics

Africa's AI & Cybersecurity Infrastructure — Qualitative Case Study

Overview

Background
Africa's digital ecosystem relies on PPPs between states and global tech providers.
Problem
Psychological barriers and misaligned institutional trust hinder collaboration.
Method
Qualitative research; thematic analysis of policy documents, AU frameworks, executive interviews.
Data
Open-access policy documents, African Union cybersecurity frameworks, public executive interviews (East Africa).
Results
Identifies sovereign risk anxieties, perceived data exploitation, asymmetrical power dynamics.
Findings
Transactional trust insufficient; need relational trust through transparent auditing & capacity building.

⚠️ Weaknesses

Qualitative only; limited to East Africa; reliance on public documents; no primary data collection.

🔮 Future Work

Empirical validation of framework; broader geographic scope; quantitative trust metrics.

Evaluation

CriterionScore
Poster Presentation Total: 0 / 60
Paper #1033

Measuring AI-Induced Technical Debt

Technical Analysis — Socio & Governance

Overview

Background
Generative AI tools (Copilot, Codex) accelerate coding but create hidden risks.
Problem
AI-Induced Technical Debt (AITD); "illusion of correctness"; epistemic debt; senior engineer burnout.
Method
Cross-sectional survey; regression analysis; Socio-Technical Systems theory + Kuhn's paradigm shift.
Data
N=51 global software professionals; survey on code churn and AI-generated PRs.
Results
Increase in code churn linked to AI PRs (explains ~25% variance); 100% of experts agree on core problem.
Findings
Short-term speed lost to long-term maintenance; need "Governance-as-Code" framework.

⚠️ Weaknesses

Small sample (n=51); pilot data; self-reported metrics; limited generalisability.

🔮 Future Work

Larger-scale replication; develop & validate Governance-as-Code framework; longitudinal tracking.

Evaluation

CriterionScore
Accept with minor comments Total: 0 / 60

Summary Dashboard

Aggregated scores & decisions across all five submissions.

Detailed Comparison

Criterion #1036 #1037 #1035 #1034 #1033