AI evidence extraction

المسؤولية الاخلاقية للنخبة الاكاديمية في توظيف تطبيقات الذكاء الاصطناعي في كتابة البحوث العلمية:دراسة ميدانية على النخبة الأكاديمية بجامعة سرت

انتصار رمضان سكوت · Journal of the Faculty of Arts and Media – Misurata University · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.36602/famj.2026.22.13

Methodology & findings

Study design

Descriptive survey-based approach using a mixed-methods design.

Sample

N = 153, 5 groups

Primary method

Cronbach's Alpha coefficient (معامل ألفا كرونباخ) was used to assess internal consistency and reliability of the questionnaire, yielding α = 0.901. The study employed a descriptive survey methodology (المنهج الوصفي بأسلوب المسح) with random simple sampling (العينة العشوائية البسيطة). Statistical software is not specified. Descriptive statistics appear to be primary (percentages reported: 69.3%, 77.3%, 63.3%), but inferential statistical tests or significance levels are not detailed in the provided excerpt.

Main result

The study found that "أن الذكاء الاصطناعي أحدث تحولات واضحة في بيئة الإعالم والبحث العلمي" (artificial intelligence has created clear transformations in the media and scientific research environment), and "إن الاعتماد المفرط عليه في العمل الإعلامي قد يؤثر في الأدوار المهنية للإعلاميين ويضعف من الجوانب الإنسانية" (excessive reliance on it in media work may affect the professional roles of journalists and weaken humanistic aspects), with results showing "وجود استخدام متقبول مرتفع لتطبيقات الذكاء الاصطناعي بين أعضاء هيئة التدريس" (high acceptance of AI applications among faculty members), though accompanied by "ختوف صاحبه وغياب التشريعات المنظمة" (accompanied by fear and absence of regulating legislation).

Reports effect sizes.

Research paradigm

Mixed methods (qualitative and quantitative); positivist with interpretivist elements

Author conclusions

The authors conclude that "استخدام الذكاء الاصطناعي في البحث العلمي أصبح أمرا لا مفر منه" (AI use in scientific research has become unavoidable) and that "غياب سياسات واضحة يؤدي إلى سوء الاستخدام وكذلك الإفراط في الاعتماد على AI يهدد الأصالة العلمية وكذلك" (absence of clear policies leads to misuse and excessive reliance on AI threatens scientific authenticity). They recommend that AI deployment in scientific research writing "ينبغي أن يتم توظيف الذكاء الاصطناعي في كتابة البحوث العلمية في إطار من المسؤولية الأخالقية التي تحافظ على أصالة المعرفة العلمية واحترام حقوق الملكية الفكرية" (should occur within a framework of ethical responsibility that preserves the originality of scientific knowledge and respects intellectual property rights). The authors stress the need for institutional policies and ethical guidelines to regulate responsible AI deployment.

Risk of bias

Selection bias (limited to single institution in Libya); potential response bias in self-reported questionnaire data; non-probability sampling (purposive sample); temporal limitation (narrow time window); potential social desirability bias in responses about ethical compliance; Selection bias: Non-random sampling within Sirte University; limited to teaching faculty, potentially excluding doctoral students and independent researchers who actively use AI tools in research writing; Response bias: Self-reported questionnaire data subject to social desirability bias regarding ethical practices and AI usage; Institutional bias: Study conducted solely within one Libyan university; findings may not generalize to other institutions or countries with different regulatory environments; Temporal bias: Snapshot data collected over a 5-week period (Feb-Mar 2026); unable to capture seasonal or long-term changes in attitudes and practices; Language bias: Paper written in Arabic; potential translation artifacts in interpretation of nuanced concepts regarding ethical responsibility; Selection bias: purposive rather than random sampling of faculty members; Institutional bias: sample limited to single university (Sirte University); Geographic bias: sample restricted to Libyan context; Institutional awareness bias: questionnaire addressed to motivated respondents willing to discuss AI ethics

Limitations

The study acknowledges limitations in scope and generalizability
The research was "محدود بنطاق جامعة سرت" (limited to Sirte University), and employed "عينة عشوائية بسيطة" (simple random sampling), which may not be fully representative of all Libyan academic institutions
The temporal limitation is noted: data collection occurred "من 22/2/2026 إلى 29/3/2026" (from February 22, 2026 to March 29, 2026), constraining the ability to track longitudinal changes in ethical practices
Additionally, the study "لم يشمل جميع عناصر ومفردات المشكلة، أو المشكلة، ومفردات عناصر" (did not encompass all elements of the problem), and was limited to teaching faculty, potentially excluding graduate students and researchers who also use AI tools.

Open questions raised

The authors identify gaps in: (1) Libyan-specific empirical research on AI ethics in academic settings—the current study is positioned as the first in Libya; (2) Integration of ethical responsibility frameworks with actual AI tool adoption (ChatGPT, Gemini, etc.) across different research phases; (3) Institutional policies and regulations governing AI use in higher education; (4) Alignment between high AI adoption rates and low ethical compliance awareness.
The authors identify the following gaps and future research directions: (1) Most previous studies focused either on technical aspects of AI or on ethical dimensions separately, lacking integrated analysis of ethical responsibility alongside technical implementation; (2) Few studies examine ethical commitment among academic elites (النخبة الأكاديمية) specifically; (3) The Libyan academic context had been understudied—this is described as "البحث الحالي هو الأولى الذي يستهدف النخبة الأكاديمية بجامعة سرت" (the current study is the first to target the academic elite at Sirte University); (4) Recommendations for future research include conducting comparative studies across different disciplines and institutions, and examining implementation of proposed ethical frameworks over time.
Lack of clear ethical guidelines and regulatory frameworks for AI use in academic institutions
Weak institutional awareness regarding ethics of AI in scientific research
Need for comparative studies across different academic disciplines and institutions beyond Sirte University
Absence of longitudinal studies tracking changes in AI adoption and ethical practices over time

Extracted from: pdfAgreement 54%

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