AI evidence extraction

Ethical Challenges in the Use of Artificial Intelligence in Academic Research in Bamyan University

Ramazan Ahmadi, Chaman Ali Hikmat, Baz Mohammad Forough, Mahdia Hussaini, Mohammad Ahmadi · Journal of Contemporary Philosophical and Anthropological Studies · 2026

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

9/10

Relevance

2/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.59652/nn96bg02

Methodology & findings

Study design

Quantitative descriptive and applied research design using stratified random sampling.

Sample

N = 104, 3 groups

Primary method

SPSS Statistics software. Descriptive statistics: frequency, percentage, mode, median, mean, standard deviation. Inferential statistics: multivariate regression analysis. Reliability assessment: Cronbach's alpha coefficient. All analyses were conducted at standard significance level (p < 0.05 for statistical significance).

Main result

The study found that "the level of AI use among faculty members at Bamyan University is assessed as 'moderate' and is primarily limited to preliminary research activities such as literature searching, initial idea generation, and text editing." Additionally, the inferential analysis revealed that "the variables 'legal issues', 'plagiarism', 'lack of transparency', and 'privacy' have a significant negative effect on the extent of AI use, while 'algorithmic bias' was not statistically significant," with legal issues showing "the highest standardized coefficient (β = -0.310, p < 0.001), is the strongest inhibiting factor."

Reports effect sizes.

Research paradigm

Positivist/quantitative empiricism

Author conclusions

"In conclusion, it can be inferred that developing effective and ethical AI use in Afghan universities requires a multi-layered approach. First, developing legal frameworks and clear institutional policies aligned with international guidelines. Second, continuous training in digital literacy and research ethics for faculty members and students. Third, strengthening technological infrastructure and reducing the digital divide. And fourth, establishing a balance between leveraging AI and preserving human judgment, such that AI acts as a research assistant rather than a substitute for critical thinking."

Risk of bias

Selection bias: all-male sample with no female participants; Representation bias: unequal faculty representation, particularly low representation from Veterinary Medicine (1.9%) and Sharia/Islamic Law (5.8%); Potential non-response bias: only 104 of 152 actively present faculty members participated (68.4% response rate); Self-report bias: reliance on self-reported attitudes and behaviors via questionnaire; Selection bias: All-male sample (100% male, 0% female) with no gender diversity; Institutional bias: Single institution (Bamyan University) limits generalizability to Afghan higher education broadly; Sampling bias: Voluntary participation may introduce self-selection bias; Response bias: Self-reported data on AI usage and ethical concerns subject to social desirability bias; Selection bias: All participants are male (100%), with no female representation; Sampling bias: Faculties represented unevenly (Veterinary Medicine 1.9%, Faculty of Sharia 5.8% vs. Faculty of Agriculture 20.2%); Institutional bias: Study limited to single institution (Bamyan University), not representative of all Afghan universities; Response bias: Entirely voluntary participation may introduce self-selection bias; Generalizability threat: Results may not apply to other Afghan universities or developing contexts

Open questions raised

The study identifies that "so far, in Afghanistan higher education institutions have not experienced structured scientific research on the ethical use of AI in educational and research contexts" and that "most of the studies conducted in other developing countries are irrelevant to cultural, social, and legal services of Afghanistan." Authors call for empirical field-based research on ethical and practical integration of AI in Afghan universities.
The authors identify that "so far, in Afghanistan higher education institutions have not experienced structured scientific research on the ethical use of AI in educational and research contexts" and that "most of the studies conducted in other developing countries are irrelevant to cultural, social, and legal services of Afghanistan." They recommend future research on: (1) developing legal and ethical frameworks specific to Afghan universities; (2) implementing educational programs on AI literacy; (3) strengthening digital infrastructure; (4) promoting responsible AI use in advanced research areas such as data analysis and scientific writing.
The authors identify the need for empirical field-based research on ethical and practical integration of AI in Afghan universities. They note that 'so far, in Afghanistan higher education institutions have not experienced structured scientific research on the ethical use of AI in educational and research contexts.' The study aims to fill this gap by providing the first comprehensive academic effort to identify and analyze ethical, social, legal, and professional dimensions of AI in Afghan higher education.

Extracted from: pdfAgreement 58%

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