AI evidence extraction

Use of AI Tools in Academic Research: Awareness, and Ethical Concerns among Social Science Researchers

Sumana Sarkar, Moyuri Sarma · International Journal For Multidisciplinary Research · 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

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.36948/ijfmr.2026.v08i03.79832

Methodology & findings

Study design

Descriptive survey method using a convenience sample of 60 research scholars from social science disciplines.

Sample

N = 60, 2 groups

Primary method

Simple percentage analysis and graphical representation. Descriptive statistics reported as percentages without inferential statistical testing, confidence intervals, or hypothesis tests.

Main result

The study found that "(96.7%) of scholars use AI tools for their research related work" and "the majority of the respondents (45%) reported using AI tools on a weekly basis." Additionally, "ChatGPT (86.7%), QuillBot (71.7%), Grammarly (65%), and Google Gemini (48.3%) are the most frequently used AI tools among social science research scholars." The findings also revealed that "(43.3%) of the scholars agreed that AI tools may unintentionally produce plagiarized or unattributed content, raising concerns about academic integrity."

Reports effect sizes.

Research paradigm

Positivist/empiricist

Author conclusions

"Artificial Intelligence (AI) has emerged as a transformative force in the digital era, influencing the field of academic research." The authors conclude that "AI holds immense potential to strengthen academic research in the social sciences" and that "adoption of AI tools with adequate training, ethical awareness, and institutional support can ensure the integrity and morality that academia requires." They further state that "research scholars need structured AI literacy programs to transition from passive consumers to critical, ethical, and proficient AI users."

Risk of bias

Selection bias: convenience sampling method is non-random and may not represent all social science researchers; Sample size: n=60 is relatively small for drawing broad conclusions; Self-selection bias: participation was voluntary through online platforms; Geographic bias: not explicitly stated, but likely limited to specific regions in India; Lack of control group: no comparison condition to assess differences in awareness or usage patterns; Convenience sampling method (selection bias); Small sample size (n=60) limiting generalizability; Self-reported data via questionnaire (social desirability bias); Limited to social science disciplines only; No stratification by discipline reported; Selection bias: convenience sampling method may not be representative of all social science researchers; Sample size: n=60 is relatively small for generalizable findings; Self-selection bias: respondents willing to complete survey may differ from non-respondents; Social desirability bias: self-reported awareness and usage patterns; Geographic limitation: appears to focus on Indian research scholars based on institutional context

Limitations

The study is "limited to research scholars belonging to social science disciplines (e.g., Education, Sociology, Psychology, Political Science, and related areas)." The study employed a convenience sampling method, which limits generalizability
The small sample size (n=60) and restriction to a single discipline area further constrain the applicability of findings beyond the studied population.

Open questions raised

The study identifies a gap in "limited familiarity with advanced AI applications" among researchers. Authors note that "research scholars need structured AI literacy programs" and recommend promoting "workshops, seminars, and better training that can gradually improve data analysis and referencing capabilities of research scholars." The authors call for "clear ethical guidelines and training for research scholars to ensure responsible use of AI tools in academic research."
Gap in limited familiarity with advanced AI applications despite high general awareness
Need for structured AI literacy training programs (70.8% of scholars unaware of such programs)
Limited use of AI for advanced research tasks such as systematic literature mapping, reference management, and methodological design
Need for clear ethical guidelines and institutional policies on AI use in research
Need for training in critical verification of AI-generated content, sources, and citations

Extracted from: pdfAgreement 65%

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