AI evidence extraction

A Comparative Study of AI Tools Adoption and Its Impact on Educational Practices in Public and Private Universities

Laraib Khan, Muhammad Junaid Siraji · Social Prism · 2026

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

7/10

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/10.69671/socialprism.2.4.2025.55

Methodology & findings

Study design

Quantitative, comparative survey research.

Sample

N = 460, 2 groups

Primary method

Descriptive statistics and independent-sample t-tests were employed to analyze disparities between the two institutions.

Main result

The study found that "the empirical study demonstrates a significantly greater rate of AI-tool use in the case of the public university, compared to the one in the private one. However, the two institutions did not show any statistically significant difference as regards to perceived effectiveness, user satisfaction, performance, or motivational outcomes." This suggests adoption gaps exist between institutional types but pedagogical perceptions converge when meaningful integration occurs.

Reports effect sizes.

Research paradigm

Positivist/Quantitative

Author conclusions

The authors conclude that "the study advances the academic discourse on the adoption of artificial intelligence (AI) in higher education, and especially in developing countries, by explaining how institutional preparedness, digital infrastructure, transparency in governance, and capacity development in the professional sphere can play a central role in supporting sustainable adoption of AI." They further state that "the results highlight the need to establish institutional policies, sound ethical frameworks, and policy interventions that are narrowly focused and with which together would help to make AI technologies in KP and similar emerging environments the subject matter of responsible, fair, and pedagogically significant integration."

Risk of bias

selection bias (non-random recruitment of faculty and postgraduate students); geographic limitation (single city); institutional context confounding (public vs private sector differences); self-report measures (perceived effectiveness, satisfaction); Selection bias (participants self-selected or recruited from two specific institutions); Geographic limitation (only two universities in one city in Khyber Pakhtunkhwa province); Potential institutional context effects not controlled for; Selection bias: Participants were recruited from only two universities in one city (Dera Ismail Khan), limiting generalizability; Sampling bias: Non-random/distributive recruitment method not fully specified; Institutional context confound: Public vs. private status conflated with other institutional differences; Self-report bias: Study relies on perceived measures (perceived usefulness, perceived ease of use, user satisfaction); Lack of control variables: No mention of controlling for faculty experience, student prior exposure to AI, or other confounders

Open questions raised

The literature of empirical studies covering emergent and resource-constrained situations is scarce although the scope of its influence has been rapidly expanding on the international stage.
The study addresses the scarcity of empirical studies in emergent and resource-constrained situations, identifying a need for research on AI adoption in developing countries and institutional contexts.
The authors identify the need for research in emergent and resource-constrained situations, stating that "the literature of empirical studies covering emergent and resource-constrained situations is scarce although the scope of its influence has been rapidly expanding on the international stage." They call for future work on institutional policies, ethical frameworks, and policy interventions for AI adoption in developing countries.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 62%

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