AI evidence extraction

ChatGPT in Higher Education and Sustainable Development Goals: A Comparative Study of Institutional Perspectives from Developing and Developed Economies

Sumaira Nazeer, Muhammad Saleem Sumbal, Naveed Yasin, Jawad Abbas, Armando Papa · Sustainable Development · 2026

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

8/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

17.44

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1002/sd.70691

Methodology & findings

Study design

Qualitative research using semi-structured interviews and reflexive thematic analysis.

Sample

N = 24, 5 groups

Primary method

Reflexive thematic analysis following Braun and Clarke's six-step approach. No quantitative statistical methods mentioned.

Main result

The study found that "The findings reveal divergent adoption patterns shaped by distinct institutional factors, and these differences carry notable implications for achieving SDGs, particularly related to quality education (SDG-4), reducing inequalities (SDG-10), and institutional strength (SDG-16)."

Reports effect sizes.

Research paradigm

Interpretivist/qualitative

Author conclusions

The authors conclude that "ChatGPT contributes towards achieving Sustainable Development Goals (SDGs) in several ways by enabling scalable, personalized learning and lowering barriers to equitable access to quality education" while noting that "divergent adoption patterns shaped by distinct institutional factors" carry "notable implications for achieving SDGs, particularly related to quality education (SDG-4), reducing inequalities (SDG-10), and institutional strength (SDG-16)."

Risk of bias

Selection bias: Faculty interviews may not represent student or administrative perspectives; Geographic selection bias: Sample drawn from specific developing and developed countries; generalizability unclear; Reflexivity bias: Thematic analysis depends on researcher interpretation and reflexivity practices; Interview bias: Semi-structured interviews subject to interviewer effects and social desirability bias; Selection bias: Interview sample of 24 faculty members may not represent broader institutional perspectives; Geographic bias: Sample drawn from both developing and developed countries but specific representation unknown; Interviewer bias: Semi-structured interviews subject to interviewer effects; Reflexivity considerations: Authors acknowledge using reflexive thematic analysis, suggesting awareness of researcher positionality; Selection bias: Sample of 24 faculty members may not represent all higher education institutions; Interviewer bias: Semi-structured interviews subject to interviewer interpretation; Geographic sampling: Limited to developing and developed countries without specification of which nations; Reflexivity concerns: Thematic analysis depends on researcher interpretation despite reflexive approach

Open questions raised

The paper explicitly identifies a gap in existing research: "existing research discourse reveals a notable gap in investigating how differing institutional factors not only influence ChatGPT's adoption, but also how these factors shape progress towards key SDGs."
The study identifies a gap in investigating how differing institutional factors influence ChatGPT's adoption and shape progress towards key SDGs in higher education contexts.
The study addresses a gap where "existing research discourse reveals a notable gap in investigating how differing institutional factors not only influence ChatGPT's adoption, but also how these factors shape progress towards key SDGs."

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