CoChat
Sign Up
Cited by 0Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEducational Innovations and Technology

Beyond Access: Socioeconomic, Academic and Disciplinary Determinants of Generative AI Use in Higher Education

Open access
Zafar, Sualeha, Shaheen, Farzana, Khan, Shahzad Ahmad·2026·Journal of Political Stability Archive
DOI 10.63468/jpsa.4.2.17
Open this paper in CoChat — free accountOpen PDF

Abstract

Although generative artificial intelligence (GenAI) is swiftly revolutionizing higher education, it prompted significant inquiries on adoption trends, equity and academic consequences. This research investigates the factors influencing the utilization of generative AI among university students, emphasizing demographic, academic, socioeconomic and technological variables. Data from a survey of 333 students were analyzed through cross-tabulations and chi-square tests to evaluate associations between the frequency of GenAI usage and variables such as gender, academic discipline, residence, parental education, household income, internet access and academic achievement. The findings demonstrate extensive adoption of GenAI, revealing notable disparities based on gender and academic specialty. Male students and individuals studying natural sciences indicated elevated levels of continuous usage. Socioeconomic characteristics, especially household income and parental education, significantly predicted both internet access and the frequency of generative AI usage, highlighting enduring digital disparities beyond mere access. GenAI usage was favorably connected with academic performance, suggesting it can boost academic success when correctly applied. Results show that discipline-specific and equity-oriented institutional regulations are needed for effective and accountable AI implementation in higher education.

AI snapshot

Written from the paper’s full text.

TL;DR

This descriptive survey of 333 university students in Punjab, Pakistan finds widespread generative AI use (88.3% ever used) with significant associations by gender, discipline, parental education, household income, internet access, and CGPA, while residence was not significant. It argues that GenAI engagement is shaped by socioeconomic and academic stratification beyond mere access.

Key findings

  • GenAI use was extensive: 11.7% never used it, 40.5% sometimes, 25.8% often, and 21.9% always. Gender was significantly associated with use (χ² = 12.031, p = .007), with male students more likely to report “always” use (32.4%) than female students (16.7%).
  • Academic discipline was significant (χ² = 22.007, p = .009): Natural Sciences had the highest frequent/consistent use (61.9% often or always) and lowest non-use (5.9%), while Arts and Humanities had the highest non-use (19.7%). Residence was not significant (χ² = 0.700, p = .873).
  • Parental education and income predicted use: father’s education χ² = 95.807, p = .000; mother’s education χ² = 69.514, p = .000; household income with GenAI use χ² = 20.677, p = .014; household income with internet availability χ² = 32.781, p = .000; internet availability with GenAI use χ² = 17.319, p = .044.
  • CGPA was significantly associated with GenAI use (χ² = 29.759, p = .000); among high performers (CGPA 3.6–4.0), 49 of 71 were in the “often” or “always” categories, and only 5 never used GenAI.

Methods

  • Quantitative descriptive survey design using a structured questionnaire distributed online via WhatsApp groups and in printed copies over six weeks.
  • Convenience sample of 333 students from 6 public universities in Punjab, Pakistan: 111 males (33.3%) and 222 females (66.7%); 291 undergraduates (87.4%) and 42 postgraduates (12.6%) across Management Sciences, Social Sciences, Natural Sciences, and Arts and Humanities.
  • Data analyzed in SPSS 20 using descriptive statistics, cross-tabulations, and chi-square tests of independence for categorical variables.

Limitations

  • The paper does not state a dedicated limitations section.
  • Evident from the text: convenience sampling, a single region/country context, and a gender-imbalanced sample (66.7% female) limit generalizability.
  • Evident from the text: the cross-sectional, self-reported survey and chi-square/cross-tabulation analysis cannot establish causality or control for confounding variables.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.63468/jpsa.4.2.17

Cites

0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

12 of 19 cited sources checked · 4 supported · 0 flagged · accuracy not yet measured

  • Premisecitation
    § Conceptual Framework

    High-achieving students (better CGPA) utilize AI tools as supplementary resources rather than replacements for learning.

    “Evidence indicates that high-achieving students (better CGPA) utilize AI tools as supplementary resources rather than replacements for learning.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Ward et al., 2025 source

      The General Feedback on AI in Education section says several students viewed AI as a supplementary tool, and the Comparative Studies section describes AI tools as complements rather than replacements. Neither passage links that approach to high-achieving students or better CGPA; the Grade Level Distribution section concerns year of study, not academic achievement. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
  • Premisecitation
    § Socioeconomic Determinants Affecting AI Engagement and Utilization

    Students with more educated parents are more confident and trusting in using AI systems for academic purposes.

    “Students with more educated parents are more confident and trusting in using AI systems for academic purposes, possibly due to more supportive home learning environments and digital culture (Limna & Kraiwanit, 2024).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Limna & Kraiwanit, 2024 source

      The Demographic factors section discusses parental education as a predictor of academic achievement, while the Discussion links students’ own education level to their intention to use Gemini. Neither passage addresses whether students with more educated parents are more confident in or trusting of AI, or whether a supportive home environment or digital culture explains such a relationship. Searches for those terms found no supporting passage in our copy. (LLM, conf=0.75)

      Experimental — accuracy not yet measured
  • Premisecitation
    § Conceptual Framework

    More than 70% of higher education students have utilised GenAI or comparable applications for academic reasons.

    “More than 70% of higher education students have utilised GenAI or comparable applications for academic reasons, underscoring the prevalence of AIassisted learning (Maxwell et al., 2025;Reyes & Contractor, 2025).”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites Maxwell et al., 2025 source
    • cites Reyes & Contractor, 2025 source
  • Premisecitation
    § INTRODUCTION

    Socioeconomic factors, especially family income and parental education, continue to be significant determinants of technological access and literacy.

    “Likewise, socioeconomic factors-especially family income and parental education-continue to be significant determinants of technological access and literacy (Bassignana et al., 2025).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Bassignana et al., 2025 source

      Section 3 says socioeconomic status shapes access to technology and digital skills, and Section 6 reports significant differences in language-technology adoption and use across socioeconomic groups. The paper lists income as an SES factor and records parents’ education as demographic information, but the cited passages do not establish that family income or parental education specifically are significant determinants of access or literacy. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
  • Premisecitation
    § AI Usage and Academic Achievement

    Students with higher academic self-efficacy and mastery goal orientation use GenAI less for academic activities.

    “Daha and Altelwany (2025) discovered that students with higher academic selfefficacy and mastery goal orientation use GenAI less for academic activities, suggesting that self-confidence may limit AI-generated aid.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Daha and Altelwany (2025) source
  • Classified as probably not a substantial claim. 12
  • May combine two claims. 37
  • The quote only partly states this claim. 2

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § AI Usage and Academic Achievement

    Students' utilization of ChatGPT correlated with enhancements in intellectual ability, critical thinking, vocabulary, grammar and language competency, content generation.

    “Zafar et al. (2024) discovered that students' utilization of ChatGPT correlated with enhancements in intellectual ability, critical thinking, vocabulary, grammar and language competency, content generation, suggesting that generative AI can augment academic talents rather than solely supplant student labor.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Zafar et al. (2024) source

      Data Analysis and Interpretation (p. 9) reports that ChatGPT users perceived improvements in critical thinking, content-idea generation, vocabulary, grammar, intellectual capacity, and language skills; the Findings (p. 10) summarize the same results. However, these are self-reported perceptions, not measured enhancements. The paper’s reported correlations concern broader variables, not the relationship between usage and each skill named in the claim. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § AI Usage and Academic Achievement

    Learning motivation strongly influences the association between GenAI attitudes and academic performance.

    “Amorin and Gallaron (2025) found that learning motivation strongly influences the association between GenAI attitudes and academic performance, suggesting that positive AI attitudes only improve performance when accompanied with intrinsic motivation.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Amorin and Gallaron (2025) source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § Generative AI Adoption in Higher Education

    Generative AI integration in higher education enhances academic engagement and originality, although it also presents ethical and pedagogical dilemmas related to plagiarism, authorship and data bias.

    “Studies demonstrate that the integration of generative AI in higher education enhances academic engagement and originality, although it also presents ethical and pedagogical dilemmas related to plagiarism, authorship and data bias (Sergeeva et al., 2025).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Sergeeva et al., 2025 source

      The Benefits of AI Implementation section describes the potential for AI tools to enhance engagement, while Challenges and Limitations identifies algorithmic bias; the Introduction also reports academic-integrity concerns. But the paper does not demonstrate increased academic engagement or originality, and the Introduction instead notes worries about loss of creativity. The passages do not specifically address plagiarism or AI-related authorship. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation
    § Conceptual Framework

    Students in engineering and computer science demonstrate higher adoption rates than those in social sciences and humanities, attributable to their increased familiarity with computational tools.

    “Students in engineering and computer science demonstrate higher adoption rates than those in social sciences and humanities, attributable to their increased familiarity with computational tools (Contractor & Reyes, 2025).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Contractor & Reyes, 2025 source

      Section 3.2 reports higher AI adoption in Natural Sciences, a category including computer science, than in several humanities fields. It also says Social Sciences students have adoption rates similar to Natural Sciences students, contrary to that part of the comparison. The paper suggests differences in AI’s usefulness for field-specific tasks, not greater familiarity with computational tools, and does not report a separate engineering adoption rate. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation
    § AI Usage and Academic Achievement

    Avoidance-oriented students utilized GenAI more, which increased procrastination and lowered CGPA.

    “Avoidance-oriented students utilized GenAI more, which increased procrastination and lowered CGPA.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Daha and Altelwany (2025) source

  • The quote only partly states this claim.

    Findingown data
    § Effect of Parental Education on Generative AI Usage

    Individuals whose fathers had greater education are significantly more inclined to utilize GenAI regularly (often or always).

    “adolescents with fathers who have advanced educational qualifications display markedly more involvement with generative AI technologies”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Effect of Parental Education on Generative AI Usage

    Individuals with 'Nowhere' access had significantly lower adoption rates, with 43.8% never utilizing such products and no respondents indicating Always use.

    “Conversely, individuals with 'Nowhere' access had significantly lower adoption rates, with 43.8% never utilizing such products and no respondents indicating Always use.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Effect of Parental Education on Generative AI Usage

    Household income serves as a significant socio-economic determinant of both electronic media access and generative AI utilization.

    “The table above illustrates that household income serves as a significant socio-economic determinant of both electronic media access and generative AI utilization.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Data Analysis and Interpretation

    25.8% reported frequent usage, 40.5% indicated occasional usage and 21.9% stated they utilized them consistently.

    “25.8% reported frequent usage, 40.5% indicated occasional usage and 21.9% stated they utilized them consistently.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Data Analysis and Interpretation

    A significant proportion of students (88.3%) reported used AI technologies, while 11.7% indicated they had never employed such tools.

    “A significant proportion of students (88.3%) reported used AI technologies, while 11.7% indicated they had never employed such tools.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Effect of Parental Education on Generative AI Usage

    Students from both high and low maternal education backgrounds utilize AI more frequently, whereas those from medium-education backgrounds depend on it to a lesser extent.

    “Students from both high and low maternal education backgrounds utilize AI more frequently, whereas those from medium-education backgrounds depend on it to a lesser extent.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Effect of Parental Education on Generative AI Usage

    The At home and At university groups exhibited intermediate patterns, with Sometimes as the modal category in both instances.

    “The At home and At university groups exhibited intermediate patterns, with Sometimes as the modal category in both instances.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    The study found notable disparities in generative AI usage based on gender and academic specialty.

    “The findings demonstrate extensive adoption of GenAI, revealing notable disparities based on gender and academic specialty.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Effect of Parental Education on Generative AI Usage

    Participants indicating Universal access (n=170, 51.1% of the sample) demonstrated the highest overall engagement, with almost half (48.2%) utilizing generative AI tools frequently or consistently.

    “participants indicating Universal access (n=170, 51.1% of the sample) demonstrated the highest overall engagement, with almost half (48.2%) utilizing generative AI tools frequently or consistently.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Abstract

    Socioeconomic characteristics, especially household income and parental education, significantly predicted both internet access and the frequency of generative AI usage.

    “Socioeconomic characteristics, especially household income and parental education, significantly predicted both internet access and the frequency of generative AI usage, highlighting enduring digital disparities beyond mere access.”
    Quote in the paperSubstantial claim

  • May combine two claims. The quote only partly states this claim.

    Findingown data
    § Effect of Parental Education on Generative AI Usage

    Individuals from low-education father backgrounds are prone to Never utilize GenAI or to employ it just sporadically.

    “pupils from less educated fathers exhibit restricted utilization”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Effect of Parental Education on Generative AI Usage

    Only 5 high-CGPA students do not utilize GenAI, whereas 29 consistently employ it.

    “Only 5 high-CGPA students do not utilize GenAI, whereas 29 consistently employ it.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning
    § DISCUSSION

    Natural Sciences students were the most frequent users of GenAI, while Arts and Humanities students were less likely to utilize it.

    “The most frequent users were Natural Sciences students, while Arts and Humanities students were less likely to utilize.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Findings

    Students with universal internet access exhibited the highest frequency and consistency of AI usage, while those with restricted access displayed negligible engagement.

    “Students with universal access exhibited the highest frequency and consistency of AI usage, while those with restricted access displayed negligible engagement.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Findings

    Mother education had a statistically significant correlation with generative AI usage (p < .001); students from both high and low mother education backgrounds reported greater AI usage than those from medium-education families.

    “mother education shown a statistically significant correlation (p < .001);notably, students from both high and low mother education backgrounds reported greater AI usage than those from medium-education families.”
    • Also stated in: § DISCUSSION · plain
      “Interestingly, students from high and low maternal education used AI more than those from medium-level homes.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § Findings

    The descriptive study revealed extensive use of generative AI technologies, with the majority of students indicating moderate to high usage levels.

    “The descriptive study revealed extensive use of generative AI technologies, with the majority of students indicating moderate to high usage levels.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given
    § METHODOLOGY

    The questionnaire was reviewed by three experts in educational technology and research methodology to ensure content validity.

    “The questionnaire was reviewed by three experts in educational technology and research methodology to ensure content validity and then tested on students to assess the clarity of language and question structure.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation
    § Conceptual Framework

    Students from affluent homes with highly educated parents have greater involvement with AI, frequently attributable to superior internet access and enhanced digital literacy chances.

    “Students from affluent homes with highly educated parents have greater involvement with AI, frequently attributable to superior internet access and enhanced digital literacy chances (Zhang et al., 2024)”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Zhang et al., 2024

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationown data
    § Effect of Parental Education on Generative AI Usage

    AI's role is as a functional academic enhancer rather than simply a technological resource.

    “underscoring AI's role as a functional academic enhancer rather than simply a technological resource.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation
    § Generative AI Adoption in Higher Education

    Engineering and computer science students adopt AI faster due to curriculum design and institutional encouragement, while social sciences and humanities students lag behind despite seeing AI's potential.

    “Further research from Spanish universities shows that engineering and computer science students adopt AI faster due to curriculum design and institutional encouragement, while social sciences and humanities students lag behind despite seeing AI's potential (Llorens-Largo et al., 2024).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Llorens-Largo et al., 2024 source

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Study design and conductnone given
    § METHODOLOGY

    Data collection took place over a six-week period, ensuring a sufficient response rate and allowing for comprehensive coverage across all faculties.

    “Data collection took place over a six-week period, ensuring a sufficient response rate and allowing for comprehensive coverage across all faculties.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data
    § METHODOLOGY

    The questionnaire was tested on students to assess clarity of language and question structure, and feedback was incorporated to improve clarity and relevance of items.

    “The questionnaire was reviewed by three experts in educational technology and research methodology to ensure content validity and then tested on students to assess the clarity of language and question structure.Their feedback was incorporated to improve the clarity and relevance of items.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation
    § INTRODUCTION

    Discipline affiliation substantially affects adoption patterns, with STEM and business students showing heightened engagement due to technical exposure and the direct relevance of AI to their academic tasks.

    “Empirical research demonstrates that discipline affiliation substantially affects adoption patterns, with STEM and business students showing heightened engagement due to technical exposure and the direct relevance of AI to their academic tasks (Kubullek et al., 2024).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Kubullek et al., 2024 source

  • May combine two claims.

    Premisecitation
    § Socioeconomic Determinants Affecting AI Engagement and Utilization

    Students from rural and low-income backgrounds encounter challenges such as restricted device availability and intermittent internet connectivity, while those from higher-income households and urban settings report better exposure to AI technologies and improved digital competencies.

    “Students from rural and lowincome backgrounds encounter challenges such restricted device availability and intermittent internet connectivity, while those from higher-income households and urban settings report better exposure to AI technologies and improved digital competencies (Vesna, 2025).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Vesna, 2025 source

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Study design and conductreasoning
    § Data Analysis and Interpretation

    To meet statistical assumptions and stabilize results, categories were logically compressed.

    “To meet statistical assumptions and stabilize results, categories were logically compressed.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning
    § METHODOLOGY

    Data were collected through both online and printed copies to accommodate students with different levels of internet access.

    “Data were collected through both online (Google Forms) and printed copies of the questionnaire to accommodate students with different levels of internet access.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given
    § METHODOLOGY

    Participants were informed about the purpose of the study, assured of confidentiality and notified that participation was voluntary.

    “Participants were informed about the purpose of the study, assured of confidentiality and notified that participation was voluntary.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data
    § Effect of Parental Education on Generative AI Usage

    Income not only affects respondents' access to the internet but also their proficiency in utilizing modern digital learning tools like GenAI.

    “Income not only affects respondents' access to the internet but also their proficiency in utilizing modern digital learning tools like GenAI.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data
    § Effect of Parental Education on Generative AI Usage

    Increased income levels are associated with enhanced electronic access and more frequent AI engagement.

    “suggesting that increased income levels are associated with enhanced electronic access and more frequent AI engagement.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data
    § DISCUSSION

    The positive gradient between AI usage and CGPA shows that GenAI-integrated pupils may benefit academically, especially in research, synthesis and problem-solving.

    “The positive gradient between AI usage and CGPA shows that GenAI-integrated pupils may benefit academically, especially in research, synthesis and problem-solving.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed correlations between socioeconomic, academic, and technological variables and GenAI usage reflect causal influence rather than mere association.
    • Rests on an assumption: CGPA is a valid measure of academic performance and improvement.

  • May combine two claims.

    Interpretationown data
    § DISCUSSION

    GenAI tools are not ancillary technology but are increasingly integrated into students' academic practices, especially among those with enhanced access to resources and superior academic performance.

    “The data indicate that GenAI tools are not ancillary technology but are increasingly integrated into students' academic practices, especially among those with enhanced access to resources and superior academic performance.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The frequency and consistency of self-reported GenAI usage accurately reflect students' actual integration of GenAI into academic practices.

  • May combine two claims.

    Interpretationreasoning
    § DISCUSSION

    Financial resources enable connectivity, digital confidence, experimentation and persistent GenAI use.

    “suggesting that financial resources enable connectivity, digital confidence, experimentation and persistent use.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed correlations between socioeconomic, academic, and technological variables and GenAI usage reflect causal influence rather than mere association.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationown data
    § Findings

    The findings indicate variations within disciplines regarding the perceived relevance and usefulness of generative AI techniques.

    “The findings indicate variations within disciplines regarding the perceived relevance and usefulness of generative AI techniques.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data
    § DISCUSSION

    GenAI may serve as a strategic academic enhancer rather than a shortcut or academic danger.

    “This contradicts the idea that GenAI is a shortcut or academic danger and instead frames it as a strategic academic booster.”
    • Also stated in: § CONCLUSION AND IMPLICATIONS · hedged
      “suggesting that AI may serve as a potential academic enhancer when employed strategically.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: CGPA is a valid measure of academic performance and improvement.

  • May combine two claims.

    Interpretationown data
    § CONCLUSION AND IMPLICATIONS

    GenAI has become a fundamental element of students' academic activities, with usage intensity differing systematically based on gender, discipline, parental education, home income and internet access.

    “The results demonstrate that GenAI has become a fundamental element of students' academic activities, with usage intensity differing systematically based on gender, discipline, parental education, home income and internet access.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The frequency and consistency of self-reported GenAI usage accurately reflect students' actual integration of GenAI into academic practices.

  • May combine two claims.

    Interpretationown data
    § Effect of Parental Education on Generative AI Usage

    There are enduring inequities despite largely positive overall access.

    “underscoring enduring inequities despite largely positive overall access.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationown data
    § DISCUSSION

    The pattern of high and low maternal education students using AI more than medium-education students may indicate compensatory use by students without academic direction at home and strategic enhancement by those from highly educated households.

    “This pattern may indicate compensatory use of AI tools by students without academic direction at home and strategic enhancement by those from highly educated households.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data
    § Findings

    Income affects both digital access and the degree of AI involvement.

    “These data suggest that income affects both digital access and the degree of AI involvement.”
    • Also stated in: § DISCUSSION · boosted
      “Household income substantially influenced GenAI use and internet access, demonstrating that economic capital continues to impact digital access and advanced learning technology engagement.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed correlations between socioeconomic, academic, and technological variables and GenAI usage reflect causal influence rather than mere association.

  • May combine two claims.

    Interpretationreasoning
    § DISCUSSION

    GenAI may multiply resources and equalize students based on their families.

    “These data show that GenAI may multiply resources and equalize students based on their families.”
    Quote in the paperSubstantial claim
44 of 44

  • The paper relies on this without support.

    Demographic and socioeconomic drivers of generative AI uptake in higher education have not been studied.

    “Students' attitudes, beliefs and behavioral intentions towards AI tools have been studied, but demographic and socioeconomic drivers of generative AI uptake in higher education have not.”

  • The paper relies on this without support.

    Current information about generative AI adoption is mostly from industrialized countries, leaving a contextual gap for developing nations like Pakistan.

    “Current information is mostly from industrialized countries, leaving a contextual gap in understanding adoption dynamics in developing nations like Pakistan, where technological access and educational fairness differ greatly.”

  • The paper relies on this without support.

    University students' use of generative AI is unequal due to academic and socioeconomic differences.

    “University students' use of generative AI is unequal due to academic and socioeconomic differences.”

  • The paper relies on this without support.

    In underdeveloped countries like Pakistan, income, domicile and parental education may limit access and use.

    “In underdeveloped countries like Pakistan, income, domicile and parental education may limit access and use.”

  • The paper relies on this without support.

    The observed correlations between socioeconomic, academic, and technological variables and GenAI usage reflect causal influence rather than mere association.

    “These data suggest that income affects both digital access and the degree of AI involvement.”
    “Household income substantially influenced GenAI use and internet access, demonstrating that economic capital continues to impact digital access and advanced learning technology engagement.”

  • The paper relies on this without support.

    The frequency and consistency of self-reported GenAI usage accurately reflect students' actual integration of GenAI into academic practices.

    “The data indicate that GenAI tools are not ancillary technology but are increasingly integrated into students' academic practices, especially among those with enhanced access to resources and superior academic performance.”
    “The results demonstrate that GenAI has become a fundamental element of students' academic activities, with usage intensity differing systematically based on gender, discipline, parental education, home income and internet access.”

  • The paper relies on this without support.

    CGPA is a valid measure of academic performance and improvement.

    “Frequent usage of Generative AI was significantly correlated with improved academic performance, suggesting that AI may serve as a potential academic enhancer when employed strategically.”
    “The positive gradient between AI usage and CGPA shows that GenAI-integrated pupils may benefit academically, especially in research, synthesis and problem-solving.”
7 of 7

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Father's education counts differ between tables

    Major

    The descriptive table reports 54 students with low-educated fathers and 163 with highly educated fathers. The father's-education-by-AI-use table reports 35 and 182, respectively, for those same categories. The discrepancy affects a reported association central to the findings.

1 of 1

Cites

The publisher hasn’t deposited this paper’s reference list.

Research works printed but not linked: 1

  1. Aldreabi, H., Dahdoul, N. K. S., Alhur, M., Alzboun, N., & Alsalhi, N. R. (2025). Determinants of Student Adoption of Generative AI in Higher Education. 23(1), 15-33.

Related

Authors
Zafar, S., Shaheen, F., & Khan, S. A.
Affiliations
Government Sadiq College Women University (PK)
ORCID
Shaheen, Farzana
Venue
Journal of Political Stability Archive
Publisher
Creative Business & Social Research
Published
2026-03-31
Volume
4
Issue
2
Pages
261-281
ISSN
  • 3006-5860 (ISSN-L)
  • 3006-5879
DOI
10.63468/jpsa.4.2.17
OpenAlex
W7151395326
Cited by
0
References
0
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
Journal of Political Stability Archive (publishedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Educational Innovations and Technology
Keywords
generative AI, AI adoption in higher education, university students, digital divide, socioeconomic factors, household income, parental education, internet access, gender differences, academic disciplines, academic performance

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-29.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-29.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review