Rafael Alves ITM

Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education

Leitura de 33 min

Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education

🎯 Resumos

Abstract Original

Aim/Purpose To examine how ethical awareness, cognitive appraisal (trust and perceived usefulness), digital competence, academic performance, and gender influence university students’ ethical use of ChatGPT and academic integrity.

📚 Bibliografia

SHISHAKLY, Rima; NACHOUKI, Mirna. Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education. Journal of Information Technology Education: Research, v. 25, p. 08, 2026.

🧠 Minhas Notas & Análise

Objetivo

  • Investigar os fatores que influenciam o uso ético e responsável do ChatGPT por estudantes universitários e suas implicações para a integridade acadêmica.
  • Examinar especificamente como quatro dimensões éticas (transparência na declaração de uso, evitação de plágio, consciência de viés algorítmico e uso responsável como complemento ao aprendizado) moldam a integridade.
  • Avaliar os papéis mediadores da “confiança na IA” e da “utilidade percebida”, além dos efeitos moderadores do letramento digital, do desempenho acadêmico (CGPA) e do gênero.

🧬 Método

  • O estudo adotou uma abordagem de pesquisa quantitativa com um design de survey transversal (cross-sectional).
  • A amostra (por conveniência) foi composta por 318 estudantes de graduação ativos no uso do ChatGPT, provenientes de cinco universidades (públicas e privadas) nos Emirados Árabes Unidos (Dubai, Sharjah e Ajman).
  • A coleta de dados foi realizada via questionário online com escala Likert de 5 pontos.
  • A análise estatística dos dados foi feita utilizando a Modelagem de Equações Estruturais por Mínimos Quadrados Parciais (PLS-SEM).

🏆 Resultados

  • Impacto Direto: Todas as quatro variáveis éticas (transparência, evitação de plágio, consciência de viés e uso responsável) preveem de forma positiva e significativa a integridade acadêmica. A transparência demonstrou o maior efeito direto.
  • Papel Mediador: A “Confiança na IA” e a “Utilidade Percebida” atuam como mediadores parciais. Por exemplo, a transparência e a consciência de viés aumentam a confiança do aluno na ferramenta, o que, por sua vez, promove o comportamento acadêmico ético.
  • Papel Moderador: O letramento digital e o histórico acadêmico (CGPA) fortalecem de forma significativa as relações entre os preditores éticos e a integridade. Alunos com maior competência digital e notas mais altas extraem maiores benefícios éticos do uso do ChatGPT.
  • Diferenças Demográficas e de Desempenho: Estudantes com CGPA alto relataram uso mais frequente e eficaz da IA em todas as tarefas. Estudantes do sexo masculino utilizam o ChatGPT consistentemente mais do que as mulheres, especialmente em tarefas técnicas como programação e resolução de problemas. O uso da ferramenta também aumenta conforme os alunos avançam em seus anos letivos.

📝 Comentários Extras

  • O artigo acerta muito ao ampliar o debate da integridade acadêmica na era da IA generativa. Sair da visão limitada de “apenas plágio e trapaça” para incluir a transparência e a consciência de vieses algorítmicos é fundamental para criar políticas institucionais modernas.
  • Uma limitação importante (e bem reconhecida pelos autores) é a dependência exclusiva de dados autorrelatados (self-reported data). O viés de desejabilidade social pode fazer com que os estudantes subnotifiquem práticas antiéticas ou exagerem em seu uso responsável.
  • A diferença de gênero no uso técnico da IA (homens usando mais para programação) reflete lacunas mais amplas de confiança e exposição em áreas STEM, um ponto que merece atenção na promoção de um letramento em IA mais igualitário.

🔗 Conexões do Cofre

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🔣 Bibliografia Latex

@article{shishakly2026,
  title = {Academic {{Integrity}} and {{Students}}' {{Ethical Use}} of {{ChatGPT}} in {{Higher Education}}},
  author = {Shishakly, Rima and Nachouki, Mirna},
  year = 2026,
  journal = {Journal of Information Technology Education: Research},
  volume = {25},
  pages = {08},
  issn = {1547-9714, 1539-3585},
  doi = {10.28945/5730},
  urldate = {2026-06-11},
  langid = {english},
}

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🖍️ Notas e Destaques

  • Most research focuses narrowly on plagiarism or cheating, with minimal attention to broader ethical dimensions such as transparency, responsible use, and algorithmic bias (p. 2) Comentário: Pontos importantes! Há mais do que apenas plágio e roubo.
  • Most research focuses narrowly on plagiarism or cheating, with minimal attention to broader ethical dimensions such as transparency, responsible use, and algorithmic bias (p. 2) Comentário: Pontos importantes! Há mais do que apenas plágio e roubo.
  • To address these gaps, this study proposes an integrative framework linking AI-specific ethical practices to academic integrity. Ethical engagement with ChatGPT is conceptualized through four interrelated dimensions: transparency in disclosing AI assistance (Lamb, 2023), responsible use as a complement to critical thinking rather than a substitute (Essel et al., 2024; Iqbal & Iqbal, 2024; Parsakia, 2023), plagiarism avoidance to preserve originality (Kotsis, 2024), and awareness of algorithmic bias to support fairness and credibility in academic work (Baker & Hawn, 2022). (p. 3) Comentário: Objetivo do trabalho e modelo conceitual
  • To address these gaps, this study proposes an integrative framework linking AI-specific ethical practices to academic integrity. Ethical engagement with ChatGPT is conceptualized through four interrelated dimensions: transparency in disclosing AI assistance (Lamb, 2023), responsible use as a complement to critical thinking rather than a substitute (Essel et al., 2024; Iqbal & Iqbal, 2024; Parsakia, 2023), plagiarism avoidance to preserve originality (Kotsis, 2024), and awareness of algorithmic bias to support fairness and credibility in academic work (Baker & Hawn, 2022). (p. 3) Comentário: Objetivo do trabalho e modelo conceitual
  • Accordingly, this study aims to examine the factors influencing university students’ ethical and responsible use of ChatGPT and their implications for academic integrity. Specifically, it investigates how transparency, plagiarism avoidance, bias awareness, and responsible use shape integrity outcomes, while considering the mediating roles of trust in AI and perceived usefulness, and the moderating effects of CGPA and gender as indicators of academic performance and demographic variation in technology-related ethical behaviour. (p. 3)
  • Accordingly, this study aims to examine the factors influencing university students’ ethical and responsible use of ChatGPT and their implications for academic integrity. Specifically, it investigates how transparency, plagiarism avoidance, bias awareness, and responsible use shape integrity outcomes, while considering the mediating roles of trust in AI and perceived usefulness, and the moderating effects of CGPA and gender as indicators of academic performance and demographic variation in technology-related ethical behaviour. (p. 3)
  • RQ1: How does transparency about using ChatGPT influence students’ academic integrity? RQ2: How does avoiding plagiarism when using ChatGPT influence students’ academic integrity? RQ3: How does awareness of bias in ChatGPT influence students’ academic integrity? RQ4: How does the responsible use of ChatGPT as a learning tool influence students’ academic integrity? RQ5: Does trust in AI mediate the relationships between ethical usage dimensions and academic integrity? RQ6: Does perceived usefulness of ChatGPT mediate the relationships between ethical usage dimensions and academic integrity? RQ7: Does digital literacy moderate the relationships between ethical use dimensions and academic integrity? RQ8: Do CGPA and gender moderate the relationships between ethical usage dimensions and academic integrity? (p. 3)
  • RQ1: How does transparency about using ChatGPT influence students’ academic integrity? (p. 3)
  • Technology Acceptance Theory (TAM) and Self-Regulated Learning Theory (p. 5) Comentário: Modelos teóricos que não são apresentados no trabalho em questão e são apenas indicados com baseados em
  • Technology Acceptance Theory (TAM) and Self-Regulated Learning Theory (p. 5) Comentário: Modelos teóricos que não são apresentados no trabalho em questão e são apenas indicados com baseados em
  • Furthermore, the mediating variables’ effects of trust in AI and perceived usefulness align with the Technology Acceptance Model (TAM) and Social Cognitive Theory (SCT), (p. 6) Comentário: Outra vez a mesma coisa. Teorias apresentadas mas não evidenciadas.
  • Furthermore, the mediating variables’ effects of trust in AI and perceived usefulness align with the Technology Acceptance Model (TAM) and Social Cognitive Theory (SCT), (p. 6) Comentário: Outra vez a mesma coisa. Teorias apresentadas mas não evidenciadas.
  • Transparency has emerged as a foundational principle in ethical AI discourse, particularly within education, where disclosure practices directly affect academic credibility (p. 8)
  • Transparency has emerged as a foundational principle in ethical AI discourse, particularly within education, where disclosure practices directly affect academic credibility (p. 8)
  • Plagiarism is another dominant concern in the literature on generative AI in education. (p. 8)
  • Plagiarism is another dominant concern in the literature on generative AI in education. (p. 8)
  • Beyond plagiarism, responsible use has become a central theme in discussions of AI-supported learning. (p. 8)
  • Beyond plagiarism, responsible use has become a central theme in discussions of AI-supported learning. (p. 8)
  • Algorithmic bias further complicates the ethical landscape of AI in higher education. (p. 9)
  • Algorithmic bias further complicates the ethical landscape of AI in higher education. (p. 9)
  • To achieve the study’s objectives, a quantitative research approach using a cross-sectional survey design was adopted (Creswell & Plano Clark, 2018). (p. 9) Comentário: Quantitativo via survey
  • To achieve the study’s objectives, a quantitative research approach using a cross-sectional survey design was adopted (Creswell & Plano Clark, 2018). (p. 9) Comentário: Quantitativo via survey
  • While this approach enabled efficient data collection in a technology-rich academic environment, it also introduced potential sampling bias, as students who are more digitally engaged or motivated may be overrepresented. Consequently, sampling error may arise from underrepresentation of students with limited AI exposure or lower digital literacy, which may restrict the generalizability of findings beyond the sampled institutions and region (Etikan et al., 2015; Jager et al., 2017). (p. 9) Comentário: Limitação importante de ser destacada.
  • While this approach enabled efficient data collection in a technology-rich academic environment, it also introduced potential sampling bias, as students who are more digitally engaged or motivated may be overrepresented. Consequently, sampling error may arise from underrepresentation of students with limited AI exposure or lower digital literacy, which may restrict the generalizability of findings beyond the sampled institutions and region (Etikan et al., 2015; Jager et al., 2017). (p. 9) Comentário: Limitação importante de ser destacada.
  • Importantly, pilot study participants were excluded from the final data collection to prevent response contamination and preserve statistical independence between the pilot and main samples. (p. 10)
  • Importantly, pilot study participants were excluded from the final data collection to prevent response contamination and preserve statistical independence between the pilot and main samples. (p. 10)
  • All construct items were measured using a 5-point Likert scale with the following response categories: 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. A neutral midpoint allowed respondents to express indifference or uncertainty, thereby reducing forced-choice bias. (p. 10)
  • All construct items were measured using a 5-point Likert scale with the following response categories: 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. A neutral midpoint allowed respondents to express indifference or uncertainty, thereby reducing forced-choice bias. (p. 10)
  • Partial Least Squares Structural Equation Modeling (PLS-SEM) (p. 10)
  • Partial Least Squares Structural Equation Modeling (PLS-SEM) (p. 10)
  • Although the quantitative survey approach enables systematic hypothesis testing, it is subject to several limitations. First, the study relies on self-reported data, which may be affected by social desirability bias, recall error, and respondents’ subjective interpretations of ethical behaviour. Students may underreport unethical practices or overstate responsible use of AI. Second, the exclusive use of a single data source restricts methodological triangulation. No behavioural logs, instructor evaluations, or qualitative interviews were incorporated to validate reported practices. As a result, causal inferences should be interpreted cautiously, and the findings reflect perceived rather than objectively observed ethical behaviour. Third, the use of convenience sampling limits statistical generalizability to the broader student population in the UAE or other regions (p. 10)
  • Although the quantitative survey approach enables systematic hypothesis testing, it is subject to several limitations. First, the study relies on self-reported data, which may be affected by social desirability bias, recall error, and respondents’ subjective interpretations of ethical behaviour. Students may underreport unethical practices or overstate responsible use of AI. Second, the exclusive use of a single data source restricts methodological triangulation. No behavioural logs, instructor evaluations, or qualitative interviews were incorporated to validate reported practices. As a result, causal inferences should be interpreted cautiously, and the findings reflect perceived rather than objectively observed ethical behaviour. Third, the use of convenience sampling limits statistical generalizability to the broader student population in the UAE or other regions (p. 10)
  • Convergent validity was established, as Average Variance Extracted (AVE) values ranged from 0.61 to 0.68, surpassing the 0.50 benchmark. (p. 12) Comentário: Parte do método pelo que parece.
  • Convergent validity was established, as Average Variance Extracted (AVE) values ranged from 0.61 to 0.68, surpassing the 0.50 benchmark. (p. 12) Comentário: Parte do método pelo que parece.
  • The confirmatory factor analysis (CFA) indicated excellent model fit (RMSEA = 0.05, CFI = 0.95), providing evidence of the instrument’s overall reliability and validity (p. 12) Comentário: Parte do método.
  • The confirmatory factor analysis (CFA) indicated excellent model fit (RMSEA = 0.05, CFI = 0.95), providing evidence of the instrument’s overall reliability and validity (p. 12) Comentário: Parte do método.
  • The findings reveal that students are aware of the importance of avoiding plagiarism when using AI tools like ChatGPT and acknowledge the need to maintain academic integrity through proper citation and responsible use. However, students also expressed that ChatGPT positively contributes to their academic integrity, regardless of the AI task or academic support involved. (p. 18)
  • The findings reveal that students are aware of the importance of avoiding plagiarism when using AI tools like ChatGPT and acknowledge the need to maintain academic integrity through proper citation and responsible use. However, students also expressed that ChatGPT positively contributes to their academic integrity, regardless of the AI task or academic support involved. (p. 18)
  • The current results suggest that male students’ higher engagement in programming and technical applications may stem from greater familiarity with computational tools and coding environments, which in turn enhances their ability to integrate AI systems effectively. (p. 18)
  • The current results suggest that male students’ higher engagement in programming and technical applications may stem from greater familiarity with computational tools and coding environments, which in turn enhances their ability to integrate AI systems effectively. (p. 18)
  • Similarly, students with higher CGPAs appear to use ChatGPT more effectively due to stronger analytical, linguistic, and self-regulatory skills, enabling them to refine prompts, critically evaluate responses, and align AI-generated outputs with academic standards (p. 18)
  • Similarly, students with higher CGPAs appear to use ChatGPT more effectively due to stronger analytical, linguistic, and self-regulatory skills, enabling them to refine prompts, critically evaluate responses, and align AI-generated outputs with academic standards (p. 18)
  • Most research focuses narrowly on plagiarism or cheating, with minimal attention to broader ethical dimensions such as transparency, responsible use, and algorithmic bias. (p. 2) Comentário: Pontos importantes! H mais do que apenas plgio e roubo.
  • To address these gaps, this study proposes an integrative framework linking AI-specific ethical practices to academic integrity. Ethical engagement with ChatGPT is conceptualized through four interrelated dimensions: transparency in disclosing AI assistance (Lamb, 2023), responsible use as a complement to critical thinking rather than a substitute (Essel et al., 2024; Iqbal & Iqbal, 2024; Parsakia, 2023), plagiarism avoidance to preserve originality (Kotsis, 2024), and awareness of algorithmic bias to support fairness and credibility in academic work (Baker & Hawn, 2022). (p. 3) Comentário: Objetivo do trabalho e modelo conceitual
  • Accordingly, this study aims to examine the factors influencing university students’ ethical and responsible use of ChatGPT and their implications for academic integrity. Specifically, it investigates how transparency, plagiarism avoidance, bias awareness, and responsible use shape integrity outcomes, while considering the mediating roles of trust in AI and perceived usefulness, and the moderating effects of CGPA and gender as indicators of academic performance and demographic variation in technology-related ethical behaviour. (p. 3)
  • RQ1: How does transparency about using ChatGPT influence students’ academic integrity? (p. 3)
  • Technology Acceptance Theory (TAM) and Self-Regulated Learning Theory. (p. 5) Comentário: Modelos tericos que no so apresentados no trabalho em questo e so apenas indicados com baseados em
  • Furthermore, the mediating variables’ effects of trust in AI and perceived usefulness align with the Technology Acceptance Model (TAM) and Social Cognitive Theory (SCT), (p. 6) Comentário: Outra vez a mesma coisa. Teorias apresentadas mas no evidenciadas.
  • Transparency has emerged as a foundational principle in ethical AI discourse, particularly within education, where disclosure practices directly affect academic credibility. (p. 8)
  • Plagiarism is another dominant concern in the literature on generative AI in education. (p. 8)
  • Beyond plagiarism, responsible use has become a central theme in discussions of AI-supported learning. (p. 8)
  • Algorithmic bias further complicates the ethical landscape of AI in higher education. (p. 9)
  • To achieve the study’s objectives, a quantitative research approach using a cross-sectional survey design was adopted (Creswell & Plano Clark, 2018). (p. 9) Comentário: Quantitativo via survey
  • While this approach enabled efficient data collection in a technology-rich academic environment, it also introduced potential sampling bias, as students who are more digitally engaged or motivated may be overrepresented. Consequently, sampling error may arise from underrepresentation of students with limited AI exposure or lower digital literacy, which may restrict the generalizability of findings beyond the sampled institutions and region (Etikan et al., 2015; Jager et al., 2017). (p. 9) Comentário: Limitao importante de ser destacada.
  • Importantly, pilot study participants were excluded from the final data collection to prevent response contamination and preserve statistical independence between the pilot and main samples. (p. 10)
  • All construct items were measured using a 5-point Likert scale with the following response categories: 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. A neutral midpoint allowed respondents to express indifference or uncertainty, thereby reducing forced-choice bias. (p. 10)
  • Partial Least Squares Structural Equation Modeling (PLS-SEM) (p. 10)
  • Although the quantitative survey approach enables systematic hypothesis testing, it is subject to several limitations. First, the study relies on self-reported data, which may be affected by social desirability bias, recall error, and respondents’ subjective interpretations of ethical behaviour. Students may underreport unethical practices or overstate responsible use of AI. Second, the exclusive use of a single data source restricts methodological triangulation. No behavioural logs, instructor evaluations, or qualitative interviews were incorporated to validate reported practices. As a result, causal inferences should be interpreted cautiously, and the findings reflect perceived rather than objectively observed ethical behaviour. Third, the use of convenience sampling limits statistical generalizability to the broader student population in the UAE or other regions (p. 10)
  • Convergent validity was established, as Average Variance Extracted (AVE) values ranged from 0.61 to 0.68, surpassing the 0.50 benchmark. (p. 12) Comentário: Parte do mtodo pelo que parece.
  • The confirmatory factor analysis (CFA) indicated excellent model fit (RMSEA = 0.05, CFI = 0.95), providing evidence of the instrument’s overall reliability and validity. (p. 12) Comentário: Parte do mtodo.
  • The findings reveal that students are aware of the importance of avoiding plagiarism when using AI tools like ChatGPT and acknowledge the need to maintain academic integrity through proper citation and responsible use. However, students also expressed that ChatGPT positively contributes to their academic integrity, regardless of the AI task or academic support involved. (p. 18)
  • The current results suggest that male students’ higher engagement in programming and technical applications may stem from greater familiarity with computational tools and coding environments, which in turn enhances their ability to integrate AI systems effectively. (p. 18)
  • Similarly, students with higher CGPAs appear to use ChatGPT more effectively due to stronger analytical, linguistic, and self-regulatory skills, enabling them to refine prompts, critically evaluate responses, and align AI-generated outputs with academic standards (p. 18)

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