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Cited by 54Psychometric Methodologies and TestingGender and Technology in EducationHealth Education and Validation

Adaptation and psychometric properties of a brief version of the general self-efficacy scale for use with artificial intelligence (GSE-6AI) among university students

Open access
Morales-García, Wilter C., Sairitupa-Sanchez, Liset Z., Morales-García, Sandra B., Morales-García, Mardel·2024·Frontiers in Education
DOI 10.3389/feduc.2024.1293437
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Abstract

Background: Individual beliefs about one’s ability to carry out tasks and face challenges play a pivotal role in academic and professional formation. In the contemporary technological landscape, Artificial Intelligence (AI) is effecting profound changes across multiple sectors. Adaptation to this technology varies greatly among individuals. The integration of AI in the educational setting has necessitated a tool that measures self-efficacy concerning the adoption and use of this technology. Objective: To adapt and validate a short version of the General Self-Efficacy Scale (GSE-6) for self-efficacy in the use of Artificial Intelligence (GSE-6AI) in a university student population. Methods: An instrumental study was conducted with the participation of 469 medical students aged between 18 and 29 ( M = 19.71; SD = 2.47). The GSE-6 was adapted to the AI context, following strict translation and cultural adaptation procedures. Its factorial structure was evaluated through confirmatory factorial analysis (CFA). Additionally, the factorial invariance of the scale based on gender was studied. Results: The GSE-6AI exhibited a unidimensional structure with excellent fit indices. All item factorial loads surpassed the recommended threshold, and both Cronbach’s Alpha (α) and McDonald’s Omega (ω) achieved a value of 0.91. Regarding factorial invariance by gender, the scale proved to maintain its structure and meaning in both men and women. Conclusion: The adapted GSE-6AI version is a valid and reliable tool for measuring self-efficacy in the use of Artificial Intelligence among university students. Its unidimensional structure and gender-related factorial invariance make it a robust and versatile tool for future research and practical applications in educational and technological contexts.

AI snapshot

Written from the paper’s full text.

TL;DR

The study adapted the 6-item General Self-Efficacy Scale into an AI-specific version (GSE-6AI) and validated it among 469 Peruvian medical students. The GSE-6AI showed a unidimensional structure, high internal consistency (α = ω = 0.91), and gender invariance.

Key findings

  • The GSE-6AI had excellent CFA fit: χ² = 17.480, df = 9, p < 0.01; CFI = 0.99; TLI = 0.98; RMSEA = 0.04 (90% CI: 0.02–0.07); SRMR = 0.02.
  • Internal consistency was high, with Cronbach’s alpha and McDonald’s omega both 0.91; all item factor loadings exceeded the recommended threshold (> 0.50).
  • Content validity was supported: all Aiken’s V values were above 0.70, and the lower 95% CI limits exceeded 0.59; items 2 and 6 reached V = 1.00 for clarity.
  • Factorial invariance by gender was supported across configural, metric, scalar, and strict models, with CFI differences of 0.003, −0.001, and 0, all below the 0.010 threshold.

Methods

  • Instrumental, cross-sectional study with 469 medical students aged 18–29 (M = 19.71; SD = 2.47) from three Peruvian universities; 53.3% were women, and inclusion required at least 8 hours per week of AI-related academic activity.
  • The GSE-6 was translated, back-translated, culturally adapted, expert-reviewed, and piloted with 13 medical students; content validity used Aiken’s V.
  • Analyses included descriptive item statistics, confirmatory factor analysis with the MLR estimator in R/lavaan, multi-group CFA for gender invariance, and reliability via Cronbach’s alpha and McDonald’s omega.

Limitations

  • The paper states that the GSE-6AI requires ongoing validation across diverse cultural and educational settings, that the cross-sectional design prevents causal conclusions, and that self-reported data may be affected by social desirability bias.
  • The paper also notes that invariance was tested only by gender, and future work should examine age, educational level, and cultural background.
  • Evident from the text: the sample was a non-random convenience sample of medical students from three Peruvian universities, which may limit generalizability to other student populations or contexts.

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

Integrity signals

Registry

Crossref · 10.3389/feduc.2024.1293437

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 16 cited sources checked · 0 supported · 0 flagged

  • Findingcitation

    The GSE-6AI has high internal consistency, with Cronbach's Alpha (α) and McDonald's Omega (ω) coefficients of 0.91.

    “Furthermore, the GSE-6AI has shown high internal consistency, with Cronbach's Alpha (α) and McDonald's Omega (ω) coefficients of 0.91, indicating adequate reliability for measuring self-efficacy in the context of artificial intelligence (Raykov and Hancock, 2005).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Raykov and Hancock, 2005

      Not checked in the free run.

  • Findingown data

    Item 3 achieved a perfect representativeness score (V = 1.00; 95% CI: 0.85-1.00).

    “and representativeness (V = 1.00;CI 95%: 0.85-1.00)for item 3”
    Quote in the paperNot a substantial claim
  • Findingown data

    A confirmatory factor analysis (CFA) of the GSE-6AI confirmed its unidimensionality.

    “Additionally, a Confirmatory Factor Analysis (CFA) was conducted for the GSE-6AI, confirming its unidimensionality.”
    Quote in the paperSubstantial claim
  • Findingown data

    Different levels of invariance (configural, metric, scalar, and strict) were tested and consistently showed good fits at all levels.

    “Through a hierarchical methodology, different levels of invariance were tested: configural, metric, scalar, and strict, consistently showing good fits at all levels and suggesting that the scale maintains its structure and meaning across genders, indicating that the scale measures the general selfefficacy construct assisted by artificial intelligence equivalently in both men and women.”
    Quote in the paperSubstantial claim
  • Findingnone given

    The GSE-6AI demonstrated superior fit indices compared to the previously established Artificial Intelligence Self-Efficacy Scale (AISES).

    “In this regard, the GSE-6AI demonstrated superior fit indices compared to the previously established Artificial Intelligence Self-Efficacy Scale (AISES).”
    Quote in the paperSubstantial claim
  • Classified as probably not a substantial claim. 14
  • May combine two claims. 14

  • Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing), S4 (based).

    Findingnone given

    Item 4 had the highest mean among GSE-6AI items (M = 2.58, SD = 0.87).

    “item 4”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The GSE-6AI CFA model had the following fit indices: χ2 = 17.480, df = 9, p < 0.01; CFI = 0.99; TLI = 0.98; RMSEA = 0.04 (90% CI: 0.02-0.07); SRMR = 0.02.

    “Specifically, the obtained indices were as follows: χ 2 = 17.480, df = 9, p < 0.01; CFI = 0.99; TLI = 0.98; RMSEA = 0.04 (90% CI: 0.02-0.07)and SRMR = 0.02.”
    Quote in the paperSubstantial claim

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

    Findingown data

    Items 1 and 6 shared the lowest mean among GSE-6AI items (M = 2.42).

    “Meanwhile, items 1”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Cronbach's alpha and McDonald's omega for the GSE-6AI were both 0.91.

    “both Cronbach's Alpha (α) and McDonald's Omega (ω) achieved a value of 0.91.”
    • Also stated in: § 3.3 Validity based on internal structure · plain
      “with a Cronbach's Alpha (α) and McDonald's Omega (ω) of 0.91”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    All GSE-6AI items had skewness and kurtosis values within ±1.5; skewness ranged from -0.21 to 0.08 and kurtosis from -0.55 to -0.70.

    “all items showed skewness (g1) and kurtosis (g2) values within the acceptable range of ± 1.5, indicating a roughly normal distribution for each item's responses.Specifically, skewness ranged from -0.21 to 0.08, and kurtosis from -0.55 to -0.70.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The CFI differences between invariance models M1-M2, M2-M3, and M3-M4 were 0.003, -0.001, and 0, respectively.

    “The analysis of differences in the Comparative Fit Index ( CFI) shows minimal variations between models, with CFI values of 0.003, -0.001, and 0 for the transitions from M1 to M2, M2 to M3, and M3 to M4, respectively.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    All GSE-6AI item-total correlations exceeded 0.30, ranging from 0.67 to 0.77.

    “all items exceeded the 0.30 acceptability threshold, with values ranging from 0.67 to 0.77.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    This study confirmed the psychometric validity and gender invariance of the GSE-6AI.

    “This study, by confirming the psychometric validity and gender invariance of the GSE-6AI, underscores the importance of technological self-efficacy for successful integration of AI in education and demonstrates the scale's universality and adaptability to different cultural and educational contexts.”
    Quote in the paperSubstantial claim

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

    Premisecitation

    Research such as Grassini (2023), Moodi et al. (2023), and Wang and Chuang (2023) generally reports high reliability coefficients for scales related to self-efficacy and attitudes toward artificial intelligence.

    “Research such as that by Grassini (2023), Moodi et al. (2023), and Wang and Chuang (2023) generally report high reliability coefficients for scales related to self-efficacy and attitudes toward artificial intelligence.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Grassini (2023) source

      Not checked in the free run.

    • cites Moodi et al. (2023) source

      Not checked in the free run.

    • cites Wang and Chuang (2023)

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    The GSE-6AI was administered to a pilot group of 13 medical students, and the results indicated clear and readable comprehension.

    “To test the readability and comprehensibility of this adaptation, it was administered to a pilot group of 13 medical students.The results indicated clear and readable comprehension (Table 2).”
    Quote in the paperSubstantial claim

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

    Premisecitation

    Wang and Chuang achieved a Cronbach's Alpha of 0.852, while Grassini reported Cronbach's Alpha and McDonald's Omega values indicating good internal consistency for different factors.

    “For instance, Wang and Chuang achieved a Cronbach's Alpha of 0.852, while Grassini reported Cronbach's Alpha and McDonald's Omega values indicating good internal consistency for different factors.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Wang and Chuang

      Not checked in the free run.

    • cites Grassini

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    Content validation of the GSE-6AI was conducted through expert judgment.

    “Additionally, content validation was conducted through expert judgment.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    The GSE-6AI items are robust in relevance, representativeness, and clarity from a broader population perspective.

    “underscoring the robustness of the items in terms of relevance, representativeness, and clarity from a broader perspective”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductcitation

    The sample size was based on a calculation with expected effect size λ = 0.10, α = 0.05, and power 1-β = 0.80; the minimum required was 200 participants, and 469 students were recruited.

    “The sample selection was based on a precise calculation using an electronic calculator (Soper, 2023), considering specific variables such as the number of observed and latent aspects in the proposed model, an expected effect size of λ = 0.10, a statistical significance of α = 0.05, and a statistical power level of 1-β = 0.80.”
    • Also stated in: § 2.1 Design and participants · plain
      “Although the minimum sample size required for the model structure was 200 participants, a total of 469 students were recruited.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Soper, 2023

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Each item of the GSE-6AI is relevant and reinforces the internal coherence of the scale.

    “Moreover, the item factor loadings exceeded the recommended threshold (λ > 0.50), indicating that each item is relevant and reinforces the internal coherence of the scale.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    The GSE-6AI requires ongoing validation across diverse cultural and educational settings to ensure its generalizability and accuracy in different populations.

    “The GSE-6AI, though validated in a specific context, requires ongoing validation across diverse cultural and educational settings to ensure its generalizability and accuracy in different populations.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationreasoning

    The findings support the idea that strengthening AI self-efficacy among students and professionals can facilitate greater acceptance and effective use of these technologies, enhancing associated educational and occupational benefits.

    “The findings support the idea that strengthening AI self-efficacy among students and professionals can facilitate greater acceptance and effective use of these technologies, enhancing associated educational and occupational benefits.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Self-efficacy is a key predictor of technology adoption, self-directed learning, and the ability to face technological challenges.

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

    Interpretationreasoning

    The adaptation and validation of the GSE-6AI in the Peruvian educational context represent a significant contribution to understanding individual perceptions of competence in using AI.

    “The adaptation and validation of the GSE-6AI in the Peruvian educational context represent a significant contribution to understanding individual perceptions of competence in using AI.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    The results confirm the clarity and applicability of the adapted scale across different cultural contexts and populations.

    “The results confirm the clarity and applicability of the adapted scale across different cultural contexts and populations, in line with research like that of Moodi et al. (2023), who analyzed the psychometric characteristics of a readiness scale for AI in medical students, demonstrating the usefulness of having specific assessment tools for different areas of AI application.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Moodi et al. (2023) source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Adapting the GSE-6 scale to the AI context broadens its scope of applicability.

    “Adapting the GSE-6 scale to the AI context not only broadens its scope of applicability but also highlights the importance of domain specificity in evaluating self-efficacy.”
    Quote in the paperSubstantial claim

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

    Study design and conductnone given

    The cross-sectional nature of the study prevents establishing causal relationships between the examined variables.

    “Additionally, the cross-sectional nature of the study prevents establishing causal relationships between the examined variables.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The GSE-6AI demonstrates universality and adaptability to different cultural and educational contexts.

    “This study, by confirming the psychometric validity and gender invariance of the GSE-6AI, underscores the importance of technological self-efficacy for successful integration of AI in education and demonstrates the scale's universality and adaptability to different cultural and educational contexts.”
    Quote in the paperSubstantial claim

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

    Premisecitation

    A common trend in the importance of validating the psychometric properties of these instruments across specific cultural contexts and various AI application domains was found when compared with similar studies.

    “When compared with similar studies, such as Wang and Chuang (2023), who developed and validated an AI self-efficacy scale, and Grassini (2023), who adapted a scale for attitudes toward AI, a common trend in the importance of validating the psychometric properties of these instruments across specific cultural contexts and various AI application domains was found.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Wang and Chuang (2023)

      Not checked in the free run.

    • cites Grassini (2023) source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The rigor in the process of translation and cultural adaptation, followed by validation by experts, ensures that the GSE-6AI is a reliable and relevant tool for measuring self-efficacy in AI use.

    “The rigor in the process of translation and cultural adaptation, followed by validation by experts, ensures that the GSE-6AI is a reliable and relevant tool for measuring self-efficacy in AI use, respecting sociolinguistic variations and adapting to the contextual reality where it is applied.”
    Quote in the paperSubstantial claim

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

    Study design and conductnone given

    Self-reported scales are susceptible to biases such as social desirability.

    “While self-reported scales are common and valuable tools, they are susceptible to biases such as social desirability.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    The validation of the GSE-6AI offers a significant contribution to the psychometric understanding of how individuals perceive their ability to interact with artificial intelligence technology.

    “The validation of the GSE-6AI offers a significant contribution to the psychometric understanding of how individuals perceive their ability to interact with artificial intelligence technology.”
    Quote in the paperNot a substantial claim
26 of 26

  • The paper relies on this without support.

    Medical students from three Peruvian universities are representative of the university student population for validating the GSE-6AI.

    “To adapt and validate a short version of the General Self-Efficacy Scale (GSE-6) for self-efficacy in the use of Artificial Intelligence (GSE-6AI) in a university student population.”
    “Furthermore, a non-random convenience sampling method was used to select medical students from three Peruvian universities who were between their first and tenth study cycles.”

  • The paper relies on this without support.

    The GSE-6 has robust psychometric properties in various cultural contexts and in both clinical and non-clinical samples.

    “Moreover, the GSE-6 has demonstrated robust psychometric properties in various cultural contexts and in both clinical and non-clinical samples.”

  • The paper relies on this without support.

    Self-efficacy is a key predictor of technology adoption, self-directed learning, and the ability to face technological challenges.

    “Since self-efficacy has been identified as a key predictor of technology adoption, self-directed learning, and the ability to face technological challenges, the GSE-6AI can be used in developing interventions aimed at improving AI-related self-efficacy among students and professionals, thereby facilitating a smoother transition toward integrating AI into various practices.”

  • The paper relies on this without support.

    AI is emerging as a critical tool in educational and professional environments.

    “The results of the psychometric validation of the GSE-6AI provide solid evidence of its utility in educational and professional environments, where AI is emerging as a critical tool.”
4 of 4

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A significant number of AI literacy scales emphasise attitudes or self-efficacy, often at the expense of deeper cognitive and ethical engagement with AI systems.

    “Moreover, a significant number of scales, such as the Attitudes Toward Artificial Intelligence Scale (Sindermann et al., 2021), the Medical Artificial Intelligence Readiness Scale for Medical Students and the General Self-Efficacy in the use of Artificial Intelligence (Morales-García et al., 2024), emphasise attitudes or self-efficacy, often at the expense of deeper cognitive and ethical”

    From A tridimensional model of AI literacy: An empirical analysis of student performance and demographic patterns in higher education (2025) · cites this paper as Morales-García et al., 2024

    The Discussion (page unavailable) identifies the GSE-6AI as a measure of self-efficacy in using AI and says that knowledge and understanding of AI also need to be addressed. This supports using the scale as an example of a self-efficacy-focused instrument, but the paper does not establish that a significant number of scales neglect deeper cognitive and ethical engagement. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • GSE-6AI, MAIRS-MC, and SNAIL have been validated specifically for medical students.

    “GSE-6AI30, MAIRS-MC43, and SNAIL45 have been validated specifically for medical students, which makes them the instruments of choice if medical students are to be assessed.”

    From A systematic review of AI literacy scales (2024) · cites this paper as [30]

  • GSE-6AI, Hwang et al.'s instrument, and Chan & Zhou's EVT-based instrument (knowledge of general AI subscale) are promising instruments.

    “GSE-6AI30, Hwang et al.’s instrument39, and Chan & Zhou’s EVT based instrument (knwl. of gen. AI subscale)37 are also promising instruments with good evidence for structural validity and internal consistency, however, their content validity needs to be checked on the higher-education students.”

    From A systematic review of AI literacy scales (2024) · cites this paper as [30]

  • GSE-6AI, Hwang et al.'s instrument, and Chan & Zhou's EVT-based instrument (knowledge of general AI subscale) showed good evidence for structural validity.

    “GSE-6AI30, Hwang et al.’s instrument39, and Chan & Zhou’s EVT based instrument (knwl. of gen. AI subscale)37 are also promising instruments with good evidence for structural validity and internal consistency, however, their content validity needs to be checked on the higher-education students.”

    From A systematic review of AI literacy scales (2024) · cites this paper as [30]

  • GSE-6AI, Hwang et al.'s instrument, and Chan & Zhou's EVT-based instrument (knowledge of general AI subscale) showed good evidence for internal consistency.

    “GSE-6AI30, Hwang et al.’s instrument39, and Chan & Zhou’s EVT based instrument (knwl. of gen. AI subscale)37 are also promising instruments with good evidence for structural validity and internal consistency, however, their content validity needs to be checked on the higher-education students.”

    From A systematic review of AI literacy scales (2024) · cites this paper as [30]

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.

  • Content-validity results cite the wrong table

    Minor

    The discussion of Aiken's V results points to Table 3, but those results appear in Table 2. Table 3 instead lists the original, translated, and adapted items.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 7

  1. Hong, 2022, I was born to love AI: The influence of social status on AI self-efficacy and intentions to use AI., Int. J. Commun., 16
  2. Jorgensen, 2021, semTools: Useful tools for structural equation modeling. In The Comprehensive R Archive Network.
  3. Kline, 2016, Principles and practice of structural equation modeling.
  4. McDonald, 1999, Test Theory: A United Treatment.
  5. Muthen, 2017, Mplus Statistical Analysis with latent variables. User’s guide.
  6. Schwarzer, 1997, Optimistic self-beliefs: Assessment of general perceived self-efficacy in thirteen cultures.
  7. Soper, 2023, A-priori Sample Size Calculator for structural equation models. Software.

Cited by

Sorted by citations

Related

Authors
Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B., & Morales-García, M.
Affiliations
  • Universidad Peruana Unión (PE)
  • Universidad Científica del Sur (PE)
Corresponding author
Morales-García, Wilter C.
ORCID
Venue
Frontiers in Education
Publisher
Frontiers Media SA
Published
2024-03-08
Volume
9
ISSN
2504-284X (ISSN-L)
DOI
10.3389/feduc.2024.1293437
OpenAlex
W4392592634
Cited by
54
References
38
Citations by year
2026: 35 · 2025: 16 · 2024: 3
FWCI
16.43
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Social Sciences › Gender Studies › Gender and Technology in Education
  • Social Sciences › Education › Health Education and Validation
Keywords
general self-efficacy, artificial intelligence, scale adaptation, psychometric validation, university students, medical students, technology adoption, gender invariance, measurement invariance, short-form scale

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