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The Illusion of Competence: Self-Perceived Digital Literacy and AI Readiness Among European Secondary Students

T0 review · 2 major / 1 minor · reviewed 2026-06-29 · grok-4.3

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Pith's one-line read European secondary students greatly overestimate their digital literacy and AI readiness, showing a sharp confidence-competence divide.

desk verdict Survey of 243 European secondary students finds self-reported digital skills exceed measured ones with a classroom-specific gender gap, but the objective tests lack any reported validation or details. read the letter →

arxiv 2605.26010 v1 pith:P3MKJNFS submitted 2026-05-25 cs.CY

Nicolas Rodriguez-Alvarez (1) , Alan Martin Blanch-Marsolini (1) , Samuel Vara-Gutierrez (2) , Hugo Gil-Garcia (3) , Javier Calzon-Dueñas (1) , Fernando Rodriguez-Merino (4) ((1) Instituto de Educacion Secundaria Parquesol , (2) Moate Community School , (3) Instituto de Educacion Secundaria Jose Jimenez Lozano
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(4) Universidad de Valladolid)
This is my paper · ORCID
classification cs.CY
keywords digitalliteracyAIreadinessDunning-Krugereffectgendergapsecondarystudentsdeepfakesalgorithmicbiasconfidencecompetencedivide
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper challenges the idea that young people are naturally proficient with technology simply because they grew up with it. Instead, it finds that students rate their skills very high for everyday passive use of devices but much lower for actively creating technology or grasping algorithmic logic. This pattern holds across the sample of 243 students, with an added twist that gender differences in perceived tech ability show up only in technology-focused classes. Students also rate their ability to spot deepfakes and biases higher than their actual skills in using AI would support. These results matter because they point to a need for different teaching approaches to build genuine competence rather than relying on assumed natural ability.

What carries the argument

The Confidence-Competence Divide, measured by contrasting self-perceived digital literacy with actual technical readiness, including the intra-pathway analysis that isolates the gender gap to specific classroom types.

What would settle it

Conducting a follow-up study with standardized, validated objective assessments of digital creation and AI operation skills on the same population and observing no overestimation compared to self-reports would falsify the central claims.

Watch

Extended reading notes

Core claim

The central claim is that there is a severe Confidence-Competence Divide characterized by a collective Dunning-Kruger effect, with near-maximum self-efficacy in passive digital consumption declining sharply for active technological creation and algorithmic logic, a context-specific technological gender gap that emerges only in Technology-oriented classrooms, and an AI Paradox where critical awareness of deepfakes and algorithmic biases is overestimated relative to operational AI skills.

Load-bearing premise

The measures used to assess actual technical readiness and operational AI skills are assumed to be objective and distinct from self-perception, though the study provides limited information on their construction and validation.

Editorial extensions

If this is right

  • Reforms should move away from passive theoretical instruction toward hands-on active technological creation.
  • The gender gap in technology skills is not universal but tied to formal STEM environments, suggesting targeted interventions.
  • Addressing the AI Paradox requires building operational skills to match critical awareness claims.
  • Student demand for pedagogical change at 76.5 percent indicates broad support for shifting teaching methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the overestimation extends to real-world decisions, students may be more susceptible to misinformation campaigns involving deepfakes.
  • Implementing hands-on AI creation programs in schools could be tested to see if it reduces the reported divide.
  • Similar patterns might appear in non-European contexts if the digital native assumption is tested elsewhere.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript reports a multicenter survey of N=243 European secondary students that claims to demonstrate a 'Confidence-Competence Divide' (Dunning-Kruger effect) between near-maximal self-reported efficacy in passive digital consumption and lower self-efficacy in active creation/algorithmic logic; an 'AI Paradox' in which students overestimate critical awareness of deepfakes and biases relative to operational AI skills; a technological gender gap that reaches significance (p=0.046) only inside Technology-oriented classrooms; and 76.5% student support for replacing passive instruction with hands-on creation.

Significance. If the objective competence instruments prove to be valid, reliable, and construct-valid after full methodological disclosure, the work would supply empirical evidence against the 'digital native' assumption and could inform European secondary curricula on AI readiness and stereotype threat. The classroom-specific gender-gap finding, if robust, would be a useful qualification to broader claims about gender and technology.

major comments (2)
  1. [Abstract / Methods] Abstract and (presumably) Methods section: the headline claims (Confidence-Competence Divide, AI Paradox, classroom-specific gender gap) all rest on a credible distinction between self-reported efficacy and independently measured 'actual technical readiness' / 'operational AI skills'. No information is supplied on instrument construction, item wording, response format, reliability statistics, pilot validation, scoring criteria, or exclusion rules for the objective tests. Without these details the observed discrepancies could be artifacts of ceiling effects, poor discrimination, or construct mismatch rather than genuine effects.
  2. [Results] Results section (gender-gap analysis): the reported p=0.046 is marginal and the manuscript does not state whether it survives correction for multiple comparisons, reports an effect size, or includes sensitivity checks for classroom-type classification or sample weighting. These omissions directly affect the load-bearing claim that the gap 'emerges significantly exclusively within Technology-oriented classrooms'.
minor comments (1)
  1. [Abstract] The abstract states 'supported by an overwhelming student demand (76.5%)' without indicating the exact survey item, response scale, or whether this percentage is conditioned on any subgroup.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the opportunity to respond to the referee's report. We value the feedback and will revise the manuscript to address the concerns raised regarding methodological details and statistical reporting. Below we provide point-by-point responses.

read point-by-point responses

  1. Referee: [Abstract / Methods] Abstract and (presumably) Methods section: the headline claims (Confidence-Competence Divide, AI Paradox, classroom-specific gender gap) all rest on a credible distinction between self-reported efficacy and independently measured 'actual technical readiness' / 'operational AI skills'. No information is supplied on instrument construction, item wording, response format, reliability statistics, pilot validation, scoring criteria, or exclusion rules for the objective tests. Without these details the observed discrepancies could be artifacts of ceiling effects, poor discrimination, or construct mismatch rather than genuine effects.

    Authors: We agree that detailed information on the objective instruments is essential for validating the reported effects. The current manuscript provides limited description in the Methods, which is insufficient. In the revision, we will expand the Methods section with comprehensive details on instrument development, including item wording, response scales, reliability (e.g., internal consistency measures), pilot testing procedures, scoring criteria, and any exclusion rules. This will allow readers to assess potential artifacts such as ceiling effects and strengthen the credibility of the confidence-competence distinctions. revision: yes

  2. Referee: [Results] Results section (gender-gap analysis): the reported p=0.046 is marginal and the manuscript does not state whether it survives correction for multiple comparisons, reports an effect size, or includes sensitivity checks for classroom-type classification or sample weighting. These omissions directly affect the load-bearing claim that the gap 'emerges significantly exclusively within Technology-oriented classrooms'.

    Authors: We acknowledge that p=0.046 is marginal and that additional statistical details are needed. In the revised manuscript, we will report appropriate effect sizes for the gender difference. We will clarify whether this was a pre-specified comparison and provide both uncorrected and multiplicity-adjusted p-values. We will also add sensitivity analyses for alternative classroom-type classifications and any sample weighting. These changes will improve the robustness assessment of the classroom-specific gender gap finding. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: purely empirical survey with direct data observations

full rationale

The manuscript reports survey results (N=243) on self-perceived vs. measured digital literacy and AI skills, including p-values and percentages as direct outputs from collected data. No equations, fitted parameters, predictions, uniqueness theorems, or self-citations appear in the provided text to support load-bearing claims. All headline findings (Confidence-Competence Divide, AI Paradox, classroom-specific gender gap) are framed as observational patterns without any derivation chain that reduces to inputs by construction.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The central claim depends on unstated assumptions about survey validity and statistical procedures without external benchmarks or raw data provided.

assumptions (2)
  • domain assumption Survey items validly distinguish self-perceived from actual digital and AI competence
    Required to interpret the reported gap and AI paradox as real rather than measurement artifact
  • standard math Standard assumptions for t-tests or equivalent hold for the gender gap analysis yielding p=0.046
    Invoked for significance testing in the intra-pathway analysis

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Cite this review

Pith. "Pith review of The Illusion of Competence: Self-Perceived Digital Literacy and AI Readiness Among European Secondary Students." pith.science (2026). https://pith.science/paper/P3MKJNFS

@misc{pith2026260526010,
  author       = {Pith},
  title        = {Pith review of: The Illusion of Competence: Self-Perceived Digital Literacy and AI Readiness Among European Secondary Students},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P3MKJNFS}},
  note         = {Machine review of arXiv:2605.26010}
}
abstract

The ubiquitous presence of digital devices has cemented the 'Digital Native' paradigm, assuming inherent technological proficiency among contemporary youth. This multicenter study ($N=243$ European secondary students) challenges this narrative by investigating the gap between self-perceived digital literacy and actual technical readiness, including Artificial Intelligence (AI) interaction. Our findings reveal a severe Confidence-Competence Divide characterized by a collective Dunning-Kruger effect: students report near-maximum self-efficacy in passive digital consumption but exhibit a sharp decline when evaluating active technological creation and algorithmic logic. Crucially, an intra-pathway analysis demonstrates that the technological gender gap is not universal; rather, it emerges significantly exclusively within Technology-oriented classrooms ($p = 0.046$), indicating the persistence of 'stereotype threat' in formal STEM environments. Additionally, the study uncovers an 'AI Paradox' wherein students significantly overestimate their critical awareness of deepfakes and algorithmic biases compared to their operational AI skills, fostering a false sense of invulnerability against modern misinformation. Ultimately, supported by an overwhelming student demand ($76.5\%$) for pedagogical reform, this research concludes that dismantling this illusion of competence requires abandoning passive theoretical instruction in favor of hands-on, active technological creation.

Figures

Figures reproduced from arXiv: 2605.26010 by the authors.

Figure 1
Figure 1. Distribution of self-perceived confidence: Passive Consumers vs. Active Creators. The compressed upper interquartile range [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Overall digital confidence distribution by gender. The upward shift in the male interquartile range indicates a systemic [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Technical Self-Efficacy by Academic Pathway and Gender. The statistical parity found in Arts and Sciences contrasts with the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: The AI Paradox. Students report higher confidence in their critical awareness (detecting biases and deepfakes) than in their [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Students’ verdict on the current IT curriculum, highlighting a strong demand for hands-on pedagogical approaches. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

8 extracted references · 8 canonical work pages

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    Sylvia Beyer. 2014. Why are women underrepresented in Computer Science? Gender differences in stereotypes, self-efficacy, val- ues, and interests and predictors of future CS course-taking and grades.Computer Science Education24, 2-3 (2014), 153–192. arXiv:https://doi.org/10.1080/08993408.2014.963363 doi:10.1080/08993408.2014.963363

    work page doi:10.1080/08993408.2014.963363 2014

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    Ziegler, Amanda K

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    work page doi:10.1037/bul0000052 2017

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    Net Generation

    Eszter Hargittai. 2010. Digital Na(t)ives? Variation in Internet Skills and Uses among Members of the “Net Generation”.Sociological Inquiry80, 1 (2010), 92–113. arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1475-682X.2009.00317.x doi:10.1111/j.1475-682X.2009.00317.x

    work page doi:10.1111/j.1475-682x.2009.00317.x 2010

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    Ammarah Hashmi, Sahibzada Adil Shahzad, Chia-Wen Lin, Yu Tsao, and Hsin-Min Wang. 2024. Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes. arXiv:2405.04097 [cs.CV] https://arxiv.org/abs/2405.04097

    work page arXiv 2024

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    Kirschner and Pedro De Bruyckere

    Paul A. Kirschner and Pedro De Bruyckere. 2017. The myths of the digital native and the multitasker.Teaching and Teacher Education67 (2017), 135–142. doi:10.1016/j.tate.2017.06.001

    work page doi:10.1016/j.tate.2017.06.001 2017

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    Nicolas Rodriguez-Alvarez, Alan Martin Blanch-Marsolini, Samuel Vara-Gutierrez, Hugo Gil-Garcia, Javier Calzon-Dueñas, Fernando Ro- dríguez Merino, and Instituto de Educación Secundaria Parquesol. 2026. Dataset for: The Illusion of Competence: Self- Perceived Digital Literacy and AI Readiness Among European Secondary Students. doi:10.5281/zenodo.18822871

    work page doi:10.5281/zenodo.18822871 2026

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    Gijsbert Stoet and David C. Geary. 2018. The Gender-Equality Paradox in Science, Technology, Engineering, and Mathematics Education.Psychological Science29, 4 (2018), 581–593. arXiv:https://doi.org/10.1177/0956797617741719 doi:10.1177/0956797617741719 PMID: 29442575

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    Ming-Te Wang and Jessica L. Degol. 2017. Gender Gap in Science, Technology, Engineering, and Mathematics (STEM): Current Knowledge, Implications for Practice, Policy, and Future Directions.Educational Psychology Review29, 1 (01 Mar 2017), 119–140. doi:10.1007/s10648-015-9355-x

    work page doi:10.1007/s10648-015-9355-x 2017

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Reviewed June 29, 2026 · model on record in the stance chip above.